Strain Specific: Microbial Strains Involved in Gut-Brain Signaling

doi:10.22186/jyi.33.3.49-54

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

Abstract

Exploration into the microbial role within behavior and neurologic regulation has been an area of growing interest and research. While in-vitro and in-vivo experimentation has suggested that commensal microbiota play a role within behavioral and neurologic functioning, little distinction has been made about the specific microbes inducing change. In order to understand and potentially utilize this complex gut-microbe-brain connection, it is imperative to distinguish between which microbes are inducing behavioral and or neurologic effects, and which biologic mechanisms are mediating said effects. To enhance the current understanding of neurologically influential microbes, this review will analyze eight microbial strains belonging to the genus types Lactobacillus and Bifidobacterium, and note the similarities and dissimilarities pertaining to modulation of inflammatory response, intestinal permeability, neurochemical concentrations, and interaction with the vagus nerve expressed amongst included microbial strains. Analysis of the selected microbes demonstrated significant distinction regarding neurochemical, inflammatory, and immunological effect amongst microbial strains belonging to the genus types Lactobacillus and Bifidobacterium. Interestingly, despite the expressed biologic variation, behavioral influence was largely uniform amongst the included microbial strains and expressed almost exclusively through a reduction in typified anxious and depressive behavior.

Introduction

Despite the myriad of pills and procedures aiming to treat psychiatric abnormalities and conditions, there is still much to be discovered about the brain. Fortunately, exploration into the effect upon commensal microbiota within behavioral and neurologic regulation has created a path in which to further decode and explore this enigmatic terrain. Recent in vitro and in vivo experimentation has demonstrated microbial influence within complex emotive states such as depression, chronic stress, anxiety, and psychiatric disorder (Bailey et al., 2011; Bercik et al., 2010; Maes, Kubera, Leunis, & Berk, 2012; Rook & Lowry, 2008). While this field is emerging and many mechanistic factors facilitating the microbial influence within gut-brain axis regulation have yet to be identified, the work done thus far suggest a future in which the brain can be indirectly targeted for therapeutic benefit through manipulation of commensal microbiota (Clarke et al., 2014; Cryan & Dinan, 2012). At a time when neuropsychiatric disorders are the leading cause of disability in the U.S (USBDC, 2013), the importance of this research cannot be overstated.
Although the blueprint outlining the microbial role within gut-brain axis regulation is far from maturation, the current understanding is that certain microbial strains are able to induce measurable neurologic and behavioral effect through the modulation of inflammatory response, neurochemical concentration, barrier-integrity, and interaction with the vagus nerve.  In order to understand, and potentially utilize these microbial capabilities, it is essential to distinguish between which microbes are inducing behavioral and or neurologic effect, and which pathway each microbe is using to do so. The complexity of this task resides in the significant microbial distinction expressed not only on a species level, but amongst microbial strains (Greenblum, Rogan, & Borenstein, 2015).
Among the more than 7,000 microbial strains (Ley, Peterson, & Gordon, 2006), majority have not demonstrated direct behavioral or neurologic effect. The collection of strains that have been shown to induce measurable neurologic and behavioral manipulation belong to one of the three genus types Lactobacillus, Bifidobacterium, and Bacteroides (Mayer, Knight, Mazmanian, Cryan, & Tillisch, 2014).  Amongst these genus types, a plethora of biologic and behavioral variances have been expressed on both a species and strain level. While variation across differing genus and species type is expected, differences expressed between microbial strains belonging to the same genus and species type is a surprising find in light of the genomic similarity expressed between them. Further investigation into these microbial variances can explain questions such as why, despite genomic similarities, only certain microbial strains are able to induce behavioral and neurologic effect. Why the microbial strains that do induce measurable effect, do so in a variety of ways that are often dissimilar from other strains belonging to the same genus and species type. And whether or not these strain mediated effects can be utilized for therapeutic benefit within neurologic and psychiatric disorders.

Lactobacillus rhamnosus JB-1.

L. rhamnosus JB-1 induces effect within gut-brain signaling through modulation of GABA receptors and inflammatory response. In an experimental study by Bravo et al. (2001), chronic treatment of L. rhamnosus JB-1 in a healthy mice model significantly increased GABA-B receptor concentrations in the cingulate and pre-limbic cortical regions, increased the concentration of GABA-A receptors in the hippocampus, decreased GABA-B receptor concentrations in the hippocampus, amygdala, and locus coerulus, and decreased GABA-A receptors concentrations in the pre-frontal cortex and amygdala. The influence over GABA receptors is supported to have psychiatric impact that is demonstrated through work by Cryan and Kaupmann (2005), whom found a reduced concentration of GABA-B receptors in the frontal cortices of a depression induced mouse model, and Jacobson-Pick, Elkobi, Vander, Rosenblum, and Richter-Levin (2008) whom found an increase of GABA-A receptors in the amygdala of a chronically stressed mice model.
Inflammatory affect was demonstrated in a study by Forsythe and Bienstock (2004), which found that L. rhamnosus JB-1 reduced intestinal inflammation through an upregulation of nerve growth factor, and inhibition of IL-8 synthesis. The neurologic impact of these noted effects is supported in work done by Angelucci, Aloe, Vasquez, and Mathé (2000), showing an altered concentration of brain derived neurotropic factor and nerve growth factor in a depression induced mice model. As well as in work done by Janelidze et al. (2015), showing an association between low serum levels of IL-8 and anxiety in suicidal patients.
L. rhamnosus JB-1 demonstrated behavioral impact in the abovementioned study by Bravo et al. (2001) through a significant decrease in typified anxious and depressive behavior in mice chronically administered with L. rhamnosus JB-1. In addition to modulation of GABA receptors, this effect was deemed to be in relation to interaction with the vagus nerve, for behavioral effect was not observed in vagotamized mice (Bravo et al., 2001). Due to the noted associating between nerve growth factor and depression, the increased concentration of nerve growth factor by L. rhamnosus JB-1 could also play a role within the noted decrease of typified anxious and depressive behavior.

Lactobacillus rhamnosus GG.

