Response of Acacia tortilis to Elephant Browsing in Tarangire National Park, Tanzania: Possible Above-Ground Compensation?
Large herbivore browsing leads to above-ground compensatory growth for some species of Acacia trees, but strength and variation of the relationship are poorly understood. Acacia tortilis is a keystone species in East African savannas and experiences a wide range of browsing pressure. In this study, terminal bud scale scars were used to measure yearly shoot elongation in A. tortilis experiencing various levels of elephant browsing at three mesic sites in northern Tarangire National Park, Tanzania. For all four years of growth, twig elongation remained very similar across elephant browsing pressure from minimal to heavy. Although never significant, twig elongation in 2013, 2011 and 2010 was somewhat higher in trees experiencing moderate elephant browsing pressure, suggesting a possible tendency toward compensatory growth. However, growth in 2012 had a slight negative correlation with browsing pressure. Further research can test whether A. tortilis compensates for moderate elephant browsing by elongating twigs more and if that growth slows slightly at higher browsing pressure. Overall A. tortilis tolerates elephant browsing extraordinarily well, but because the elephant population in Tarangire National Park is large and growing, managers should continue to monitor this keystone for decreased growth and tree mortality.
Woody plants provide energy and nutrients for many mammals in African savannas. Throughout Africa, Acacia spp. trees provide the primary food source for numerous browsers, and also serve as important habitat for birds (Dharani, Kinyamario, Wagacha & Rodrigues, 2009). Many species, including black rhinoceros, giraffe, grey duiker, dik-dik, grysbok, klipspringer, gerenuk, dibatag, bushbuck, and kudu, feed exclusively on woody browse (Owen-Smith, 1982). Because Acacia trees fix nitrogen, this also leads to higher forage quality in grasses underneath them compared to areas not under their canopies (Ludwig, De Kroon & Prins, 2008; Mopipi, Trollope & Scogings, 2009). Understanding the conditions that allow continued Acacia growth despite browsing will allow conservationists to monitor and prevent mortality of this keystone savanna species.
There are many variables in woody savanna which affect the productivity of trees, including Acacia spp. Disease decreases growth, rainfall increases growth, and fire has variable effects on growth based on the severity of the fire and the characteristics of various Acacia species (Dharani et al., 2009; Fornara, 2008; Mopipi et al., 2009; Otieno, Kinyamario & Omenda, 2001; Scogings, Johansson, Hjalten & Kruger, 2012). However, effects of herbivory and the dynamic interactions between browsers and woody plants have been heavily disputed. Several researchers found that intense large herbivore browsing results in compensatory above-ground plant growth and can lead to alternative stable states (Dublin, Sinclair & McGlade, 1990; Jachmann & Bell, 1985; Smallie & O’Connor, 2000). However, others have found that large herbivore populations, such as elephants (Loxodonta africana) or giraffes (Giraffa camelopardalis), have a negative effect on woody vegetation growth (Guldemond & Van Aarde, 2008; Chira & Kinyamario, 2009; Pellew 1984).
Past findings on the effects of browsing on sub-Saharan African Acacia species vary by mammal, tree species and response measurement. Browsing by non-elephant large mammals resulted in compensatory growth, leading to higher stem diameter growth of A. xanthophloea (Dharani et al., 2009). Similarly, simulated browsing of A. xanthophloea, A. tortilis, A. hockii (Pellew, 1984) and A. karroo (Stuart-Hill & Tainton, 1989) resulted in increased shoot growth and competitive ability when defoliation rates were between 25-50%. Du Toit, Bryant & Frisby (1990) and Chira & Kinyamario (2009) respectively found that heavy browsing by elephants led to an increase in shoot growth and higher nitrogen concentration in A. nigrescens foliage and coppice growth in A. brevispica. Conversely, Gandiwa, Magwati, Zisadza, Chinuwo, & Tafangenyasha (2011) found that South African A. tortilis with moderate to high levels of elephant browsing had smaller mean tree density, height, and basal area, compared to trees with low level of browsing. While recent patterns in precipitation, defoliating insects, disease, and soil fertility can also affect above-ground growth, current evidence suggests that compensatory growth responses occur only under certain levels of browsing pressure before tree mortality and/or resource depletion occurs.
