Varying Sugars and Sugar Concentrations Influence In Vitro Pollen Germination and Pollen Tube Growth of Cassia alata L.

doi:10.22186/jyi.33.1.42-45

Abstract | Introduction | Methods | Results | Discussion | Conclusions |Acknowledgements | 
References | PDF

Abstract

This study investigates the effects of varying sugars and sugar concentrations on the in vitro germination and tube growth of pollens of Cassia alata L., a known Philippine ornamental and medicinal plant. This aims to add information on the pollination fertilization mechanism of the plant for its possible extensive cultivation. Using a pollination germination medium with different sugar concentrations (2.5, 5.0, 7.5 and 10.0%), pollen germination and pollen tube growth is highly influenced by all sucrose concentrations and by certain glucose (2.5%) and lactose (2.5 and 7.5%) concentrations. Maltose and fructose, on the other hand, are determined to be inhibitory sugars for pollen germination.

Introduction

The total count of pollen grains on a stigma usually surpasses the number required to fertilize all ovules; thus, the process of pollen growth in the carpel is highly competitive (Okusaka & Hiratsuka, 2009). In higher plants, the elongation of pollen tube is extremely fast making the pollen tube the plant cell with the fastest growth rate. Accordingly, this swift growth of pollen tubes is essential for male reproductive success (Okusaka, & Hiratsuka, 2009) and for the subsequent plant development.
Pollen development and tube growth (due to its high growth rate) are high energy-requiring processes (Selinski, & Scheibe, 2014). Carbohydrates act as energy source during the two processes (Okusaka, & Hiratsuka, 2009). The storage compounds and sugars stored in mature pollen can adequately sustain survival of pollen and germination; however, the rapid pollen tube elongation requires secretions of carbohydrates (exogenous sugars) from the stylar canal to proceed (Reinders, 2016). Exogenous sugars also provide and maintain suitable osmotic environment not only for germination of pollen but also for sustained pollen tube growth (Baloch & Lakho, 2001).
Most of the studies conducted on C. alata L. are on its therapeutic properties. Leaves of C. alata L. contain anthraquinone derivatives which exhibit antimicrobial, antitumor, antioxidant, cytotoxic and hypoglycemic activities (Alalor, Igwilo, & Jeroh, 2012). Crude extracts of the plant are being used to treat various skin diseases (Balinado, & Chan, 2017) and are effective against Staphylococcus aureus and Bacillus subtilis (Alalor, Igwilo, & Jeroh, 2012). Also, C. alata L. based soap was proven effective against opportunistic yeasts (Esimone, 2007).
Preliminary investigation of the developmental morpho-anatomy of the male gametophyte of C. alata L. was already conducted (Tolentino, 2011), but limited information is known regarding its sugar metabolism and investigating this will immensely contribute to the extensive cultivation of the plant taking into consideration its medicinal properties. This study, therefore, would add light to the developmental biology of C. alata particularly to its pollen germination and pollen tube growth.
The study specifically aims to determine the effect of varying sugars and sugar concentrations on the in vitro pollen germination and tube growth of C. alata by calculating the germination percentage and measuring the pollen tube length after exposure to different sugars. In numerous studies on in vitro pollen germination of different plant species, sucrose exhibited strong stimulatory effects (Baloch, & Lakho, 2001; Patel, 2017; Zhang, & Croes, 1982), together with glucose and lactose (Ismail, 2014); thus, may also promote pollen germination in C. alata. Maltose and fructose, on the other hand, were reported to have varied effects on pollen germination of various plant species (Ismail, 2014; Okusaka, & Hiratsuka, 2009; Nakamura, & Suzuki, 1985).  

Methods

Pollen Collection

Cassia alata L. flowers at anthesis were collected randomly from Cavite State University, Indang, Cavite during daytime. Flowers were immediately transported to the Department of Biological Sciences of the same institution for the conduct of the experiment. Pollen grains were collected by carefully tapping and brushing the anthers of each flower on a clean petri dish.

Preparation of Pollen Germination Medium

A Brewbaker and Kwack medium was used as pollen germination medium. It was composed of 100mg 1-1 boric acid, 200mg 1-1 magnesium sulfate, 100mg 1-1potassium nitrate, 300-mg 1-1 calcium nitrate, 1% agar and sugars (Jayaprakash, & Sarla, 2000). Five sugars were utilized, namely; fructose, glucose, lactose, maltose and sucrose. For each sugar, four different concentrations were prepared: 2.5%, 5.0%, 7.5%, and 10.0%. A medium with no sugar added was used as negative control. The resulting medium was finally autoclaved to maintain sterility.

Preparation of a Humid Chamber and Germination Slides

A filter paper was placed in each petri dish before pouring distilled water sufficient enough to obtain a moist environment for the pollen. A glass slide with several (two to three) drops of hot liquid pollen germination medium at the center was then placed in each petri dish. This allowed the agar to completely cool and harden. With the aid of a nylon brush, pollen grains were transferred onto the solidified agar medium. Resulting petri dishes were then incubated in the dark for a total of three hours. This was performed in triplicate.

Observation of Pollen Germination and Pollen Tube Growth

Observation for signs of pollen germination and pollen tube growth was done by microscopy thrice at one-hour interval. A single field of view per replicate that contained at least 30 solitary pollen grains was observed and photographed. A pollen grain was considered germinated when its tube length doubled the diameter of the pollen grain.  The total number of pollens that germinated was determined and percent germination was calculated using the following formula.

VS equation 1 (1)

Pollen tube lengths were then measured (in μm) with the aid of ImageJ free software using the images obtained from microscopy.

Statistical Treatment

Descriptive statistics, such as means and percentages, were utilized in determining pollen germination percentage and mean pollen tube lengths. One-way Analysis of Variance (ANOVA) was used to determine the significant differences in pollen tube growth among sugar concentrations.

Results

Examination of Pollen Germination

Humid chambers containing germination slides with pollen grains were incubated for a total of three hours. The total number of germinated pollen grains per sugar concentration was obtained as shown in Figure 1. As presented, pollen grains only germinated in media containing glucose (i.e. 2.50%) and lactose (i.e. 2.50% and 7.50%) and all concentrations of sucrose (with 100% germination in 5.00-10.00% sucrose concentrations). On the other hand, germination was not observed in solitary pollen grains exposed to fructose and maltose.

Figure 1. Mean number of germinated pollen grains per sugar concentration after 3 h of incubation.

Figure 1. Mean number of germinated pollen grains per sugar concentration after 3 h of incubation.

As shown in Table 1 and Figure 2, pollen tube growth of C. alata L. was only observed in 2.5- and 7.5-% lactose concentrations. Increase in pollen tube length under 7.5-% concentration was found to be directly proportional to increasing time of incubation and was significantly different from other concentrations.

Table 1. Mean pollen tube lengths (in μm) in response to increasing lactose concentrations.

Table 1. Mean pollen tube lengths (in μm) in response to increasing lactose concentrations.

 

Figure 2. Graph showing mean pollen tube lengths (in μm) in response to increasing lactose concentrations.

Figure 2. Graph showing mean pollen tube lengths (in μm) in response to increasing lactose concentrations.

In addition, as presented in Table 2 and Figure 3, C. alata pollens only responded to 2.5-% glucose concentration. Pollen tube lengths increased as incubation time also lengthened. No pollen tube length was observed in glucose concentrations higher than 2.5%.

Table 2. Mean pollen tube lengths (in μm) in response to increasing glucose concentrations.

Table 2. Mean pollen tube lengths (in μm) in response to increasing glucose concentrations.

 

Figure 3. Mean pollen tube lengths (in μm) in response to increasing glucose concentrations.

Figure 3. Mean pollen tube lengths (in μm) in response to increasing glucose concentrations.

Varying sucrose concentrations differently influenced pollen tube growth, results were statistically significant (Table 3, Figure 4). In all concentrations, an increase in pollen tube length was observed in response to increasing time of incubation. A representative photograph of pollen tube growth on germination medium with sucrose is shown in Figure 5.

Table 3. Mean pollen tube lengths (in μm) in response to increasing sucrose concentrations.

Table 3. Mean pollen tube lengths (in μm) in response to increasing sucrose concentrations.

 

Figure 4. Mean pollen tube lengths (in μm) in response to increasing sucrose concentrations.

Figure 4. Mean pollen tube lengths (in μm) in response to increasing sucrose concentrations.

 

Figure 5. Pollen germination on sucrose.

Figure 5. Pollen germination on sucrose.

Discussion

Varying sugars and sugar concentrations differently influenced pollen germination and pollen tube growth of C. alata L. Pollens successfully germinated in the sugar sucrose and acted more effectively than glucose and lactose; while fructose and maltose strongly inhibited germination on agar medium.
The observation that glucose permitted pollen tube growth could be explained by the fact that glucose is natural pollen constituent, together with other sugars, such as arabinose and galactose (Loo & Hwan, 1944). This sugar acts as an essential signaling molecule that controls plant growth and development and gene expression (Zhou et al., 1998). In addition, the effect of lactose in this study was similarly reported by Bishop (2009) and Ismail (2014). Most significant pollen tube growth on lactose compared to other sugars was also observed by Takao et al. (2006). Bishop (2009) even suggested that a higher concentration of lactose could be used as substitute for the normally used sucrose. The positive influence of sucrose to pollen germination and growth, on the other hand, could be attributed to the condition it provides to pollen that is similar to the condition of the stigmatic tissue of a flower; this stigma that secretes a fluid substance to rehydrate the pollen (Zhang, & Croes, 1982). Sucrose is the most common sugar form found in the translocation stream and is transported to other non-photosynthetic plant tissues, such as flowers, for direct metabolic use (Hopkins, & Huner, 2009).The growth of pollen tube on sugar-free medium, in addition, could be attributed to the use of endogenous carbohydrates of the pollen without the influence—be it stimulatory or inhibitory—of other sugars present in the medium.
Similarly to the results obtained by Nakamura and Suzuki (1985), maltose strongly inhibited pollen tube growth in Camella japonica. Okusaka and Hiratsuka (2009), in addition, reported that fructose causes pollen inhibition. It was suggested that the pollen on fructose medium predominantly uses other sugars (e.g. sucrose and glucose) as respiration substrates and cannot maintain the constant level of these sugars.
This study reveals that different sugars have a considerable influence on pollen germination and pollen tube growth in C. alata L. Pollen tube growth is influenced by glucose, lactose and sucrose sugars; the latter being the most effective. Maltose and fructose were, on the other hand, found inhibitory of germination. This study therefore adds information on the developmental biology of pollens of C. alata L., a known ornamental and medicinal plant in the Philippines, which can further be used for its extensive cultivation in the country.

Acknowledgement

The researchers would like to acknowledge with deep and warm gratitude the Department of Biological Sciences, College of Arts and Sciences, Cavite State University for the laboratory materials and equipment used in the study.

References

Okusaka, K., & Hiratsuka, S. (2009). Fructose inhibits pear pollen germination on agar medium without loss of viability. Scientia Horticulturae, 122(1):51-55. doi:10.1016/j.scienta.2009.03.024
Selinski, J., & Scheibe, R. (2014, November 3). Pollen tube growth: Where does the energy come from? Plant Signaling and Behavior, 9(12). Retrieved November 23, 2016, from  doi: 10.4161/15592324.2014.977200
Reinders, A. (2016). Fuel for the road – sugar transport and pollen tube growth. Journal of Experimental Botany,67(8):2121-2123. doi:10.1093/jxb/erw113
Baloch, M. J., & Lakho, A. R. (2001). Impact of sucrose concentrations on in vitro pollen germination of okra, Hibiscus esculentus.Pakistan Journal of Biological Sciences, 4(5):402-403.
Alalor, C., Igwilo, C., & Jeroh, E. (2012). Evaluation of the antibacterial properties of aqueous and methanol extracts of Cassia alata. Journal of Pharmacy and Allied Health Sciences, 2(2):40-46. doi:10.3923/jpahs.2012.40.46
Balinado, L., & Chan, M. (2017). An ethnomedicinal study of plants and traditional health care practices in District 7, Cavite, Philippines.2017 International Conference on Chemical, Agricultural, Biological and Medical Sciences (CABMS-17).doi: 10.17758/URUAE.AE0117622
Esimone, C. (2007). Evaluation of the Antiseptic properties of Cassia alata-based herbal soap.International Journal of Alternative Medicine, 6(1).
Jayaprakash, P., & Sarla, N. (2000). Development of an improved medium for germination of Cajanuscajan (L.) Millsp.Pollen in vitro.Journal of Experimental Botany, 52(357):851-855
Loo, T., & Hwang, T. (1944). Growth stimulation by manganese sulphate, indole-3-acetic acid and colchicine in pollen germination and pollen tube growth.American Journal of Botany, 31(6):356-367
Zhou, Li, Jang, Jang-chyun, Jones, Tamara Sheen. (April 1998). Glucose and ethylene signal transduction crosstalk revealed by an Arabidopsis glucose-insensitive mutant. Harvard Medical School, Boston, MA.
Bishop, C. J. (2009). Pollen tube culture on a lactose medium. Stain Technology, 24(1):9-12. doi: 10.3109/10520294909139572
Ismail, O. M. (2014). In vitro germination of date palm pollen grains affected by different sugar types.Research Journal of Pharmaceutical, Biological, and Chemical Sciences, 5(1):880-886.
Takao, S., Yoshiomi, S., & Norio, N. (2006). Japanese Journal of Palynology, 52(2):97-105
Zhang, H. Q., Croes, A. F. (1982). A New Medium for Pollen Germination in vitro. 31(1|2):113-119
Hopkins, W., & Huner, N., 2009, Introduction to plant physiology: 4th ed, John Wiley & Sons, Inc: USA, 503 pp.
Nakamura, N., & Suzuki, H. (1985). Inhibition of Camellia japonica pollen tube growth by maltose.Plant and Cell Physiology, 26(6):1011-1018
Patel, E. (2015). Sucrose Needs for Pollen Germination of Impatiens balsamina L. International Journal of Innovative Research in Science, Engineering and Technology, 4(10). doi:10.15680/IJIRSET.2015.0410104
Tolentino, V. (2011). A preliminary study on the developmental morpho-anatomy of the male gametophyte of Cassia alata L. Retrieved March 16, 2017, from http://rcw-portal.ateneo.edu/rvp/details.php?id=2011-C0393

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A Transcriptome Study of Borrelia burgdorferi Infection in Murine Heart and Brain Tissues

doi:10.22186/jyi.33.1.28-41

Abstract | Introduction | Methods | Results | Discussion | Conclusions |Acknowledgements | 
References | PDF

Abstract

Lyme disease is the most common vector-borne disease in the United States and is typically caused by the bacterium Borrelia burgdorferi. Although often curable, delayed diagnosis due to nonspecific symptoms risks systemic complications, and some patients experience symptoms despite bacterial clearance from the body. We hypothesized that B. burgdorferi infection induces a self-perpetuating cascade of immunological responses such that symptoms remain after infection or causes residual damage to patients’ immune system and tissues. We present a transcriptome study of B. burgdorferi infection in murine heart and brain tissues using the Next Generation Sequencing technology and computational methods to identify differentially expressed genes, particularly for evidence of active inflammatory pathways. Our results reveal differential expression of five genes in an infected heart. These differentially expressed genes are enriched in pathways related to immune functions in heart tissue. Our study indicated that B. burgdorferi infection triggers immune response pathways similar to other pathogens, and some genes were found to be unique to infection by B. burdorferi, suggesting the potential for development of specific therapeutic targets to treat B. burgdorferi infection. In the brain, 66 genes were differentially expressed. These genes were enriched in pathways that facilitate the pathogen’s crossing of the blood-brain barrier. Although the mouse model of B. burgdorferi infection fails to recapitulate human neuroborreliosis, we observed damage to the integrity of the blood-brain barrier upon peripheral infection. This study elucidates mechanism of infection unique to Borrelia and clarifies the role of a mouse model of Lyme disease.

Introduction

Lyme disease is prevalent from southern Scandinavia to the northern Mediterranean countries and in the northeastern United States (U.S.). In the U.S., Lyme disease is the most common vector-borne disease: over 251,000 cases were reported between 2005 and 2014, with about 25,000 confirmed cases each year. Most cases occur in the northeast; however, notable expansion was observed in the Great Lakes region (CDC 2014). Lyme disease is caused by the infection of Borrelia burgdorferi sensu lato (family Spirochaetaceae), a diderm, microaerophilic spirochete bacteria (Wang et al., 1999). Within the genus Borrelia, three other species (B. afzelii, B. garinii, and possibly B. valaisiana) can cause the disease, but are more prevalent on the European continent (WHO, 2006). Other Borrelia species are carried by soft-bodied ticks and cause relapsing fevers (Garcia-Monco et al., 1997). All four pathogenic species of Borrelia are spread to humans by the bite of an infected tick. In the U.S., two blacklegged, or deer, tick species (Ixodes scapularis and Ixodes pacificus) are known to carry B. burgdorferi. The bacteria infect several mammal and bird species and are transmitted during the tick’s blood meals (Rosa et al., 2005).
Although Lyme disease is usually curable with prompt antibiotic treatment, nonspecific symptoms make early diagnosis difficult, and untreated infection can induce rheumatic, cardiac, and neurologic complications. The current screening test is still suboptimal in detecting Lyme reliably (Centers for Disease, & Prevention, 1995; Dressler et al., 1993). Lyme is often diagnosed after the emergence of the classic bulls-eye-shaped rash at the site of the tick bite, which occurs in over 70% of patients (McConville, 2014). The infection spreads throughout the body, causing general inflammation during the early dissemination stage, and years after initial infection, painful arthritis and joint swelling are observed among 60% of patients (McConville, 2014). Borrelia are transported throughout the body, and persistent infections are established in the skin, joint, heart, bladder, and, in only humans and primates, the central nervous system (Rosa et al., 2005).
Some of these tissues are particularly affected by infection-induced inflammation. Lyme carditis (inflammation of the heart tissue, interfering with its electrical activity) occurs in 4-10% of infections during the early dissemination stage. Carditis responds well to antibiotic treatment; however, because it occurs so early in the infection process and Lyme disease is difficult to diagnose, it can be fatal (McAlister et al., 1989). Additionally, 10-15% of Lyme disease cases manifest neurological conditions, such as pain caused by temporary or permanent inflammation of the nerves, meningitis, memory and anxiety problems, depression, and both cranial and peripheral neuritis (Narasimhan et al., 2003; Pachner, & Steere, 1984; Rupprecht et al., 2008).
Some patients will experience Post-Treatment Lyme Disease Syndrome (PTLDS), a chronic manifestation of Lyme disease. PTLDS is diagnosed when symptoms continue despite bacterial clearance from the body (McConville 2014). Unlike many other gram-negative bacteria, little epidemiological evidence shows antibiotic-resistant Borrelia infections to be a threat; however, the prevalence of persistent symptoms is concerning. Because antibiotic treatment does not necessarily resolve PTLDS, understanding how Borrelia affects the body, especially the heart and brain tissues, is crucial in reducing the burden of this disease. We hypothesize that Borrelia infection induces a self-perpetuating cascade of immunological responses, such that symptoms remain after infection. The B31 B. burgdorferi genome has been fully sequenced, consisting a small linear chromosome (~900kb) and 21 unique plasmids (5-56kb) (Fraser et al., 1997), but does not reveal any obvious virulent elements (Rosa et al., 2005). Thus, looking at the transcriptional activities of the infected host rather than the genome of B31 B. burgdorferi may shed light the immune response and on its pathogenesis.
A microarray study of Borrelia genes during infection of heart and CNS tissue in non-human primates revealed elevated expression of over 90 genes in bacteria in the CNS when compared to bacteria in the heart (Narasimhan et al., 2003), indicating that parasite-host responses are different in the two tissues. Infection induces a macrophage response and upregulated cytokine expression in the murine macrophage cell line (Wang et al., 2008). However, little is know about the host’s transcriptional response at tissue level upon Borrelia infection. Here, we present a transcriptome study that integrates experimental and computational methods to probe for the effect of B. burgdorferi infection on gene expression, and subsequently, biological pathways of inflammation in murine heart and brain tissues. We have designed a dual-method redundant pipeline to overcome issues arising from the lack of replicates owing to the scarcity of samples and the high cost of RNA-sequencing (RNA-seq). This method will allow us to better study and characterize acute and persistent Borrelia infection.

Materials and Methods

Culture and Infection

B31-MI B. burgdorferi, from ATCC (Manassas, VA), was grown in BSK-H (Sigma BSK-H Complete, St. Louis, MO) at 37°C to a concentration of 7.2×107 viable spirochetes/mL at the Baumgarth lab at University of California Davis and shipped on ice for next-day infections.
Six female C3H/HeJ mice (The Jackson Laboratory, Bar Harbor, ME), aged 6-8 weeks old, were infected, and four female C3H/HeJ mice, also aged 6-8 weeks old, were used as controls. Two injections of approximately 0.5mL each were injected into each mouse subcutaneously in the mid-back with a 21-gauge needle. Control mice were injected with BSK-H media via the same protocol. C3H/HeJ mice carry a chromosomal inversion on Chromosome 6 (Chang, 2015), which yields no phenotypic change, as well as mutations in the Pde6b and Tlr4 genes. The Pde6b mutation causes retinal degeneration and eventual blindness. The Tlr4 mutation makes these mice more tolerant to endotoxin in bacterial infections. Higher than the minimum dose (3.6×105 times higher) (Barthold et al., 1993; Rego et al., 2014) of spirochetes was injected to the mice to ensure infection. Arthritic swelling was observed in all three experimental mice collected on day 14, and in one mouse collected on day 42. All mice were used in accordance with Lafayette College’s Institutional Animal Care and Use Committee approved protocol that followed the guidelines for ethical conduct in care and use of animals.

RNA Extraction

Only mice infected for 14 days were selected for RNA-seq to explore the acute phase of the disease. They were sacrificed with carbon dioxide gas and then cervical dislocation. Samples of heart and brain tissue were collected at 14 and 42 days. RNA extraction using TRIzol Reagent (Ambion, Austin, TX) was conducted, following the manufacturer’s protocol, from sample tissues. A preliminary analysis of one sample from control and experiment was conducted. Both samples, brain and heart, were extracted from control and experimental animals after 14 days of infection. These samples were chosen as a condition-constant (day) control-experimental pair for their high concentrations and 260/280 ratios indicative of higher RNA purity (Table S1).

RNA-Seq

One sample was selected from control and experimental conditions from heart and brain tissues for RNA-seq. Each sample consisted of 5ug of poly(A)+ total RNA. Single-end RNA-seq was performed in Illumina HiSeq platform offsite by GENEWIZ (GENEWIZ 2013). Sequencing results were returned in FASTQ files in which short read was about 50 bps long on average. The total number of short reads ranged from 47 million to 59 million per sample. The quality of short reads was checked by FASTQC (Andrews); average Q-score was 37 and over 94% of the short reads was above 30 (Table S2).

Differentially Expressed Gene Analysis

We built a dual, redundant pipeline to circumvent the scarcity of replicates, in which each dataset was processed twice by two principally distinct methods. The advantages of this pipeline include the elimination of method bias and the confidence of identifying truly differentially expressed genes (DEGs).
DEGs were identified largely by the Tuxedo pipeline (Trapnell et al., 2012) with some modifications (Figure 1A-B). Before making DEG calls, short reads obtained from Next Generation Sequencing (NGS) were mapped to the mouse genome (mm10 (Browser)) by Tophat (v2.0.10) (Trapnell et al., 2009) via alignment engine bowtie2 (v2.2.1.0) (Langmead, & Salzberg 2012). Following short reads mapping was the assembly of overlapping short reads into long transcripts. By counting the number of transcripts mapped to genes, gene expression levels were determined.

Figure 1. Workflow of the dual-method approach to differential gene expression analysis and signaling pathway identification. (A) Short reads mapping and differentially expressed gene (DEG) identification using DESeq2. (B) RNA-seq short reads mapping and DEG identification using the Tuxedo pipeline. (C) Signaling pathway analysis of DEGs using WebGestalt and SPIA.

Figure 1. Workflow of the dual-method approach to differential gene expression analysis and signaling pathway identification. (A) Short reads mapping and differentially expressed gene (DEG) identification using DESeq2. (B) RNA-seq short reads mapping and DEG identification using the Tuxedo pipeline. (C) Signaling pathway analysis of DEGs using WebGestalt and SPIA.

Two DEGs callers were used in our dual-method pipeline: cufflinks (v2.1.1) (Trapnell et al., 2010) and DESeq2 (v3.2.1) (Love et al., 2014). Figure 1A illustrates the overview of the DESeq2 pipeto line. A slight alteration was done the Tuxedo pipeline in which the number of transcripts mapped to each gene (raw count) was prepared by htseq-count (v0.6.1p1) (Anders et al., 2015) per sample before running DESeq2. This pipeline identified 365 (p < 0.10) and 168 (p < .04) DEGs in heart and brain, respectively (Supplemental File 2, Tables S4 & S6). To corroborate with the DEGs found by DESeq2, we also used the standard Tuxedo cufflinks package as an alternative method to analyze the genome-wide gene expression levels between the two conditions. This package comprises of three programs, namely cufflinks, cuffmerge, and cuffdiff. The result is a list of DEGs that show statistical significance expression patterns between control and infection.
For quality assurance purpose, RNA-seq and DEG results were inspected by a visualization method CummeRbund (v2.10.0), an R package (cummeRbund). Bias in harvesting RNA samples from control and experimental conditions may cause misleading conclusion in gene differential expression analysis. Thus, we used CummeRbund to reveal genome-wide expression distribution plots under two conditions of two tissues. Figure S1 shows similar distributions of genes in control and experimental conditions, meaning the absence of sequencing bias among our samples and both control and experimental mice of each tissue type had a similar quantity of total reads on a genome scale. Thus, expression levels are comparable on locus-focused basis.
CummeRbund also generates scatterplot to show the widespread of DEGs in experiments. Scatterplots of differential gene expression (Figures S2 and S3) showed the presence of a small set of differentially expressed genes between the two conditions in the brain and heart tissues. By using cuffdiff, 136 and 100 genes in heart tissue and brain tissue were discovered to express differentially, respectively (Supplemental File 1, Tables S3 & 35).