L. rhamnosus GG induced neurologic influence primarily through modulation of inflammatory processes. In an experimental study by Turner (2009), children with atopic dermatitis administered with L. rhamnosus GG resulted in significant reductions of serum levels of IL-10, which has been shown to reduce intestinal inflammation through the suppression of regulatory T cells (Park et al., 2005). The behavioral impact of this effect is suggested in a previous study by Dhabhar et al. (2009), which observed a decreased concentration of IL-10 levels in adults with major depression syndrome. Additional inflammatory effect was observed in an experimental study by Pessi et al. (2001), which noted a decreased concentration of regulatory T cells in casein degraded by L. rhamnosus GG. While no direct associations between regulatory T cell concentration and behavioral and or neurologic impact was observed, an indirect link pertaining to decreased inflammation by regulatory T cells (Park et al., 2005) which has been correlated with depression and anxiety (Bercik et al., 2010; Maes, Kubera, Leunis, & Berk, 1999; Smith & Rudolph, 1991;) supports the psychiatric influence of this inflammatory effect.
Behavioral impact was observed in a study by Kantak, Bobrow, and Nyby (2013), which observed an attenuation of obsessive compulsive disorder (OCD) typified behaviors in a house mice model administered with L. rhamnosus GG, that worked as well as the positive study control, fluoxetine, which is common medication used in OCD treatment (Bandelow et al., 2012). The noted behavioral affect was presumed to be in relation to a decrease in the anxiety often preceding or accompanying OCD, and not from a direct modulation of the neurologic mediators associated with OCD typified behavior (Kantak et al., 2013). In reference to the abovementioned effects upon inflammation, it is plausible to infer that the noted behavioral modulation could be mediated, at least partially, through inflammatory modulation which has been previously associated with anxiety (Maes et al., 1999; Smith & Rudolph, 1991).

Lactobacillus helveticus NS8.

L. helveticus NS8 induces behavioral and neurologic impact through modulation of neurotransmitters and inflammatory response. Research conducted by Liang et al. (2015) found that when administered in a pathogen free rat model, L. helveticus NS8 restored hippocampal concentrations of serotonin and norepinephrine. The psychological significance of this effect can be noted in previous work by Stanton and Sarvey (1985), which found that hippocampal depletion of norepinephrine reduces the frequency and magnitude of long term potentiation, which is a synaptic mechanism associated with cognitive functions pertaining to learning and memory (Eichenbaum & Otto, 1992), as well as attention and arousal (Shors & Matzel, 1997). Hippocampal effect was also noted in a study by Martinowich et al (2007), which observed an increase of hippocampal brain derived neurotrophic factor mRNA, which has been noted to play a role within depression and psychiatric disorders such as schizophrenia and bi-polar disorder (Martinowich et al., 2007; Palomino et al., 2006). L. helveticus NS8 was also shown to induce inflammatory effect through the attenuation of LPS induced inflammation which was mediated through an increased synthesis of IL-10 (Rong et al., 2015).
L. helveticus NS8 induced behavioral effect in an experimental study by Luo et al. (2014), which observed an attenuation of anxiety and improved cognition in a hyperammonemia induced rat model administered with L. helveticus NS8. These results were supported in a proceeding study by Liang et al. (2015), which observed a significant reduction of chronic stress induced anxiety, depression, and cognitive dysfunction in mice orally administered with L. helveticus NS8. All abovementioned induced effects including the synthesis of serotonin and norepinephrine (Liang et al., 2015), increased concentration of hippocampal brain derived neurotrophic factor mRNA (Martinowich, Manji, and Lu, 2007), and increased synthesis of IL-10 (Rong et al., 2015), appear to be plausible, and likely, mechanisms mediating the observed behavioral impact by L. helveticus NS8.

Lactobacillus helveticus R0052.

L. helveticus R0052 induced cerebral effect through mechanisms pertaining to increased barrier integrity and amelioration of stress induced irregularities in hypothalamic-pituitary-adrenal axis (HPA-axis) and automatic nervous system (ANS) functioning. Influence upon barrier integrity was supported in a study by Johnson-Henry, Hagen, Gordonpour, Tompkins, and Sherman (2007), which observed that L. helveticus R0052 prevented against pathogenic infiltration of Escherichia coli 0157:H7 by binding to epithelial cells and blocking pathogenic entry. This impact suggests that L. helveticus R0052 may play a beneficial immunologic role through the enhancement of intestinal barrier security and protection against pathogenic infection. This is a neurologically relevant action, for previous studies have shown a connection between pathogenic infection and behavior (Quinn et al., 1984) as well as psychological stress and barrier integrity (Ait-Belgnaoui, Bradesi, Fioramonti, Theodorou, & Bueno, 2005). HPA-axis modulation was supported in a study by (Ait-Belgnaoui et al., 2014), which observed a reversal of stress-induced HPA-axis and ANS irregularities, which was mediated through a decreased plasmatic concentration of corticosterone, adrenaline, and noradrenaline in a stress-induced mice model administered with a combination of L. helveticus R0052 and Bifidobacterium longum R0175. The modulation of the HPA-axis presents possible psychological relevance, for the HPA-axis has domain over coordinating all bodily stress responses (Tsigos & Chrousos, 2002).
L. helveticus R0052 induced behavioral effect in a study by Ohland et al. (2013), which observed an amelioration of diet-induced anxiety and memory deficits in mice administered with L. helveticus R0052. Interestingly, this effect appeared to be diet-dependent, for behavioral effect was only noted when mice were followed a high fat “western” diet, but not when following a low fat “chow” diet. These results were supported in another study by Gilbert, Arseneault-Bréard, and Flores Monaco (2013), which noted a diet-dependent decrease in typified depressive behavior in a post-myocardial infarction induced mice model administered with a combination of L. helveticus R0052 and Bifidobacterium longum R0175. Although future experimentation is required to be able to accurately ascertain the likely factors mediating the noted behavioral influence, it is reasonable to conclude that in addition to dietary intake, the abovementioned influence over HPA-axis regulation (Ait-Belgnaoui et al., 2014) could be a contributing factor to the noted behavioral modulation.

Lactobacillus johnsonii N6.2

L. johnsonii N6.2 induced neurologic effect through modulation of neurotransmitters concentrations, gut-barrier integrity, and oxidative stress concentrations. In an experimental study by Valladares et al. (2013), L. johnsonii N6.2 increased ileum and peripheral serotonin concentrations, decreased peripheral kynurenine concentrations, and decreased tryptophan activity when administered to a “BioBreeding” rat model. The significance of this alteration within behavior and neurologic functioning can be seen in previous work showing an association between low levels of serotonin and depression (Lucki et al., 1998), as well as an association between tryptophan depletion and depression and panic disorder symptom relapse (Bell et al., 2001). Further microbial influence was shown in a study by Valladares et al. (2010), which observed that when administered to a diabetic induced rat model, L. johnsonii N6.2 induced effect within barrier integrity by increasing concentrations of tight junction protein claudin and decreasing host oxidative stress response.  In addition to the previously noted association between barrier integrity and psychological stress (Ait-Belgnaoui et al., 2005), the behavioral significance of the modulation of oxidative stress response is supported in a study by Gorrindo, Lane, Lee, McLaughlin, and Levitt (2013), which found a correlation between increased oxidative stress response levels and increased severity of autism spectrum disorder associated behavioral abnormalities pertaining to language impairment, OCD typified behavior, and aversion to social communication. While no studies have tested to see whether or not L. johnsonii N6.2 induces effect within behavior, the noted induced effects pertaining to modulation of neurotransmitters and effect upon oxidative stress levels warrants future research to assess the degree of possible behavioral modulation.