Although compensatory growth in response to browsing has been observed in many Acacia species, the mechanism(s) and evolutionary trade-offs have been heavily disputed. Du Toit et al. (1990) found lower levels of tannins – naturally occurring plant polyphenols that discourage herbivore browsing by lowering metabolic efficiency (Chung, Wong, Wei, Huang & Lin, 1998) – and higher nitrogen and phosphorus content in heavily browsed A. nigrescens. They concluded that there is a tradeoff between shoot growth and defensive allo-chemicals for the species. The subsequent increase in palatability would lead to increased browsing, and create a positive feedback loop leading to the evolutionary development of Acacia compensatory growth as a necessary mechanism for the tree survival. Similarly, Fornara & du Toit (2007) found an inverse relationship between Acacia tolerance (regrowth abilities) and reproduction (seeds produced) in response to browsing, suggesting that above-ground growth came as a trade off with reproduction. Dharani et al. (2009) also observed increased browsing tolerance of A. xanthophloea after browsing. However, the study found that compensatory growth was limited to plant stems, while browsing still had a negative effect on tree height and canopy growth. They concluded that trees reallocated resources from leaves to stems and roots. Fornara (2008) similarly found that large herbivore pruning of A. nigrescens contributed to enhanced root size and subsequent re-sprouting abilities while negatively impacting maximum tree branch height, suggesting nutrient shunting from foliage to stems and roots.
It has also been hypothesized that the mechanism of shoot growth stimulation after browsing is not a reallocation of nutrients within the individual tree, but increased energy flow from the recycling of nutrients bound in leaf growth (Mopipi et al., 2009). However, Tanentzap & Coomes (2012) argued that herbivore grazing and browsing lead to decreased terrestrial carbon stock in general, and that the previous theory applies only in environments with sufficient resources to compensate for lost photosynthetic ability of consumed foliage. Other proposed mechanisms relating to resource allocation include: reduced intershoot competition due to woody browse loss, reduced leaf shading enabling enhanced photosynthesis capability, and reduced transpiration allowing more soil water retention (Fornara & du Toit, 2007; McNaughton, 1979). Numerous studies have resulted in various, sometimes contradictory, evidence and theories for compensatory plant growth mechanisms. Since no general conclusion has been reached, compensatory growth is likely species and location-dependent, influenced by browsing type and pressure, nutrient availability, inter- or intra-species competition, and perhaps other factors.
The mesic Acacia woodland in northern Tarangire National Park is composed largely of A. tortilis, one of the most prevalent and widespread Acacia species throughout Africa (Otieno et al. 2001), but has become less dense due to elephant utilization (Tanzania National Parks, 2012). Elephant herbivory can drive vegetation change in savanna ecosystems, and dense elephant populations can lead to over browsing and significant plant mortality (Van Aarde & Guldemond, 2008). Elephant browsing was found to be a more significant factor than rainfall behind changes in savanna vegetation (Hayward & Zawadska, 2010). With an average annual growth rate of about 7% from 1993-2006, there is no doubt that the Tarangire elephant population has intensified browsing pressure in the area, but it is unclear how the composition and woody plant-browser dynamics of the park savanna have changed (Foley, 2010). If this population growth continues, increased browsing pressure could threaten the survival and resilience of the park’s Acacia population.
Understanding the risk of A. tortilis decline and growth response to elephant browsing will help reduce tree mortality and prevent the loss of its ecosystem services. In this study, we inferred above-ground compensatory growth if greater growth occurred at moderate levels of branch removal by elephants. We hypothesized that A. tortilis trees under moderate levels of elephant browsing will have higher levels of annual shoot elongation because moderate levels of browsing pressure will stimulate more compensatory growth.
Tarangire National Park is located between S03°40’ and S03°35 and between E35°45’ and E37°0’ in the Arusha province of northern Tanzania and covers 2,850km2. As previously described by Ludwig et al. (2008), the park experiences a short rainy season from October to November, and a long rainy season from March to May, with the majority of the 650mm/year rainfall occurring between March and April. The park has several major habitat types including Acacia tortilis parkland, tall Acacia woodland dominated by A. xanthophloea and A. sieberiana, drainage line woodland, and deciduous savanna dominated by Combretum spp. and Commiphora spp. (Ludwig et al., 2008). This study focuses on three mesic sites located in the Lemiyon, Matete and Gursi areas of the northern part of the Park in order to focus on A. tortilis specifically.
At each of the three sites, we selected five to six trees with little (<30%), moderate (30-50%), and severe (>50%) elephant damage (47 trees total) from trees with multiple accessible unbrowsed twigs to measure. Tree measurements were taken for A. tortilis which fit strict criteria (<200m from roads, diameter at breast height (DBH) >10cm, and height >5m).