Signaling Pathway Analysis

Similar to the discovery of DEGs, two distinct signaling pathway analysis tools were used to search for biological pathways perturbed by DEGs due to B. burgdorferi infection: WebGestalt (Zhang et al., 2005), and SPIA (v3.2.1) (Tarca, 2013). Both methods sourced biological pathway information from KEGG pathway database (Kanehisa, & Goto, 2000; Kanehisa et al., 2014). WebGestalt detects enriched pathways by identifying over-represented Gene Ontology (GO) (Ashburner et al., 2000) terms associated with DEGs. The underlying statistical test used to substantiate over-representation of GO terms is the hypergeometric test. Thus, it assumes DEGs are independent of each other. Such a condition may not hold, as DEGs belonging to the same pathway inherently interact directly or indirectly with each other.
We used SPIA to cross-examine results obtained from WebGestalt. SPIA harnesses genes’ topological relationship in assessing the degree of perturbation exerted on the network by DEGs. More DEGs in a pathway indicates greater significance that the experimental condition induced a perturbation in that pathway. The location of the gene in the pathway is also taken into consideration by SPIA. For example, insulin receptor anchored at the cell surface functions as an on/off switch in the insulin signaling pathway. Thus, its differential expression induces a larger ripple effect to the downstream cellular processes than genes situated at the end of the cascades.
In our dual-method approach, each pathway analysis tool received two lists of DEGs, one from each aforementioned DEG calling methods, and produced four lists of predicted pathways for each tissue (Figure 1C). We then overlapped these lists to reliably identify pathways perturbed by B. burgdorferi infection. Pathways found in at least three out of four lists were selected for analysis (Table 1).

Table 1.  Altered pathways associated with differentially expressed genes (DEGs). Top ten pathways ordered by the number of DEGs are listed in below. Only nine pathways in brain tissue were identified by WebGestalt and DESeq2. pPERT is the p-value for a pathway to be perturbed by DEGs. Pathways shared by at least three datasets are in bold.

Table 1. Altered pathways associated with differentially expressed genes (DEGs). Top ten pathways ordered by the number of DEGs are listed in below. Only nine pathways in brain tissue were identified by WebGestalt and DESeq2. pPERT is the p-value for a pathway to be perturbed by DEGs. Pathways shared by at least three datasets are in bold.

 

Table 1.  Altered pathways associated with differentially expressed genes (DEGs). Top ten pathways ordered by the number of DEGs are listed in below. Only nine pathways in brain tissue were identified by WebGestalt and DESeq2. pPERT is the p-value for a pathway to be perturbed by DEGs. Pathways shared by at least three datasets are in bold.

Table 1. Altered pathways associated with differentially expressed genes (DEGs). Top ten pathways ordered by the number of DEGs are listed in below. Only nine pathways in brain tissue were identified by WebGestalt and DESeq2. pPERT is the p-value for a pathway to be perturbed by DEGs. Pathways shared by at least three datasets are in bold.

Results

Differentially Expressed Genes

Cufflinks and DESeq2 identified 136, and 365 DEGs in heart tissue, respectively (Tables S3 and S4). Surprisingly, these two sets of DEGs overlapped meagerly. We then checked whether the two sets of DEGs still pertained coherent biological functions. We grouped DEGs by molecular functions using WebGestalt’s GO Slim Classification function. This function virtually determines molecular function GO terms enrichment among genes. As seen in Figure 2A, the two sets of DEGs exhibited highly similar GO molecular function profile; 15 out of 17 molecular functions were shared between the two sets. Moreover, the order of five functions was preserved between the two profiles. These five functions are protein binding, ion binding, molecular transducer activity, transporter activity, and chromatic binding. Furthermore, we repeated the same analysis using DAVID (Huang da et al., 2009a; Huang da et al., 2009b), a similar method but independently developed by a different research group. The two sets shared the top two biological process GO terms: immune response, and defense response (Tables S7 and S8).
In brain tissue, cufflinks and DESeq2 identified 100 and 168 DEGs, respectively (Tables S5 and S6). The two sets of brain DEGs shared 67 genes or 67% of cufflinks’s predictions. Out of the 14 molecular functions of DEGs, 12 functions were common between the two groups (Figure 2B). Additionally, the two methods found the same top six functions in the two datasets but in a slightly different order. Similarly, the two sets of brain DEGs were analyzed using DAVID. Biological processes behavior and locomotive behavior were shared between the two sets (Tables S9 and S10).

Figure 2. Comparison of Gene Ontology (GO) molecular function terms. (A ) Molecular function GO terms of heart DEGs generated by DESeq2 (left) and cufflinks (right). In both panels, the numbers below the captions of the bars represent the order sorted by the number of GO terms in each group. Underlined numbers on the right panel signify the conservation of their order on both panels. (B) Molecular function GO terms of brain DEGs generated by DESeq2 (left) and cufflinks (right).

Figure 2. Comparison of Gene Ontology (GO) molecular function terms. (A ) Molecular function GO terms of heart DEGs generated by DESeq2 (left) and cufflinks (right). In both panels, the numbers below the captions of the bars represent the order sorted by the number of GO terms in each group. Underlined numbers on the right panel signify the conservation of their order on both panels. (B) Molecular function GO terms of brain DEGs generated by DESeq2 (left) and cufflinks (right).

Immune Response to B. burgdorferi Infection in Heart Tissue

As genes do not function alone, the infected host is expected to launch concerted biological processes to battle against B. burgdorferi. Thus, we examined whether or not the list of DEGs originated from common pathways in response to B. burgdorferi infection. We used a dual-method approach to overcome the issue of replicate-depletion in biological pathway analysis in which four sets of predicted pathways were generated for each tissue (Figure 1C). The top ten pathways, by the highest number of DEGs with applicable p-values, were selected for analysis in this study (Table 1). Ten genes belonging to the chemokine signaling pathway were up-regulated in B. burgdorferi infection (Figure 3A). Upregulation of β-arrestin 1 (Arrb1) was detected and is upstream of a myriad of downstream factors in response to infection. This result suggests that B. burgdorferi infection activated Arrb1, which stimulated a broad inflammatory response.

Figure 3. Biological pathways altered by B. burgdorferi infection in heart tissue. Pathway diagrams were generated by Pathview (Luo, & Brouwer 2013). (A) Chemokine signaling pathway (mmu04062).

Figure 3. Biological pathways altered by B. burgdorferi infection in heart tissue. Pathway diagrams were generated by Pathview (Luo, & Brouwer 2013). (A) Chemokine signaling pathway (mmu04062).

 

Figure 3. Biological pathways altered by B. burgdorferi infection in heart tissue. Pathway diagrams were generated by Pathview (Luo, & Brouwer 2013). (B) FcγR-mediated phagocytosis (mmu04666).

Figure 3. Biological pathways altered by B. burgdorferi infection in heart tissue. Pathway diagrams were generated by Pathview (Luo, & Brouwer 2013). (B) FcγR-mediated phagocytosis (mmu04666).

Our enrichment results indicated that Borrelia infection in heart also perturbs the same set of genes as Staphylococcus aureus (Figure 3C) and Leishmaniasis (Figure 3E). Unlike Borrelia, Staphylococcus aureus is a gram-positive bacterium and Leishmaniasis is caused by parasites (genus Leishmania). We have found that similar to S. aureus infection (as defined by a gene cluster in DAVID), the complement genes C3 and C1Q were up-regulated in B. burgdorferi infection (Figure 3C). Similarly, three genes from the complement system (C3b, C3bi, CR3) were up-regulated by B. burgdorferi infection and Leishmaniasis (Figure 3E). The complement system supplements the activities of antibodies and aforementioned phagocytosis to remove pathogenic particles and cells.

Figure 3. Biological pathways altered by B. burgdorferi infection in heart tissue. Pathway diagrams were generated by Pathview (Luo, & Brouwer 2013). (C) Response to S. aureus infection (mmu05150).

Figure 3. Biological pathways altered by B. burgdorferi infection in heart tissue. Pathway diagrams were generated by Pathview (Luo, & Brouwer 2013). (C) Response to S. aureus infection (mmu05150).

 

Figure 3. Biological pathways altered by B. burgdorferi infection in heart tissue. Pathway diagrams were generated by Pathview (Luo, & Brouwer 2013). (D) Osteoclast differentiation (mmu04380).

Figure 3. Biological pathways altered by B. burgdorferi infection in heart tissue. Pathway diagrams were generated by Pathview (Luo, & Brouwer 2013). (D) Osteoclast differentiation (mmu04380).

 

Figure 3. Biological pathways altered by B. burgdorferi infection in heart tissue. Pathway diagrams were generated by Pathview (Luo, & Brouwer 2013). (E) Response to Leishmanias infection (mmu05140).

Figure 3. Biological pathways altered by B. burgdorferi infection in heart tissue. Pathway diagrams were generated by Pathview (Luo, & Brouwer 2013). (E) Response to Leishmanias infection (mmu05140).

Calcium Signaling in Response to B. burgdorferi Infection in Brain Tissue

When compared with heart tissue, only three pathways in brain tissue were identified by SPIA and WebGestalt: calcium signaling, genes involved in gap junction, and melanogenesis (Table 1). Two G-Protein Coupled Receptors (GPCRs) implicated in calcium signaling, glutamate metabotropic receptor 5 (Grm5) and adenosine A2a receptor (Adora2a) showed differential expression (Figure 4A). Upregulation of Grm5 triggered a higher expression of a downstream factor PLCβ (phospholipase C beta) in the pathway. In contrast, Adora2a was down-regulated compared to heart tissue.
The second neurological pathway perturbed by B. burgdorferi infection was the gap junction (Figure 4B). The gap junction serves intercellular exchange of ions or small molecules between neighboring cells’ cytosolic compartments. Inflammatory response attributed to infection is often associated with the loss of such an exchange channel (Eugenin et al., 2012). Five genes in this pathway were perturbed, with subpathways both up- and down-regulated (Figure 4B). To our knowledge, no literature suggests an association between melanogenesis and bacterial infection. Two genes from this pathway, adenylate cyclase 4 (Adcy4) and phospholipase C beta 1 (Plcb1; Figures 4A-C), also participate in calcium signaling and gap junction pathways discussed above.

Figure 4. Biological pathways altered by B. burgdorferi infection in brain tissue. (A) Calcium signaling pathway (mmu04020).

Figure 4. Biological pathways altered by B. burgdorferi infection in brain tissue. (A) Calcium signaling pathway (mmu04020).

 

Figure 4. Biological pathways altered by B. burgdorferi infection in brain tissue. (A) Calcium signaling pathway (mmu04020). (B) Gap junction (mmu04540). Circled are Adcy4 and Plcb1 genes, discussed in text.

Figure 4. Biological pathways altered by B. burgdorferi infection in brain tissue. (B) Gap junction (mmu04540). Circled are Adcy4 and Plcb1 genes, discussed in text.

 

Figure 4. Biological pathways altered by B. burgdorferi infection in brain tissue. (C) Melanogenesis (mmu04916). Circled are Adcy4 and Plcb1 genes, discussed in text.

Figure 4. Biological pathways altered by B. burgdorferi infection in brain tissue. (C) Melanogenesis (mmu04916). Circled are Adcy4 and Plcb1 genes, discussed in text.

Discussion

Differentially Expressed Genes Identification

We built a dual, redundant pipeline to identify DEGs associated with B. burgdorferi infection in heart and brain tissue, in which each dataset analyzed by two principally distinct methods (cufflinks and DESeq2) and interpreted with multiple enrichment approaches. We found hundreds of genes to be differentially expressed via the two methods in each tissue.
The set of DEGs identified in heart tissue by our two methods (Cufflinks and DESeq2) overlapped meagerly. This could be partly attributed to the difference of bowtie2 and htseq-count in mapping of non-unique short reads to genes/genome. Bowtie2 considers short reads to be mappable if the number of mismatches against genome falls below a specified threshold. This permits the possibility of double-counting non-unique reads in multiple genomic locations. Besides mismatch factor, htseq-count only counts short reads that can be mapped to a single location in the genome. Thus, fewer reads are mapped by htseq-count than bowtie2, and this difference in mapping strategy likely contributed to the difference in DEGs being identified. Intriguingly, regardless of which differentially expressed gene calling methods we used, results from WebGestalt and DAVID coherently point to the activation of immune system despite the meagerly overlapping of the two DEG lists.

Immune Response to Borrelia Infection in Heart Tissue

In heart tissue, chemokine signaling, Fc gamma R-mediated phagocytosis, osteoclast differentiation, and both S. aureus and Leishmania infection-response are associated with B. burgdorferi infection. Chemokine signaling and phagocytosis events are to be expected, as B. burgdorferi actively infects heart tissue during early stages of the disease (Armstrong et al., 1992). Our results indicated that B. burgdorferi infection activated Arrb1, which is known to stimulate a broad innate and adaptive inflammatory responses (Jiang et al., 2013). Little is known about the difference in pathogenicity between S. aureus, Leishmania, and Borrelia; however, it is not surprising to see similar genes and biological pathways are mobilized to defend the host against pathogens as many innate (early) immune responses are non-specific. Osteoclast differentiation was likely perturbed because S. aureus infects osteoblasts (osteomyelitis) (Rasigade et al., 2013; Webb et al., 2007) and associates with osteoblast differentiation pathways (Figure 3D). Although Borrelia does not infect osteoclasts, the similarities in infection response may produce this observation. The shared pathways predicted by the two distinct pathway methods unequivocally indicate upregulation of phagocytosis and pathways associated with response to infection in heart tissue. The altered gene expression indicates activation of white blood cells, induction of the complement system, and cellular targeting for immune destruction of tissue cells through alterations in receptor proteins. We observed these immunological reactions in heart only, suggesting tissue-specific targeting of B. burgdorferi primarily in the heart. However, the number of DEGs (>100) may be too extensive to draw any actionable therapeutic conclusions at this stage. Further investigation is needed to elucidate essential symptomatic genes.

Blood-Brain Barrier Disruption in Response to Borrelia Infection in Brain Tissue

Only three consensus pathways were perturbed in the brain tissue by our dual-method pipeline. Moreover, these pathways had fewer differentially expressed genes compared to the heart tissue results (Table 1). This is expected: because B. burgdorferi does not actively infect murine brain tissue (Radolf et al., 2012), a less cohesive response occurs upon host infection as a variety of cell types are responding to inflammation, not generating an inflammatory response. We observed perturbations in calcium signaling, gap junction, and melanogenesis. Calcium signaling has been shown to influence bacterial infection (Soderblom et al., 2005; TranVan Nhieu et al., 2004). We propose that this phenomenon is exploited by B. burgdorferi to cross the blood-brain barrier (Coureuil et al., 2013; Grab et al. 2005; Halperin, 2015), even if these bacteria fail to establish infection (Radolf et al., 2012) once across the barrier. This perturbation of the blood-brain barrier could be used to study human neuroborreliosis. Previous studies indicate that neurological symptoms exhibited by Borrelia infection in humans may be attributed to the success of Borrelia in crossing the blood-brain barrier and attacking the CNS (Grab et al., 2009). Our results are consistent with these findings, suggesting that the bacterium may also disrupt the blood-brain barrier in mice by dysregulated calcium signaling and gap junctions. This suggests the potential of targeting bacterial crossing of blood-brain barrier for therapeutic use.
The GPCR Grm5 and a downstream factor PLCβ in the calcium signaling pathway show elevation of transcriptional activities in response to infection. Infection of the bacterium Neisseria meningitidis (meningococci) has also been shown to activate calcium signaling (Asmat et al., 2014). It was found that elevation of cytoplasmic calcium concentration elevated in N. meningitides-infected cells is for the adherence of the bacteria to the cells. The activity of PLCβ facilitates the adherence of N. meningitidis through the upregulation of cytoplasmic calcium concentration. This result suggests that B. burgdorferi infection may also harness similar regulatory mechanism used by N. meningitides in elevating cytoplasmic calcium concentration in order to achieve high adherence to blood vessels, facilitating the crossing of the blood-brain barrier for subsequent CNS infection.
Additionally, the activation of Adora2a reveals the dampening of immune response. Adora2a has been shown to be involved in the infection of Plasmodium falciparum, a common pathogen of malaria (Auburn et al., 2010; Gupta et al., 2015). The ligand adenosine activates the Adora2a receptor, which in turn triggers other downstream processes. To prevent cells from over-stimulation, a negative feedback mechanism is launched to impede further excitation by mediating the dissociation of a subunit from Adora2a (Auburn et al., 2010; Metaye et al., 2005). This down-regulation of Adora2a may be to shield the cells from over-excitation. This indicates Adora2a expression could be a biomarker of infection and that anti-inflammatory drugs may exacerbate Lyme treatment.
We also found perturbations in the expression of genes involved in gap junctions. Inflammatory responses due to infection often associate with increased expression of ion channels like gap junctions to facilitate cellular communication (Eugenin et al., 2012). Moreover, studies have shown that gap function regulation plays a role in triggering cell death in virus-infected cells in the CNS (Eugenin et al. 2012). Our results reveal that some neurological responses caused by B. Burgdorferi, including the role of gap junctions,  are similar to bacterial and viral infections.
We found differential expression of genes involved in melanogenesis. There is no evidence suggesting melanogenesis and bacterial infection are linked. However, two genes from this pathway, Adcy4 and Plcb1, are also involved with calcium signaling and gap junction pathways. We propose that melanogenesis was highlighted by the two signaling pathway discovery methods because of this overlap.
Enzymes involved in cAMP synthesis, like Adcy4, were perturbed; this likely affects cellular signaling. (Tanaka et al., 2013) conducted a transcriptome analysis of murine brain tissue infected with Toxoplasma gondii, an intracellular pathogenic protozoan. The parasite causes systemic infection and persists in the brain and muscle tissue. They found over 30 genes to be significantly upregulated, including Cxcl9, H2-Eb1, Ccl8, H2-Aa, Zbp1 and Igtp. We observed these genes to be also upregulated in Lyme heart infection. However, no genes were found in both the T. gondii study and on our list of DEGs in the brain. This suggests more work needs to be done to understand the molecular basis of neuroborreliosis.
In conclusion, we present a dual-method pipeline to analyze the host transcriptome Borrelia infection using RNA-seq. Many immune response-related genes were differentially expressed in heart tissue and far fewer were identified in the brain. We propose that Borrelia may disrupt the blood-brain barrier in mice and induces a peripheral inflammatory cascade.
First, although infection was not established in the brain, the tissue is affected as many genes are differentially expressed and we found that neuronal gap junctions and calcium signaling are disrupted. This is a hallmark of loss of integrity of the blood-brain barrier. Thus, the damage is occurring irrespective of direct brain infection. Moreover, this suggests that in human infection, the crossing of the blood-brain barrier and infection of the central nervous system are two events. It may be possible to study Borrelia’s effect on the blood-brain barrier in mice, even though the central nervous system is not infected in a mouse model of the disease.
Second, none of the predicted cytokine genes were significantly differentially expressed in this experiment even though the chemokine pathway was perturbed by Borrelia infection. This indicates that these cytokines are induced by the peripheral immune response. However, cytokine-cytokine receptor interaction via Gm2023 (Figure 3A) and receptor CD45 were over-expressed in the infected heart tissue (Figure 3B), allowing phagocyte recruitment to destroy infected cells. Thus, the heart tissue is responding to inflammation but is not producing these cytokines. These results not only elucidate the transcriptional basis of self-perpetuating cascade alluded to immunological responses found in Borrelia infection but also affirm the utility of the dual-method approach proposed in our study.
Challenges facing diagnosis and treatment of Lyme are significant. Prolonged symptoms after antibiotic treatment are still afflicting a small percentage of patients, making the topic of “chronic Lyme disease” interesting but understudied. Although the mouse is not a perfect model of human Lyme disease, we show that the mouse can be used to examine unique features of Borrelia infection and the crossing of the blood-brain barrier. A thorough molecular study to explore these pathways over time is needed to elucidate the etiology of lingering Lyme symptoms in the host in order to improve patient outcome.

Acknowledgments

We thank the financial support of Lafayette College for MC and EH. Thanks to Drs. Laurie Caslake, Elaine Reynolds, and Robert Kurt for lab facilities, assistance in the infection process, and discussion. Lastly, thank you to Kimberly Olsen of the Baumgarth lab at the University of California, Davis, for providing the Borrelia samples.

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Development of a Methodology to Determine Antibiotic Concentrations in Water Samples Using High-Pressure Liquid Chromatography

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Abstract | Introduction | Methods | Results | Discussion | Conclusions |Acknowledgements | 
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Abstract

Antibiotic concentrations are typically measured using solid-phase extraction along with liquid chromatography, but this process is not practical due to a large number of man hours involved. The use of a lyophilizer with high-pressure liquid chromatography (HPLC) is an accurate and cost-effective method of analyzing antibiotics in water samples. An initial antibiotic analysis methodology was developed with the goal of concentrating antibiotics in water samples for greater detection; however, it was observed that the methodology required additional refinement to improve accuracy, particularly when manure was present in the water samples. Based on prior tetracycline antibiotic research, we hypothesized that sample preparation techniques and HLPC characteristics would influence our ability to detect these antibiotics in water samples. We anticipated that analysis of larger sample volumes would improve antibiotic detection while higher manure concentrations would decrease detection capabilities. The objective of this study was to examine the effects of a secondary sample preparation step (filtration), mobile phase solution, HPLC column, sample volumes, wavelengths, and manure concentrations on the recovery rates of three common antibiotics, specifically chlortetracycline (CTC), tetracycline (TC), and oxytetracycline (OTC). The study examined three filtration methods, two mobile phase solutions, two HPLC columns, five sample volumes, three wavelengths, and four manure concentrations. Best results were obtained with a mobile phase solution of acetonitrile with 0.05% formic acid, the Acclaim® RSLC C18 PA2 column, smaller sample volumes, and a wavelength of 356nm. This study highlighted some of the challenges associated with detecting antibiotics in water samples. The accurate detection of antibiotics in water samples is an important step in developing and testing methods to reduce antibiotic transport in the environment.

Introduction

The U.S. Environmental Protection Agency (USEPA) classifies antibiotics as a contaminant of emerging concern (CEC) because they are detected in the environment at higher than expected levels and may negatively impact human and aquatic ecosystems (USEPA, 2013). The risk these antibiotics pose to humans and aquatic life is not known; however, the primary concern is that the antibiotic-resistant strains of bacteria will develop. Utilization in human healthcare and livestock care are the two main sources of antibiotics in the environment. Unlike human waste, which is treated via treatment plants or septic systems, livestock waste is oftentimes directly applied to the land as part of a nutrient management plan (NRCS, 2012). Baguer, Jensen, and Krogh (2000) noted that land application of manure is the main pathway for veterinary antibiotic introduction into the terrestrial and aquatic environments. In agriculture, antibiotics are used for both therapeutic as well as non-therapeutic purposes. The two main non-therapeutic uses of antibiotics in livestock are growth additives and illness prevention (Shore, & Pruden, 2009). Estimates are that 11 million kg of antibiotics were used in 2002 along for non-therapeutic uses (Davis et al., 2006). Unfortunately, large amounts of administered antibiotics are not metabolized by animals but instead are excreted in manure. Rates of unmetabolized antibiotics are as high 70-90% as in the case of tetracyclines, which are one of the most used classes of antibiotics (Kumar Gupta, Chander, & Singh, 2005; USEPA, 2013).
Manures are commonly applied across croplands as part of farm nutrient management plans. Hence, the antibiotics in these manures are land applied as well. Once applied to the land, antibiotics are transported to surface waters, via runoff, or ground waters, through infiltration. To date, only a limited amount of research has been conducted on the transport of antibiotics in the runoff, but this research indicates that the mechanisms of transport vary with antibiotic type. Some antibiotics bind to and are transported with soil, while others do not (Tolls, 2001). Limited studies have examined the use of best management practices (BMPs), such as vegetated filter strips, and the addition of alum to minimize antibiotic transport (Enlow, 2014; DeLaune, & Moore, 2013; Lin et al., 2011).
One challenge, in studies involving antibiotics, is the sensitivity and reliability of the methods used to detect the antibiotics. Another is the time required, and hence labor costs, associated with performing antibiotic analyses. Because antibiotic concentrations are so low, they require concentration before extraction. Typically, solid-phase extraction is used along with liquid chromatography. However, when a study requires analyzing many samples, solid-phase extraction is impractical due to the high amount of human labor involved. To address this issue, Enlow (2014) developed a methodology using a lyophilizer for use in the analysis of the antibiotic oxytetracycline. The lyophilizer was used to concentrate antibiotics in water samples with the goal of improving antibiotic detection. Results indicated the methodology worked well at high oxytetracycline concentrations but performed somewhat poorly at low levels in the presence of manure. The poorer performance was due to the presence of one or more unknown constituents which appeared on the chromatograph near the peak of chlortetracycline. Enlow (2014) hypothesized that a secondary filtration step, larger sample volumes, and different wavelengths on the HPLC would improve antibiotic recovery rates. These assumptions were made based on the presence of visible solids in samples following one filtration, which was thought to interfere with antibiotic detection, total suspended solids methodology, which uses larger sample volumes to improve accuracy in the presence of low concentrations (Eaton et al., 1998), and a subsequent literature review which identified the use of a range of wavelengths to measure tetracycline antibiotics (personal communication). Wavelengths are significant to the determination of the substance in a sample because different compounds absorb different wavelengths of UV light (Kay, Blackwell, & Boxall, 2005). Questions regarding the effects of different manure concentrations, in water samples, on antibiotic recovery rates remained, as did the effects of different mobile phase solutions and HPLC columns.
Based on prior tetracycline antibiotic research, we hypothesized that sample preparation techniques, namely an additional filtration step to remove remaining particulates that can interfere with HPLC performance (CDER, 1994), and HLPC characteristics, such as mobile phase solution (Jia, Xiao, Hu, Asami, & Kunikane, 2009), column type (Ritorto et al., 2014) and wavelength (Ng, & Linder, 2003), would influence antibiotic detection in water samples. We anticipated that analysis of larger sample volumes would improve antibiotic detection, as we would have a more material from which to develop a concentrate, while higher manure concentrations would decrease detection capabilities due to the presence of more impurities requiring removal to not inhibit HPLC performance. This study aimed to examine the effects of a secondary sample preparation step (filtration), mobile phase solution (mobile phases), HPLC column, sample volumes, wavelengths, manure concentrations on the recovery rates of three common antibiotics, specifically chlortetracycline (CTC), tetracycline (TC), and oxytetracycline (OTC). The laboratory analyses were first conducted and refined on manure-free samples prior to examining samples with manure.

Materials and Methods

Antibiotics

Three commonly used antibiotics were examined: CTC, TC, and OTC. Antibiotics (chlortetracycline hydrochloride ≥ 75% HPLC; tetracycline hydrochloride ≥ 95% European Pharmacopeia HPLC assay; oxytetracycline hydrochloride ≥ 95% (HPLC) crystalline) were obtained from Sigma-Aldrich (St. Louis, Mo.). These antibiotics were evaluated at concentrations of 1, 10, 20, 100 and 200 μg/mL. Additionally, an equal combination of the three antibiotics (COMBO) was examined at final concentrations of 1, 10, 20, 100 and 200μg/mL (individual antibiotic concentrations of 0.33, 3.33, 6.67, 33.3, 66.7μL/mL, respectively, were used to create COMBO).