Bifidobacterium longum, subspecies longum JCM1217

Bifidobacterium longum subsp. longum JCM1217 induced effect within gut-brain axis regulation through immunological modulation. In a study by Fukuda et al. (2011), pathogenic infection of Escherichia coli 0157:H7 was prevented in mice administered with B. longum, subspecies longum JCM1217 through production of acetate. Fukuda et al. (2011) proposed that increased acetate production protected against pathogen infection by attaching to epithelial cells and preventing the pathogen from translocating from the gut lumen to host blood supply. In addition to the increased barrier integrity, which has been previously noted to have an association with psychological stress (Ait-Belgnaoui et al., 2005), the induced production of acetate could have possible behavioral ramifications, for experimentation by MacFabe et al. (2007) observed that autism spectrum disorder behavioral abnormalities were induced in a rat model when short chain fatty acid levels were manually increased. More research should be conducted on B. longum, subspecies longum JCM1217 to assess whether or not the noted immunological effect induces behavioral or neurologic modulation.

Bifidobacterium longum NCC3001.

In an experimental study by Bercik et al. (2010), Bifidobacterium longum NCC3001 ameliorated colitis associated behavioral alterations and brain derived neurotrophic factor depletions, which has been previously associated with stress and depression (Martinowich et al., 2007; Angelucci et al., 2000). However, when tested in a follow-up study by Bercik et al. (2011), colitis associated alterations brain derived neurotrophic factor was not observed, which demonstrates that this was likely not a factor mediating the noted behavioral change. Additional neurologic effect was demonstrated in a study Khoshdel et al. (2013), which observed that B. longum NCC3001 significantly reduced the excitability of enteric neurons when applied to ileal segments of adult mice. This effect upon enteric neurons was supported in a study by Bercik et al. (2011), and presumed to be a possible method in which the central nervous system is signaled through activation of vagal pathways within the enteric nervous system.
Behavioral effect was noted in the abovementioned studies (Bercik et al., 2010; Bercik et al., 2011) through the attenuation of colitis associated typified anxious behavior.  Both studies also observed in the abovementioned study by Bercik et al. (2011) through an attenuation of typified anxious behavior and an increase in typified exploratory behavior.  Interaction with the vagus nerve appeared unclear, for behavioral effect was found independent of the vagus nerve in the first study by Bercik et al. (2010) and dependent of the vagus nerve in the follow-up study by Bercik et al. (2011). While the noted effect regarding decreased excitability of the vagus nerve supports interaction with the vagus nerve within behavioral modulation (Bercik et al., 2010; Khoshdel et al., 2013), more research is required to adequately determine the degree of vagal involvement.

Bifidobacterium infantis 35624.

Bifidobacterium infantis 35624 induced both behavioral and neurologic effect through modulation of neurotransmitters, inflammatory response, and intestinal permeability.  Research by Desbonnet, Garrett, Clarke, Bienenstock, and Dinan (2008) observed that B. infantis 35624 reduced frontal cortex serotonin concentrations, increased plasmatic concentrations of noradrenaline and tryptophan, and inhibited the production of IL-10. The significance of these mediated effects are supported in previous work by (Amat et al., 2005; Bland et al., 2003) showing a connection between prefrontal cortex serotonin concentration and the modulation of anxiety, in work by Vijayakumar and Meti (1999) showing a connection between noradrenaline and depression, in work by Myint et al. (2007) showing a connection between decreased plasmatic tryptophan concentrations and depression, and work by (Dhabhar et al., 2009) showing a connection between IL-10 and major depressive syndrome.
Behavioral influence was demonstrated in the abovementioned study by (Desbonnet et al., 2008) which noted an amelioration of typified depressive behavior in rats administered with B. infantis 35624 that worked as effectively as the positive study control Citalopram supporting the effectiveness of B. infantis 35624 as a behavioral regulator.

DISCUSSION

The variation of induced effects expressed by the 8 included microbial strains demonstrate that even amongst strains belonging to the same genus type, generalizations pertaining to induced effects should be avoided. For example, while four out of the five included Lactobacillus strains induced influence over a collection of 9 neurotransmitters, modulation over the same neurotransmitter was only expressed one time in the modulation of serotonin by both L. helveticus NS8 and L. johnsonii N6.2 (Liang et al., 2015; Valladares et al., 2013). Interestingly, in some instances microbial strains belonging to the same genus type induced oppositional effects. For example, while both L. rhamnosus GG and L. helveticus NS8 impacted the concentration of anti-inflammatory cytokine IL-10, L. rhamnosus GG decreased IL-10 concentrations (Turner., 2009) while L. helveticus NS8 increased them (Rong et al., 2015). Oppositional impact was also expressed amongst strains belonging to differing genus types, such as L. johnsonii N6.2, which increased peripheral serotonin concentrations (Valladares et al., 2013), and B. infantis, which reduced peripheral serotonin concentrations (Desbonnet et al., 2008).
Despite the diversity of microbially induced effects, behavioral modulation was largely uniform amongst the included strains. Six out of the eight strains demonstrated behavioral influence through the attenuation of of typified anxious and or depressive behavior. The apparent dichotomy expressed by the divergent mediated effects and uniform behavioral influence demonstrates the complexity of assessing the microbe-brain connection. As with many organically occurring events, no one factor determines whether or not an effect will take place. It is a consortium of many factors, some yet to be identified, and some yet to be understood.
While the noted behavioral modulation is promising and undoubtedly warrants further investigation, it is essential to remember the vast differences between human and animal physiologies and the methodology in which behavioral influence is assessed in animal models. The clinical testing done thus far has suggested promising results. For example, a study examining the effect of a multispecies probiotic supplement upon depressive indicators in healthy individuals found a significant decrease in a cognitive reactivity to sad mood amongst those given the probiotic supplement when compared to participants supplied with the placebo (Steenbergen, Sellaro, Hemert, Bosch, & Colzato, 2015). This effect was attributed to a decrease in frequency of negative and aggressive thoughts which further supports the potential therapeutic benefit of varying microbial strains (Steenbergen et al., 2015). It will be interesting to see whether or not this effect is viable in participants who have pre-existent conditions such as depression or acute anxiety as well as other psychiatric disorders.