The percentage of canopy removed by elephant browsing was estimated qualitatively to the nearest 10%. Similar manual methods of estimating and quantifying browsing damage on tree canopy were described in Okula & Sis (1986), Riginos & Young (2007), Dharani et al. (2009) and Moncrieff (2011). It was not possible to judge the age of tree damage so that some years of measured growth could have occurred before any or some of the recorded damage occurred.
Elongation was measured using terminal bud scale scars on 3-4 twigs for each of the four previous years. We assigned twig measurements to the year in which growth began. To focus on growth response at the tree level, we averaged annual twig measurements within each tree.
Precipitation data to examine differences among years are unfortunately not available from this site, and reproduction occurred after the study time. All data was collected in the park in a short four-week period due to resource limitation.
Minitab ver. 17 (Minitab, 2014) was used to test for differences in growth among sites and years using a repeated measures ANOVA with year (fixed factor) crossed with site (random factor) and with trees nested within sites. Before analysis, a square root transformation was used to meet assumptions of normality and equality of variances. For each year linear and quadratic regressions from JMP ver. 11 (JMP®, 2007) were used to model relationships between annual shoot elongation and elephant damage. The best model fit was defined as that which had a larger R2 value. Due to the similarity of growth sites we combined them in further analyses and considered all trees independent.
Consistency of Growth Across Sites
Twig elongation ranged between 110-240mm across the three sites and four years, with the highest average growth recorded for site A in 2012 and the lowest for site B in 2013 and site A in 2010 (Figure 1).
The repeated measures ANOVA showed no differences in twig elongation between growth sites and years. It also measured the effects of an individual tree, and the interaction between site and year (Table 1). While growth year and the mesic site did not affect twig elongation individually (p-values > .05), the interaction between the two variables was significant (p = .01), as was the variation among trees within sites (p = .04). The repeated measures ANOVA explained 46% of the variation in annual growth measurements.
Effects of Browsing on Shoot Elongation
The relationships between elephant browsing damage and annual A. tortilis twig elongation had model p-values > .05 for all years (Figure 2). For 2013, 2011 and 2010 quadratic regression models were a better fit to the observed data, with higher R2 values (Figure 2). The general trend for these years showed that growth at moderate browsing levels (30-60% damage) was about 20mm greater than at low and high levels. For 2012, a linear regression line provided a better fit, showing a very slight (<5mm) decrease in shoot elongation as browsing pressure increases (Figure 2). Model R2 were low for all relationships, indicating substantial unexplained variation, but the quadratic models for 2013, 2011 & 2010 were higher than the linear model of 2012 growth (Figure 2).
The effects of herbivory on compensatory growth in woody plants have been heavily disputed. (Chira & Kinyamario, 2009; Dublin et al., 1990; Jachmann & Bell, 1985; Pellew, 1984; Smallie & O’Connor, 2000). It is clear that elephant herbivory can drive vegetation change in savanna ecosystems, and dense elephant populations can sometimes lead to over-browsing and significant plant mortality (Van Aarde & Guldemond, 2008).
Soil moisture and available nutrients are major confounding factors influencing Acacia growth (Mopipi et al., 2009). To reduce these variations, we measured trees only on mesic sites, i.e. sites with moderate and consistent soil moisture levels and nutrient availability in the context of the larger study area. Trees less than 200m from roads were excluded to limit the impact of dust from nearby vehicle traffic. We excluded immature trees (DBH < 10cm) since differences in plant developmental stages have been shown to influence Acacia browsing response (Bergstrom, 1992). Trees shorter than 5m were also excluded because they are more likely to be affected by the browsing of smaller mammals, which reduces Acacia growth due to the differences in browsing behavior of smaller browsers compared to elephants (Augustine, McNaughton & Samuel, 2004). Although Dharani et al. (2008) showed that rainfall plays a significant role in Acacia growth, levels of precipitation were not a major confounding factor in this study since sites were within about 20km of each other. Ben-Shahar (1996) also showed that fire and burn damage can significantly influence Acacia growth, but there was no recent history of fire in these areas (Tarangire, 2012), nor did we observe evidence of recent fires.
The three mesic sites used in this study were chosen to control for proximity to roads, tree maturity, and small mammal browsing; factors known to affect browsing response. The repeated measures ANOVA showed no significant difference between twig elongation across the three mesic sites (p = .52), which indicates that location does not explain the observed variations (Table 1) and growth across sites can be regarded as the approximately the same.