Secondary Sample Preparation Step (Filtration)

Three sample preparation methods were examined: solid-phase extraction (SPE), lyophilization (LYO), and a combination of the SPE and lyophilization (BOTH). Prior to SPE, samples were centrifuged for 10 minutes at 1500RPM using a Thermo Scientific Sorvall Legend XTR Centrifuge. Three replications of all antibiotics (CTC, TC, OTC, and COMBO) at all five concentrations were used to examine the three filtration methods. One replication was used per filtration method.
In SPE, the sample is manually pulled through a SPE cartridge; it is the SPE cartridge that retains the antibiotics. First, SPE cartridges are preconditioned prior to use with 1mL of Methanol(MeOH) followed by 4mL of deionized water. Samples are then manually loaded into the SPE cartridges using a 10mL syringe at 2mL/min, a rate which is quite slow especially for large sample volumes. Next, the SPE cartridge is washed with 0.05% MeOH in deionized water. This step is important when analyzing samples containing particulates as they can inhibit sample movement through the cartridges. Finally, the sample is eluted from the SPE cartridge using 2mL MeOH. To conduct the SPE, 60 mg bed weight, 3 mL column volume Thermo Scientific Hypersep Retain PEP was used.
LYO, or freeze drying, instead removes the liquid from a sample to concentrate any remaining constituents. LYO is especially beneficial for large sample volumes as it can greatly reduce their size without impacting constituents in the sample. For the LYO, SP Scientific VirTis Wizard 2.0 lyophilizer (Gardiner, New York) was used.
For the LYO and BOTH filtration methods, two replications were frozen at a temperature of -44°C until the sample was completely solid and then placed in the lyophilizer until all liquid was removed (approximately six days). For the LYO filtration method, samples were rehydrated with 2mL of methanol (MeOH) and then analyzed on the A Dionex Ultimate 3000 HPLC along with an Ultimate 3000RS Variable Wavelength detector (Sunnyvale, California) (0.05% formic acid in acetonitrile mobile phase solution; RSLC PAC column; wavelengths of 230, 290 and 356nm). For the BOTH filtration method, samples were rehydrated with 5mL of deionized water and analyzed via SPE following standard procedures (Sigma-Aldrich, 1998).

Mobile Phase Solution

Mobile phase solutions are used with HPLC methodologies to dissolve and transport constituents, improve constituent separation, and maintain pH as to improve accuracy and precision (Shimadzu, n.d.). Two mobile phase solutions were examined: 0.05% acetic acid solution in methanol (MeOH) and 0.05% formic acid (C2H4O2) in acetonitrile (C2H3N). These weakly acidic solutions were chosen for their compatibility with antibiotics extraction from the solid phase to liquid phase (Kim and Carlson, 2007; Suárez, Santos, Simonet, Cárdenas, & Valcárcel, 2007) and from their prior use in other research focused on HPLC use to evaluate antibiotics (Hernádez, Sancho, Ibáñez, & Guerrero, 2007; Lindberg, Jarnheimer, Olson, Johansson, & Tysklind, 2005; Yang, Cha, & Carlson, 2005). Manure free water samples were spiked with one of three types of antibiotics (CTC, TC, and OTC) to final concentrations of 10, 50, 100, and 1000μg/mL to see how the mobile phase solutions worked with a range of concentrations. Spiked water samples were used to ensure distinctly visible antibiotic peaks on the chromatogram. When examining the influence of mobile phase solution type on antibiotic recovery rates, only the RSLC column was used; however, all three wavelengths examined in this study (section 2.6) were examined.

HPLC Column

In the HPLC process, the solution passes through a column composed of unique material. The interaction between sample constituents and column material allows for the separation of the constituents as their pass-through rate varies. A Dionex Ultimate 3000 HLPC (Sunnyvale, CA) and an Ultimate 3000RS Variable Wavelength Detector (Sunnyvale, CA) were used. Two HPLC columns were examined: Acclaim® Rapid Liquid Separation Liquid Chromatography (RSLC) C18 Polar Advantage II (PA2) (polar-embedded reversed-phase, 3µm particle size, 2.1mm diameter, 150mm length, 120Å average pore diameter) and Acclaim® 120 C18 (conventional reversed-phase, 3µm particle size, 2.1mm diameter, 100mm length, 120Å average pore diameter). The Acclaim® 120 C18 was chosen because of its use in other studies involving tetracycline antibiotics (Enlow, 2014; Haghedooren et al., 2008; Yang et al., 2004; Tong, Wang, & Zhu, 2009). The Acclaim® RSLC C18 PA2 is a newer column type, so its uses in antibiotic studies is less documented (Bean et al., 2016). As with the mobile phase solution, manure free water samples were spiked with one of three types of antibiotics (CTC, TC, and OTC) to concentrations of 10, 50, 100, and 1000μg/mL.

Sample Volume

Five sample volumes were examined: 100, 200, 300, 400 and 500mL. Each sample volume was spiked to create a final OTC concentration of 20µg/mL. This concentration was chosen based on work done in Enlow (2014). Due to budget and time constraints, multiple antibiotics at multiple concentrations were not examined. All samples were frozen for at -80°C and then placed in the lyophilizer for two weeks. Samples were then reconstituted with 2mL of MeOH and analyzed on the HPLC at 356nm using a mobile phase of 0.05% formic acid in acetonitrile and a RSLC PAC column.

Wavelength

Three wavelengths (230, 290 and 356nm) were examined using water samples with containing 0.01, 0.05, 0.15, and 0.25g/mL swine manure that had been spiked with antibiotics (Table 1). Briefly, antibiotic-free swine manure was collected from a nearby small heritage hog farm and transported to the Biosystems and Agricultural Engineering Department at the University of Kentucky and stored at 0°C until analysis. Once thawed, antibiotics (CTC, TC, OTC, and COMBO) were added to subsamples at concentrations of 10 and 20µg/mL. For the COMBO samples, equal parts CTC, TC, and OTC were added to the manure to arrive at final antibiotic concentrations of 10 and 20µg/mL. All water samples (20mL deionized water; n=96) were created in triplicate to evaluate the three methods of filtration (SPE, LYO, and BOTH). The small sample volume (20mL) allowed for more rapid analysis as it decreased the time required for the filtration process.

Table 1. Manure concentrations for tested water samples.

Table 1. Manure concentrations for tested water samples.

Manure Concentration

The effect of manure concentration (0.01, 0.05, 0.15, and 0.25g/mL) on antibiotic recovery rate was examined. Wet manure was weighed and then placed in 20mL of deionized water and vigorously mixed. An initial antibiotic concentration of 20µg/mL, LYO filtration, Acclaim® RSLC C18 PA2 column, and a wavelength of 356nm, were used. A secondary filtration step was not used.

Statistical Analysis

An Analysis of Covariance (ANCOVA) was used to compare the parameters wavelength, antibiotic type, antibiotic concentration, and manure concentration to antibiotic recovery rates (%) in SAS (p > .05). Both wavelength and antibiotic type served as class (categorical) variables.

Results

Secondary Sample Preparation Step (Filtration)

As the antibiotic analysis methodology was first developed on samples without manure, the effects of a secondary sample preparation step (filtration) were not examined until later in the experiment due to funding limitations. The time required to filter the samples was substantial. Filtering one 100mL sample required nearly one hour. Preliminary results from this study indicated that samples containing large amounts of manure (e.g. > 5% by volume) will likely require a third filtration step to remove solids before lyophilization. Without this step, a lot of solids remains after lyophilization. Ideally, after lyophilization, the only desired remnants are the antibiotics, which can be easily saturated with a mobile phase solution and tested directly in the HPLC.

Mobile Phase Solution

When 0.05% acetic acid in MeOH was used as a mobile phase solution, the peaks for TC and OTC overlapped while the peak for CTC was distinct (Figure 1). Using 0.05% formic acid in acetonitrile as the mobile phase solution improved peak separation between the OTC and TC while maintaining the clear distinction in CTC. Thus, 0.05% formic acid in acetonitrile was used as a mobile phase solution in the remainder of the experiments.

Screen Shot 2017-05-31 at 12.05.46 AM

Figure 1. (A) The peaks for oxytetracycline (OTC) and tetracycline (TC) overlap when a MeOH with 0.05% acetic acid mobile phase solution is used. (B). Using a mobile phase solution of acetonitrile with 0.05% formic acid, the peaks between TC and OTC are distinct.

HPLC Column

Peak separation amongst the antibiotics was better using the Acclaim® RSLC C18 PA2 column as compared to the Acclaim® 120 C18 column. Figure 2a shows the clear and symmetric peaks associated with Acclaim® RSLC C18 PA2 column while Figure 2b shows that the peaks associated with the Acclaim® 120 column are less distinct.

Figure 2. (A) Clear, symmetric peaks of tetracycline (TC) and chlortetracycline (CTC) were seen with the Acclaim® RSLC C18 PA2 column. (B) The Acclaim® 120 C18 column produced slightly less distinct and symmetric peaks.

Figure 2. (A) Clear, symmetric peaks of tetracycline (TC) and chlortetracycline (CTC) were seen with the Acclaim® RSLC C18 PA2 column. (B) The Acclaim® 120 C18 column produced slightly less distinct and symmetric peaks.

Sample Volume

Smaller sample volumes are more efficient to analyze due to lesser times required for filtration. With LYO, for example, large sample volumes can require multiple weeks to dry. Oxytetracycline was evaluated at a concentration of 20µg/mL in clean, deionized water. Samples were run on the Acclaim® RSLC C18 PA2 column and with a mobile phase solution of 0.05% formic acid in acetonitrile. Sample volume had no significant effect on antibiotic recovery rates (α = .05) (Table 2).

Table 2. Antibiotic recovery (%) associated with sample size.

Table 2. Antibiotic recovery (%) associated with sample size.

Wavelength

Results indicated that the most distinct peaks on the chromatograms occurred using a wavelength of 356nm. Figure 3 shows a sample with a 20µg/mL of COMBO and 1mg/L manure at the wavelengths 230, 290, and 356nm. The baseline of Figure 3c is close to zero, and the peaks for OTC and TC are clear and defined at 356nm, which cannot be said of the other two wavelengths.

Figure 3. Chromatographs showing peaks for oxytetracycline (OTC), tetracycline (TC), and chlortetracycline (CTC) at different wavelengths. (A) 230 nm, (B) 290 nm, and (C) 356 nm.

Figure 3. Chromatographs showing peaks for oxytetracycline (OTC), tetracycline (TC), and chlortetracycline (CTC) at different wavelengths. (A) 230 nm, (B) 290 nm, and (C) 356 nm.

Manure Concentration

The recovery rates for TC and CTC were quite low across all lev
els of manure concentration, averaging 0.5% for TC and 1.5% for CTC. As the concentration of manure increased, the recovery rates of OTC decreased, as seen in Figure 4. The decreasing trend does not appear in CTC. This impurity was seen in the control (manure, no antibiotics) and in OTC and TC only (manure) samples (Figure 5). The presence of this impurity makes determining the amount of CTC in a sample challenging.

Figure 4. Antibiotic recovery rates (y-axis) decreased for oxytetracycline (OTC) as the concentration of manure (x-axis) increased. No significant trends were noted for tetracycline (TC) or chlortetracycline (CTC).

Figure 4. Antibiotic recovery rates (y-axis) decreased for oxytetracycline (OTC) as the concentration of manure (x-axis) increased. No significant trends were noted for tetracycline (TC) or chlortetracycline (CTC).

 

Figure 5. (A) This chromatogram shows the control, which contained manure and deionized water. (B) The impurity in the control peaks at the same time as chlortetracycline (CTC), making it difficult to discern the CTC in the CTC spiked sample.

Figure 5. (A) This chromatogram shows the control, which contained manure and deionized water. (B) The impurity in the control peaks at the same time as chlortetracycline (CTC), making it difficult to discern the CTC in the CTC spiked sample.

Discussion

Measurement of antibiotics in water samples containing manure, using the HPLC, was best accomplished by the following methodology.

.  Mobile phase solution of acetonitrile (C2H3N) with 0.05% formic acid (C2H4O2) (best separation between OTC and TC),

.  Acclaim® RSLC C18 PA2 column,

.  Smaller sample volumes (more time-efficient, especially for lyophilization), and

.  Wavelength of 356nm.

.  While we hypothesized that the factors mobile phase solution, HPLC column, sample volume, and wavelength would influence the measurement of antibiotics in   water samples, we did not know which treatment would yield the best results for OTC, TC, and CTC.

Mobile Phase Solution

Using a mobile phase solution comprised of 0.05% formic acid in acetonitrile produced the best separation between OTC, TC, and CTC. These results agreed with other studies that found that the ability to detect antibiotics increased when using formic acid. Jia et al. (2009) examined the effect of formic acid on HPLC sensitivity in antibiotic detection and found that formic acid increased signal intensities for OTC and CTC but not TC. Suárez et al. (2007) recommended using a volatile acid mobile phase solution for detecting tectracyline compounds. The researchers examined a 1:1 (v:v) methanol to water mixture, with different percentages of formic acid (from 0.2% to 2%) as a sheath liquid and found formic acid at 0.5% yielded the best results in terms of mass spectrometry signal intensity. Improved antibiotic identification using acetonitrile may be linked to methanol’s role in TC degradation. Liang, Denton, and Bates (1998) found that the degradation of TC is increased in methanol solutions via functional group substitutions or additions on TC. The results of this study agreed with findings from these prior studies.

HPLC Column

Of the two HPLC columns examined, separation of OTC, TC, and CTC was best when using the Acclaim® RSLC C18 PA2 column.
Similar results were found by Ritorto et al. (2014) who compared the performance of the Acclaim® 120 C18 and the Acclaim® RSLC C18 PA2 to separate tryptic digested proteins from cell lysate. The researchers found that the Acclaim® RSLC C18 PA2 had higher efficiencies and exhibited higher polarity of selectivity. Unlike the Acclaim® 120 C18, the Acclaim® RSLC C18 PA2 is compatible with 100% aqueous environments and has a wider pH range (1.5-10.0) (Thermo Fisher Scientific Inc., 2016). HPLC columns are stable over a specific pH range. The presence of manure can influence pH levels in streams, though such waters are likely to have a pH range between 4 and 8 (Harden, 2015). Haghedooren et al. (2008) examined the performance of 65 reversed-phase liquid chromatographic (RP-LC) C18 columns, including the Acclaim 120 C18 but not the Acclaim® RSLC C18 PA2, to separate antibiotics, one of which was TC, from impurities. The Acclaim 120 C18 was a lower performing column for separation of TC.

Wavelength

Acquiring the most distinct peaks at 356nm agreed with results from other studies. Ng and Linder (2003) reported minimal differences in maximum peak absorption between TC, OTC, and CTC, with wavelengths of 369, 358, and 374nm, respectively. Liang et al. (1998) found peak absorbance of TC standards mixture at 269nm and peak absorbance of the degraded TC sample (e.g. OTC, CTC, and other such components) at 303 and 338nm. Kay et al. (2005) used a wavelength of 355nm for OTC. The agreement of our findings with these studies is viewed as positive. Our methodology, with respect to the other factors examined, differed from these studies. Our results confirmed that a wavelength of 356nm is appropriate for tetracycline antibiotic detection.

Manure Concentrations

The actual amount of manure in the sample impacted antibiotic recovery rates. Higher manure concentrations yielded significantly lower antibiotic recovery rates for OTC (Figure 4). The reason for this relationship is not known but possibly related to affinity for OTC to bind to manure (Loke, Tjørnelund, & Halling-Sørensen, 2002). The addition of larger amounts of manure to the water samples would mean more potential for OTC-manure binding. Manure concentrations did not have a significant effect on TC and CTC recovery rates. The low levels of recovery of antibiotics from these manure-laced samples are concerning and indicate the methodology requires further refinement. We hypothesize that an impurity, possibly chloride, in swine manure is appearing at the same time as the CTC in the chromatograph, and thus is influencing this result.

Secondary Sample Preparation Step (Filtration)

Additional work is needed to evaluate the benefits of a secondary filtration step on antibiotic recovery rates. If these constraints were not present, additional sample analyses would improve our ability to draw more definitive conclusions regarding the effect of a secondary filtration on antibiotic recovery rates. We could conclude that the method of secondary filtration chosen must consider the time allotted for the study. While lyophilization takes several days, it is a process that can be left unattended. In contrast, SPE can be done immediately; however, the process of pulling a sample through the cartridges at 2mL/min is very time-consuming. For example, a 100mL sample required 50 minutes to filter while a 500mL sample required over 4 hours. We noted that a third filtration step may be needed when analyzing samples with high manure concentrations (e.g. > 5% by volume). However, a balance is needed between removing sufficient amounts of impurities to maximize HPLC performance and removing antibiotics. With each filtration, the potential exists to remove significant amounts of antibiotics from the sample.

Acknowledgements

The authors would like to thank the University of Kentucky’s Office of Sustainability for funding the project, Brent Howard and Hoods Heritage Hogs for donating the antibiotic-free swine manure used in this study, and the entire Biosystems and Agricultural Engineering Department for their understanding when an odorous bucket of swine manure exploded in the laboratory.

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Comparison of Dark Matter Proportions Across Types of Spiral Galaxies

doi: 10.22186/jyi.33.1.1-7

Abstract | Introduction | Methods | Results | Discussion | Conclusions |Acknowledgements | 
References | PDF

Abstract

A large obstacle on the path to better understanding the evolution of the Universe is knowing the extent to which “nature” and “nurture” affect structures in our Universe. Recent studies have observed that many galactic properties such as luminosity and morphology are dependent on their environment and in particular, their halos, from the galactic cluster scale down to galaxy groups. In this study, we investigate the relationship between dark matter (as a fraction of the total mass of the galaxy) and morphology of individual galaxies to determine if a similar relationship between galaxies and their environment exists at this scale. Our approach differs in the sense that we look at the proportion rather than the actual value of the characteristic we are studying to control for the size of the galaxies. We select the sample from Sa, Sb, and Sc type galaxies, where the spiral arms of Sa galaxies are the tightest and those of Sb, Sc are increasingly more unwound. While unable to statistically prove due to the sample size, an increasing trend in the dark matter fractions was observed between Sa and Sb type galaxies apart from NGC 4594. Little to no trend was discernable in dark matter between Sb and Sc type galaxies. We suggest a larger sample size and controlling for the environment in future experiments.

Introduction

The current cosmological model of the early formation of stars and galaxies in the Universe involves dark matter, a type of theorized matter that interacts only through the gravitational force and possibly the weak force, that grouped together to form halos that provided a framework for the structure of the Universe. Clouds of baryonic gas converged in these dark matter halos in the early Universe. As more gas was accumulated through mergers and fell into the halos, the gas formed rapidly spinning disks that were the first protogalaxies. Astrophysicists today continue to study dark matter halos and their evolution to better understand their role in forming the different types of galaxies we observe.
Galaxies are sorted by their structure into morphological classes using criteria established by Edwin Hubble and others (Hubble, 1926; van den Bergh, 1960a, 1960b). Studying the shape and structure of galaxies can provide valuable information about their birth and evolution. As advancements have been made over the past decades in the observation techniques and instruments used to study galaxies, astrophysicists have been able to study galaxies in more depth across the electromagnetic spectrum. Identifying galactic structure from multiple wavelengths has brought about a broader and more detailed classification of galaxies in the Universe.
The bottom-up theory of structure formation in the Universe argues that galaxy groups and clusters formed from smaller structures and grew through mergers and other interactions between structures (White, & Rees, 1978). In the past 40 years, it has become more apparent that galactic properties such as morphology and luminosity are linked to their environments (Postman, & Geller, 1983; Zabludoff, & Mulchaey, 1998). Weinmann, van den Bosch, Yang and Mo (2006) found relationships between galaxy properties and halo mass scale smoothly from clusters to groups, providing evidence towards the bottom-up scenario and precedence for studying the relationship between dark matter and galaxy characteristics.
In this study, we investigate the dark matter in a sample of regular spiral galaxies. We are looking to find a trend between dark matter content and morphological type. This could suggest that the influence dark matter has on the structure of the Universe begins on as small a scale as individual galaxies. To test this, we will take light data for seven different galaxies in visible wavelengths. Combined with published rotation curves data and published gas mass data, we will compute dark matter fractions for three Sa, two Sb, and two Sc type galaxies and discuss any trends observed.

Materials and Methods

The aim of this experiment is to test for a trend between morphological type and dark matter content. There are many processes the light data and rotational velocity data go through to produce dark matter fractions, so it is important to be cognizant of the uncertainty present in the calculations. To minimize uncertainties, we control as many factors as possible. Controlled factors are as follows:

Johnson-Cousin Filter Images:

We take all filtered photometric data in Johnson-Cousins B, V, and R filters (Cousins, 1974a, 1974b; Johnson, 1953).

SA Spiral Galaxies:

All galaxies in the sample are unbarred spiral galaxies to eliminate uncertainty in the event that bar structures affect the dark matter fraction (or vice versa).

Hubble Constant:

We adopt a value of H0 = 74.4 (kms-1)Mpc-1 for distances and radii (de Vaucouleurs et al., 1991). We adjust all distance and radii measurements using this number to produce precise and homogenized results.

Kroupa Initial Mass Function:

We calculate mass to light ratios assuming the Kroupa Initial Mass Function (IMF). This is chosen for its modernity and its low uncertainty in higher solar mass values (Kroupa, 2001).

Absolute Magnitude of Sun:

We adopt the value 4.83 for the absolute magnitude of the Sun (Williams) for magnitude and luminosity calculations.

Solar Mass:

We use the value 1.9885×1030 kg for one solar mass (Williams). This parameter is used for luminosity calculations.

Gravitational Constant:

We use a recently published value, 6.67408×10-11m3kg1s-2, for the Gravitational constant in dynamical mass calculations (Mohr, Newell, & Taylor, 2015).

Galaxy Sample:

We selected the galaxies with the aim to avoid introducing unwanted variables into the data. The profile of a “normal” spiral galaxy was adopted by looking at galaxies from Zombeck (1990, pp. 83-85). All galaxies chosen fell into the similar ranges that Zombeck observed (Table 1, Table 2):

.  Mass of 109 to 1012 Solar masses

.  Absolute Magnitude of -18 to -22

.  Diameter of ~5 to 40 kpc

NGC 4565 has a diameter outside the range seen in Zombeck (1990) but was still included because it has been in previous studies involving dark matter (Table 1).

Table 1. Properties of Observed Galaxies.

Table 1. Properties of Observed Galaxies.

 

Table 2. Derived Stellar Mass values of Observed Galaxies.

Table 2. Derived Stellar Mass values of Observed Galaxies.

Seyfert AGN:

Three of the galaxies included in the study are Seyfert galaxies (NGC 4378, NGC 4565, and NGC 7314). Seyferts have been observed to fluctuate in luminosity over periods as long as years and as short as days because of their active nuclei (that are very luminous). This may affect the stellar mass calculations because these fluctuations come from non-stellar sources.

Observations

A summary of the observations is visible in Table 3. The 1m SARA-North Telescope operates at the Kitt Peak National Observatory in Arizona, USA, and the 0.6m SARA-South Telescope operates at the Cerro Tololo Inter-American Observatory in Chile. The galaxies studied were NGC 4378, NGC 4594, NGC 6314, NGC 2841, NGC 4565, NGC 4682, and NGC 7314 (Figure 1, Figure 2, Figure 3).

Table 3. Log of Observations.

Table 3. Log of Observations.

The 1m SARA-North Telescope operates at the Kitt Peak National Observatory in Arizona, USA, and the 0.6m SARA-South Telescope operates at the Cerro Tololo Inter-American Observatory in Chile. The galaxies studied were NGC 4378, NGC 4594, NGC 6314, NGC 2841, NGC 4565, NGC 4682, and NGC 7314 (Figure 1, Figure 2, Figure 3).

Figure 1. Composite image with Johnson B, V, and R filters of NGC 4565, The Needle Galaxy. Taken with the 0.6m SARA-South telescope.

Figure 1. Composite image with Johnson B, V, and R filters of NGC 4565, The Needle Galaxy. Taken with the 0.6m SARA-South telescope.

 

Figure 2. Composite image with Johnson B, V, and R filters of NGC 4594, The Sombrero Galaxy. Taken with the 1m SARA-North telescope.

Figure 2. Composite image with Johnson B, V, and R filters of NGC 4594, The Sombrero Galaxy. Taken with the 1m SARA-North telescope.

 

Figure 3. Johnson V filter image of NGC 7314 taken with the 0.6m SARA-South telescope.

Figure 3. Johnson V filter image of NGC 7314 taken with the 0.6m SARA-South telescope.

Techniques

Radii to the 25 mag arcsec-2 surface brightness level measured in the B band were calculated manually from published values of the distance to the galaxies and their apparent size (that use the same blue 25 mag arcsec-2 criterion). The formula,

Equation 1` (1)

was used, where r is the radius, D is the distance in megaparsecs and θ is the apparent size of half of the major axis, in arcseconds.
Aperture Photometry Tool (APT) was employed to calculate the apparent magnitude of each galaxy. When available, we manipulate visual band images for calculating apparent magnitude, but empty filter images are used as an alternative when visual band images are unavailable. It is still valid to use empty filter images for visual apparent magnitude calculations because they do not subtract any visual band light out, and all images in APT must be calibrated to nearby stars to produce accurate results anyways. For each galaxy, we select multiple nearby stars to calibrate the apparent magnitude results by measuring their magnitudes in APT and comparing them to published visual apparent magnitude values in the WikiSky database (Wikisky.org). We then use the difference in these values to determine a zero-magnitude constant for APT.
Absolute magnitudes for each galaxy were calculated using the previously measured apparent magnitudes and published distance values. The formula,

Equation 2 (2)

is used, with K as the K correction constant, a value that corrects for comparing sources with different redshifts. Blain et al. (2002) addressed the use of the K-correction constant in magnitude calculations and argued that including it does not make a significant difference until redshifts of about 5. Because none of the galaxies in this study have redshifts that exceed 1, we have excluded K correction constants from the calculation of absolute magnitudes.
Since luminosity is directly related to absolute magnitude, it was simple to calculate solar luminosities. The formula reads,

Equation 3 (3)

where the absolute visual magnitude of the Sun is Mo.
To calculate stellar mass from luminosity, one needs a stellar mass to light ratio. If no ratio was applied and the luminosity was determined to be equal to the stellar mass, one would be assuming that every star in the galaxy observed is comparable to the Sun in the power of light it emits to the amount of mass it contains. This obviously is not the case, but it is practically impossible to take photometric counts of every star in a galaxy and determine its mass to light ratio, so astronomers have developed other methods of determining mass to light ratios for entire galaxies based on their color. We employ a formula of Bell et al. (2003) with a 0.15 dex adjustment for the Kroupa IMF,

Equation 4 (4)

along with published B-V color indices, to calculate stellar mass to light ratios for each galaxy. Included in the Bell et al. (2003) paper are zero point (y-intercept) adjustments for different published initial mass functions. Because we have assumed the Kroupa IMF for the galaxies, we adjusted accordingly. Once we calculated the ratios using the above formula, we multiplied the luminosity by that factor to arrive at the galaxy’s stellar mass.
As mentioned above, dynamical mass can be calculated with rotational velocity and distance from the center of the galaxy using a rearranged version of the circular rotational velocity formula,

Equation 5  (5)

Using published rotation curves, we calculated the dynamical mass of the galaxies using the formula,

Equation 6 (6)

Statistical Tests

A linear regression test was performed to test for a relationship between gas mass (by percentage) and morphological type. As the value for NGC 4682 appeared to be an outlier, a second test was performed excluding it (Table 4). An adjusted R-squared value was calculated by,

Equation 7 (7)

where p is the total number of explanatory variables in the model (not including the constant term), and n is the sample size. This adjusted value accounts for the small sample size in this study.