Acknowledgements

This review was completed as an exterior project for a graduate level course facilitated by Dr. Colette LaSalle, at San Jose State University.

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Brain Tumor Segmentation Using Morphological Processing and the Discrete Wavelet Transform

doi:10.22186/jyi.33.3.55-62

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

Abstract

Medical imaging is key for the successful diagnosis and treatment of brain tumors, but the initial detection of tumors is, by nature, difficult. Image segmentation, a technique often used to aid detection, is highly dependent on the resolution of the segmented image. Many common morphological segmentation methods often suffer from a lack of resolution which hinders tumor detection. Thus, in this paper, two tumor segmentation techniques are developed and compared using MATLAB – one based on morphological processing, and a second which combines the discrete wavelet transform with morphological processing. Both proposed approaches begin with skull stripping via binary erosion, followed by image contrast enhancement and histogram thresholding. In the wavelet-based technique, the key step is to perform a fourth level discrete wavelet decomposition followed by manipulations of the wavelet. The resulting image is then morphologically opened, contrast enhanced, and gray thresholded. Both approaches were successfully tested on several magnetic resonance images, and it was shown that the wavelet transform method generally produces higher resolution segmented images. Additionally, it was found that the choice of wavelet basis function used plays a key role in the resolution of segmentation, with the Symlet 20 wavelet basis able to segment out almost 18% more pixels on average from an MR image than the Haar wavelet basis. These results can serve as a useful future reference as they provide convincing evidence of the necessity for careful choice of wavelet basis and suggest a basis that seems well suited for this application.

Introduction

Within Magnetic Resonance (MR) image processing, one major problem is the segmentation of brain tumors. Segmentation is the process of partitioning an image into several distinct sections to simplify the image or to focus on a region for further study (Kaur, G., & Rani, 2016). In the last several decades, there has been a push for automating the process of segmentation of pathological regions in the brain in MR scans. Current research on brain tumor segmentation uses a wide variety of methods which can be grouped as “intelligent” or “non-intelligent.” “Intelligent” techniques include machine learning methods such as neural-networks and support vector machine. The focus of this investigation, however, was on non-intelligent techniques, which include local and global thresholding, histogram operations, and morphology (Kaur & Banga, 2013).
Each of these methods has certain deficiencies. For example, global thresholding is not localized enough to produce segmentations with the desired resolution, but local thresholding is often not robust enough because of variations in how tumors present on MR images. Statistical models and morphological processing can also struggle to differentiate tissues in complex cases (Kaus et al., 1999). Wavelet-based techniques have become more popular in recent years because of the robustness they provide, and many hybrid wavelet methods are present in the literature (e.g., Sawakare & Chaudhari, 2014). When the correct method is found for a specific application, image segmentation can be a highly effective and important process because it allows clinicians to better understand the nature of a tumor and to plan more effective treatments (Xuan & Liao, 2007). Non-intelligent segmentation techniques are particularly important because they are often used for refining and improving intelligent segmentation methods (Kaur & Banga, 2013). In this research, a non-intelligent segmentation method, different from but inspired by those that are currently in the literature (Saini & Singh, 2015), was created based on morphology. Additionally, enhancements to this technique were developed using the discrete wavelet transform. These improvements were tested using several different wavelet basis functions. The major result of this investigation shows the key role that the choice of wavelet basis function plays in the resolution of the segmented image, a notion generally not discussed in the literature.

Morphology

The first of the two main mathematical concepts underlying this research is morphological image processing. Morphology consists of several operations that can be performed on a given image, represented by an image matrix, using other matrices called structuring elements (SE’s) in order to alter the image in desirable ways. Morphological processing is driven by operations performed by the SE’s on the image matrix, which use ones and zeroes to perform geometrical transformations based on the distributions of said ones and zeroes. The morphological operation used for segmentation in this investigation is called morphological opening. Morphological opening is made up of the successive operations of morphological erosion and dilation, which are both performed using the same SE.
Morphological erosion is defined in terms of set notation as follows. Given image A and structuring element B, both sets in Euclidean N-space, then the erosion of A by B, denoted A A B, “is the set of all elements x for which (x+b) ϵ A for every b ϵ B” (Sternberg, Haralick, & Zhuang, 1987):

BT equation 1 (1)

Essentially, erosion shrinks the geometric features within an image based on the distribution of ones and zeroes within the SE. Morphological dilation, denoted A A B (where A and B are the same as above), is defined as (Sternberg et al., 1987):

BT eq 2 (2)

Essentially, dilation inflates the geometric features within an image based on the nature of the SE.
The combination of these operations in succession, denoted  A o B, is called morphological opening, and it is defined as (Sternberg et al., 1987):

BT eq 3 (3)

Discrete Wavelet Transform

The second concept central to this investigation is the discrete wavelet transform. Traditionally, Fourier processing is used in most signal and image processing applications. Fourier bases are frequency localized however, meaning that small changes in space produce great changes in frequency and vice versa. As a result, Fourier based processing methods work very well for analyzing periodic signals, but abrupt changes are not easily detected through Fourier transform based analysis (Ingale, 2014). Due to these limitations, the decision was made to use the wavelet transform in support of morphological segmentation instead of the Fourier transform. The most important property of wavelets, the localization of their basis functions, sharply contrasts with the nature of Fourier basis functions; wavelet basis functions provide a degree of localization in both the space and frequency domains, meaning that small changes in space create small changes in frequency and vice versa.
Wavelet basis functions consist of a father wavelet (scaling function) notated φ(x), and a mother wavelet function, the first level of which is notated ψ(x). These mother wavelet functions can be scaled and shifted so that they cover the entire x-axis. These shifted, scaled functions are used to decompose a signal into its component parts. This decomposition, which allows for a more in-depth analysis of a particular region of the signal, is known as the discrete wavelet transform (DWT).
The DWT in one dimension is used to decompose and analyze signals such as an electrocardiogram or audio signal (a signal which can be represented by a single row or column vector). However, an image is not a one-dimensional signal like an EKG, but can instead be thought of as a two-dimensional signal (represented by a matrix instead of a column or row vector). Thus, for the computation of the DWT of an image, the first step is to extend the wavelet basis function to two dimensions, which is done using both the father wavelet φ(x) and the mother wavelet ψ(x). The wavelet function is extended to two dimensions in the following manner (Tolba, Mostafa, Gharib, & Megeed, 2001):