Annual variation in precipitation has been shown to be a significant driver of overall Acacia productivity (Mopipi et al., 2009; Otieno et al., 2001). However, in this study, tree measurements were taken for each of the last four years individually to control for variations in annual environmental conditions, such as drought, that might affect tree growth. There was no significant difference between twig elongation across years (p = .17) (Table 1). Thus, growth year did not explain the observed variations in twig elongation, and trees grew similar amounts across the four years. This is in line with Hayward & Zawadska (2010), who found that precipitation variables had less of an impact on above ground growth than elephant browsing.
Recent studies have found evidence that intense large herbivore browsing can sometimes result in compensatory above-ground plant growth (Dublin et al., 1990; Jachmann & Bell, 1985; Smallie & O’Connor, 2000), while at other times having a negative effect on woody vegetation growth (Chira & Kinyamario, 2009; Gulemond & Aarde, 2008; Pellew, 1984). In this study A. tortilis shoot elongation varied little across the very broad continuum of browsing pressure (Figure 2), and browsing pressure explained little of variations in growth (R2 = .07). Thus we conclude that, in terms of aboveground shoot elongation, the Tarangire National Park population tolerates elephant browsing remarkably well. This may help explain the dominance of A. tortilis in the park and other East African savanna ecosystems.
Our data show equivocal results on whether moderate levels of elephant browsing stimulate compensatory growth. Insignificant trends in 2013, 2011 and 2010 (Figure 2) suggest support for the hypothesis and the conclusions of Du Toit et al. (1990), Chira & Kinyamario (2009), and Stuart-Hill & Tainton (1989) that compensatory growth occurs. The slight, insignificant decrease in 2012 shoot elongation as browsing increases (Figure 2) suggests support for the conclusions of Dharani et al. (2009) and Fornara (2008) that compensatory growth does not occur and that all levels of elephant browsing have a negative effect on above-ground growth. However, in contrast to these studies cited above, our results for four years in northern Tarangire National Park indicate a striking robustness to elephant browsing in A. tortilis, since even trees with heavy browsing damage had approximately the same shoot growth as trees with light or no browsing damage.
It is important to note that a large proportion of above ground growth variation among trees within sites remain unexplained by elephant browsing damage (Table 1). Substantial genetic differences, small-scale variation in soil nutrients and other soil properties, differences in above- and below-ground competition, time of elephant damage, and other environmental factors likely account for differences in shoot growth among replicate trees. This large variation will tend to mask patterns in growth with browse damage. Also, since important influences on growth such as fire, water, and nutrient availability will vary at other sites, resistance to browsing at other sites could show different patterns.
The variation in Acacia growth response to herbivory studied over the last 20 years, as well as within this paper, means that a unified understanding of Acacia compensatory growth remains elusive. Future studies might attempt to measure multiple responses, e.g., shoot elongation, the radial increment of trunks, reproduction, concentration of secondary compounds, and below-ground growth in order to better understand this phenomenon. Since responses likely vary with water availability, studies across a water availability gradient and across years of varying precipitation might prove fruitful. Additionally, genetic variation within A. tortilis, seasonal timing of elephant browsing, previous elephant browsing, effects of other herbivores, and time since the last fire may also influence responses to elephant browsing. Future studies should also consider tree mortality and seedling establishment since growth measurements do not take mortality into account.
Based on shoot elongation data from four years, A. tortilis in northern Tarangire National Park appears to be tolerating browsing of the large elephant population well, with some insignificant suggestion of modest compensatory growth at moderate browsing levels. In one particular year, a slight decline in shoot growth in trees with heavier browsing is observed. Such an anomalous reverse in growth trends in this year could be evidence of herbivory tipping point, rather than the linear relationship suggested in other years, after which browsing is harming not stimulating above ground growth for A. tortilis. Since this tree species comprises such a large proportion of tree cover in the Park (Tanzania National Parks, 2012) and because the recent trend in elephant numbers is upward (Foley, 2010), we suggest like Gandiwa et al. (2011) in Zimbabwe, that Park managers should monitor growth responses and mortality of A. tortilis to ensure the vitality of this keystone species.
Thanks to my fellow students, Heather Hagerling, Nikki Hernandez, Mark Parlier, and Maxine Quinney, who suffered many an Acacia wound during the blistering late mornings of data collection. And thanks to our wonderful driver Walter Humphreys for navigating us daily throughout Tarangire National Park.