Table 4. Linear Regression models for Gas Mass content vs Type.

Table 4. Linear Regression models for Gas Mass content vs Type.

 Results

From the data, there appears to be a decrease in stellar mass content between Sa and Sb type galaxies, apart from NGC 4594 (Figure 4, Table 2). There also appears to be a slight decrease in stellar mass content between Sb and Sc type galaxies, but because of the size of the sample, the significance of this decrease cannot be tested.

Figure 4. Graph of dark matter content vs morphological type. Error bars shown in black.

Figure 4. Graph of dark matter content vs morphological type. Error bars shown in black.

We also present the variation of neutral hydrogen gas content as a function of morphological type (Figure 5). Although there appears to be an increasing trend in gas content in later type galaxies, NGC 4682 seems not to follow this trend. With all conditions met, two linear regression models were calculated: one inclusive of all the data from the sample and one that ignored the data from NGC 4682. The coefficient of determination greatly increased with the exclusion of the data point from NGC 4682. The second model produces an R-squared value of 0.84, thus 84% of the variation in gas content is accounted for by the morphological type (Table 4). While this is an indicative result, the adjusted R-squared value is a more representative number to explain the strength of correlation because it accounts for the size of our small sample. Still, at 0.63, the adjusted R-squared value shows a moderately strong positive correlation between gas content and morphological type.
We believe that the large deviation seen in the gas content of NGC 4682 is not intrinsic, but rather due to the method used to obtain that value. All other gas mass values were sourced from published papers, but the gas mass value for NGC 4682 was calculated from a proportion given in Young and Scoville (1991). In a survey of 150 galaxies, they also present a positive trend in gas content versus later morphological types. While their proportions do not agree with the data that has been collected with this sample, the similarity of their findings adds validity to this experiment.

Figure 5. Graph of gas mass content percentage vs morphological type.

Figure 5. Graph of gas mass content percentage vs morphological type.

Uncertainties were accounted for in the dark matter fractions for both uncertainties found in the published values as well as those calculated from the data taken. We use the absolute uncertainties published alongside the distances from astronomical papers cited. The relative uncertainties of these range from 0.2% to 1.3%. When using APT to calculate apparent magnitudes, an uncertainty of +/- 0.01 mag is adopted because although APT returns values with more than two decimal places, most published values only specify magnitudes to the hundredths place. Therefore, we take 0.01 as an artificial smallest increment for the uncertainty. Lastly, uncertainty in the mass to light ratios was accounted for per the note made in Bell et al. (2003) that “Scatter in the above correlations is ~0.1 dex for all optical M/L ratios…” These uncertainties were propagated through the calculations and are visible as error bars in the figure of the total mass content breakdown (Figure 1).
Considering the abnormality in the gas mass content of NGC 4682, the dark matter fractions seem to have an upwards trend towards later type galaxies, with the exception of NGC 4594 (Table 5). Because the number of galaxies from each morphological type does not exceed 10, the dark matter fractions are neither averaged nor used to conduct a statistical test as the sample size would greatly decrease the power of the test. While NGC 4594 disrupts the trend in the data, it is beneficial to the study because it opens the experiment to further investigation.

Table 5. Derived Dark Matter proportions of Observed Galaxies.

Table 5. Derived Dark Matter proportions of Observed Galaxies.

Discussion

In the data that have been presented, a negative trend between stellar mass and morphological type is observed. While this trend is notable in evaluating the possible causes for a trend in dark matter content as a function of galaxy morphology within this sample, it is not universally significant. Calvi, Poggianti, Fasano, & Vulcani (2011) provided evidence that the morphological-mass relation changes with global environment and concluded that galaxy stellar mass cannot be the only factor influencing the morphological distribution of galaxies.
The validity of the luminosity data is supported by comparing the observed apparent magnitudes of the sample with published values. Most observed magnitudes were within a few tenths of a magnitude from published values, with the largest deviation being 0.9 mag (Table 6).

Table 6. Comparison of Apparent Magnitudes with Published Values.

Table 6. Comparison of Apparent Magnitudes with Published Values.

A positive trend in gas mass content with morphological type is observed, and disregarding data from NGC 4682 as a possible outlier, a moderately strong positive correlation is found in a linear regression model. Dark matter fractions appear to increase from Sa galaxies to Sb galaxies, except for NGC 4594. The relationship between Sb and Sc galaxy dark matter fractions is harder to discern if there is a trend at all between them.
The properties of the Seyfert galaxies in the sample appeared similar to the non-active galaxies for the most part. Although NGC 7314’s gas content fraction was less than half that of the other type Sc galaxy, NGC 4682, we have already pointed out above that the method for obtaining the gas mass value for NGC 4682 was different than the rest of the galaxies, so we do not attribute this to its active nuclei characteristics. NGC 4565 (Seyfert 1) had a comparable gas mass proportion to NGC 2841, another type Sb spiral galaxy, but a significantly lower stellar mass percentage. This is puzzling because Seyferts are noted for their luminous nuclei, which would give a larger stellar mass value. On the other hand, the Seyfert 2 galaxy NGC 4378 produced very similar proportions of gas and stellar mass as the regular type Sa galaxy, NGC 6314 (Figure 1, Table 5).
While we are unable to statistically prove that there is an increasing trend in dark matter content in later-type spirals, the results hint that there may be some authenticity to this relationship that would require further experimentation to confirm.
If this study were to be expanded on, a larger sample of galaxies would make any trends in dark matter or otherwise more apparent. As Calvi et al. (2011) found that environment was a confounding variable that affected the stellar mass-morphological distribution of galaxies, and it is also known that there are multiple correlations between galactic properties and environment (Weinmann et al., 2006), we would recommend sampling from a variety of galactic environments to eliminate this variable in the event that dark matter is also tied to environment. Radio astronomy observations could be performed to gather gas mass data from atomic hydrogen lines as well as rotation curves data to add consistency to the variables.
It is thought that in the early universe, dark matter and gas halos clustered and merged to form spiral galaxies (Coil, 2013). This study provides an opportunity to understand more about the role dark matter plays in the evolution of galaxies. As we discover more information about how different types of spiral galaxies are formed, a trend found between dark matter and galaxy morphology could be useful in predicting the life cycles of spiral galaxies.
Acknowledgements
I would like to thank Dr. Amy Lovell at Agnes Scott College for her help arranging telescope time at both Kitt Peak National Observatory and Cerro Tololo Inter-American Observatory, her training given on using these telescopes and the software necessary to process the images, and her continual support throughout this process.

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Is Unpaved Road Dust Near Fairbanks, Alaska a Health Concern? Examination of the Total and Bioaccessible Metal(loid)s

doi:10.22186/jyi.33.1.8-18

Abstract | Introduction | Methods | Results | Discussion | Conclusions |Acknowledgements | 
References | PDF

Abstract

Recent studies highlight the health risks associated with toxic metal(loid)s [e.g., arsenic (As), zinc (Zn), and lead (Pb)] in dust from mining operations, urban settings, and rural roads. To have a deleterious health effect, inhaled or ingested metal(loid)s must dissolve under conditions in the lung or gastrointestinal tract. In this study, we determined total and physiologically-soluble fractions of metal(loid)s in road dust from four sites in east-central interior Alaska.  Total As and antimony (Sb) were enriched up to 26.2 and 53.7, respectively, in dusts relative to average crustal abundance. Several elements such as nickel (Ni), As, and Sb were highly to moderately soluble in simulated lung fluids (7-80%, 15-51%, and 5-42%, respectively). Nickel and As exceeded the EPA inhalation risk unit, which is an exposure level of minimal risk. Despite several elements being highly soluble in simulated gastric fluids, including Ni, copper (Cu), As, and Pb, only As samples exceeded the oral reference dose for children (based on total elemental concentrations) in some samples. The highest exposure risks identified in this study are inhalation of As and Ni present in road dust and ingestion of As-containing dust, especially by children. Additional studies would be needed to further quantify the health risk posed by road dust in this region.

Introduction

Road Dusts

Numerous studies report enrichment of potentially toxic metal(loid)s (e.g., As, cadmium (Cd), Cu, Pb, Zn, and Ni) along roadways of all kinds (e.g., Apeagyei, Bank, & Spengler, 2011; Meza-Figueroa et al., 2016; Witt, Shi, Wronkiewicz, & Pavlowsky, 2014). However, few have focused on dust in (sub)arctic environmental conditions (Brumbaugh, Morman, & May, 2011; Hasselbach et al., 2005; Moghadas et al., 2015; Norman et al., 2016; Shotyk et al., 2016; Walker & Everett, 1987). Road dust is a potential source of human exposure to toxic metal(loid)s (Colombo, Monhemius, & Plant, 2008; DeWitt et al., 2016; Garcia-Rico et al., 2016; Witt et al., 2014) and is of particular concern because the small particles (< 70 µm) are wind-transportable (Gillette & Walker, 1977; Kok, Parteli, Michaels, & Bou Karam, 2012). Small particles (< 45 µm) are commonly also enriched in toxic metal(loid)s (Meunier, Koch, & Reimer, 2011).

Metal(loid) enrichments in road dust have been correlated with a variety of natural and anthropogenic sources (Charlesworth, De Miguel, & Ordonez, 2011). Tires are a source of Zn, and brake parts have high concentrations of iron (Fe) and Cu (Apeagyei et al., 2011). Catalytic converters on vehicles are a source of platinum group elements (Colombo et al., 2008). Use of studded tires, a common practice across Alaska, is also a source of metal(loid)s in road dust (Norman et al., 2016). Studies of metal deposition in paved roadside snow banks in high latitude environments, where snows persist for several months, reported enrichments in Pb, Zn, Cd, Cu, and Ni (Moghadas et al., 2015).
The Alaskan Department of Environmental Conservation has examined road dust in several villages and measured 24-hour averages of particulate matter up to 608 µg m-3, which exceed the federal standard of 150 µg m-3 (AK DEC, 2011). A study that looked at total and bioaccessible metal concentrations along mining haul road in Alaska found enrichment of Zn, barium (Ba), Cd, and Pb in road dust, moss, and other higher vegetation (Brumbaugh et al., 2011), with a spatial extent of at least 25 km in the downwind direction. Another study reported deposition rates of 500 g m-2 (at 8 m from the road), resulting in a layer of dust up to 10 cm deep and measurable deposition of dust at 1000 m from the road along the Dalton Highway, an unpaved road from Fairbanks to Prudhoe Bay, Alaska (Walker, & Everett, 1987). Dust generated on the haul road contains elevated metal(loid) concentrations and has also been linked to local vegetation changes, increased pH near the road, and thawing of ground ice (Myers-Smith, Arnesen, Thompson, & Chapin, 2006; Walker & Everett, 1987). These studies highlight the potential for unhealthy dust concentrations and enrichment of toxic metal(loid)s in road dust around Fairbanks.
Fairbanks has a long history of mining and is located in the Tintina Gold Province, containing a series of epizonal mercury (Hg)-Sb-As-gold (Au) gold vein deposits (Gough & Day, 2007). In addition to the mineralization, mafic and ultramafic lithologies present in the region may be sources of Ni, chromium (Cr), Fe, manganese (Mn), and Co (Gough & Day, 2007; Wang et al., 2007). Wang et al. (2007) also found the surficial soil concentrations of As and Sb ranging between 3 to 410 mg kg-1 and 0.4 to 24 mg kg-1, respectively, elsewhere in Alaska’s interior. In interior Alaska, unpaved roads may sometimes be constructed using mine overburden and waste rock, which could contain elevated concentrations of potentially toxic metal(loid)s (FHA, 2016; H. Schaefer, personal interview, November 16, 2016). Road construction and driving on unpaved roads where potentially toxic elements are present may loft metal(loid)-bearing particles, thereby making them available for ingestion or inhalation.

Health Effects of Toxic Metals

Nutrient and micronutrient metal(loid)s have biochemical and physiological roles within the body, but may also be toxic depending on concentration. However, As, Ni, Sb, and Pb have no known biological function and have well-documented deleterious health effects (e.g., Chang, Magos, & Suzuki, 1996). Exposures are classified as either acute, meaning a short-term, generally higher dose exposure, or chronic, meaning a lower dose that is encountered over a longer period. Both acute and chronic exposures can have deleterious health effects, but chronic exposures are commonly harder to connect directly with health effects.
Arsenic has been reported to disrupt biological processes, including cellular respiration, DNA replication and DNA repair (Tchounwou, Yedjou, Patlolla, & Sutton, 2012). Additionally, As is highly carcinogenic and has been linked to respiratory disease, cardiovascular disease, anemia, gastrointestinal distress, nervous system disorders, and other negative health conditions (ATSDR, 2007). Nickel is also carcinogenic and has been linked to respiratory and renal necrosis, birth defects, immune system alteration, and other disorders (ATSDR, 2005). Antimony is also potentially carcinogenic and has been linked to decreased respiratory function, gastrointestinal distress, optic disorders and reproductive disruption (ATSDR, 1992). These are just a few of the potential health impacts of exposure to three specific metal(loid)s, which lead to concerns regarding elevated levels of potentially toxic metal(loid)s found along roadways.
Although regulation is determined by total elemental concentrations, negative health impacts are controlled by the fraction of metal(loid) solubilized under physiologically relevant conditions or bioaccessibility. Bioaccessibility is controlled by mineralogy, speciation, oxidation state, particle size, and encapsulation (Plumlee, Ziegler, & Lollar, 2005). Small particles (< 45 µm) are typically more bioaccessible than larger particle sizes, likely due to higher surface area (Meunier et al., 2011; Ruby et al., 1999). The two major exposure routes for dust and soil are ingestion or inhalation (Fig. 1). Accidental ingestion may result from hand-to-mouth transfer (especially by children) or food-bound particles (Taylor & Williams, 1995). Particles less than 250 µm typically stick to children’s hands and can be swallowed (EPA, 2012). Inhalation depends largely on particle size. Particles that are less than 4 µm will enter the lungs, and particles that are approximately 2 µm can reach and remain in the alveoli for months to years (Lundborg, Falk, Johansson, Kreyling, & Camner, 1992; Plumlee et al., 2005). Physiologically based extraction tests (PBETs) provide insight to the bioaccessible fraction of metal(loid)s by interaction with gastric or lung fluids (EPA, 2012). PBET analysis is becoming a widely used and verified alternative for animal models in examining bioaccessibility of several common contaminant metal(loid)s (Wragg et al., 2011).

Figure 1. Exposure mechanisms for dust particles.

Figure 1. Exposure mechanisms for dust particles.

Goals of This Study

The combination of enrichment of toxic elements in road dust, high numbers of unpaved roads in Alaska, and high concentrations of dust measured on unpaved roads highlights the need for direct examination of road dust to assess the health impact of dust from unpaved roads near growing population centers. This study examines the total and bioaccessible fractions of potentially toxic metal(loid)s in Alaskan road dust collected in interior Alaska, mostly near Fairbanks (Fig. 2). The goal is to compare the total and bioaccessible metal(loid) concentrations to relevant soil screening levels and the EPA reference dose or inhalation unit risk to determine if these road dusts represent a health risk to the Fairbanks population. This study will provide initial assessment of the health risk from unpaved road dust in interior Alaska.

Figure 2. Major Alaskan roads outlined in blue with study areas in red. Expanded views of Fairbanks sample sites and Denali Highway. Images from Google earth and Alaska Department of Transportation (AK DOT, 2016).

Figure 2. Major Alaskan roads outlined in blue with study areas in red. Expanded views of Fairbanks sample sites and Denali Highway. Images from Google earth and Alaska Department of Transportation (AK DOT, 2016).

Methods

All glassware was acid washed prior to use, and all chemicals were ACS grade or better.

Sampling Sites and Collection Procedures

Dust samples were collected from interior Alaska in summer 2014 using a variety of methods, including passive samplers mounted on the roadside and mounted on a vehicle as well as artificial agitation (Table 1). Sample sites were selected to represent a variety of environments, including: within Fairbanks, AK city limits, residential communities near Fairbanks, and along the Denali Highway, an unpaved secondary highway often used for recreational activities (Fig. 2). Passive samplers (TE-200-PAS from Tisch, Village de Cleves, OH) were continuously deployed for 28 days from July until August. Because of the low dust volumes collected in the passive samples, additional samples were collected at the end of the summer using a leaf blower to agitate dust. The leaf blower was used to loft particles into a clean plastic garbage bag. Low dust accumulations in the passive samples are attributed to the unusually wet conditions during the summer of 2014. From June and the end of August 2014, interior Alaska received 29.5 cm of precipitation. The 30-year average for the same period is 13.7 cm (Wendler, 1995). The Denali Highway sample was collected in a passive sampler mounted on the back of a vehicle during a single round trip from Fairbanks across the Denali Highway from the Cantwell side. Another sample was collected from undisturbed boards underneath a house located approximately 35 m from the proximal road, which is assumed to represent years of metal(loid) deposition.

Table 1. Sample descriptions and summary of collection methods.

Table 1. Sample descriptions and summary of collection methods.

Size Fractionation

A brass sieve set (numbers: 10, 18, 35, 60, 120, 200, 325, and 400 mesh, Cole-Parmer USA Standard Test Sieve, Vernon Hills, IN) was used to separate dust particles into size fractions of greater than 2 mm, 2 – 1 mm, 1 – 0.5 mm, 500 – 250 µm, 250 – 125 µm, 125 – 75 µm, 75 – 45 µm, 45 – 37 µm, and smaller than 37 µm.  After manual dry sieving each sample to less than 0.5 mm, the remaining sample was added in the top of the stacked sieve set, and the whole assembly was dry-agitated by a sieve shaker (Cole-Parmer SS-3CP, Vernon Hills, IN) on setting 10 for 30 minutes. After 30 minutes, the sieve assembly was removed from the agitator, and each size fraction was weighed, transferred to labeled glass vials, and stored at room temperature. PBETs, described in section 2.4, were performed on the less than 250 µm fraction for gastric and the less than 37 µm fraction for lung extractions.

Total Metal(loid) Concentrations

The total metal(loid) concentrations were determined by dissolving duplicate samples using sodium peroxide sinter followed by inductively-coupled mass spectrometry (ICP-MS) elemental analysis (Cotta & Enzweiler, 2012; Longerich, Jenner, Fryer, & Jackson, 1990; Meisel, Schoner, Paliulionyte, & Kahr, 2002). Briefly, each sample was ground by hand using an agate mortar and pestle until the sample passed through a 200-mesh sieve. To a clean, glassy carbon crucible, 100 mg of sample and 600 mg of sodium peroxide (Alfa Aesar, L11306) were added. This was then mixed thoroughly with a plastic spatula, and an additional 10 – 30 mg of sodium peroxide was sprinkled over the top of the mixture. The crucibles were heated at 480°C for 30 minutes in a muffle furnace, cooled to room temperature, and placed in acid washed Nalgene bottles. To each sample, 10 mL of 18 MΩ H2O was slowly added. Then, 2 mL of 13% HNO3 followed by an additional 2 mL of 35% HCl were added. Additional 18 MΩ H2O was added to bring the total mass of sample, sodium peroxide, acids, and water to 100 g. The samples were diluted by a factor of 10 and analyzed using ICP-MS.

Physiologically-Based Extraction Tests (PBETs)

PBETs have been applied to a variety of samples, including mine wastes (Ruby, Davis, Schoof, Eberle, & Sellstone, 1996; Schaider, Senn, Brabander, McCarthy, & Shine, 2007), reference minerals (Colombo et al., 2008; Lundborg et al., 1992; Takaya et al., 2006), soils (Drysdale et al., 2012), and road dust (Brumbaugh et al., 2011; Dodd, Rasmussen, & Chenier, 2013; Witt et al., 2014).  PBETs were performed to determine the fraction of the dust that would dissolve in simulated gastric (EPA, 2012) and alveolar fluids (Drysdale et al., 2012; Takaya et al., 2006). Briefly, a 0.4 M glycine solution adjusted to pH 1.5 using OmniTrace HCl and a freshly prepared modified Gamble’s solution (Drysdale et al., 2012) were heated to 37°C using an incubator-shaker table (Lab-Line 4628; Melrose Park, IL) to mimic the gastric and alveolar fluids, respectively. In a 15 mL Falcon tube, 0.1 g of sieved dust (less than 250 µm  for gastric and less than 37 µm for lung) was combined with 10 g of the simulated gastric or lung fluids. These tubes were incubated and shaken at 37°C at 60 rpm in the dark for one hour for the gastric and seven days for the lung extraction. The experiments were terminated by centrifuging at 8,500 g for ten minutes prior to decanting and filtering the supernatant using an acid washed 0.2 µm polypropylene filter (Acrodisc GHP). The pH of the supernatant was measured, and the supernatant was acidified to a pH less than two with OmniTrace nitric acid prior to dilution and analysis by ICP-MS.

Elemental Analysis

Elemental analyses of PBETs and sodium peroxide sinter digestions were performed by ICP-MS. Analyses were performed using a 7500ce ICP-MS (Agilent; Santa Clara, CA) at the Advanced Instrumentation Laboratory (AIL) at the University of Alaska Fairbanks. Both external and internal standards (i.e., scandium (Sc), germanium (Ge), yttrium (Y), rhodium (Rh), and iridium (Ir)) were used in calibration to measure the elements of interest (vanadium (V), Cr, Mn, Ni, Cu, Zn, As, molybdenum (Mo), silver (Ag), Sb, Ba, Pb, and thorium (Th)). Reagent blanks, method blanks, and aqueous standard reference materials (NIST 1640 and SLRS-5) were measured once during the ICP-MS run, in addition to 2% nitric acid blanks and mid-level standards analyzed at least every 15 samples as quality control measures.

Enrichment Factors

Similar to previous studies (e.g., Meza-Figueroa et al., 2016), enrichment factors were calculated based on the average crustal abundance for the given element (Fig. 3; Rundick, 2006). A value above one indicates enrichment, a value of one indicates no enrichment or depletion, and a value below one indicates depletion relative to the average crustal abundance. Total crustal abundance values, shown in Table 2, were used to calculate enrichment factors using the following equation:

UR equation 1                                              (1)
Figure 3: Enrichment factors of dust relative to average crustal abundance. Values above one are enriched whereas values below one are depleted relative to average crustal abundance values tabulated in Table 2.

Figure 3. Enrichment factors of dust relative to average crustal abundance. Values above one are enriched whereas values below one are depleted relative to average crustal abundance values tabulated in Table 2.

Health-Based Screening Levels

The Environmental Protection Agency (EPA) of the United States publishes tables of health-based generic screening levels for chemicals, including metal(loid)s of health concern, for the purpose of establishing screening levels at contaminated sites (EPA, 2016). Within these tables, values for oral reference doses (RD) and inhalation reference concentrations (RfCi) can be found, which represent estimates of the maximum daily oral or inhalation dose of a chemical that is “likely to be without an appreciable [noncancerous] risk of deleterious effects during a lifetime,” even for sensitive subgroups (EPA, 2016). Similarly, values are also tabulated for inhalation risks as inhalation unit risk (IUR), representing “the upper bound excess lifetime cancer risk from continuous exposure to an agent at a concentration of 1 µg m-3 in air.” These values will be used to contextualize total elemental concentrations of potentially toxic elements.

Table 2. Total metal(loid) concentrations. For comparison, average crustal abundance, U. S. Environmental Protection Agency residential soil screening levels, oral references doses, and reference inhalation concentrations are also tabulated. Average and one standard deviation of duplicate measurements are reported for unsieved samples. Values below detection limit are indicated by BDL, and values not reported are indicated by NR. a Values for upper continental crust from (Rudnick, 2006). b From Regional Screening Level (RSL) Resident Soils Table (EPA, 2016). c Tabulated as reference dose (RfD0) or SFO, an estimate of a daily oral exposure to the human population that is likely to be without an appreciable risk of deleterious effects during a lifetime in EPA (2016). d Tabulated as inhalation unit risk (IUR), an upper bound excess lifetime cancer risk estimated from continuous exposure to an agent at a concentration of 1 µg m-3 in air (EPA, 2016). e Tabulated as chronic inhalation reference concentration (RfCi; EPA, 2016).

Table 2. Total metal(loid) concentrations. For comparison, average crustal abundance, U. S. Environmental Protection Agency residential soil screening levels, oral references doses, and reference inhalation concentrations are also tabulated. Average and one standard deviation of duplicate measurements are reported for unsieved samples. Values below detection limit are indicated by BDL, and values not reported are indicated by NR. a Values for upper continental crust from (Rudnick, 2006). b From Regional Screening Level (RSL) Resident Soils Table (EPA, 2016). c Tabulated as reference dose (RfD0) or SFO, an estimate of a daily oral exposure to the human population that is likely to be without an appreciable risk of deleterious effects during a lifetime in EPA (2016). d Tabulated as inhalation unit risk (IUR), an upper bound excess lifetime cancer risk estimated from continuous exposure to an agent at a concentration of 1 µg m-3 in air (EPA, 2016). e Tabulated as chronic inhalation reference concentration (RfCi; EPA, 2016).