 Screen Shot 2017-07-29 at 7.46.16 PM

(4)

 

 

(5)

 

 

(6)

 

 

(7)

where the superscripts denote horizontal (h), vertical (v), and diagonal (d) basis functions. These new wavelet functions are used to define high and low pass decomposition and reconstruction filters (Lo-D and Hi-D in the above figure represent said decomposition filters), which are used to compute the DWT of the image by the convolution algorithm pictured in Figure 1 (“DWT2,” n.d.). This decomposition creates sub-images, the “CA,” “CD-horizontal,” “CD-vertical,” and “CD-diagonal,” in Figure 1. These sub-images each contain unique data about the original image; the approximation image, “CA,” contains a smaller, lower resolution version of the original image, and the detail (“CD”) images contain information about the vertical, horizontal, and diagonal aspects of the original image. Manipulation of these sub-images allows for effective analysis of important data, such as a tumor region, and has proved to be the powerful analysis tool that enhances morphological segmentation in this investigation.

Figure 1. Algorithm for DWT decomposition computation.

Figure 1. Algorithm for DWT decomposition computation.

Methods

Step 1: Skull Stripping

For both segmentation methods, the first step is to remove the skull from the original MR image. This is an important step because in many MR images, the skull appears as one of the brightest regions of the image, and is sharply contrasted with other regions of the brain, such as grey matter. In the types of MR images used in this investigation, tumors also appear as bright regions, and thus to limit false positive segmentations from occurring, the skull must be removed from the image. To do this, the original Red, Green, and Blue (RGB) or Grayscale image was converted to a binary image. Next, the pixels that corresponded to the skull were eroded away (i.e. removed from the outside in, until the skull was eliminated). Then, the smaller, eroded binary image was used as a mask and placed over top of the original image, so that only the pixels in the original image that correspond to ones in the eroded binary image are kept as they are in the original image, and all other pixels (those of the skull) are set to 0 (eliminated or set to black). The result is a skull stripped version of the original MR image. This is shown in Figure 2 (Clark et al., 2013).

Figure 2. The results of performing skull stripping. A. Original brain MR image obtained from The Cancer Imaging Archive. B. The results of skull stripping the image a via the processes described in Methods: Step 1.

Figure 2. The results of performing skull stripping. A. Original brain MR image obtained from The Cancer Imaging Archive. B. The results of skull stripping the image a via the processes described in Methods: Step 1.

Step 2: Contrast Enhancement and Thresholding

The next step in the proposed segmentation method is to perform contrast enhancement and thresholding based on Otsu’s thresholding method (Otsu, 1979). These operations are performed as preliminary, global segmentation steps which help to illuminate the tumor region. The contrast enhancement method used is based on the shape of a specified curve. All the MR images used in this investigation are weighted such that pathological regions appear as brighter than other regions of the brain. Thus, to illuminate the tumor region and fade other regions, the general shape of the curve used made light regions lighter and dark regions darker. The results of contrast enhancement are shown in Figure 3.

Figure 3. The results of performing contrast enhancement on the skull-stripped image shown in Figure 2B.

Figure 3. The results of performing contrast enhancement on the skull-stripped image shown in Figure 2B.

After contrast enhancement is performed, the next step is to perform thresholding by Otsu’s method. The goal of thresholding is to decide which pixels in the image correspond to the foreground and which correspond to the background. Once the background and foreground pixels have been determined based on the distribution of pixel values within the image, the image is converted to binary, sending the background pixels to a value of 0 and the foreground pixels to a value of 1. The skull stripped image is then masked with this thresholded binary image to produce the final image for this step in the segmentation (Figure 4).

Figure 4. The results of performing Otsu thresholding on the image shown in Figure 3.

Figure 4. The results of performing Otsu thresholding on the image shown in Figure 3.

Step 3: The DWT

For the technique involving the discrete wavelet transform, the next step is the wavelet step (for the technique not involving the DWT, the next step is morphological opening, which will be discussed in Step 4). The first aspect of this step is the choice of wavelet basis function. Since different basis functions provide different degrees of localization in the space and frequency domains, they also allow for different degrees of resolution in segmentation. The four main bases tested in this investigation are shown in Figure 5.

Figure 5. Wavelet Basis Functions Tested. A-D from left to right, top to bottom. A. Haar Basis. B. Daubechies 2 Basis (‘db2’). C. Symlet 4 Basis (‘sym4’). D. Symlet 20 Basis (‘sym20’).

Figure 5. Wavelet Basis Functions Tested. A-D from left to right, top to bottom. A. Haar Basis. B. Daubechies 2 Basis (‘db2’). C. Symlet 4 Basis (‘sym4’). D. Symlet 20 Basis (‘sym20’).

After much testing, it was found that the best wavelet basis for this application was the Symlet 20 wavelet, pictured in Figure 6 with the corresponding high and low pass filters. Comparisons between bases will be examined in the results section.

Figure 6. Father and mother wavelet functions, as well as the high and low pass decomposition and reconstruction filters, for the Symlet 20 wavelet.

Figure 6. Father and mother wavelet functions, as well as the high and low pass decomposition and reconstruction filters, for the Symlet 20 wavelet.

Once the wavelet basis is chosen, the DWT can be performed on the image. The computation of the DWT in MATLAB decomposes the image into four outputs: the approximation image and the three detail images (“CA,” “CD-horizontal,” “CD-vertical,” and “CD-diagonal). Once the first DWT is computed, a second level DWT is computed using the first level approximation image as the input, and the small magnitude coefficients of the approximation matrix are set to 0. This pattern is repeated two more times for a total of four operations of the DWT.
Then, the detail sub-images are set to 0. The first reconstruction produces an image which is then used to reconstruct the second image and so on until the final image is the result of four reconstructive processes using the inverse DWT (IDWT) (four operations of the IDWT are needed for reconstruction to “undo” the four operations of the DWT used for deconstruction). The resultant image is then contrast enhanced and thresholded, for a second time, via the methods used in Step 2 above. The results of these operations are used as a mask which is overlaid on the original image (Figure 7).

Figure 7. The results of performing wavelet decomposition and reconstruction on the image pictured in Figure 4.

Figure 7. The results of performing wavelet decomposition and reconstruction on the image pictured in Figure 4.