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Neurogenesis Unchanged by MTHFR Deficiency in Three-Week-Old Mice
The primary pathway for removing homocysteine, a potentially neurotoxic molecule, from circulation is via 5-methyltetrahydrofolate. This molecule is converted from folate via an enzyme known as methylenetetrahydrofolate reductase (MTHFR). Polymorphisms in the MTHFR gene have been linked to various pathologies (e.g. neurological disease) and animal models have been developed to study the in vivo effects of the deficiency. These models have revealed increased levels of apoptosis in the cerebellum and hippocampus and potential modulation of neurogenesis, which may contribute to the pathologies viewed. The aim of this study was to evaluate neurogenesis during late development. Brain tissue from 3-week-old male mice with different MTHFR genotypic was collected and stained for phosphohistone H3 (PH3) and 4′,6-diamidino-2-phenylindole (DAPI). Cell counts were performed in order to quantify PH3 positive cells in the hippocampus (dentate gyrus), cerebellum and cortex. There were no significant differences between genotype groups in all areas analysis. This result suggests that MTHFR status does not affect neurogenesis levels at this particular stage of development. Future research should consider the potential for confounding methylation pathways, such as the betaine-mediated mechanism, and investigating the effect on earlier developmental periods.
Folate metabolism is a key mechanism in the brain that allows the downstream alteration of a variety of proteins and plays a role in the synthesis of nucleotides (Kamen, 1997). These folate-mediated effects are necessary for the production of new neural cells and thus are essential to the overall health of the brain during development, adulthood and aging (McGarel, Pentieva, Strain & McNulty, 2015). One mechanism through which folates affect protein function is through the initial methylation of homocysteine to methionine. Homocysteine is a cytotoxic molecule when in high levels that produces a variety of negative effects, including endoplasmic reticulum stress, excitatory amino acid receptor over-activation, kinase hyperactivity and DNA damage (Ho, Ortiz, Rogers & Shea, 2002). These effects have been associated with many clinical pathologies in humans, being indicated as a contributing factor to cognitive impairment (Almeida et al., 2005), neural tube defects (Felkner, Suarez, Canfield, Brender & Sun, 2009), brain atrophy (den Heijer et al., 2003), stroke (Hankey & Eikelboom, 2001) and cardiovascular disease (Frosst et al., 1995; Wierzbicki, 2007). Thus, the ability for folates to methylate homocysteine to its nontoxic derivative methionine plays a large role in protecting against neurotoxicity. However, the links between these illnesses and homocysteine are still not fully understood, with many downstream biochemical pathways still needing to be discovered. Research into increased homocysteine levels and altered folate metabolism confirms a variety of cytotoxic effects in Caenorhabditis elegans (Ortbauer et al., 2016), Drosophila melanogaster (Blatch, Stabler & Harrison, 2015) and Sacchaomyces cerevisiae (Kumar et al., 2011). Thus, the need to utilize mammal models of increased levels of homocysteine is necessary to produce potential theories of illness.
One such model looks at the knockout of a particular enzyme in the homocysteine cycle, known as methylenetetrahydrofolate reductase (MTHFR). Folate itself cannot directly methylate homocysteine, thus it must first be converted from the form it is ingested to its primary circulating form, 5-methyltetrahydrofolate (5-methyl-THF). The key enzyme to this process is the aforementioned MTHFR, which catalyzes the production of 5-methyl-THF from a less abundant form 5,10-methyl-THF, which then methylates homocysteine. Thus, this enzyme is essential to both the metabolism of folate and homocysteine. This cycle is highlighted in Figure 1.
The occurrence of MTHFR deficiency is not uncommon in humans, with two common mutations producing reduced or lack of function. One of these deficiency-causing mutations is homozygous in approximately 18 percent of humans (Zittan et al., 2007). As many as 34 mutations in this gene, however, have been identified in individuals with homocystinuria, a genetic condition resulting in elevated levels of homocysteine that is associated with neurological and vascular problems (Leclerc, Sibani & Rozen, 2000). Studies into MTHFR in animal models have confirmed its role in preventing homocysteine-mediated neurotoxicity and associated pathologies (Chen et al., 2001; Chen, Schwahn, Wu, He & Rozen, 2005; Jadavji et al., 2012). Some of these pathologies include changes to motor control, mood and cognitive function, all of which have some connection to the altered morphology of the hippocampus and cerebellum specifically (Jadavji et al., 2012). The combination of the modelled results and the clinical implications of this gene thus offer significant intrigue into the role of folates in protecting against neurotoxicity.