Comparison of Data with Ingestion Reference Dose

In order to compare measured values with oral reference dose (Table 2), several assumptions are required to arrive at the same units. These calculations were performed using a precautionary approach and are intended to provide an upper safe exposure limit. However, the calculations presented do not account for any hand-to-mouth transfer of metal(loid)-bearing particles or enrichment of metal(loid)s in smaller size fractions that are more readily lofted by vehicles. For ingestion calculations, it was assumed that all inhaled dust particles were captured in the mouth, nose and throat, and ingested rather than inhaled. The data shown in Figure 4A estimates the mass of each metal(loid) in air an adult may be exposed to per hour of metal(loid) exposure [Eq. (2)]:

ur equation 2     (2)

where total metal(loid) concentrations from Table 2 were converted to milligram metal(loid) per microgram dust. The dust concentration, mass of dust per volume air, was assumed to be 300 µg m-3, a value arrived at using roughly half the highest measured dust concentration in an Alaskan village (608 µg m-3) and double the state and federal exposure standard (24-hour average of 150 µg m-3; AK DEC, 2011). Ingestion exposure calculations [Eq. (2)] require an estimation of the air volume exchange by an average adult (80.7 kg and 15 breaths min-1) or child (20 kg and 20 breaths min-1) assuming that 7 mL kg-1 of air is exchanged (Fleming et al., 2011; McDowell, Fryar, Ogden, & Flegal, 2008; Ricard, 2003). This calculation yielded 0.508 m3 hr-1 and 0.168 m3 hr-1 tidal volume for adults and children, respectively. The individual element hourly exposures were calculated using adult tidal volumes.
The daily exposure reference dose (listed in Table 2) were converted to milligram metal(loid) per hour and adjusted for body mass for comparison with measured values using [Eq. (3)] and shown in Figure 4A:

ur equation 3            (3)

Comparison of Data with Inhalation Unit Risk

Dust exposure from inhalation (Fig. 4B) was calculated by multiplying the concentration of metals in dust by the concentration of dust, according to the following equation, and directly compared with inhalation unit risk listed in Table 2:

ur equation 4  (4)

 This assumes that all material was of a particle size suitable for inhalation and was all inhaled into the lungs and remained in residence for seven days, and that none of the inhaled material was trapped in the upper respiratory tract and ingested.

Figure 4. Health risk associated with road dust. Exposure potentials for each sample in addition to EPA reference doses or concentrations are shown for A. gastric, and B. inhalation exposures to road dust.

Figure 4. Health risk associated with road dust. Exposure potentials for each sample in addition to EPA reference doses or concentrations are shown for A. gastric, and B. inhalation exposures to road dust.

Percent Bioaccessibility

The percent bioaccessibility (Table 3) was calculated to better evaluate the fraction of the total element present in the sample liberated in simulated physiological conditions. Reported errors were propagated using the standard deviations of replicate measurements of the total and bioaccessible metal(loid)s using standard methods (Harris, 2010). The following equation was used to calculate percent bioaccessibility:

ur equation 5  (5)

Results

Total Metal Concentrations

The dust examined were generally below the EPA residential soil screening levels, based on the total elemental analysis results shown in Table 2 (EPA, 2015). However, all of the dust exceed the screening levels for As and possibly Cr. The reference dose for Cr depends on the oxidation state of Cr, and Cr (III) has a much higher screening level than Cr (IV) as shown in Table 2. Most Cr-bearing minerals contain Cr (III) and would, thus, not exceed screening levels. Additionally, several soils were above the screening level for V, Mn, As and Sb. There are also distinct differences in the elemental content of the two Gold Hill Rd. dust samples, with the dust from under the house being higher in all elements except Sb, Mo, and As (Table 2).
Compared with the average crustal abundance (Table 2), which results in enrichment factors shown in Figure 3, all samples are at least somewhat enriched in As, Ag, Mo, and Sb, and most are enriched in Mo and Ba. The highest enrichment factors were observed in As and Sb, the two elements with the lowest oral reference doses (Table 2), which is indicative of their toxicity. However, these values are within the range of As and Sb values for surficial soils previously reported elsewhere in the Tintina Gold Province (Wang et al., 2007). The highest observed enrichment factors were for As and Sb in rural residential road dust (Moose Mountain and Gold Hill Rd.; Fig. 3). The Denali Highway sample is enriched relative to crustal abundance in Cu, Zn, As, Mo, Ag, Sb, Ba, Pb, and Th. In contrast, only As, Mo, Ag, Sb, and Ba are enriched relative to crustal abundance in the O’Connor Rd (Fig. 3). Of the two Gold Hill Rd. samples, the dust sample from under the house is closer to average crustal abundances for nearly all elements than the road dust sample (Fig. 3).
When total toxic metal(loid) composition is compared with EPA reference doses or concentrations, the potential gastric exposures are well below the reference doses for all elements except As, for which total concentrations exceed the reference dose for children at the Moose Mountain Rd. and Gold Hill Rd. sites (Fig. 4A). However, for inhalation, road dust also exceed the inhalation risk unit for Ni and for As in some samples (Fig. 4B).

Bioaccessibility

Gastric bioaccessibility, shown in Table 3, demonstrates that the elements examined have a wide range of bioaccessibility. Vanadium, Cr, Mo, and Th have low gastric bioaccessibility with less than 4% of the total element solubilized in simulated gastric fluids. Other elements, including Ba, Zn, and Sb exhibit low to medium bioaccessibility with 1-22% gastric liberation. Moderate to high gastric bioaccessibility was observed in Mn, As, and Ag (5-60% liberation). Consistently high bioaccessibility was observed in Ni, Cu, and Pb with gastric liberations up to 82%. The percent bioaccessibility of metal(loid)s in artificial lung fluid extractions are also shown in Table 3, and in general, the lung bioaccessibility is much lower than in the gastric extractions. Elements with concentrations below detection limits for all samples (Cr, Mn, Zn, Ag, Pb, and Th) were excluded from the table. Several other elements had lung bioaccessibility < 5% for all samples, including V, Cu, and Ba, with most values less than 1%, indicating low bioaccessibility. Molybdenum lung bioaccessibility was mostly below detection limits, but 21% of Mo was liberated in the Denali highway sample. This sample has a similar total Mo concentration as other samples, thus the differences in bioaccessibility points to mineralogical control over physiological solubility. Moderate to high bioaccessibility were observed for Ni, As, and Sb at 7-80%, 15-52%, and 5-43% bioaccessibility, respectively. These trends are not consistent with the trends observed in the gastric extractions.

Table 3. Percent gastric and lung bioaccessibility of metal(loid)s. Values are reported as the average percent solubility relative to the total and one standard deviation of triplicate measurements. Physiologically-based extraction tests were performed on less than 250 µm for gastric and less than 37 µm for lung extractions isolated by dry sieving. For lung bioaccessibility, Cr, Mn, Zn, Ag, Pb, and Th were excluded from the table because all extracted solutions were below detection limits.

Table 3. Percent gastric and lung bioaccessibility of metal(loid)s. Values are reported as the average percent solubility relative to the total and one standard deviation of triplicate measurements. Physiologically-based extraction tests were performed on less than 250 µm for gastric and less than 37 µm for lung extractions isolated by dry sieving. For lung bioaccessibility, Cr, Mn, Zn, Ag, Pb, and Th were excluded from the table because all extracted solutions were below detection limits.

Discussion

Given the mafic and ultra-mafic lithologies and mineralization in the Fairbanks region, it is perhaps unsurprising that As and Sb are enriched in all samples (Fig. 3). The urban residential site of the O’Connor Road is the least contaminated with only moderate enrichments (Fig. 3). This sample site has higher population density, and thus, the dust has the largest potential to affect residents. It is positive that it was found to be the less enriched in As, Sb, and other potentially toxic elements. The elemental profile of the Gold Hill Under House site dust is much more similar to average crustal abundances than other samples, including Gold Hill Road dust, potentially indicating multiple sources of dust contribute to the dust accumulation under the house.
Conversely, the Denali Highway site has the highest number of elements enriched, although enrichments were modest. Despite the isolated location, this unpaved highway receives heavy use during the summer, especially during hunting seasons by off road vehicles. Since many of the vehicles traveling the road are not equipped with microparticulate air filtration, these travelers could be exposed to a substantial amount of dust that is enriched in toxic elements (Fig. 1) at concentrations that are near or above EPA reference doses (Fig. 4). All-terrain vehicles have been observed to loft up to 160 mg m-3 of less than 10 µm particles at the breathing level of ATV operators (Goossens & Buck, 2014).  The same study demonstrated that these particles remain suspended for at least one minute, creating excellent conditions for inhalation and ingestion of suspended particulates. Thus, land use is an important factor when assessing acute and chronic exposures. The Denali Highway site would likely provide acute exposure while recreating whereas residents near the Moose Mountain Rd., Gold Hill Rd., or O’Conner Rd. sites would more likely be exposed to chronic doses.
The total and bioaccessible fractions of toxic metal(loid)s in Alaskan road dust were collected to assess if these road dust represent a health threat to nearby communities. Schaider et al. (2007) observed generally higher rates for gastric rather than lung bioaccessibility in Pb and Zn reference minerals, which is consistent with the findings of this study.The wide range of metal(loid)s liberated under physiological conditions is typical because bioaccessibility depends on particle size, identity of metal(loid)-bearing mineral phases, metal(loid) speciation, exposure of metal(loid)s to extraction fluid, and identity of extraction fluid (Ruby et al., 1999). Evidence of this point is seen in the very different rates of metal(loid) release that are observed in the fraction of metal(loid)s liberated in the PBET literature. In addition, the variety of PBET procedures may also affect the results, as has been reported in a review of 96 lung bioaccessibility articles (Wiseman, 2015).

Health Risk Associated With Alaskan Road Dust

This study identifies Ni and As as possible inhalation health risks and As as a potential ingestion risk based on the potential for exceeding recommended reference doses or concentrations (Fig. 4). Several elements were quite soluble under simulated gastric conditions, indicating they could be bioaccessible, most notably Ni, Cu, and Pb (Table 3). Nickel and Cu are micronutrients; however, Pb has no known biological function and is toxic and highly soluble under gastric conditions. This highlights that bioaccessibility largely controls the potential for toxicity and should be considered.
Arsenic and Sb show enrichment in all samples (Fig. 3), especially the rural residential samples, and both are highly toxic metalloids with low inhalation concentrations and the lowest oral reference doses of all elements examined in this study (Table 2). Both Sb and As form trivalent and pentavalent oxyanions in the surficial environment. In both cases, the trivalent form is both more mobile and more toxic than the pentavalent form, and more reduced forms are even less soluble and toxic (ASTDR, 1992; Ruby et al., 1999). The connection between As mineralogy and bioaccessibility has been extensively examined. Arsenic bioaccessibility can be summarized as: arsenopyrite is less bioaccessible than As-bearing Fe (oxy)hydroxides and Mn oxides, which is less bioaccessible than As oxides and oxyanions (Ruby et al., 1999). Substantially less work has been performed on Sb bioaccessibility, but Sb in 16 soil types yielded low (< 10%) gastric bioavailability in juvenile swine models (Denys et al., 2012). Additionally, a low correlation was observed between the bioaccessibility in swine models and simulated gastric extractions, which liberated up to 18% Sb. Other studies have reported up to 6% liberation of Sb from mine tailings in simulated gastric extractions (Li et al., 2014). The authors are not aware of any studies examining liberation of Sb in lung fluids.
In simulated lung fluids, Drysdale et. al (2012) reported Ni bioaccessibility of less than 4.2% in soils from a mining and smelting-impacted region. Others have examined Ni and Ni oxide solubility in artificial lysosomal lung fluids and found up to 88% release of NiO within 24 hrs (Mazinanian, Hedberg, & Wallinder, 2013). Again, the range of these results highlights the importance of speciation.
In this study, moderate to high rates of Pb liberation from gastric solutions were observed (16-82%), whereas no liberation was detected in simulated lung fluids. Schaider et al. (2007) observed widely variable rates of gastric liberation in reference minerals PbS and PbCO3 (3% and 97%, respectively).  The same study reports lung bioaccessibilities of 0.4 and 14%, for PbS and PbCO3, respectively, using a PBET solution mimicking conditions inside alveolar macrophages. The high bioaccessibility lends insight into the Pb mineralogy, as many studies have examined the link between Pb mineralogy and bioaccessibility or bioavailability (Casteel, Weis, Henningsen, & Brattin, 2006; Ruby et al., 1996; Ruby et al., 1999). The most bioaccessible phases with gastric liberation values near 100% include lead associated with Mn oxides and PbCO3, and medium bioaccessibility (~50%) include lead associated with Fe (oxy)hydroxides and Pb phosphates (Casteel et al., 2006), which may be present in these samples. Although high bioaccessibility is somewhat concerning due to the potential for hand-to-mouth transmission and ingestion of Pb-bearing particles, the total Pb concentrations in these dust are well below the EPA soil screening level for residential soils (Table 2).

Limitations of Study

The limited number of samples and variety of sampling methods employed were the principal limitations of this study. While a variety of sampling methods were necessary to collect enough sample mass, this may affect the apparent composition of dust samples. Ideally, dust samples would all have been collected passively. The unusually wet weather in summer 2014 may have lowered the number of airborne particulate concentrations and may have also facilitated surface water transport of small particles that would otherwise have been available for lofting. Additionally, the moisture present may have altered the mineralogical composition of dust or rainwater may flush ions that might otherwise form small, bioaccessible, readily lofted mineral salts at the road surface. Artificial agitation was capable of lofting larger particles than a passing vehicle. Thus, sieving was used to minimize the impact of this on the study results, and the results presented here are expected to be illustrative of potential human exposures.
This work relied on the ability of PBETs to predict the bioaccessibility of a variety of elements. Whereas PBETs are an attractive method for supplementing resource-intensive animal model studies, they have not been validated for all elements. PBETs have been extensively applied to assessing the bioaccessibility of Pb, As, and Ni in geological media, but less work has been done on Sb (Denys et al., 2012; Ng, Juhasz, Smith, & Naidu, 2013; Wiseman, 2015; Wragg et al., 2011). Despite the method not having validation for all elements, the method still lends qualitative insight into solubility of elements under physiological conditions for elements that have yet to be validated.

Conclusions

This work provides an initial assessment of the health risk represented by metal(loid)s in Alaskan road dust. The dust was consistently enriched in As, Ag, and Sb, but perhaps the most significant risk observed based on comparisons to reference doses or concentrations is from inhalation of As and Ni or ingestion of As, especially at the rural residential locations where As is most enriched. These results are consistent with the medium to high bioaccessibility of As and Sb in lung fluids and As in the gastric extraction. Lead is highly bioaccessible in the gastric extraction but is not identified as a health risk based on total Pb concentrations. Although road dust is not the only source of dust to residents, this study shows that road dust can be highly enriched in As and Sb, and highlights the potential for health risks associated with chronic exposure to road dust from the Fairbanks area.
Additional measurements should be conducted using passive samplers to collect dust dispersed by passing vehicles rather than artificial agitation, which were used in this study due to the wet summer. Additionally, a direct measurement of lofted particulate matter concentrations at adult and child breathing levels would further refine the assumptions used in this work. It is clear that further study of exposure risks is warranted to evaluate the risk posed to residents.

Acknowledgements

The authors gratefully acknowledge the contributions of Karen Spaleta at the Advanced Instrumentation Laboratory (AIL) at the University of Alaska Fairbanks for excellent technical support. Material support for this work was provided by the University of Alaska Fairbanks Office of Undergraduate Research & Scholarly Activity (URSA) Student Project Award and Biomedical Learning and Student Training (BLaST) Scholars Program. Work reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under three linked awards number RL5GM118990, TL4 GM 118992 and 1UL1GM118991.

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The Efficacy of Aqueous False Yam (Icacina oliviformis) Tuber Extract Against Cowpea Aphids (Aphis craccivora Koch)

doi:10.22186/jyi.32.3.7-22-24

Abstract | Introduction | Methods | Results | Discussion | Conclusions |Acknowledgements | 
References | PDF

Abstract

Cowpea aphids (Aphis craccivora) are a major pest of cowpeas (Vigna unguiculata), which feed on the plant at the vegetative stage by sucking the sap. In an endeavour to find an new method of controlling aphid infestation in the Northern Ghana, an in vitro study was carried out to investigate the effect of different concentrations of aqueous false yam (Icacina oliviformis) tuber extract against cowpea black aphids (Aphis craccivora Koch). It was observed that a 55% dilution was as effective as undiluted extract after 72 hours of exposure. However, mortality was significantly affected (p = 0.05) with respective least significant differences (LSD’s) values of 0.63 and 0.75, by soaking periods, concentration, and duration of exposure when extract was applied topically to the aphids as well as the soaked leaf, indicating that the most plausible mode of action of the false yam tuber extract would be toxic by direct contact and through feeding. The effectiveness of this study may help poor farmers save the cost of chemical pesticides and prevent their hazardous impact on the environment.

 Introduction

Cowpea (Vigna unguiculata) is a grain legume grown widely in the tropics and the subtropics. It has been used as the primary substitute for a protein source in many urban and rural homes in Nigeria, Niger, Burkina Faso, Myanmar, Cameroon and Mali (Ishiyaku et al., 2010). However, the crop is subject to significant loss or complete failure due to severe pest infestation (Singh & Emdam, 1979). Some of these insect pests may include Aphis craccivora, Maruca vitrata, Megalurothrips sjostedti and Callosobrucus maculates. It is estimated that the average yield for monoculture crop is about 1500kg/ha in the United States, 6500kg/ha in South America and Asia but often below 400kg/ha in Nigeria and other growing African countries (Singh, 1986). In spite of the extensive production and the significance of cowpea in Africa, yield per hectare is relatively low (Ofuya, 1997). This has been attributed mainly to the severe infestation and losses caused by various insect pests in the field and during storage (Jackai & Daoust, 1986).

Cowpea aphids (Aphididae: Hemiptera) are predominant class of insect pest with global distribution. Aphids attack about 50 crops in 19 plant families due to their high association with host plants in the family Leguminosae (Radha, 2013).

The promising option to farmers in controlling these pests was the indiscriminate use of chemical agro-pesticides (Shannag, Capinera, & Freihat, 2014). This phenomenon has led to the eradication of beneficial insect species, which is a threat to human health and environmental hazards. This calls for the search and use of environmentally friendly biopesticides instead of conventional pesticides to control aphid’s outbreak (Leake, 2000). False yam tuber (Icacina oliviformis) extract has been shown to have some toxic compounds that need to be investigated to find its effect on cowpea aphid. Its effect on aphids has been attributed to the presence of inhibitory factors (resins) which make sap unpleasant to feed on (Dei, Bacho, Adeti, & Rose, 2011). The false yam plant’s year-round availability makes it economically affordable to be used as a substitute biopesticide for the conventional pesticides. This study investigates the efficacy of aqueous false yam tuber extract used as a biopesticide as an alternative for chemical pesticides against cowpea aphids.

Materials and Methods

Sourcing and Processing of False Yam

The false yam tubers were obtained from the wild within the environments of the Nyankpala campus of the University for Development Studies, Tamale, Ghana. The tubers were then manually dug with a hoe and a cutlass before processing.

The dug tubers were peeled and chopped into pieces of about 2cm with a cutlass and washed to remove any soil particles and dirt. The treatments were prepared by soaking 2.5kg of chopped tubers in water for 48 hours, 72 hours and 96 hours in order to obtain varying concentrations. After the respective periods of soaking, the preparation was decanted to obtain the solution.

Insect culture

A stock culture of A. craccivora was maintained on cowpea under laboratory conditions at room temperature and 70.0±5.0% relative humidity (RH) and a photoperiod of 16 hours of light: 8 hours of dark (L16:D8) for several generations. In all experiments, the insects were put on fresh cowpea plants cultivated in small plots and enclosed individually in netted cloth, the tops of which were covered with muslin held in place with rubber bands.

Toxicity and Mortality Test – Leaf Soaking

Cowpea leaves were soaked in 15-20mls of test solution for 1min. The leaves were removed and allowed to dry for 1hr. Fifteen aphids (3-14 day old) were then released to feed on the leaves. The leaves together with the aphids were transferred into plane/transparent bottle containers and covered with muslin cloth to allow air circulation. Five different concentrations were used and each was replicated three times. Adult mortality was calculated after 24, 48 and 72 hours of exposure.

Contact Toxicity by Topical Application

Tests for contact toxicity by topical application were carried out in the laboratory at room temperature, 65–70% RH and under an L16:D8 photo-regime. Three- to fourteen-day-old adult aphids of mixed sex were transferred into glass Petri dishes (7.0cm diameter) lined with moist filter paper to keep the aphids immobile. The immobilized insects were picked individually and 10µl of each diluted false yam tuber extract was applied to the dorsal surface of the abdomen of each using a micro-pipette applicator. Thirty adults in three replicates of 10 insects each were treated with false yam tuber extract preparation. Water only was used as a control. After treatment, the adults were transferred into glass Petri dishes (10 insects/Petri dish) containing fresh cowpea leaves to serve as food. Insect mortality was recorded at 24, 48 and 72 hours after treatment.

Results

Mortality was significantly affected at p = 0.023 by soaking periods, concentration, and duration of exposure (Figure 1). Aphid mortality increased with increasing concentration of false yam tuber extract except at 70% concentration, where a decrease in mortality was observed, after which mortality rate increased again with increasing concentration. It is of interest to note that 55% concentration was as effective as 100% concentration (zero dilution) and this was true for all soaking periods.

Figure 2. Effect of concentration of false yam tuber extract and period of exposure of aphids on mortality. LSD (p < 0.05) = 0.63.

Figure 1. Effect of soaking period and concentration of false yam tuber extract on aphid mortality by topical application. LSD (p < 0.05) = 0.63

 Effect of Concentration of False Yam Tuber Extract and Period of Exposure of Aphids on Mortality

The longer the exposure period of the different treatments on the aphids, the more toxic they were (Figure 2). The difference in mortality rate was statistically different (p = 0.01).

Figure 2. Effect of concentration of false yam tuber extract and period of exposure of aphids on mortality. LSD (p < 0.05) = 0.63.

Figure 2. Effect of concentration of false yam tuber extract and period of exposure of aphids on mortality. LSD (p < 0.05) = 0.63.

Effect of Concentration and Duration of Exposure on Aphid Mortality by Soaked Leaves

Aphid mortality was significantly affected at p < 0.05 by concentration, soaking period and duration of exposure. Concentration-duration of exposure effect was also observed. The relative efficacy of the false yam tuber extract was significantly affected by soaking periods. Data showed that the concentration of the extract increased with increasing soaking period. Results showed significant differences with increasing concentration and duration of exposure at p= 0.028. The highest mortality of aphids was recorded after 72 hours of exposure to the various treatment concentrations as shown in Figure 3.

Figure 3. Effect of concentration of false yam and the duration of aphid exposure on mortality by indirect leaf spray (p = 0.028).

Figure 3. Effect of concentration of false yam and the duration of aphid exposure on mortality by indirect leaf spray (p = 0.028).

Discussion

The present study shows that high concentration of false yam tuber extract has a significant impact on aphid mortality. Aphid mortality increased with increasing concentration of false yam tuber extract.

The reduction in aphid numbers of the false yam treated plants was due to the antifeedant effect of the tuber extract which led to the starvation and the death of the aphids. According to Fay (1987), the false yam contains an active compound known as gum resin which makes it toxic. This might have been the major cause of the death of the aphids. However, the effects were not immediate since aphids were found actively moving on the leaves a few days after the treatment application. Radha (2013) reported similar incidence of the delay of the effect of neem kernel extract against cowpea aphids. The average aphid mortality increased with the increasing concentration and the duration of aphid exposure. However, extremely high concentrations may cause dryness to the leaves which might kill the plant at field conditions. The pre-trial experiment carried out showed that when high amount of undiluted false yam tuber extract was applied to the cowpea plant, the leaves dried up within 24 hours. The mean aphid mortalities were higher at 72hrs of exposure and at 55-85% of the false yam tuber extract mixed with their respective percentages of water.

Aphid mortality was significantly affected by the soaking period, concentration and duration of exposure. Vanhaelen et al., (1987) reported that the natural product of the false yam plant contains toxic compounds called terpenes that limit its utilization as food. The components of the compounds such as Icacenone, sitosterol 3-0-B-D-glucopyranoside and sigmasterol 3-0-B-D-glucopyranoside could act as insecticides (Vanhaelen et al., 1987).

Aphids have soft bodies that contain openings called spiracles used for respiration, delivering oxygen to the insect’s body tissue (SF Gate, Home Guide, 2007). The ability of the extract to kill aphids when topically applied may be as a result of two reasons: either the extract is toxic resulting in direct death or indirectly by blocking the aphid’s spiracles. SF Gate (2007) report in the control of A. craccivora showed that insecticidal soap killed most aphids within one hour, suggesting suffocation as a cause of death. Insecticidal soaps and oils (petroleum-based horticultural oils or plant derived oils such as neem or canola oils) have best been used to kill aphids primarily by smothering (Flint, 2013). However, the effect of the false yam tuber extract was realized after 24hrs with increasing concentration, suggesting that aphid mortality was more likely to be due to the toxicity of the extract rather than suffocation. This results agrees with Flint (2013) who stated that insecticidal soaps, neem oils and horticultural oils kill aphids present on the day they are sprayed.

Conclusion

The present study demonstrated that aqueous extract from the false yam tuber has potential as an insecticide for cowpea black aphids. This study shows that the different concentrations of the aqueous I. oliviformis had significant effects on the cowpea aphids. The results obtained revealed the effectiveness of the tuber extract with increasing concentration and soaking period. They further suggested that 55% & 70% concentrations of the false yam extracts proved effective after 72 hours soaking.

Among the trials to determine the plausible mode of action of the extract, toxicity by topical application and indirect leaf spray proved to be the effect mode of action. Toxicity and mortality of aphids were greatly influenced by both topical application and antifeeant test. The results obtained also revealed that the extract requires 72 hours after application for it to be effective as control mechanism on the target organism. This may explain why the extract was not effective in the field due to the frequent rains soon after the treatment applications.

Acknowledgements

To the almighty God, to whom I really owe my thanks for making my dreams come true for this work would not have been realized. I might not have come this far without the outstanding support of Mr. Samuel Erasmus Afrane for funding this work. My greatest gratitude also goes to my project supervisor, Dr. Nelson Opoku. God bless you for your advice, direction, and motivation to undertake this great piece of work. To Dr. Charles Adarkwah, thank you for your immense contribution and ideas which helped to accomplish the objectives of this work. I would also like to thank Dr. Francis K. Amagloh for his help during my data analysis.

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The in vitro Studies of the Inhibitory Effect of Green Tea (Camellia sinensis) on Pseudomonas aeruginosa Treated Contact Lenses

doi: 10.22186/jyi.32.4.25-29

Abstract | Introduction | Methods | Results | Discussion | Conclusions |Acknowledgements | 
References | PDF

Abstract

Pseudomonas aeruginosa is the leading cause of ocular infections in those who wear contact lenses. Others have previously done a study using the antioxidant selenium-coated contact lenses to inhibit the bacteria in an animal model. However, selenium is very toxic even in small quantities. In this study, green tea which is known for its antioxidant property was used to treat contact lenses. We did a disc diffusion assay using different concentrations of green tea and compared with black tea to study their inhibitory effect on P. aeruginosa. The 100mg/mL of green tea was the most effective concentration that maintained a uniform solution and produced the clear zone. Contact lenses were treated with 100mg/mL of green tea before being exposed to P. aeruginosa and another experiment was done by coating the contact lenses with the bacteria and treated with the green tea afterward. We found that green-tea-treated contact lenses had fewer bacteria, with a 41.9% inhibition rate when compared to the control but the results were not significant. However, green tea significantly reduced the bacteria present on contact lenses (p < 0.05). In conclusion, green tea shows an inhibitory effect on Pseudomonas aeruginosa and has the potential to be used as a cleaning solution on contact lenses.