Step 4: Morphological Opening

In this step, the two approaches converge again. For the hybrid, wavelet-morphology based approach, the next step is to morphologically open the results of the wavelet processing step (Figure 7) to remove all regions that do not correspond to the tumor. For the morphological (non-wavelet) approach, the step is to morphologically open the skull-stripped, thresholded image (Figure 4). After much testing, the most suitable structuring element for this application was found to be a “disk” (i.e. a circle of 1’s with 0’s outside of the circle in the SE matrix). The radius of the SE used in this research was 14 pixels.
The results of morphologically opening the wavelet reconstructed image with the above structuring element are shown below in Figure 8a, and the results of morphologically opening the skull-stripped, thresholded image without the wavelet step are pictured in Figure 8b.

Figure 8. A. The results of morphologically opening the image shown in Figure 7. B. The results of morphologically opening the image shown in Figure 4.

Figure 8. A. The results of morphologically opening the image shown in Figure 7. B. The results of morphologically opening the image shown in Figure 4.

Step 5: Final Contrast Enhancement and Thresholding

The final step is to perform another contrast enhancement and thresholding on the morphologically opened images created in the previous step. This makes it so that the only object left in the image after this step is the segmented tumor region (Figure 9). This region is shown in red (Figure 10) overlaid on the original MR image for qualitative comparison.

Figure 9. A. The results of contrast enhancing and thresholding the image shown in Figure 8A. B. The results of contrast enhancing and thresholding the image shown in Figure 8B.

Figure 9. A. The results of contrast enhancing and thresholding the image shown in Figure 8A. B. The results of contrast enhancing and thresholding the image shown in Figure 8B.

Results

This section will present two sets of results:  a comparison of the two segmentation methods developed in this paper (i.e. segmentation with and without the DWT) and a comparison of the resolutions produced when using the different wavelet bases which shown in Figure 5.
The comparisons made in this section show the difference in the ability of each technique to segment out data. They indicate the percent difference in how much data was segmented out of the image by each technique (they are shown in the form of percent difference instead of actual pixel numbers because the size of each tumor varies greatly from image to image, making raw pixel number comparisons meaningless).

Comparison Between Segmentation Methods

Morphological segmentation was successfully used as a baseline for tumor segmentation in this paper. With the addition of the wavelet transform step, the resolution of segmentation (i.e. the number of correct pixels that were partitioned from the image) fluctuated based on the choice of wavelet basis. The following tables shows comparisons between morphological segmentation and hybrid wavelet-morphological segmentation for each image tested and each of the four wavelet bases shown in Figure 5 above.

Comparison Between Wavelet Bases

From the comparisons made in Table 1, it is clear that certain wavelet bases perform better than others. Since the Symlet 20 basis consistently outperformed the other three in comparison to morphological processing, the following section serves to compare the resolution of the Symlet 20 basis with the Symlet 4, Daubechies 2, and Haar bases. The following table illustrates how the Symlet 20 wavelet basis compared with each of the wavelet bases used above for each of the images tested.

Table 1. Comparisons Between Techniques. The percent difference in resolution represents the number of pixels that were segmented using the given wavelet basis for the DWT compared with the number of pixels.

Table 1. Comparisons Between Techniques. The percent difference in resolution represents the number of pixels that were segmented using the given wavelet basis for the DWT compared with the number of pixels.

Discussion

As the data shows, the hybrid wavelet-morphological technique performs consistently better segmentation than just morphological segmentation when using the Symlet 4 and Symlet 20 wavelet bases, but is inconsistent when using the Daubechies 2 and Haar bases. Table 1 indicates that the Symlet 20 basis is the best suited basis tested, as the worst resolution that it produced was still about thirteen percent better than the morphological approach. Additionally, the Symlet 20 basis consistently outperformed each other wavelet basis, with an average of seventeen percent more data segmented when compared with the Haar basis and two and a half percent better resolution when compared with the Symlet 4 basis. This underscores the importance of the choice of wavelet basis function for tumor segmentation, a result not previously reported in the literature. Thus, with the right choice of wavelet basis, using a combination of wavelet processing and morphological techniques instead of just morphological processing can help to eliminate data loss in tumor segmentation. On average, the Symlet 20 wavelet basis could segment out about 20 percent more data than morphological processing alone.
Even though it is clear that the Symlet 20 basis is best suited for this technique, it is still uncertain as to exactly why this is the case. One thought, at least when it comes to comparing the Haar and Symlet 20 bases, is that the Haar basis is more ‘rigid’ and is less able to detect smaller changes in pixel characteristics than the Symlet 20 basis. This is an unproven idea, meaning that one main area of future work is to develop a more concrete theory as to why some wavelet bases perform so much better than others. An additional area of future work is to improve on the skull stripping method used in this technique. This skull stripping method is somewhat “primitive” and not highly adaptable, meaning that for tumors near the skull region, significant data loss is a distinct possibility. As a result, another highly important area of future work would be to develop a more robust, wavelet-based skull stripping method.

Conclusion

Brain tumor segmentation is an inherently difficult problem because of the widely varying nature of pixel distributions of pathological tissues in MR images. The segmentation methods and results presented in this paper are just two of many methods that have been developed over the past several decades with the goal of automating and improving brain tumor segmentation. The results underscore the effectiveness of the wavelet transform, as well as present novel findings about the impact of basis function choice on the resolution of the segmented image. Although many techniques exist that use the wavelet transform in some way, none that combine morphology and the DWT in the way presented in this paper have been reported in the literature. Additionally, there have never been clear results reported on the impact of the wavelet basis function choice on the results of segmentation. The results of the investigation in this paper can therefore serve as a useful guide for the development of future segmentation techniques involving the wavelet transform because they provide convincing evidence that the choice of wavelet basis can be vital to the resolution of a segmented image, as well as point to a basis with useful properties for this application.

Acknowledgments

The author would like to thank his mentors, Dr. Marcus Fries, Dr. Pierre-Richard Cornely, and Dr. Jill Macko for their invaluable ideas and support.