Many of the associated pathologies, including cognitive impairment, brain atrophy and neural tube defects would suggest that the production of new and fully-functioning neurons is altered in response to high levels of homocysteine (Boot et al., 2003; Black, 2008). Neurogenesis, or the production of new neurons, occurs most prominently prenatally across species (Clancy, Darlington & Finlay, 2001, and is tightly linked to overall brain and cognitive function (Siwak-Tapp et al., 2007). Neurogenesis is also inherently linked with apoptosis, or cell death, as during development, neural pathways are trimmed via apoptosis. Additionally, adult neural death must be replaced by new cells in order to maintain the function of that brain region. Adult neurogenesis almost exclusively occurs in the dentate gyrus of the hippocampus and the olfactory bulb (Ming & Song, 2011), although there is research suggesting adult cell proliferation in the cerebellum (Ponti, Peretto & Bonfanti, 2008) and the cortex (Gould, Reeves, Graziano & Gross, 1999). As a result of this, it is of great interest to study the effect of increased homocysteine levels on the rates of neurogenesis in these regions to determine if blunted neurogenesis occurs as a result of increased homocysteine levels and if this mediates any of the observed pathologies.
Previous research has already begun to look in some of these regions with regards to neurogenesis and homocysteine levels. Research conducted by Jadavji et al. in 2012, used adult MTHFR knockout mouse models to test the association between neurogenesis and hyperhomocysteinemia-mediated pathologies. The results showed that homozygous knockouts for MTHFR had severely decreased cognitive functioning in object recognition tests, while apoptosis levels in the dentate gyrus were also elevated in the same genotype (Jadavji et al., 2012). This would suggest that without cell replacement in the dentate, cognitive function is greatly reduced, as a result of elevated homocysteine.
Similarly, research into other areas of the brain associated with neurogenesis appears to produce comparable results. In 2005, Chen, Schwahn, Wu, He & Rozen analyzed cell death levels in the cerebellum of MTHFR knockout mice, and found that homozygous knockouts also showed increased apoptosis of cerebellar neurons in both the intragranular and extragranular layers during the first two weeks of postnatal development (Chen, Schwahn, Wu, He & Rozen, 2005). As previously mentioned, pruning of neural pathways occurs during this time frame and alterations to cell death would result in changes to cerebellar patterning and could potentially contribute to neuropathology.
Thus, in order to determine if the pathologies viewed as a result of elevated homocysteine levels are partially mediated by alterations to neurogenesis levels, this study aimed to analyze the levels of neurogenesis in three of the regions associated with adult cell proliferation; the dentate gyrus, the cerebellum and the cortex. Analysis of neurogenesis in these regions in MTHFR knockout mice was used to determine if and propose how neurogenesis reductions may mediate homocysteine-related neuropathology.
Materials and Methods
All experiments were approved by Montreal Children’s Hospital Animal Care Committee in accordance to the Canadian Council on Animal Care guidelines. The mice used were from the C57B1/6 genetic background. The breeding of each status of MTHFR has been described previously (Jadavji et al., 2012). Mice were fed standard mouse chow (Envigo) and water ad libitum. They were sacrificed at 3 weeks from male mice.
Slides of sagittal-oriented brain tissue from 3-week-old animals were deparaffinized in fresh xylene twice for 5 minutes each. They were then rehydrated in absolute alcohol for 5 minutes, followed by reduced concentration alcohol gradually at 95, 80 and 70% alcohol for 3 minutes each. The slides were then rinsed using PBS twice for 5 minutes each before Antigen Unmasking Solution was used for 20 minutes at 95°C to retrieve antigens on the slides. 10 μg/mL Proteinase K in Tris/EDTA solution was used to perform cell permeabilization for 10 minutes at room temperature before 5% goat serum in PBS blocked non-specific binding sites in the samples for 30 minutes. This allows for the targeted antibody-antigen interaction to occur. These slices were then stained using 1:100 concentration rabbit anti-phospho histone H3 mitosis marker (PH3; Cell Signalling) as the primary antibody to identify cells that were proliferating overnight at 4°C. Alexa Fluor (Cell Signalling) antibody at a dilution of 1:200 was then used for 40 min at room temperature. 4′,6-diamidino-2-phenylindole (DAPI) was used as a general stain for the visualization of all cells and section were coverslipped with Vectashield Hardset Mounting Media in order to preserve the staining and were stored at 4°C.