Introduction 

Contact lenses are known to be susceptible to bacterial attachment and result in infections such as corneal ulcers and microbial keratitis in the eyes (Preechawatmd, Ratananikommd, Lerdvitayasakul, & Kunavisarut, 2007; Stenson, 1986). The risk of infection is often due to poor personal hygiene in the handling of the lenses and the storage cases, which provide the ideal environment for the growth of bacteria (Dantam et al., 2016; Szczotka-Flynn, Pearlman, & Ghannoum, 2010). Many lens cleaning solutions also risk being contaminated with the bacteria from the contact lens (Lin, Kim, Chen, Kowalski, & Nizet, 2016; Posch, Zhu, & Robertson, 2014; Szczotka-Flynn et al., 2010). Pseudomonas aeruginosa is the leading cause of contact lens-related ocular infections due to the nature of the bacteria’s ability to survive in the eye, on the contact lens, and in the storage case (Stapleton & Carnt, 2012; Weissman, Mondino, Pettit, & Hofbauer, 1984; Willcox, 2007). It is an opportunistic pathogen in humans and can typically be found in a biofilm environment with some surface or substrate (Todar, n.d.). In addition, P. aeruginosa is able to attach and cause an infection of the cornea of the eye (Fleiszig, Efron, & Pier, 1992; Klotz, Misra, & Butrus, 1990; Willcox, 2007).

Many studies have been done by using organic and inorganic substances as the coating for the contact lens to prevent bacterial attachment. Concanavalin A, a lectin, was used in an injured rabbit’s cornea in order to compete with P. aeruginosa for the binding of cornea cells (Blaylock, Yue, & Robin, 1990). Despite the ability to reduce the number of bacteria found on the cornea, it is toxic in high amounts (Nopanitaya, Hanker, & Tyan, 1976; Tiegs, Hentschel, & Wendel, 1992). Matthews et al. (2006) performed another study on the coating of contact lenses with selenium to inhibit the growth of P. aeruginosa in vitro and in vivo. It was found that the coating allowed for extended-wear over a period of two months and prevented P. aeruginosa colonization with no adverse effects on the cornea. Selenium is an antioxidant but it is also toxic even in small quantity and can cause neurotoxicity, cancer, and harm to an unborn child (Vinceti et al., Wei, Malagoli, Bergomi, & Vivoli, 2001).

Tea is another very powerful antioxidant, and has been shown to have antibacterial, anti-inflammatory and anticancer properties (Chan, Lim, Chong, Tan, & Wong, 2010; Chan, Soh, Tie, & Law, 2011; Hamilton-Miller, 1995; Piljac-Žegarac, Šamec, & Piljac, 2013; Siddiqui et al., 2016). Flayyih et al. (2013) found black tea was able to inhibit P. aeruginosa isolated from the corneal scrapings of various eye infections. Green tea, which comes from the same plant as black tea, Camellia sinensis, has also been associated with many medical properties, including anticancer properties, improvement in cardiac health, and lowering stress (Cooper, Morré, & Morré, 2005; Thangapazham et al., 2007). The green tea contains more of the specific antioxidant polyphenols, catechins, than black tea (Hamilton-Miller, 1995), which makes it more potent in antioxidant properties (Ojo, Ladeji, & Nadro, 2007; Serafini, Ghiselli, & Ferro-Luzzi, 1996; Yokozawa et al., 1998). In addition, the catechins play an important role in the inhibition of bacterial growth (Bai et al., 2016; Kumar et al., 2012; Taylor, Hamilton-Miller, & Stapleton, 2005;) by inducing the stress-related genes (Liu et al., 2013).

P. aeruginosa is also known for its biofilm properties and its antibiotic resistance (Costerton, Stewart, & Greenberg, 1999; Mah et al., 2003). Abidi et al. (2014) found different plant extracts exhibited antimicrobial properties against the biofilm of the bacteria and Radji et al. (2013) also incorporated green tea into drug therapy to combat antibiotic resistant bacteria.

The goal of this research was to study the effect of green tea on inhibiting P. aeruginosa from attaching and growing on contact lenses in vitro. We hypothesized that the use of green tea, through coating, would effectively inhibit P. aeruginosa from attaching and growing on contact lenses and that the use of green tea would effectively reduce the amount of P. aeruginosa present on infected contact lenses. The alternate use of organic products as cleaning materials are common nowadays and green tea has been used as a cosmetic for repairing dry skin (Aburjai & Natsheh, 2003). The significance of our study is the indication of the possibility of using green tea as an alternate cleaning solution for contact lenses.

Materials and Methods

Relationship Between the Optical Density and Cell Number of P. aeruginosa

Seven 1:2 serial dilutions were done using a prepared culture of P. aeruginosa (Carolina Biological Supply Company, NC) in 0.1M Phosphate Buffered Saline (PBS) to obtain the relationship between the optical density and cell number of bacteria. Bacteria were grown at 37ºC for 24 hours, and the optical densities (OD) of the stock and each dilution (1:2, 1:4, 1:8, 1:16, 1:32, 1:64, and 1:128) were measured at a 600nm wavelength using a DU 720 General-Purpose UV/Vis Spectrophotometer (Beckman Coulter, NJ). Each dilution was further diluted to obtain the countable numbers between 30-300 colony forming units (CFU), and 0.1ml or 0.5ml were placed on two nutrient agar plates (Carolina Biological Supply Company, NC). All plates were then incubated at 37ºC for 24 hours. The number of bacteria that grew on the plates was then used to calculate the original amount of bacteria and was plotted against the optical density. Four trials were done, and a linear regression curve was plotted to obtain the number of bacteria in which OD =1.

Preparation of Green Tea and Black Tea Solution and Disk Diffusion Assay

Four different concentrations (25mg/mL, 50mg/mL, 100mg/mL, and 200mg/mL) of green tea and black tea solutions were prepared with autoclaved water. We purchased and used organic Green tea Matcha (Kiss Me Organics, WY) and organic Black tea Matcha (Pure Matcha, JP) to prepare 1mL of each concentration. We also measured the pH of each tea solution.

We plated the 107CFU of P. aeruginosa on Mueller Hinton Agar plates (Difco Laboratories, MD). Sterile plain disks (Fisher Scientific, MA) were dipped into each of the four different concentrations of prepared black and green tea respectively. The disks were dipped, dried and placed in the center of each section of the plates and grew at 37ºC for 24 hours. After examining the plates, we measured the diameter of the zone of inhibition for each concentration. Four trials were done and the averages of the inhibition clear zones were calculated.

Testing the P. aeruginosa on the Green Tea-Treated Contact Lenses

Six new Acuve Moist brand contact lenses (Johnson & Johnson, NJ) of -3.00 prescription strength were dripped dry from the original packaging using forceps sterilized in ethanol and transferred to sterile vials. Three vials each containing 1mL of PBS and three vials each containing 1mL of 100mg/mL of green tea were used. For each vial, one contact lens was placed in the solution for one hour. These treated contact lenses and three more from the original packaging used for a positive control were then removed, placed in separate sterile vials containing 1mL of 106CFU of P. aeruginosa and incubated for another hour. The contact lenses from the original packaging were also placed in a 1mL solution of PBS to serve as a negative control. We diluted the solutions from each treatment were diluted at a 1:10 dilution with PBS, and 0.1mL of each was placed on two nutrient agar plates to recover the bacteria. The plates were incubated at 37ºC for 24 hours and the bacteria were enumerated. A total of seven trials were done.

The Inhibitory Properties of Green Tea on P. aeruginosa Treated Contact Lenses

Another six new -3.00 prescription contact lenses were dripped dry from the original packaging using sterilized forceps and transferred to separate sterilized vials containing 1mL of 106CFU/mL of P. aeruginosa and incubated on an orbital shaker rotator (Model KJ-201BD, Laboratory Sky, CN) for one hour. After that, we dripped, dried and transferred the contact lenses to sterile vials with three containing 1mL of autoclaved water, and three containing 1mL of 100mg/mL of green tea. The contact lenses were incubated in these solutions for one hour at room temperature. All these treated contact lenses, along with three more from the original packaging used for a negative control, were removed and placed in sterile vials containing 1mL of PBS to recover the bacteria. The solutions were diluted at a 1:10 dilution with PBS and 0.1mL of each solution was placed on two nutrient agar plates. The plates were incubated at 37ºC for 24 hours and the bacteria were enumerated. A total of seven trials were done.

Statistical Analysis

A one-way ANOVA with a post-hoc Tukey test was run on vassarstats.net. The test was used to determine the difference between the treatment groups within each experiment. The significance was set at p < 0.05. Figures were created using Microsoft excel with values shown as mean and Standard Error of Mean (SEM) for the discrepancy between different trials.

Results

Optical Density and Cell Number

The relationship between optical density at 600nm and the concentration of P. aeruginosa in CFU/mL is shown in Figure 1. The resulting equation of the curve was y=9×108 x-3×107. It was then determined the concentration of the P. aeruginosa was 8.7×10^8CFU/mL when OD600 nm is equal to 1.

Figure 1. The correlation of the number of P. aeruginosa (CFU/ml) versus optical density.

Figure 1. The correlation of the number of P. aeruginosa (CFU/ml) versus optical density.

Disk Diffusion Assay

The pH of both black tea and green tea were 7. Both black tea and green tea were found to have an inhibitory effect on P. aeruginosa in the disc diffusion assay, although green tea demonstrated a stronger effect than black tea (Figure 2). Green tea produced larger diameters of the zones of inhibition when compared to those of black tea in the same concentration. The differences between the green and black tea at the concentrations of 50mg/ml and 100mg/ml were significant (p < 0.05). However, the differences between the concentrations 25mg/ml and 200mg/ml in both types of tea were marginally significant (p = 0.06). The 100mg/ml concentration depicts the most significant difference between the them, with an average of a 2.05cm diameter clear zone from the green tea.

Figure 2. The average diameter (+SEM) (cm) of the clear zones when 107 P. aeruginosa was treated with green tea and black tea disc at different concentrations.

Figure 2. The average diameter (+SEM) (cm) of the clear zones when 107 P. aeruginosa was treated with green tea and black tea disc at different concentrations.

The Inhibitory Properties of Green Tea on Contact Lenses

Contact lenses were treated with different solutions followed by the incubation of 1mL of 106CFU of P. aeruginosa for an hour. Contact lenses that were treated with 100mg/ml green tea had recovered 5645.45 ± 2399.70 CFU of P. aeruginosa when compared to the control with PBS that yielded 8690 ± 5232.05 CFU (Table 1). The original package of contact lenses that were incubated with an equal amount of P. aeruginosa had recovered 9722.2 ± 6287.8 CFU of the bacteria (Table 1). The bacterial inhibition rate in percentage was calculated by the difference between the number of bacteria recovered from the original contact lenses without any treatment and the number of bacteria recovered from the treated contact lenses by PBS or green tea, divided by the bacteria recovered from the original contact lenses without any treatment. The green tea-treated contact lenses had a 41.9% inhibition rate when compared to the control that was not treated (p = 0.062). There was no significant difference in inhibiting bacteria when comparing contact lenses treated with green tea to contact lenses treated with PBS (p > 0.05). There was also no significant difference in the number of bacteria found in the original package and PBS treatment (p > 0.05).

Table 1. The average number (± SEM) in CFU and the bacterial inhibition rate in percentage of P. aeruginosa recovered from contact lenses when treated with different solutions, followed by the incubation with 106 P. aeruginosa for an hour.

Table 1. The average number (± SEM) in CFU and the bacterial inhibition rate in percentage of P. aeruginosa recovered from contact lenses when treated with different solutions, followed by the incubation with 106 P. aeruginosa for an hour.

Testing of Green Tea on Bacteria Treated Contact Lenses

The contact lenses that were incubated with 106CFU of P. aeruginosa for an hour and treated with green tea afterwards were found to have significantly less bacteria (2887 ± 1441.18 CFU) when compared to contact lenses with equal amount of bacteria and treated with autoclaved water (61500 ± 3535.53 CFU) (Table 2) (p < 0.05). The control with the original contact lenses had no bacteria recovered.

Table 2. The average number (±SEM) in CFU of P. aeruginosa recovered from contact lenses when treated with different solutions after the contact lenses were incubated with 106 P. aeruginosa for an hour.

Table 2. The average number (±SEM) in CFU of P. aeruginosa recovered from contact lenses when treated with different solutions after the contact lenses were incubated with 106 P. aeruginosa for an
hour.

Discussion

In this study, we used green tea in an attempt to inhibit the P. aeruginosa from growing on the contact lens. The Kirby Bauer disk diffusion assay was done to confirm the bactericidal properties of the green tea. Our results showed that green tea produced a larger diameter of the zone of inhibition when compared to the same concentration of black tea. In previous studies, the minimum inhibitory concentration (MIC) of black tea alcohol extract was found to be 400mg/mL on P. aeruginosa isolates with a 20mm clear zone on the agar gel diffusion experiment (Flayyih et al., 2013). This coincides with our findings with the use of green tea on P. aeruginosa in the similar experiment, which produced a 20.5mm sized clear zone; however, the concentration of green tea used was only 100mg/mL. When compared to the same concentration of black tea (100mg/ml), only a 12.5mm sized clear zone was produced in this study. Several attempts to obtain the MIC of the green tea used in our studies were failed because of the dark green color of the tea interfering with the optical density reading (data not shown). Overall, green tea is more potent than black tea in inhibiting bacteria as demonstrated in previous studies (Almajano, Carbó, Jiménez, & Gordon, 2008). The stronger antioxidant properties of the catechins in green tea may attribute to its stronger antibacterial power. A previous study confirmed the antibacterial properties of the catechin was correlated to its antioxidant capacity on a phospholipid membrane model (Caturla, Vera-Samper, Villalaín, Mateo, & Micol, 2003).

In the in vitro contact lens studies, when the green tea was used for the coating of the contact lens before the treatment with P. aeruginosa, results showed that green tea does not effectively prevent P. aeruginosa from attaching and growing on contact lenses. Another experiment was performed by incubating contact lenses with P. aeruginosa followed by the treatment of green tea. The bacteria recovered from the contact lenses treated with green tea afterward showed a significant difference when compared to the control treated with autoclaved water. From our studies, it can be concluded that green tea showed a significant inhibitory property on the P. aeruginosa treated contact lenses though it was not able to remove all the bacteria from the contact lenses. This may be due to the high inoculum of bacteria (106) used in the experiment and the short treatment time of an hour. In a previous in vitro study, maximum numbers of P. aeruginosa were found to adhere to the contact surface within an hour, however, it would take generally 24 hours for the biofilm to be formed (Dutta, Cole, & Willcox, 2012). In addition, different isolates of P. aeruginosa may affect the ability of their attachment to the contact lenses (Klotz, Butrus, Misra, & Osato, 1989). The strain used in this experiment was for laboratory teaching purpose and not a clinical isolate, and their attachment on the contact lenses may vary. The Etafilcon A type of contact lenses of Acuve Moist with a high water content of 58% with ionic polymers was chosen to use in our study. The nature of the contact lens material also affects the attachment of the bacteria (Dutta et al., 2012; Miller & Ahearn, 1987). Different strains of P. aeruginosa were found to have less adhesion on the lens composed of ionic polymers than non-ionic polymers (Miller & Ahearn, 1987). Therefore, the adhesion measured in this experiment should have fewer bacteria. The pH 7 was found to be the optimal environment for the attachment of P. aeruginosa on the contact lens (Miller & Ahearn, 1987) and in our studies, the pH of the green tea was found to be neutral at 7.

Lastly, green tea’s active ingredient, epigallocatechin gallate (EGCG), has been shown to have the greatest antioxidant and antibacterial properties (Gordon & Wareham, 2010; Steinmann, Buer, Pietschmann, & Steinmann, 2013; Vidigal et al., 2014). Further studies can be done by testing this active ingredient against the P. aeruginosa on the contact lens. In addition, the green tea we used is the Matcha-powdered form, and future experiments can use other forms of green tea to reconfirm the hypothesis. For the future application, the effects of green tea on the contact lenses material and human eye should be tested.

Acknowledgements

We would like to thank Natcha Rummaneethorn and Charlene Caoili for their support in this research. This research project was also supported by Manhattanville College Biology Department and was funded by the Castle Scholars Honors Program at Manhattanville College.

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Addition of Zinc, Manganese, and Iron to Growth Media Triggers Antibiotic Production in Bacterial Isolates From the Lower Atmosphere

doi:10.22186/jyi.32.2.7-11

Abstract | Introduction | Methods | Results | Discussion | Conclusions |Acknowledgements | 
References | PDF

Abstract

With the growing number of antibiotic-resistant pathogens, there is a need for new antibiotics. Bacteria within the order Actinomycetales produce the majority of known antibiotic compounds but harbor cryptic secondary metabolic pathways that likely produce thousands more antibiotics awaiting discovery. This has recently renewed interests in bioprospecting for novel Actinomycetales in underexplored environments, such as the lower atmosphere, and activating cryptic secondary metabolic pathways in previously characterized members of this order. Many antibiotic-producing Actinomycetales have complex metabolic interactions with trace metals (such as iron, zinc, and manganese); adding these metals to bacterial growth media may trigger antibiotic production, which could lead to the discovery of novel drugs that may otherwise be overlooked during screening in the absence of such metals. To test this hypothesis, a collection of bacterial isolates, composed primarily of taxa within the order Actinomycetales, was obtained from the lower atmosphere. In support of our hypothesis, we found that several isolates produced antibiotics in the presence of trace metals, but not in their absence. Further investigation is required to identify the compounds being produced, but our results suggest that adding trace metals to growth media during high-throughput screening efforts may be a fruitful approach for identifying antibiotic-producing bacteria in large culture collections.

Introduction

According to the World Health Organization (2015), pathogens are becoming more antibiotic-resistant than ever before, which is a problem caused and exacerbated by the overuse and misuse of existing antibiotics. As a result, there is a desperate need for novel antibiotics, but the approval rate of clinical antibiotics continues to decline (Donadio, Maffioli, Monciardini, Sosio, & Jabes, 2010). The order Actinomycetales within the phylum Actinobacteria, includes the genus Streptomyces, which produces two-thirds of known antibiotics (Barka et al., 2016; Watve, Tickoo, Jog, & Bhole, 2001). This genus is predicted to produce 150,000 to almost 300,000 antimicrobial compounds still awaiting discovery (Watve et al., 2001). Therefore, predictions about the next source of novel antibiotics often point to Actinomycetales (Bérdy, 2012; Goodfellow & Fiedler, 2010). 

Goodfellow & Fiedler (2010) stated that by using selective techniques, such as sampling from understudied and extreme environments, novel Actinobacteria may be discovered. One such environment, is the lower atmosphere (Weber and Werth, 2015), which is defined by as the first 20km above ground level (Womack, Bohannan, & Green 2010). A multitude of both culture-dependent and culture-independent studies demonstrate that Actinobacteria are an omnipresent component of the aerial environment (Bowers et al., 2011; Fahlgren, Hagström, Nilsson, & Zweifel, 2010; Polymenakou, 2012; Shaffer & Lighthart, 1997; Weber & Werth, 2015). The lower atmosphere has several distinct advantages in the search for novel Actinomycetales. The lower atmosphere is a highly variable environment (Fahlgren et al., 2010) with dramatically oscillating temperatures (-56°C to 15°C), low relative humidity and high levels of ultraviolet radiation (Womack, Bohannan, & Green, 2010). These conditions may select for Actinomycetales over faster-growing bacterial taxa, such as many Proteobacteria (Weber & Werth, 2015). Exploring the lower atmosphere, given its potential to harbor antibiotic-producing bacteria, with selective cultivation methods may lead to the discovery of novel species and antibiotics. While not as commonly studied for their antibiotic-producing capabilities, Bacillus is another genus of bacteria that contains antibiotic-producing members and is commonly found in the lower atmosphere (Athukorala, Dilantha Fernando, & Rashid, 2009; Fahlgren et al., 2010; Shaffer & Lighthart, 1997). 

Another approach to discover novel antibiotic compounds is to place a single organism under a wide array of culture conditions in an attempt to activate cryptic metabolic pathways that may produce uncharacterized metabolites with antibiotic properties known as the ‘One-Strain-Many-Compounds’ approach (Bode, Bethe, Höfs, & Zeeck, 2002). This method focuses on elucidating the up to 50 different pathways that have been found in a single Streptomyces strain, instead of screening massive numbers of isolates (Barka et al., 2016) in one set of conditions. The disadvantage of the latter approach is that it frequently results in the disposal of isolates, which do not produce antibiotic compounds under the one set of conditions tested (Bode et al., 2002). Culturing an isolate under altered conditions has been shown to encourage the production of antibiotics. Modifications of growth media that have been found to elicit the expression of secondary metabolite pathways include metals and amino acid additions (notably tryptophan; Palazzotto et al., 2015), as well as varied phosphate concentrations, carbon sources, nitrogen sources, and pH, to name a few (Bode et al., 2002; Iwai & Ōmura, 1982; Marwick, Wright, & Burgess, 1999; Weinberg, 1990).

Along these lines, amending growth media with trace metals is possibly a fruitful approach for activating cryptic secondary metabolic pathways, as trace metals are often required for the expression of secondary metabolite pathways (Haferburg et al., 2009; Huck, Porter, & Bushell, 1991; Iwai & Ōmura, 1982; Weinberg, 1990). Ochi and Hosaka (2013) indicated that rare earth metals and trace metals, notably manganese, zinc and iron may trigger the production of various secondary metabolic pathways. Actinobacteria have complex metabolic interactions with metals (Abbas & Edwards, 1989; Abbas & Edwards, 1990; Haferberg & Kothe, 2007; Paul & Banerjee 1983; Pettit, 2011). For instance, Yamasaki, Furuya, & Matsuyama (1998) showed that the impact of a metal ion on an isolate’s antibiotic production varies depending on the anionic species to which it is associated. Additionally, a single trace metal may inhibit antibiotic production in some cases, but encourage production in others, making trace metals potentially useful in the discovery of novel antibiotics (Abbas & Edwards, 1989; Abbas & Edwards, 1990; Bundale et al., 2015; Huck et al., 1991; Iwai & Ōmura, 1982; Weinberg, 1990). 

In this study, the impact of amending media with three different metals (iron, manganese, and zinc) on the ability of bacterial isolates to produce antibiotics was examined. Isolates were obtained from the lower atmosphere and their ability inhibit the growth of Gram-positive and Gram-negative bacteria was assessed.

Materials and Methods

Air Sampling and Selection of Isolates

Air samples (180L) were collected onto 90mm petri plates containing Tryptic Soy Agar (TSA; Honeywell Specialty Chemicals, Seelze, Germany) amended with 50mg/mL cycloheximide (TSA-C), using a SAS SUPER 180 (Bioscience International, Rockville, MD) on the roof of the Gale Life Science Building on the Idaho State University campus (Pocatello, ID, USA). Plates were incubated at room temperature until Actinomycetales-like morphologies developed (approximately 5 days). Colonies with unique morphologies, several of which were Bacillus-like, were selected for isolation and purification. 

Inhibition Assays

After several streaks for purification, seven isolates of Actinomycetales-like and Bacillus-like morphologies and twelve isolates identified using the 16S rRNA gene from air samples collected between 2013 and 2015 were plated onto 60mm x 15mm petri dishes containing Luria Broth (LB; Thermo Fisher Scientific, Waltham, MA), King’s B (KB; Coldspring Harbor, 2009), tryptic soy agar (TSA), TSA with cycloheximide (TSA-C), TSA with 10mM (NH4)5Fe(C6H4O7)2 (TSA-Fe), TSA with 10mM MnCl2 (TSA-Mn), and TSA with 10mM ZnSO4•7H2O (TSA-Zn), KB with 10mM (NH4)5Fe(C6H4O7)2 (KB-Fe), KB with 10mM MnCl2 (KB-Mn), and KB with 10mM ZnSO4•7H2O (KB-Zn). The isolates were grown for one week at room temperature and then overlaid with molten LB agar amended with 1μL/mL of TSA broth containing either S. aureus or E. coli with average optical densities (measured at 600nm) of 1.885 and 1.648, respectively. Plates were incubated and monitored for zones of clearance around the isolates for seven days, indicating inhibition of S. aureus or E. coli growth.

Determination of Airborne Isolates Taxonomy

The twelve isolates utilized in the second set of inhibition assays were taxonomically identified by sequencing their 16S rRNA genes. DNA was extracted from isolates using the UltraClean Microbial DNA Isolation Kit (Mo Bio, Carlsbad, CA) following the manufacturer’s protocol, except only 25μL of the kit’s solution MD5 rather than 50μL was used in the final DNA elution step. The 16S rRNA gene was amplified using a Mastercycler Pro S (Eppendorf, Hamburg, Germany) in 20μL reaction volumes containing: 1x buffer solution, 200μM dNTPs, 3% DMSO, 0.5μM 27F primer, 0.5μM 1492R primer, 0.03UμL-1 Phusion High-Fidelity DNA Polymerase (Thermo Fisher Scientific, Waltman, MA) using universal 27F 5’-AGAGTTTGATCMTGGCTCAG-3’ and 1492R 5’-TACGGYTACCTTGTTACGACTT-3’ primers using the following PCR conditions: initial denaturation at 98°C for 30 seconds; thirty cycles of 98°C for 30 seconds, 64°C for 45 seconds, 72°C for 30 seconds; final extension at 72°C for five minutes. Amplicons were purified using the MinElute PCR Purification Kit (QIAGEN, Hilden, Germany) and delivered to the Idaho State University Molecular Research Core Facility (Pocatello, ID, USA) for bidirectional sequencing. Contigs were formed from the bidirectional sequence reads using Sequencher version 5.1 (Gene Codes Corporation, Ann Arbor, MI). Sequences were taxonomically identified using the Basic Local Alignment Search Tool (BLAST).

Results

The impact of four different complex media types with and without three different trace metal amendments on the ability of isolates to inhibit the growth of E. coli and S. aureus was tested. Antibiotic production was observed more frequently in media types containing trace metal amendments than in media lacking such amendments (Table 1). Three of the seven isolates inhibited S. aureus growth on all of the complex medias, while six of the seven isolates inhibited the growth of S. aureus on at least one of the trace metal-amended mediums. Many isolates failed to grow on TSA during the E. coli inhibition assay. Therefore, the impact of trace metals on the production of antibiotics that were effective against Gram-negative bacteria is unclear. 