References

Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moorse, S., Phillips, S., Maffit, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 26(6), 1045-1057. doi:10.1007/s10278-013-9622-7.  DWT2. (n.d.). Retrieved from https://www.mathworks.com/help/wavelet/ref/dwt2.html. x
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Kaur, M., & Banga, V. K. (2013). Thresholding and Level Set Based Brain Tumor Detection Using Bounding Box as Seed. International Journal of Engineering Research & Technology, 2(4). Retrieved from https://www.ijert.org.
Kaus, M. R., Warfield, S. K., Nabavi, A., Chatzidakis, E., Black, P. M., Jolesz, F. A., & Kikinis, R. (1999). Segmentation of Meningiomas and Low Grade Gliomas in MRI. In Medical Image Computing and Computer-Assisted Intervention – MICCAI’99: Second International Conference, Cambridge, UK, September 19-22, 1999 (Vol. 1679, Lecture Notes in Computer Science, pp. 1-10). Springer Berlin Heidelberg.
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Prevalence and Outcomes of Electrolytes Deficiency in Children under Five with Diarrhea in Mwanza, Tanzania

doi:10.22186/jyi.33.2.46-48

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

Abstract

Dehydration from diarrhea leads to a loss of vital electrolytes in the body. The prevalence of electrolytes deficiency and its outcomes due to diarrhea among children under five in Mwanza, Tanzania was not clearly known, thus this study was performed to determine this statistic. A cohort study was conducted among 66 children less than five years old suffering from diarrhea attended and admitted to health centers in Mwanza, Tanzania. Vein puncture was performed to obtain peripheral blood, processed, and analyzed for two major electrolytes, Potassium and Sodium. This study was conducted because the loss of vital electrolytes (sodium and potassium) from diarrhea can be fatal if poorly treated. The median age of study participants was 1 year, ranged from 0.6 to 3 years. The prevalence of electrolytes deficiency in the cohort was determined to be 54.5%. Sodium deficiency (Hyponatremia) was the most prevalent (37.9%). After medication and oral rehydration therapy, all of the diarrheagenic children recovered, and those with electrolytes deficiency had their electrolytes balanced. Proper medication with oral rehydration therapy ensures complete recovery from diarrhea and electrolytes balance.

Introduction

Diarrhea is the passage of three or more loose or liquid stools per day; it may be a result of eating contaminated food and water or from food poisoning and is a common symptom of gastrointestinal infections caused by a wide range of pathogens, including bacteria, viruses and protozoa (Black et al., 1980; Kothari VR, & Thakur NA, 2014). Dehydration can be identified by experiencing dizziness, thirst, fatigue, infrequent urination and dark colored urine, nausea and headaches can leave the body without the electrolytes necessary for survival (Hirschhorn, 1980; Mackenzie, Barnes, & Shann, 1989). The electrolytes found in the body are potassium, calcium, sodium, magnesium, bicarbonate and chloride for cells functioning and signaling. According to WHO estimates; diarrheal disease is the leading cause of under-five mortality and is responsible for killing around 760,000 children every year (Kothari VR, & Thakur NA, 2014).
Epidemiological studies have shown that in developing countries, there are an estimated 1.3 billion episodes and 3.2 million deaths  of those under age five each year due to diarrhea (Rahman, 2014). Overall, these children experience an average of 3.3 episodes of diarrhea per year, but in some areas, primarily in developing countries, the average exceeds 9 episodes per year (Rahman, 2014). Where episodes are frequent, children may spend more than 15% of their days with diarrhea and about 80% of deaths that occur in the first two years of life are due to the condition. In developing countries 50% of pediatric hospitalizations  are due to acute diarrhea (Rahman, 2014).
A study carried out  by the BP Koirala Institute of Health Sciences in Dharan, Nepal that examined acid, base, and electrolyte disturbance in diarrhea showed 56% sodium  deficiency (hyponatremia), 46% potassium deficiency (hypokalemia) and 26% combined (hyponatremia and hypokalemia) (Shah, Das, Kumar, Singh, & Bhandari, 2007). The same study reported five children out of 57 had died due to electrolytes loss from diarrhea (Shah et al., 2007).
Statistical information about the prevalence of electrolyte deficiency and the outcomes among children under five years old with diarrhea that attended or was admitted to healthcare centers in Mwanza, Tanzania was not clearly known. We hypothesized that, vital electrolytes (sodium and potassium) are lost together with water due to excessive diarrhea among children under five years old. The outcome of vital electrolytes lost can be fatal, so this study was designed to guide management of children under five years old with diarrhea for early and complete recovery.

Materials and Methods

A cohort study was conducted between July and August 2016. All children less than five years old suffering from diarrhea who was admitted at the pediatric wards in Mwanza Healthcare centers and whose parents or guardians gave their consent to take part in this study were used in the study. A serial sampling method was used to determine eligibility of these study participants. About 2.5 to 5ml of two blood samples were collected from each participant and placed in a plain vacutainer tubes whereby the serum was extracted for electrolytes (sodium and potassium) analysis. Blood sample A was collected on the first day of participant admission or visit and sample B, as a follow up to sample A, was collected three days after administering oral rehydration solution (ORS) or antibiotics to all participants with electrolyte(s) [sodium and/or potassium] deficiency results.
Laboratory Procedure
Extracted sera were analyzed within two hours after specimen collection for sodium and potassium following the internal standard operating procedures, and as per reagents manufacturer guidelines for the SP Twin Electrolytes Test Kit (ARKRAY Healthcare Pvt. Ltd, India) in the Corolimeter manual analyzer (CL 157 Colorimeter).

Results

Demographic and Clinical Characteristics of Participants

During this study, a total of 66 children were enrolled. Out of those, 53.0% (35/66) were females. The median age of study participant was 1 (IQR: 0.6-3) year (Table 1).

Table 1. Demographic and clinical information of study participants.

Table 1. Demographic and clinical information of study participants.

Electrolyte Deficiency Results

The overall prevalence of electrolytes deficiency in diarrheagenic under-five children was 54.5% (36/66). Hyponatremia, hypokalemia and both (hyponatremia and hypokalemia) electrolytes deficiency were observed in 37.9% (25/66), 16.7% (11/66) and 15.2% (10/66) of the cohort respectively.

Factors Associated with Electrolytes Deficiency

Factors found to be connected with electrolytes deficiency among diarrheagenic under-five children in the bivariate analysis were 1) Present of symptoms like fever, vomiting and dehydration, 2) Duration of diarrhea, and 3) Diarrhea treatment. Children with symptoms such as fever, vomiting and dehydration showed electrolyte deficiency; 20/58 (34.5%) had hyponatremia, 10/58 (17.2%) had hypokalemia, and 8/58 (13.8%) had both (p = .707) (Table 2).
Considering the duration of diarrhea, most of children showed electrolyte deficiency during the early three up to seven days of diarrhea; 13/36 (36.1%) had hyponatremia, 6/36 (16.7%) had hypokalemia, and 8/36 (22.2%) had both hypokalemia and hyponatremia (p = .651).
Following diarrhea treatment with ORS, antibiotics or both, for those who did not received any treatment; 12/34 (35.3%) had hyponatremia, 4/34 (11.8%) had hypokalemia and 9/34 (26.5%) had both hypokalemia and hyponatremia (p = .031) (Table 2).