The stained slides were imaged using Infinity Analyze software, where an image under fluorescence for PH3 and DAPI were captured for each of the regions of interest; the dentate gyrus of the hippocampus, the cerebellum and the cortex. The images for each fluor respectively, were combined using Image J software (NIH) and the final combined images were manually quantified for co-localization of the two immunofluorescent tags. As the cerebellum and cortex were larger and unable to be imaged all in one photo, three separate lobes were imaged for each cerebellar sample, and three sections from each cortex (anterior, medial and posterior) and the counts were averaged from every sample for each specimen into one mean per mouse.
Data was analyzed using SPSS and GraphPad software. One-way analysis of variance (ANOVA) was conducted on the three levels of MTHFR in each of the regions of interest, with p < .05 set as the significance threshold.
Previous research into MTHFR knockout mice would suggest that neurogenesis would likely be reduced in the mice with lost function of the enzyme. Previous research by Chen et al. (2001) confirmed the knockout status of MTHFR of this strain of mice, and can be referred to for Western blot results. As the goal of the research was to determine if MTHFR status had an impact on neurogenesis in the 3 week old mice, the co-localization of DAPI and PH3 staining was compared to determine if there was any difference between genotype groups.
First, in the dentate gyrus (Figure 2), the three statuses of MTHFR produced mean co-localized cell counts of 4.10 (n = 4, SD = 1.49) for the wild-type, 4.33 (n = 3, SD = 4.04) for the heterozygotes and 4.50 (n = 4, SD = 1.78) for the full knockouts. There was no difference between genotype groups (Figure 2(C), F(2,8) = 0.03, p = 0.974).
Next, the same measures were taken for the cerebellum (Figure 3). This procedure produced a mean co-localized cell count for the wild-type mice of 9.30, (n = 5,SD = 4.51), 8.55 (n = 6, SD = 4.49) for the MTHFR heterozygotes and 5.64 (n = 5, SD= 1.52) for the complete knockouts. The analysis of variance showed no difference between groups (Figure 3(C), F(2,13) = 1.29, p = 0.309).
Finally, the levels of co-localized cells were recorded for the cortex of the mice, as imaged in Figure 4. The mean co-localized cell count for the wild-type mice was 8.06 (n = 4, SD = 1.93), 6.19 (n = 4, SD= 3.71) for the heterozygous knockouts and 7.42 (n = 5, SD= 4.62) for the complete knockouts. There was no difference between groups (Figure 4(C), F(2,10) = 0.26, p = .773).
This research produced no statistically significant changes in the co-localization counts of PH3 and DAPI in any of the three main regions of interest, the dentate gyrus, cerebellum and cortex in response to MTHFR status. This would suggest that the relative level of MTHFR does not have a significant impact on neurogenesis at this point of development in male mice, and is not an ideal candidate for explaining altered neurogenesis and neuropathology seen in response to increased homocysteine. Despite this unexpected result, there are numerous reasons why this study may have failed to reject the null hypotheses.
The first reason may simply have to do with a small sample size. The levels of MTHFR had a range of n-values of 3 to 6, thus even after procedures to reduce variance by calculating a mean for each mouse before the genotype mean, there was still a high level or variance for most statuses. This high variance greatly contributes to the failure to reject the null hypothesis of the one-way ANOVAs, especially for the cerebellum which had the highest F-ratio. If the sample size was increased for each MTHFR level, it is possible that the resulting reduced variance could lead to a statistically significant effect. Procedural issues were not the only potential source of confound producing this result. As mentioned in the introduction, mice are generally into their “adolescent” period by three weeks, and thus have completed the majority of their developmental neurogenesis (Clancy, Darlington & Finlay, 2001). As no significant change in neurogenesis was viewed at this stage of development, it could perhaps be beneficial to look at younger mice that are still going through developmental neurogenesis. It is possible that earlier neurogenesis may be more sensitive to MTHFR status, suggested by the many studies showing developmental neurological defects, especially in the neural tube (Felkner, Suarez, Canfield, Brender & Sun, 2009). Future research should consider looking at mice earlier in neurological development for alterations to neurogenesis as a result of MTHFR status.