Screen Shot 2017-01-30 at 11.48.32 AM

Table 1. Inhibition of E. coli and S. aureus by isolates. + indicates inhibition of Gram-positive, – indicates inhibition of Gram-negative, a blank space indicates there was no inhibition, ? indicates the results were not conclusive, and X indicates the isolate did not grow.

The inhibitory activity of isolate 31RC1 is of particular interest. This isolate only inhibited the growth of E. coli on TSA media. However, when iron and manganese were added, it became effective only to S. aureus. This indicates that the addition of metals may result in the production of completely different inhibitory compounds.

Eleven of the twelve isolates utilized in the second experiment were genera within Actinomycetales (Streptomyces, Nocardia, Rhodococcus) representing seven different species, and one isolate was a Staphylococcus spp. (Table 2). Inhibition assays were performed on KB media, and KB media amended with 10mM of iron, zinc, or manganese (Table 2).

Table 2. Inhibition of S. aureus by 16s rRNA gene identified isolates. ++ indicates major inhibition, + indicates inhibition, (+) indicates mild inhibition, and – indicates no inhibition.

Table 2. Inhibition of S. aureus by 16s rRNA gene identified isolates. ++ indicates major inhibition, + indicates inhibition, (+) indicates mild inhibition, and – indicates no inhibition.

None of the isolates inhibited E. coli on any media types. When the isolates were assayed against S. aureus, isolates that were identified as the same strain had distinct antibiotic-producing behaviors when trace metals were added. This was the most notable for the three isolates identified as Streptomyces pratensis, which had different inhibition capabilities, but all were able to inhibit S. aureus with the addition of zinc to the media. In some instances, metals improved antibiotic production capabilities especially in isolate 4oA2, where the addition of zinc alone resulted in inhibition. For every isolate that inhibited S. aureus on KB, inhibition was also observed on at least one medium with a metal amendment; while isolate QLW 21 produced no antibiotic on the zinc-amended media, it still produced antibiotics on manganese-amended and iron-amended media. Furthermore, replicate assay plates using the same isolate showed inconsistencies in inhibition such as in the case of isolate QAS 5, which inhibited S. aureus on manganese-amended media on only one of the four replicate assay plates. 

Discussion

With the need for novel antibiotics, there is renewed interest in screening large collections of bacteria to identify antibiotic-producing cultures. There is also renewed interest in identifying alternative sets of culturing conditions to trigger the production of uncharacterized secondary metabolites. By restricting screening assays to one culture condition, bacterial isolates of interest may be prematurely removed from further study. Using an array of conditions for initial antibiotic screens will likely facilitate the discovery of new antibiotics. Our data supports the hypothesis that adding trace metals to bacterial growth media may be an effective way to activate cryptic, secondary metabolite-producing pathways (Haferberg & Kothe, 2013). 

Few metal-facilitated, antibiotic producing pathways have been studied in enough detail to determine the exact role that the metal ions play. It was hypothesized that metal ions are essential cofactors in antibiotic pathways (Iwai & Ōmura, 1982). The well-studied antibiotic production of actinorhodin in S. coelicolor A3(2) found that the addition of zinc to media may have allowed antibiotic producing, zinc-dependent enzymes to function when most intracellular zinc would otherwise be unavailable near the end of the exponential phase, thus predicting that the zinc affects translation directly and indirectly through feedback (Hesketh, Kock, Mootien, & Bibb, 2009).

Our results add to the growing collection of data that indicates that strain-to-strain variation in antibiotic production capacity is large, and culture identification at the species-level is not a reliable predictor of an organism’s ability to produce compounds with antibiotic properties. Schmidt et al. (2009) observed that bacterial isolates identified as the same species, respond to nickel using different mechanisms. This is perhaps a result of genetic mutations affecting antibiotic production akin to those examined by Higo, Hara, Horinouchi and Ohnishi (2012) or plasmid acquisition or loss. For instance, metabolic pathways encoding the production of molecules such as methylenomycin A have been found on transmittable plasmids (Wright & Hopwood, 1976).

Inconsistencies in the antibiotic production between inhibition assay replicates of the same isolate may be a result of the developmental stage reached by the isolate at the time of the assay. The stage of development triggers antibiotic production; generally occurring at the end of the exponential phase (Abbas & Edwards, 1990; Hesketh et al., 2009; Weinberg, 1990), but the mechanism is not entirely understood (Bibb, 2005). Nonetheless, trace metals, even if not the only determining factor, certainly appear to encourage antibiotic production and their addition to bacterial growth media should be considered when screening large collections of isolates or even single organisms for their ability to produce novel antibiotics. 

Conclusions

The lower atmosphere is an accessible source of antibiotic-producing bacteria that should be further studied. Trace metals can trigger the production of antibiotics and should be included while screening isolates for antibiotic activity. Using replicate assay plates of isolates should also be considered during screening, as isolates inconsistently produced antibiotics. Although further study of this phenomenon is needed, it may be the result of the stage of development reached by the test colonies. Lastly, even if a bacterial strain is determined to be common and “well-studied” based on the identity of its 16S rRNA gene sequence, incorporating trace metal amendments into the ‘one-strain-many-compounds’ approach may result in the discovery of novel antibiotics.

Acknowledgements

We thank Abdullah Aljadi, Rachel Clinger, Naomi Veloso and Sora Matsunaga for their work on this study via their participation in the AMOEBA program at Idaho State University (National Science Foundation (NSF DUE 1140286; J.P. Hill, PI), and Jason T. Werth for technical support. Funding for this project was provided by a Dimensions of Biodiversity grant from the NSF (NSF DEB 1241069; C.F. Weber, PI). 

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rClone: A Synthetic Biology Tool That Enables the Research of Bacterial Translation

doi:10.22186/jyi.32.3.7-12-19

Abstract | Introduction | Methods | Results | Discussion | Conclusions |Acknowledgements | 
References | PDF

Abstract

Understanding bacterial translation is of interest to many high school and college biology students, but hands-on research of this process has traditionally been inaccessible because of the expertise required for molecular cloning. There is also a need for more and better translational control elements that can be used for genetic circuits with applications in medicine, biotechnology, bioremediation, and biomaterials. We addressed these needs by inventing, constructing, and testing two new tools called rClone Red and rClone Blue that use Golden Gate Assembly for easy cloning of ribosome binding sites (RBSs), which control the initiation of translation. We validated rClone Red for student research by building libraries of mutated RBSs, selecting members of the library, determining their sequences, and measuring their strengths using a red fluorescence protein reporter gene (RFP). We compiled consensus sequences and tested them in rClone Blue with a blue chromoprotein reporter gene. Because our results showed higher RBS strengths for the blue chromoprotein gene than for the RFP gene, we developed and supported a hypothesis that an anti-RBS sequence was present in the RFP gene that sequestered the RBS with intramolecular mRNA base pairing. We demonstrated that rClone Red and rClone Blue are student-friendly research tools for mutational analysis of bacterial RBSs. In addition, we increased the number of RBS and bicistronic RBS elements for use in genetic circuits. We submitted 65 RBS and 62 bicistronic RBS sequences to the Registry of Standard Biological Parts to make them available to the worldwide synthetic biology research community. 

 Introduction

Gene expression, the process by which the inherited information of genes is used to direct the function of cells, is regulated in all cells because not all genes are needed all the time or under all circumstances (Hijum, Medema, & Kuipers, 2009). Gene expression begins with transcription, the process by which the DNA base sequence of a gene is converted into RNA sequence information. For genes that encode proteins, the messenger RNA (mRNA) product of transcription is used during translation to encode the sequence of amino acids in a protein. The sequence of bases in mRNA is translated by the ribosome, which is composed of a large (50S) and a small (30S) subunit. Translation is initiated when the 16S ribosomal RNA (rRNA) of the small ribosomal subunit base pairs to a conserved sequence in the mRNA, called the ribosome binding site (RBS; Figure 1).

Figure 1. The RBS of a mRNA base pairs with the 16S rRNA of the 30S ribosomal subunit. A. Simple RBS (B24 from this study). B. Bicistronic C dog RBS (C10 from this study). Dashed lines indicate hydrogen bonds from paired RNA bases. mRNA = messenger RNA; rRNA = ribosomal RNA; RBS = ribosome binding site; C dog = bicistronic RBS.

Figure 1. The RBS of a mRNA base pairs with the 16S rRNA of the 30S ribosomal subunit. A. Simple RBS (B24 from this study). B. Bicistronic C dog RBS (C10 from this study). Dashed lines indicate hydrogen bonds from paired RNA bases. mRNA = messenger RNA; rRNA = ribosomal RNA; RBS = ribosome binding site; C dog = bicistronic RBS.

After the small ribosomal subunit binds to the RBS, the large ribosomal subunit attaches to the small subunit to begin translation of the mRNA into a chain of amino acids. The mRNA bases are read as triplet codons that interact by base pairing with anticodons in transfer RNA (tRNA) molecules, which carry amino acids to the growing protein chain (Malys & McCarthy, 2010). As shown in Figure 1, RNA-RNA base pairing typically involves the Watson-Crick base pairs of G with C, and A with U, but G can also base pair with U. The conventional understanding is that the strength of a given RBS is determined by the strength of its base pairing interactions with the 16S rRNA (Shine & Dalgarno, 1974). In natural bacterial genomes, there is a wide variety of RBS sequences and RBS translational strengths that have resulted from natural selection for global patterns of gene expression. The relationship between RNA base pairing and the strength of an RBS also explains how synthetic RBSs can be produced with widely varying strengths. 

In addition to intermolecular base pairing, intramolecular base pairing affects the strengths of RBSs. The ability of RNA to engage in intramolecular base pairing is well established (Busan & Weeks, 2013). RBS elements can be disabled by RNA folding, as is the case in riboswitches (Breaker, 2012). The RNA in riboswitches adopts an OFF state when the RBS is bound by a complementary anti-RBS sequence within the mRNA. The addition of a small molecule ligand that binds to the folded RNA changes the RNA shape so that the RBS is available for interaction with the 16S rRNA during the ON state.

Understanding the function of RBSs informs the new discipline of synthetic biology, which uses engineering principles and molecular cloning methods for the construction of parts, devices, and systems, with applications in areas such as medicine, energy, and the environment (Khalil & Collins, 2010). Synthetic biologists have studied the function of RBSs as interchangeable parts that retain their strengths in the context of any gene expression device, but the interchangeability of simple RBSs has come under question (Mutalik et al., 2013). An RBS that functions efficiently upstream of one gene will not necessarily function efficiently upstream of a different gene. The C dog bicistronic RBS was intended to solve this inconsistency (Mutalik et al., 2013). As shown in Figure 1, the C dog RBS uses two RBSs to preserve the strength of the second RBS upstream of any gene of interest. The first RBS initiates translation of mRNA into a short leader polypeptide. The coding sequence for the leader polypeptide extends past the second RBS and ends at the start codon of the gene of interest. Translation of the mRNA into the leader polypeptide is hypothesized to disrupt gene-specific mRNA folding that could sequester the second RBS and reduce its strength of translation initiation.

We still have more to learn about the translation initiation in natural and synthetic systems. However, mutational analysis of RBSs has historically been challenging for high school and undergraduate researchers, because it requires substantial expertise in molecular cloning methods. For example, the introduction of mutations into bacterial translational control elements usually involves purification of restriction-digested DNA from agarose gels, a method that is difficult to master and time-consuming. In addition, an often complicated strategy involving compatible restriction enzyme sites must be tailored for the assembly of DNA parts during each molecular cloning project. We addressed these two problems with the design, construction, and testing of a new molecular tool called rClone. rClone uses Golden Gate Assembly (GGA), which eliminates the need for gel purification of DNA and enables the standardization of assembly for molecular cloning (Weber et al., 2011). We leveraged the simplicity and reliability of GGA for rClone, which enables the cloning of RBSs, as we did previously for pClone, which enables the cloning of promoters (Campbell et al., 2014). After RBSs and promoters are cloned, rClone and pClone help one measure their function easily using convenient reporter genes. These two plasmids make the mutational analysis of bacterial gene expression faster, cheaper, and more accessible to high school and college student researchers. We demonstrated the power of rClone for RBS mutational analysis by producing libraries of thousands of mutant simple RBSs and C dog RBSs, and investigating the libraries to learn about the sequence requirements for the control of bacterial translation. We used this mutational analysis to address the hypothesis that mutations in both simple RBSs and bicistronic C dog RBSs would affect the efficiency of translation. Our results culminated in consensus sequences that represent new and testable hypotheses about the sequence requirements for RBSs, as well as a collection of 127 new RBSs that can be used by the synthetic biology community.

Materials and Methods

For the construction of rClone Red, we used PCR and GGA to remove a RBS from tClone Red, which we built previously for the study of transcriptional terminators. We built rClone Blue by using PCR and GGA to replace the RFP reporter gene in rClone Red with a reporter gene that encodes a blue chromoprotein. We submitted rClone Red and rClone Blue to the Registry of Standard Biological Parts as part numbers BBa_J119384 and BBa_J119389, respectively (MIT Working Group, 2005; http://parts.igem.org/Part:Bba_J119384; http://parts.igem.org/Part:Bba_J119389). We used rClone Red and rClone Blue to construct four different mutant RBS libraries. We explored the libraries by picking colonies, determining the DNA sequences of the mutant RBSs they carry, and measuring the strengths of the RBSs with the fluorescence produced by the RFP reporter gene. Additional details for the experimental procedures by which we constructed and used rClone Red and rClone Blue can be found in Supplemental Information

Results 

Cloning RBSs with rClone Red and rClone Blue

rClone plasmids use the type IIs restriction enzyme BsaI and GGA to clone RBSs (Figure 2A). rClone Red contains a “backward facing” Green Fluorescent Protein (GFP) expression cassette between a “forward facing” promoter and a “forward facing” RFP reporter gene. The GFP cassette is flanked by BsaI binding sites. The GFP and the BsaI sites are removed during GGA when BsaI cuts out the GFP cassette, leaving behind sticky ends. Annealed oligonucleotides that encode the RBS to be cloned also have sticky ends complementary to those produced by BsaI digestion of rClone Red. DNA ligase present in the GGA reaction attaches the oligonucleotides that have annealed to rClone Red. Two categories of clones result from the transformation of the GGA reaction into E. coli bacteria (Figure 2B). Transformation of the original rClone Red plasmid results in colonies that express GFP because the GFP cassette is still within the RFP cassette. Successful cloning of a new RBS sequence produces colonies that are not green. The strength of the RBS determines the amount of RFP produced in non-green colonies.

Figure 2. rClone Red and rClone Blue allow RBSs to be cloned into a reporter gene expression cassette. A. BsaI and DNA ligase enable cloning of RBSs via GGA. B. Photographs show typical colony colors after GGA with rClone Red (right) or rClone Blue (left). RBS = ribosome binding site; GGA = Golden Gate assembly.

Figure 2. rClone Red and rClone Blue allow RBSs to be cloned into a reporter gene expression cassette. A. BsaI and DNA ligase enable cloning of RBSs via GGA. B. Photographs show typical colony colors after GGA with rClone Red (right) or rClone Blue (left). RBS = ribosome binding site; GGA = Golden Gate assembly.

Cloning RBSs with rClone Blue works the same way as cloning RBSs with rClone Red (Figure 2A). rClone Blue contains a “backward facing” GFP expression cassette between a “forward” promoter and the AmilCP blue reporter gene. The GFP cassette is flanked by BsaI binding sites and is removed during GGA when BsaI cuts out the GFP cassette, leaving behind sticky ends. An RBS, or a library of RBSs, with complementary sticky ends, can be cloned using GGA. As with rClone Red, green and not green colonies can occur (Figure 2B, left side). Unsuccessful GGA results in green colonies because the GFP cassette was not removed. Colonies that do not express GFP are the products of successful GGA. 

Library Design and Exploration

We used rClone Red and rClone Blue to study simple RBSs and C dog bicistronic RBSs. We developed two mutation strategies for both types of RBSs. One strategy produced 65,536 possible RBS sequences by varying all eight bases of the RBS and replacing them with N’s, where N refers to A, T, C or G. The other strategy preserved the middle two highly conserved bases, resulting in 4,096 different RBS sequences. We constructed libraries using both strategies for the simple RBS (Figure 3A) and the C dog bicistronic RBS (Figure 3B). The strengths of the RBSs determines the amount of blue chromoprotein produced in the non-green bacterial colonies. The strength of a given RBS determines the phenotype of colonies in a library (Figure 4). Colonies that resulted from failed assemblies are green whereas colonies from a successfully cloned RBS are not green. The intensity of the red fluorescence of a colony is determined by the strength of the RBS it contains. A strong RBS results in a visibly red colony whereas a weak RBS results in a colony that is not red. 

Figure 3. Strategies for mutant RBS library construction. A. Plan for simple RBS libraries. Bottom. B. Plan for C dog libraries. C. List of oligonucleotides ordered for library construction. RBS = ribosome binding site; C dog = bicistronic RBS.

Figure 3. Strategies for mutant RBS library construction. A. Plan for simple RBS libraries. Bottom. B. Plan for C dog libraries. C. List of oligonucleotides ordered for library construction. RBS = ribosome binding site; C dog = bicistronic RBS.

 

Figure 4. Results from rClone experiments. Diagrams show various RBS strengths in rClone Red and photograph is an example of an rClone Red RBS N8 library plate. RBS = ribosome binding site.

Figure 4. Results from rClone experiments. Diagrams show various RBS strengths in rClone Red and photograph is an example of an rClone Red RBS N8 library plate. RBS = ribosome binding site.

We explored our simple RBS N6 library by picking 33 clones (Figure 5A).  The RBS strengths are expressed as percentages compared to the strongest RBS we found in our libraries, which was C dog mutant C10. The strongest simple RBS we found in the library was RM, with a relative strength of 31.1%. Of the 33 clones we examined, 19 of them had a strength of less than 10%.  We also picked 32 mutant clones from the simple RBS N8 library (Figure 5B). The strongest simple RBS among the clones we picked from the N8 library was B24 with a relative strength of 51.3%. Twenty-six of the 32 clones we picked from the simple RBS N8 library had a strength less than 10%. 

Figure 5. Selected clones from simple RBS mutant libraries. A. Simple RBS N6 library. B. Simple RBS N8 library. Numbers represent the percentage of RFP produced compared to the strongest RBS, C10 in Figure 6. RBS = ribosome binding site.

Figure 5. Selected clones from simple RBS mutant libraries. A. Simple RBS N6 library. B. Simple RBS N8 library. Numbers represent the percentage of RFP produced compared to the strongest RBS, C10 in Figure 6. RBS = ribosome binding site.

We picked 26 clones from the C dog N6 library (Figure 6A). The strongest one we found in the N6 C dog library was D18, which had a relative strength of 84.9%. Only 3 of the 26 clones had a strength less than 10%. We picked 36 clones from the C dog N8 library (Figure 6B). The strongest C dog RBS we picked from the N8 C dog library was C10, which was the strongest of all the examined RBS clones and it was used as the relative standard for all comparisons. Ten of the 36 clones had a strength less than 10%. 

Figure 6. Selected clones from C dog RBS mutant libraries. A. C dog N6 library. B. C dog N8 library. Numbers represent the percentage of RFP produced compared to the strongest RBS, C10. RBS = ribosome binding site; C dog = bicistronic RBS.

Figure 6. Selected clones from C dog RBS mutant libraries. A. C dog N6 library. B. C dog N8 library. Numbers represent the percentage of RFP produced compared to the strongest RBS, C10. RBS = ribosome binding site; C dog = bicistronic RBS.

Building and Testing RBS Consensus Sequences

A consensus sequence expresses the frequency at which each base occurs in each position of a DNA or RNA sequence for a library of sequences (Schneider & Stephens, 1990). We used the information from Figures 5 and 6 to construct a weighted consensus (see details in Supplemental Information) sequence for the RBS N6, RBS N8, C dog N6, and C dog N8 libraries, and the results are displayed in Figure 7. Figure 7 also shows a consensus sequence from 149 RBSs in the E. coli genome (Schneider et al., 1990). To validate our consensus sequences, we selected the base with the highest score and the second strongest base if its score was within half a standard deviation of the highest. For example, simple RBS N6 had a consensus formula of CRCGAGGT, where R stands for purine (A or G). Therefore, testing the simple RBS N6 consensus required two sequences: CGCGAGGT (RBS1) and CACGAGGT (RBS2). We cloned each validating consensus sequence into rClone Red and measured their relative RBS strengths (Figure 8). The strengths of the validating consensus clones for simple RBSs are lower than some members of the RBS N6 library from which the consensus was generated. For the simple RBS N8 library, one of the validating clones was weaker than some members of the simple RBS N8 library, but the other validating clone was stronger than all but one of the simple N8 sequences we had tested. For C dog N6, the validating sequence had the second highest score among the tested clones. Of the two C dog N8 validating clones, one was stronger than all of the tested clones and the other was the fifth strongest in its source library.  

Figure 7. Comparison of RBS consensus sequences. Consensus sequences are shown from simple RBS (left) and C dog (right) libraries and the consensus sequence from 149 naturally occurring E. coli RBSs (top). RBS = ribosome binding site; C dog = bicistronic RBS.

Figure 7. Comparison of RBS consensus sequences. Consensus sequences are shown from simple RBS (left) and C dog (right) libraries and the consensus sequence from 149 naturally occurring E. coli RBSs (top). RBS = ribosome binding site; C dog = bicistronic RBS.

We also cloned all of the consensus validating sequences into rClone Red in their reciprocal simple RBS and C dog contexts. We gave consensus testing sequences two-letter abbreviations based on their context and origin, using R for simple RBS and C for C dog RBS. For example, we cloned the consensus validating sequence RBS1, derived from the simple RBS N6 library consensus, into a C dog RBS and called it CR1. Likewise, we tested the validating sequence called Cdog1 as a standalone simple RBS called RC1 (Figure 8). For three of the simple RBS sequences tested in the C dog context, the strength increased by an average of 2.4-fold. All of the C dog RBS sequences decreased by an average of 28-fold when they were tested as simple RBSs. 

Figure 8. Strengths of consensus validating sequences in simple RBS and C dog RBS contexts. A. Strengths of consensus validating sequences in their original contexts.  B. Strengths of consensus validating sequences in the reciprocal contexts. The first letter of each clone name indicates the context in which the sequence is being tested, with R for RBS and C for C dog. The second letter of each clone name indicates the type of library from which the consensus was developed, with R for RBS and C for C dog. Underlined bases vary in a given consensus sequence. Colors highlight related validation sequences. Strength numbers represent the percentage of RFP produced compared to C10 in Figure 6. RBS = ribosome binding site; C dog = bicistronic RBS; RFP = red fluorescent protein.

Figure 8. Strengths of consensus validating sequences in simple RBS and C dog RBS contexts. A. Strengths of consensus validating sequences in their original contexts. B. Strengths of consensus validating sequences in the reciprocal contexts. The first letter of each clone name indicates the context in which the sequence is being tested, with R for RBS and C for C dog. The second letter of each clone name indicates the type of library from which the consensus was developed, with R for RBS and C for C dog. Underlined bases vary in a given consensus sequence. Colors highlight related validation sequences. Strength numbers represent the percentage of RFP produced compared to C10 in Figure 6. RBS = ribosome binding site; C dog = bicistronic RBS; RFP = red fluorescent protein.

To investigate the interchangeability of simple RBSs and C dog RBSs upstream of a different reporter gene, we cloned all of the consensus validating sequences into rClone Blue (Figure 9). The amount of blue chromoprotein produced was proportional to RBS strengths. We categorized each clone as strong, medium or weak based on how blue the colonies appeared in white light. We used RFP strengths to categorize the same validating sequences when tested in rClone Red.  rClone Blue and rClone Red shared three of the five strong validating sequences (CC1, CC2 and CC3).  Only two of the five medium validating sequences (RR1 and RR4) and one weak validating sequence (RR3) were shared in rClone Red and rClone Blue. 

Figure 9. Comparison of consensus sequence validating sequences in rClone Blue (top) and rClone Red (bottom). Clones listed at the bottom are the consensus validating sequences that are shared between rClone Red and rClone Blue.

Figure 9. Comparison of consensus sequence validating sequences in rClone Blue (top) and rClone Red (bottom). Clones listed at the bottom are the consensus validating sequences that are shared between rClone Red and rClone Blue.

Base Pairing of the 16S rRNA with RBSs

Our results provide support for the hypothesis that base pairing between the 16S rRNA of the ribosome and the RBS affects the strength of a given RBS. The strongest RBSs from all four libraries have an average of 6.75 out of 8 bases pairs with the 16S rRNA. The weakest RBSs from all four libraries have an average of only 2.25 base pairs with the 16S rRNA. Our results also showed that the strengths of simple and C dog RBSs were lower in rClone Red than they were in rClone Blue. We hypothesized that the RFP mRNA has a translation blocking anti-RBS that is absent in the blue chromoprotein mRNA. An anti-RBS within RFP mRNA would explain why many of the validating sequences worked better in rClone Blue than in rClone Red. To investigate the validity of our anti-RBS hypothesis, we explored the possible secondary structures formed in both rClone Red and rClone Blue using a web-based program called mFold (Zuker, 2003). The program searches all the possible intramolecular base pairing interactions for an RNA sequence. It presents the most stable structures and calculates their free energies as ΔG in kcal/mol. Free energies are a measure of the stability of structures and more negative free energies indicate more stable ones. We used mFold to predict the secondary structure and calculate the ΔG for the RC1 RBS in the context of both rClone Red and rClone Blue mRNAs (Figure 10). The RC1 RBS had a strength of only 4.0 in rClone Red but was among the strongest RBSs in rClone Blue (see Figure 9). The mFold results show that in the rClone Red mRNA, the RC1 RBS is base paired to a part of the RFP coding sequence immediately downstream of its start codon, forming a stem and loop structure. The RC1 RBS does not form a stable a stem and loop structure with the blue chromoprotein mRNA. The mFold results support our hypothesis that the RFP gene has an anti-RBS which sequesters simple RBSs and inhibits the initiation of translation. 

Figure 10. mFold RNA folding analysis of rClone Red and rClone Blue mRNAs.  The RC1 RBS forms a stable anti-RBS hairpin in rClone Red but not in rClone blue. mRNA = messenger RNA; RBS = ribosome binding site.

Figure 10. mFold RNA folding analysis of rClone Red and rClone Blue mRNAs. The RC1 RBS forms a stable anti-RBS hairpin in rClone Red but not in rClone blue. mRNA = messenger RNA; RBS = ribosome binding site.

We looked for base pairing between the anti-RBS in the RFP coding sequence and each of the consensus validating RBS sequences. Validating sequences that base pair with the 16S rRNA also base pair with the anti-RBS, which explains the preponderance of low strength RBSs in the rClone Red libraries. Every RBS sequence that had the potential to be a high strength RBS also was more likely to interact with the RFP anti-RBS. For example, seven of the eight bases of RC2 can pair with the 16S rRNA and with the anti-RBS. RC2 could be a strong RBS because it base pairs well with the 16S rRNA, but it also binds to the anti-RBS in the RFP mRNA.