Table 2. Factors associated with electrolytes deficiency.

Table 2. Factors associated with electrolytes deficiency.

Electrolyte Deficiency Outcomes

Out of 66 diarrheagenic children, 40 recovered completely, 18 were still suffering from diarrhea and 8 were lost prior to follow-up (discharged or did not attend next visit) with no record of death. Among the cohort, 32 received management with antibiotics, ORS or both, whereby 62.5% (20/32) recovered completely from diarrhea (p = .0069). All diarrheagenic children who had recovered after treatment, 20/20 (100%) had balanced electrolytes from day three of follow up (Table 3).

Table 3. Outcome of electrolytes deficiency from day three after diarrhea treatment with ORS/antibiotics.

Table 3. Outcome of electrolytes deficiency from day three after diarrhea treatment with ORS/antibiotics.

Discussion

In this study, the prevalence of electrolytes deficiency among under-five diarrheagenic children was 54.5%, with the most prevalent depleted electrolyte being sodium (hyponatremia), 37.9% followed by potassium depletion (hypokalemia), 16.7%. This is comparable to another study done in Dharan, Nepal which found that the most prevalent depleted electrolyte among children with diarrhea was sodium (hyponatremia), 56% (Shah et al., 2007). Another study done in Nigeria in 2015 on serum electrolyte profiles in children admitted with dehydration due to diarrhea showed that hyponatremia and hypokalemia ranked first and second by 60.5% and 44.3% respectively (Onyiriuka, & Iheagwara, 2015). Sodium and potassium are the major lost electrolytes in diarrhea because they form intracellular and extracellular fluids respectively at the sodium-potassium pump (Skou, 1989).
The current study found that, electrolytes deficiency among diarrheagenic children to be associated with clinical symptoms like fever, duration of diarrhea and treatment type as previously reported (Bahl et al., 2002; Donowitz, Kokke, & Saidi, 1995; Thapar, & Sanderson, 2004; Weiner, & Epstein, 1970). The more episodes of diarrhea a child experiences, the greater amount of water and electrolytes are lost (Thapar, & Sanderson, 2004). Lack of treatment therapy to replace water and electrolytes results  in a high level of deficiency (Thapar, & Sanderson, 2004).
This study found that most of the children experienced electrolyte deficiency after suffering from diarrhea during the early three to seven days. This may be because during the early days they had not yet received treatment; thus, the results showed electrolytes had decreased. However, as the days went on they underwent treatment and their levels began to rise as ORS replaced the lost electrolytes. Diarrhea treatment either with ORS or antibiotics ensures recovery and a rise in electrolyte  levels (Hirschhorn, 1980). But for those children who received antibiotic treatment and ORS, still in diarrhea may be the exact aetiological cause of diarrhea was not bacteria (Black et al., 1980; Hirschhorn, 1980).
Most patients with electrolyte deficiency recovered completely after receiving treatment; those who had not received any treatment but recovered may have been affected by a bacterial toxin, in which case the diarrhea usually stops itself after some time (self-limiting) (Challapalli, Tess, Cunningham, Chopra, & Houston, 1988).

Conclusion

The current study found high prevalence of electrolytes deficiency, 54.5% among children under five years old with diarrhea. Electrolytes deficiency was connected with fever, vomiting and dehydration. The use of ORS as part of diarrhea management to replace water and the lost electrolytes is recommended. This study was unable to determine the aetiological causative agent of diarrhea among children under five years old. Further studies should attempt to determine the aetiological causative agent of diarrhea cases.

References

Bahl, R., Bhandari, N., Saksena, M., Strand, T., Kumar, G. T., Bhan, M. K., &
Sommerfelt, H. (2002). Efficacy of zinc-fortified oral rehydration solution in 6-to 35-month-old children with acute diarrhea. The Journal of Pediatrics, 141(5), 677-682.
Black, R. E., Merson, M., Rahman, A. M., Yunus, M., Alim, A. A., Huq, I., Curlin,
G. (1980). A two-year study of bacterial, viral, and parasitic agents associated with diarrhea in rural Bangladesh. Journal of Infectious Diseases, 142(5), 660-664.
Challapalli, M., Tess, B. R., Cunningham, D. G., Chopra, A. K., & Houston, C. W.
(1988). Aeromonas-associated diarrhea in children. The Pediatric Infectious Disease Journal, 7(10), 693-697.
Donowitz, M., Kokke, F. T., & Saidi, R. (1995). Evaluation of patients with chronic
 diarrhea. New England Journal of Medicine, 332(11), 725-729.
Hirschhorn, N. (1980). The treatment of acute diarrhea in children. An historical
and physiological perspective. The American Journal of Clinical Nutrition, 33(3), 637-663.
Hsiao, A. L., & Baker, M. D. (2005). Fever in the new millennium: a review of
recent studies of markers of serious bacterial infection in febrile children. Current Opinion in Pediatrics, 17(1), 56-61.
Mackenzie, A., Barnes, G., & Shann, F. (1989). Clinical signs of dehydration in
 children. The Lancet, 334(8663), 605-607.
Onyiriuka, A., & Iheagwara, E. (2015). Serum electrolyte profiles of under-five
Nigerian children admitted for severe dehydration due to acute diarrhea. Nigerian Journal of Health Sciences, 15(1), 14.
Rahman, H. (2014). Molecular characterization of Necrotoxigenic Escherichia Coli
NTEC of man and animals.
Shah, G., Das, B., Kumar, S., Singh, M., & Bhandari, G. (2007). Acid base and
electrolyte disturbance in diarrhoea.
Skou, J. C. (1989). Sodium-potassium pump Membrane transport (pp. 155-185):
Springer.
Thapar, N., & Sanderson, I. R. (2004). Diarrhoea in children: an interface between
 developing and developed countries. The Lancet, 363(9409), 641-653.
VR, Kothari., & NA, Thakur. (2014). A Cross Sectional Study of Risk Factors for
Development of Dehydration in Children under 5 Years Having Acute Watery Diarrhea.
Weiner, M., & Epstein, F. (1970). Signs and symptoms of electrolyte disorders. The
 Yale Journal of bBology and Medicine, 43(2), 76.

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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

doi:10.22186/jyi.33.1.19-27

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

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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