Testing the Anti-RBS Hypothesis

To test whether the anti-RBS was the cause of the reduced RFP production of rClone Red RBS constructs, we mutated five bases in the three codons predicted to base pair with RC1 without changing the amino acids they encode (Figure 11). Two of the three codons specify serine which can be encoded by six codons. Having six codons to choose from provided more mutational options to reduce base pairing between the RFP mRNA and RC1 RBS. The result of introducing three synonymous codons is called RFP version 2. We used mFold to predict the structure and stability (ΔG) of the encoded mRNA of RFP version 2 and compared it to that of RFP version 1. As shown in Figure 11, the RBS is not sequestered in RFP version 2, and the RFP version 2 stem and loop structure is less stable than that of RFP version 1. We also compared the RFP produced with RC1 in the original rClone Red to the RFP produced with RC1 in rClone Red version 2 encoding RFP version 2. Consistent with the mFold analysis, rClone Red version 2 produced ten times more RFP than rClone Red version 1 when both used the same simple RBS. With a relative RFP production of 36%, RC1 would be moved from the weak category to the medium category in Figure 9.

Figure 11. Testing anti-RBS hypothesis to explain reduced RBS function in rClone Red.  A. Two versions of RFP mRNA encoding the same three amino acids immediately following the start codon. The underlined letters indicate mutated bases. RNA structure and ΔG were calculated by mFold. B. The percentage of RFP protein produced (relative to C10 in Figure 6) by the two versions of rClone Red. RBS = ribosome binding site; RFP = red fluorescent protein.

Figure 11. Testing anti-RBS hypothesis to explain reduced RBS function in rClone Red. A. Two versions of RFP mRNA encoding the same three amino acids immediately following the start codon. The underlined letters indicate mutated bases. RNA structure and ΔG were calculated by mFold. B. The percentage of RFP protein produced (relative to C10 in Figure 6) by the two versions of rClone Red. RBS = ribosome binding site; RFP = red fluorescent protein.

Discussion

Codon Usage Bias in the Anti-RBS 

Our investigation uncovered several new RBS consensus sequences that differ from those published over forty years ago (Shine & Dalgarno, 1974). Because of the modular nature of both rClone plasmids, it was straightforward for us to clone the consensus verifying sequences for simple RBS and C dog RBS in rClone Red and rClone Blue. Because we compared the verifying sequences directly in both plasmids, we uncovered a previously undocumented anti-RBS in the RFP mRNA. When we produced rClone Red version 2, we confirmed that RC1 was stronger than it had been in rClone Red version 1. It is interesting that RC1 placed upstream of RFP version 2 had medium strength (36% in Figure 11) whereas RC1 in rClone Blue was among the strongest of the constructs we tested (Figure 9). A possible explanation for these observations might be connected to the fact that, for a given amino acid, E. coli uses some codons more often than others. This is referred to as codon usage bias. Researchers who want to produce a foreign protein in E. coli often conduct a process called codon optimization to pick the most frequently used codons to gain translation efficiency (Shin, Bischof, Lauer, & Desrosiers, 2015). Perhaps part of the reduction in the efficiency of translation for RFP mRNA was because the UCC serine codon found in rClone Blue is more optimal (17% usage in E. coli) than the UCA (12% usage) and AGU (13% usage) serine codons in RFP version 2 (Maloy, Stewart, & Taylor, 1996). As with any engineering solution to a problem, compromises must be made such as reducing the stem and loop structure while maximizing codon optimization. Nevertheless, we successfully redesigned and tested rClone Red to be more responsive to strong RBS sequences. 

Research Applications

In the context of synthetic biology, understanding gene expression allows us to engineer bacterial cells to produce pharmaceuticals, attack cancer cells, neutralize environmental pollutants, and synthesize biofuels (Khalil & Collins, 2010). The Registry of Standard Biological Parts contains over 20,000 DNA parts and devices for use in synthetic biology (MIT Working Group, 2005). We submitted our collection of 65 simple RBSs and 62 bicistronic RBSs as four Registry parts (Bba_J119390 to BbaJ119393). Our contribution has increased the number of RBSs in the Registry from 56 to 183, all of which can be used for the design and construction of genetic circuits that enable bacteria to produce pharmaceuticals, biofuels, and chemical commodities. Various RBS strengths can be used to fine-tune the expression of genes encoding enzymes in a biosynthetic pathway. We also submitted information to the Registry about rClone Red (Bba_J119384) and rClone Blue (Bba_J119389) to make them available to the global synthetic biology research community, including high school and college student researchers. 

Future Prospects for Mutational Analysis of Translation Initiation

There are many opportunities for mutational analysis of bacterial translation. For example, students could search for anti-RBS sequences in thousands of bacterial genomes to discover new examples of translational regulation (Li, Oh, & Weissman, 2012). For synthetic biology applications, learning how to disable anti-RBS elements would be important for improving the interchangeability of RBSs and achieving higher levels of gene expression. Another interesting topic for exploration would be the fitness cost of gene expression (Pope, McHugh, & Gillespie, 2010). There is a cost to bacterial cells of producing orthogonal proteins such as RFP and blue chromoprotein.  Fitness cost could be measured by comparing growth rates of clones that produce RFP or blue chromoprotein with various RBS strengths. Fitness costs of promoters, RBSs, alleles, and protein degradation tags could be used to determine optimal orthogonal gene expression.  

Suite of Synthetic Biology Cloning Tools

rClone Red and rClone Blue are members of a suite of synthetic biology tools that enables high school and undergraduate research students to perform original research on bacterial gene regulation and expression. Each tool leverages the ease with which GGA can be used to clone user-defined regulatory elements and measure their effects on expression of a reporter gene. pClone Red (Bba_J119137) and pClone Blue (Bba_J119313) enable students to conduct promoter experiments of their own design (Campbell & Eckdahl, 2015; Campbell et al., 2014; Eckdahl & Eckdahl, 2016; Eckdahl & Campbell, 2015). tClone Red (Bba_J119367) facilitates investigation of transcriptional terminators that use alternative RNA folding states to control gene expression. We have designed and constructed actClone (Bba_J100204) to study DNA binding sites for activator regulatory proteins, and repClone (Bba_J100205) to study repressor protein binding sites. Each of these tools use the same GGA strategy as rClone Red and rClone Blue to enable students to explore the sequence requirements of bacterial DNA regulatory elements. Our suite of synthetic biology tools is appropriate for high school and undergraduate researchers to conduct original research in bacterial gene regulation and expression.

Acknowledgements

We would like to thank Dr. Jay Meyers of Saint Joseph Central High School for his support and encouragement. Support from National Science Foundation (http://www.nsf.gov/) RUI grants MCB-1329350 to Missouri Western State University and MCB-1120578 to Davidson College is gratefully acknowledged, as well as the James G. Martin Genomics Program at Davidson College. 

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Campbell, A. M., & Eckdahl, T. T. (2015). CourseSource pClone Intro Biology Lesson. Retrieved from http://www.coursesource.org/

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Eckdahl, T. T., & Campbell, A. M. (2015). CourseSource pClone Genetics Lesson. Retrieved from http://www.coursesource.org/

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Response of Acacia tortilis to Elephant Browsing in Tarangire National Park, Tanzania: Possible Above-Ground Compensation?

doi: 10.22186/jyi.32.1.1-6

Abstract | Introduction | Methods | Results | Discussion | Conclusions |Acknowledgements | 
References | PDF

Abstract

Large herbivore browsing leads to above-ground compensatory growth for some species of Acacia trees, but strength and variation of the relationship are poorly understood. Acacia tortilis is a keystone species in East African savannas and experiences a wide range of browsing pressure. In this study, terminal bud scale scars were used to measure yearly shoot elongation in A. tortilis experiencing various levels of elephant browsing at three mesic sites in northern Tarangire National Park, Tanzania. For all four years of growth, twig elongation remained very similar across elephant browsing pressure from minimal to heavy. Although never significant, twig elongation in 2013, 2011 and 2010 was somewhat higher in trees experiencing moderate elephant browsing pressure, suggesting a possible tendency toward compensatory growth. However, growth in 2012 had a slight negative correlation with browsing pressure. Further research can test whether A. tortilis compensates for moderate elephant browsing by elongating twigs more and if that growth slows slightly at higher browsing pressure. Overall A. tortilis tolerates elephant browsing extraordinarily well, but because the elephant population in Tarangire National Park is large and growing, managers should continue to monitor this keystone for decreased growth and tree mortality.

Introduction

Woody plants provide energy and nutrients for many mammals in African savannas. Throughout Africa, Acacia spp. trees provide the primary food source for numerous browsers, and also serve as important habitat for birds (Dharani, Kinyamario, Wagacha & Rodrigues, 2009). Many species, including black rhinoceros, giraffe, grey duiker, dik-dik, grysbok, klipspringer, gerenuk, dibatag, bushbuck, and kudu, feed exclusively on woody browse (Owen-Smith, 1982). Because Acacia trees fix nitrogen, this also leads to higher forage quality in grasses underneath them compared to areas not under their canopies (Ludwig, De Kroon & Prins, 2008; Mopipi, Trollope & Scogings, 2009). Understanding the conditions that allow continued Acacia growth despite browsing will allow conservationists to monitor and prevent mortality of this keystone savanna species.

There are many variables in woody savanna which affect the productivity of trees, including Acacia spp. Disease decreases growth, rainfall increases growth, and fire has variable effects on growth based on the severity of the fire and the characteristics of various Acacia species (Dharani et al., 2009; Fornara, 2008; Mopipi et al., 2009; Otieno, Kinyamario & Omenda, 2001; Scogings, Johansson, Hjalten & Kruger, 2012). However, effects of herbivory and the dynamic interactions between browsers and woody plants have been heavily disputed. Several researchers found that intense large herbivore browsing results in compensatory above-ground plant growth and can lead to alternative stable states (Dublin, Sinclair & McGlade, 1990; Jachmann & Bell, 1985; Smallie & O’Connor, 2000). However, others have found that large herbivore populations, such as elephants (Loxodonta africana) or giraffes (Giraffa camelopardalis), have a negative effect on woody vegetation growth (Guldemond & Van Aarde, 2008; Chira & Kinyamario, 2009; Pellew 1984).

Past findings on the effects of browsing on sub-Saharan African Acacia species vary by mammal, tree species and response measurement. Browsing by non-elephant large mammals resulted in compensatory growth, leading to higher stem diameter growth of A. xanthophloea (Dharani et al., 2009). Similarly, simulated browsing of A. xanthophloea, A. tortilis, A. hockii (Pellew, 1984) and A. karroo (Stuart-Hill & Tainton, 1989) resulted in increased shoot growth and competitive ability when defoliation rates were between 25-50%. Du Toit, Bryant & Frisby (1990) and Chira & Kinyamario (2009) respectively found that heavy browsing by elephants led to an increase in shoot growth and higher nitrogen concentration in A. nigrescens foliage and coppice growth in A. brevispica. Conversely, Gandiwa, Magwati, Zisadza, Chinuwo, & Tafangenyasha (2011) found that South African A. tortilis with moderate to high levels of elephant browsing had smaller mean tree density, height, and basal area, compared to trees with low level of browsing. While recent patterns in precipitation, defoliating insects, disease, and soil fertility can also affect above-ground growth, current evidence suggests that compensatory growth responses occur only under certain levels of browsing pressure before tree mortality and/or resource depletion occurs.

Although compensatory growth in response to browsing has been observed in many Acacia species, the mechanism(s) and evolutionary trade-offs have been heavily disputed. Du Toit et al. (1990) found lower levels of tannins – naturally occurring plant polyphenols that discourage herbivore browsing by lowering metabolic efficiency (Chung, Wong, Wei, Huang & Lin, 1998) – and higher nitrogen and phosphorus content in heavily browsed A. nigrescens. They concluded that there is a tradeoff between shoot growth and defensive allo-chemicals for the species. The subsequent increase in palatability would lead to increased browsing, and create a positive feedback loop leading to the evolutionary development of Acacia compensatory growth as a necessary mechanism for the tree survival. Similarly, Fornara & du Toit (2007) found an inverse relationship between Acacia tolerance (regrowth abilities) and reproduction (seeds produced) in response to browsing, suggesting that above-ground growth came as a trade off with reproduction. Dharani et al. (2009) also observed increased browsing tolerance of A. xanthophloea after browsing. However, the study found that compensatory growth was limited to plant stems, while browsing still had a negative effect on tree height and canopy growth. They concluded that trees reallocated resources from leaves to stems and roots. Fornara (2008) similarly found that large herbivore pruning of A. nigrescens contributed to enhanced root size and subsequent re-sprouting abilities while negatively impacting maximum tree branch height, suggesting nutrient shunting from foliage to stems and roots.

It has also been hypothesized that the mechanism of shoot growth stimulation after browsing is not a reallocation of nutrients within the individual tree, but increased energy flow from the recycling of nutrients bound in leaf growth (Mopipi et al., 2009). However, Tanentzap & Coomes (2012) argued that herbivore grazing and browsing lead to decreased terrestrial carbon stock in general, and that the previous theory applies only in environments with sufficient resources to compensate for lost photosynthetic ability of consumed foliage. Other proposed mechanisms relating to resource allocation include: reduced intershoot competition due to woody browse loss, reduced leaf shading enabling enhanced photosynthesis capability, and reduced transpiration allowing more soil water retention (Fornara & du Toit, 2007; McNaughton, 1979). Numerous studies have resulted in various, sometimes contradictory, evidence and theories for compensatory plant growth mechanisms. Since no general conclusion has been reached, compensatory growth is likely species and location-dependent, influenced by browsing type and pressure, nutrient availability, inter- or intra-species competition, and perhaps other factors.

The mesic Acacia woodland in northern Tarangire National Park is composed largely of A. tortilis, one of the most prevalent and widespread Acacia species throughout Africa (Otieno et al. 2001), but has become less dense due to elephant utilization (Tanzania National Parks, 2012). Elephant herbivory can drive vegetation change in savanna ecosystems, and dense elephant populations can lead to over browsing and significant plant mortality (Van Aarde & Guldemond, 2008). Elephant browsing was found to be a more significant factor than rainfall behind changes in savanna vegetation (Hayward & Zawadska, 2010). With an average annual growth rate of about 7% from 1993-2006, there is no doubt that the Tarangire elephant population has intensified browsing pressure in the area, but it is unclear how the composition and woody plant-browser dynamics of the park savanna have changed (Foley, 2010). If this population growth continues, increased browsing pressure could threaten the survival and resilience of the park’s Acacia population.

Understanding the risk of A. tortilis decline and growth response to elephant browsing will help reduce tree mortality and prevent the loss of its ecosystem services. In this study, we inferred above-ground compensatory growth if greater growth occurred at moderate levels of branch removal by elephants. We hypothesized that A. tortilis trees under moderate levels of elephant browsing will have higher levels of annual shoot elongation because moderate levels of browsing pressure will stimulate more compensatory growth.

Methods

Study Site

Tarangire National Park is located between S03°40’ and S03°35 and between E35°45’ and E37°0’ in the Arusha province of northern Tanzania and covers 2,850km2. As previously described by Ludwig et al. (2008), the park experiences a short rainy season from October to November, and a long rainy season from March to May, with the majority of the 650mm/year rainfall occurring between March and April. The park has several major habitat types including Acacia tortilis parkland, tall Acacia woodland dominated by A. xanthophloea and A. sieberiana, drainage line woodland, and deciduous savanna dominated by Combretum spp. and Commiphora spp. (Ludwig et al., 2008). This study focuses on three mesic sites located in the Lemiyon, Matete and Gursi areas of the northern part of the Park in order to focus on A. tortilis specifically.

Data Collection

At each of the three sites, we selected five to six trees with little (<30%), moderate (30-50%), and severe (>50%) elephant damage (47 trees total) from trees with multiple accessible unbrowsed twigs to measure. Tree measurements were taken for A. tortilis which fit strict criteria (<200m from roads, diameter at breast height (DBH) >10cm, and height >5m).

The percentage of canopy removed by elephant browsing was estimated qualitatively to the nearest 10%. Similar manual methods of estimating and quantifying browsing damage on tree canopy were described in Okula & Sis (1986), Riginos & Young (2007), Dharani et al. (2009) and Moncrieff (2011). It was not possible to judge the age of tree damage so that some years of measured growth could have occurred before any or some of the recorded damage occurred.

Elongation was measured using terminal bud scale scars on 3-4 twigs for each of the four previous years. We assigned twig measurements to the year in which growth began. To focus on growth response at the tree level, we averaged annual twig measurements within each tree.

Precipitation data to examine differences among years are unfortunately not available from this site, and reproduction occurred after the study time. All data was collected in the park in a short four-week period due to resource limitation.

 Data Analysis

Minitab ver. 17 (Minitab, 2014) was used to test for differences in growth among sites and years using a repeated measures ANOVA with year (fixed factor) crossed with site (random factor) and with trees nested within sites. Before analysis, a square root transformation was used to meet assumptions of normality and equality of variances. For each year linear and quadratic regressions from JMP ver. 11 (JMP®, 2007) were used to model relationships between annual shoot elongation and elephant damage. The best model fit was defined as that which had a larger R2 value. Due to the similarity of growth sites we combined them in further analyses and considered all trees independent.

Results

Consistency of Growth Across Sites

Twig elongation ranged between 110-240mm across the three sites and four years, with the highest average growth recorded for site A in 2012 and the lowest for site B in 2013 and site A in 2010 (Figure 1).

Figure 1. Annual Growth by Site. Variations in growth is represented by mean (+SE) shoot elongation in A. tortilis, measured using a terminal bud scar scale, at three mesic sites (A, B & C) in the northern woodlands of Tarangire National Park. Measurements were taken for years 2013, 2012, 2011 & 2010.

Figure 1. Annual Growth by Site. Variations in growth is represented by mean (+SE) shoot elongation in A. tortilis, measured using a terminal bud scar scale, at three mesic sites (A, B & C) in the northern woodlands of Tarangire National Park. Measurements were taken for years 2013, 2012, 2011 & 2010.

The repeated measures ANOVA showed no differences in twig elongation between growth sites and years. It also measured the effects of an individual tree, and the interaction between site and year (Table 1). While growth year and the mesic site did not affect twig elongation individually (p-values > .05), the interaction between the two variables was significant (p = .01), as was the variation among trees within sites (p = .04). The repeated measures ANOVA explained 46% of the variation in annual growth measurements. 

Table 1. Repeated Measures ANOVA. The Summary table shows the effect of mesic site and year on annual growth (shoot elongation) in A. tortilis. Significant relationships were found (p-value < 0.05 indicated by * symbol) between above ground growth and mesic site, as well for the interaction of site and growth year on growth. Model R2 (adj.) = 0.46 which indicates relatively high model performance.

Table 1. Repeated Measures ANOVA. The Summary table shows the effect of mesic site and year on annual growth (shoot elongation) in A. tortilis. Significant relationships were found (p-value < 0.05 indicated by * symbol) between above ground growth and mesic site, as well for the interaction of site and growth year on growth. Model R2 (adj.) = 0.46 which indicates relatively high model performance.

Effects of Browsing on Shoot Elongation

The relationships between elephant browsing damage and annual A. tortilis twig elongation had model p-values > .05 for all years (Figure 2). For 2013, 2011 and 2010 quadratic regression models were a better fit to the observed data, with higher R2 values (Figure 2). The general trend for these years showed that growth at moderate browsing levels (30-60% damage) was about 20mm greater than at low and high levels. For 2012, a linear regression line provided a better fit, showing a very slight (<5mm) decrease in shoot elongation as browsing pressure increases (Figure 2). Model R2 were low for all relationships, indicating substantial unexplained variation, but the quadratic models for 2013, 2011 & 2010 were higher than the linear model of 2012 growth (Figure 2).

Figure 2. Effects of elephant browsing on A. tortilis growth by year. Best fit regression models (linear or quadratic) represent the relationship between above ground growth (included as square root transformed shoot elongation) and elephant browsing (measured as percent of canopy damaged by increments of 10) of A. tortilis over four years (2010, 2011, 2012 & 2013). Each point is the mean growth measurement taken from four to five shoots from one tree.

Figure 2. Effects of elephant browsing on A. tortilis growth by year. Best fit regression models (linear or quadratic) represent the relationship between above ground growth (included as square root transformed shoot elongation) and elephant browsing (measured as percent of canopy damaged by increments of 10) of A. tortilis over four years (2010, 2011, 2012 & 2013). Each point is the mean growth measurement taken from four to five shoots from one tree.

Discussion

The effects of herbivory on compensatory growth in woody plants have been heavily disputed. (Chira & Kinyamario, 2009; Dublin et al., 1990; Jachmann & Bell, 1985; Pellew, 1984; Smallie & O’Connor, 2000). It is clear that elephant herbivory can drive vegetation change in savanna ecosystems, and dense elephant populations can sometimes lead to over-browsing and significant plant mortality (Van Aarde & Guldemond, 2008).

Soil moisture and available nutrients are major confounding factors influencing Acacia growth (Mopipi et al., 2009). To reduce these variations, we measured trees only on mesic sites, i.e. sites with moderate and consistent soil moisture levels and nutrient availability in the context of the larger study area. Trees less than 200m from roads were excluded to limit the impact of dust from nearby vehicle traffic. We excluded immature trees (DBH < 10cm) since differences in plant developmental stages have been shown to influence Acacia browsing response (Bergstrom, 1992). Trees shorter than 5m were also excluded because they are more likely to be affected by the browsing of smaller mammals, which reduces Acacia growth due to the differences in browsing behavior of smaller browsers compared to elephants (Augustine, McNaughton & Samuel, 2004). Although Dharani et al. (2008) showed that rainfall plays a significant role in Acacia growth, levels of precipitation were not a major confounding factor in this study since sites were within about 20km of each other. Ben-Shahar (1996) also showed that fire and burn damage can significantly influence Acacia growth, but there was no recent history of fire in these areas (Tarangire, 2012), nor did we observe evidence of recent fires.

The three mesic sites used in this study were chosen to control for proximity to roads, tree maturity, and small mammal browsing; factors known to affect browsing response. The repeated measures ANOVA showed no significant difference between twig elongation across the three mesic sites (p = .52), which indicates that location does not explain the observed variations (Table 1) and growth across sites can be regarded as the approximately the same.

Annual variation in precipitation has been shown to be a significant driver of overall Acacia productivity (Mopipi et al., 2009; Otieno et al., 2001). However, in this study, tree measurements were taken for each of the last four years individually to control for variations in annual environmental conditions, such as drought, that might affect tree growth. There was no significant difference between twig elongation across years (p = .17) (Table 1). Thus, growth year did not explain the observed variations in twig elongation, and trees grew similar amounts across the four years. This is in line with Hayward & Zawadska (2010), who found that precipitation variables had less of an impact on above ground growth than elephant browsing.

Recent studies have found evidence that intense large herbivore browsing can sometimes result in compensatory above-ground plant growth (Dublin et al., 1990; Jachmann & Bell, 1985; Smallie & O’Connor, 2000), while at other times having a negative effect on woody vegetation growth (Chira & Kinyamario, 2009; Gulemond & Aarde, 2008; Pellew, 1984). In this study A. tortilis shoot elongation varied little across the very broad continuum of browsing pressure (Figure 2), and browsing pressure explained little of variations in growth (R2 = .07). Thus we conclude that, in terms of aboveground shoot elongation, the Tarangire National Park population tolerates elephant browsing remarkably well. This may help explain the dominance of A. tortilis in the park and other East African savanna ecosystems.

Our data show equivocal results on whether moderate levels of elephant browsing stimulate compensatory growth. Insignificant trends in 2013, 2011 and 2010 (Figure 2) suggest support for the hypothesis and the conclusions of Du Toit et al. (1990), Chira & Kinyamario (2009), and Stuart-Hill & Tainton (1989) that compensatory growth occurs. The slight, insignificant decrease in 2012 shoot elongation as browsing increases (Figure 2) suggests support for the conclusions of Dharani et al. (2009) and Fornara (2008) that compensatory growth does not occur and that all levels of elephant browsing have a negative effect on above-ground growth. However, in contrast to these studies cited above, our results for four years in northern Tarangire National Park indicate a striking robustness to elephant browsing in A. tortilis, since even trees with heavy browsing damage had approximately the same shoot growth as trees with light or no browsing damage.

It is important to note that a large proportion of above ground growth variation among trees within sites remain unexplained by elephant browsing damage (Table 1). Substantial genetic differences, small-scale variation in soil nutrients and other soil properties, differences in above- and below-ground competition, time of elephant damage, and other environmental factors likely account for differences in shoot growth among replicate trees. This large variation will tend to mask patterns in growth with browse damage. Also, since important influences on growth such as fire, water, and nutrient availability will vary at other sites, resistance to browsing at other sites could show different patterns.

The variation in Acacia growth response to herbivory studied over the last 20 years, as well as within this paper, means that a unified understanding of Acacia compensatory growth remains elusive. Future studies might attempt to measure multiple responses, e.g., shoot elongation, the radial increment of trunks, reproduction, concentration of secondary compounds, and below-ground growth in order to better understand this phenomenon. Since responses likely vary with water availability, studies across a water availability gradient and across years of varying precipitation might prove fruitful. Additionally, genetic variation within A. tortilis, seasonal timing of elephant browsing, previous elephant browsing, effects of other herbivores, and time since the last fire may also influence responses to elephant browsing. Future studies should also consider tree mortality and seedling establishment since growth measurements do not take mortality into account.

 Conclusion

Based on shoot elongation data from four years, A. tortilis in northern Tarangire National Park appears to be tolerating browsing of the large elephant population well, with some insignificant suggestion of modest compensatory growth at moderate browsing levels. In one particular year, a slight decline in shoot growth in trees with heavier browsing is observed. Such an anomalous reverse in growth trends in this year could be evidence of herbivory tipping point, rather than the linear relationship suggested in other years, after which browsing is harming not stimulating above ground growth for A. tortilis. Since this tree species comprises such a large proportion of tree cover in the Park (Tanzania National Parks, 2012) and because the recent trend in elephant numbers is upward (Foley, 2010), we suggest like Gandiwa et al. (2011) in Zimbabwe, that Park managers should monitor growth responses and mortality of A. tortilis to ensure the vitality of this keystone species.

 Acknowledgments

Thanks to my fellow students, Heather Hagerling, Nikki Hernandez, Mark Parlier, and Maxine Quinney, who suffered many an Acacia wound during the blistering late mornings of data collection. And thanks to our wonderful driver Walter Humphreys for navigating us daily throughout Tarangire National Park.

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