In addition to changing the age of the mice used, another possible reason for viewing this result could be an alternative method of methylating homocysteine. Betaine is well established as a supplementary methyl donor for the homocysteine to methionine conversion known to reduce homocysteine concentrations (Olthof & Verhoef, 2005). Derived from choline, betaine is regularly produced in the body through the catalytic activity of two enzymes, choline dehydrogenase (CHDH) and betaine aldehyde dehydrogenase (BADH). Betaine is then converted into its methyl-donating form via betaine homocysteine methyltransferase (BHMT). Thus, these results viewed may show that at this key stage of development there is an alternative method of reducing homocysteine and neuroprotection in response to MTHFR deletion. This method could involve the upregulation of any of the CHDH, BADH or BHMT enzymes, resulting in increased betaine-mediated methylation of homocysteine, preventing the neurotoxic effects. This theory is not completely hypothetical, as previous research mentioned by Chen, Schwahn, Wu, He & Rozen, in 2005, showed that betaine augmentation reduced the effect of MTHFR deletion in cerebellar development, the region closest in this study to having statistically significant effects (Chen, Schwahn, Wu, He & Rozen, 2005). Although the supplementation of that study came from an external source, it could be valuable to assess the relative transcript levels of betaine-related enzymes in future research of folate metabolism and the homocysteine cycle.
It may also be of value in future research to sample levels of methyl marks on methylated acceptors in these regions of interest. As methionine can be metabolized to S-adenosylmethionine, protein methylation would be expected to be viewed in mice with effective folate metabolism. This could potentially provide a valuable measure of whether homocysteine was being converted to methionine at regular levels or not, perhaps confirming confounding effects from betaine.
MTHFR has continuously been indicated in numerous pathologies and has been modelled effectively as a knockout paradigm. Age of the mice and confounding enzymatic pathways should both be considered in future research into the models. Despite no statistically significant results here, this study still contributes essential information into the mechanisms of neurogenesis and folate metabolism.
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Structural Dynamics of Amyloid-β Aggregation in Alzheimer’s Disease: Computational and Experimental Approaches
The nucleation of amyloid-β (Aβ) oligomers, and the fibril formation that follows represents an important pathologic mechanism for Alzheimer’s disease (AD). This has motivated the search for therapeutics that specifically target Aβ, which holds promise to be a cure for AD. However, conventional biophysical approaches like X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy fall short of capturing this highly dynamic process. The aggregation of amyloid fibrils has been unravelled by a mix of novel approaches. For example, computational methods like molecular dynamics (MD) simulations provide atomistic predictions of structural dynamics of protein fibrils. On the other hand, various spectroscopy and spectrometry approaches allow unprecedented resolution and details to be experimentally observed and complement predictions offered by computational investigations. This review surveys these approaches in the context of studying Aβ peptide aggregation. We shall discuss how these methods help us understand the mechanistic aspect of the aggregative process. We emphasise that computational and experimental approaches must go together to obtain a comprehensive view on the structural dynamics of critical protein players in health and diseases.
Aβ aggregates as causative agent for Alzheimer’s disease
AD is the most common form of dementia, featuring irreversible memory loss and deterioration in linguistic, cognitive, and learning capacities. Insoluble Aβ plaques are a hallmark of AD, with elevated levels of the 42-residue Aβ42 in the brain compared to a more abundant Aβ40 in healthy individuals (Näslund et al., 1994). Researchers have long followed the path of “amyloid hypothesis” in finding AD’s pathologic mechanism. This hypothesis holds that aggregation and deposition of Aβ peptides are the earliest initiating factors of AD, leading to the formation of paired helical filaments of tau aggregates and eventually neuronal cell death (Karran, Mercken & De Strooper, 2011).
Both soluble Aβ oligomers and insoluble Aβ fibrils are neurotoxic. The former is a better correlate of cognitive dysfunction; Aβ oligomers block long-term potentiation in hippocampal neurons (Tomic, Pensalfini, Head & Glabe, 2009). In Aβ fibrils, β-strands of individual Aβ peptides aggregate to form cross-β structures with inter-strand hydrogen bonds (Figure 1A). This provides surfaces onto which Aβ peptides are favourably “docked-and-locked” (Karran et al., 2011). Therefore, it is intuitive for therapeutics to attempt preventing Aβ aggregates, in both oligomer and fibril states.
To design these therapeutic interventions, we need to thoroughly understand the nucleating step, stability, and reversibility of these aggregates. While techniques like NMR contributed greatly to studying the structure of Aβ fibrils, Aβ oligomer structure is less clear due to its heterogeneity, instability, and variability across experimental conditions (Cerasoli, Ryadnov & Austen, 2015) (Figure 1B).
The dynamic process of how Aβ is transiently misfolded, aggregates into oligomers, and subsequently forms protofibrils and fibrils is still elusive, due to the limitations of conventional biophysical techniques. In this review, we compare newer spectrometry and spectroscopy techniques with conventional biophysical approaches used in studying Aβ aggregation. In addition, computational strategies like MD simulation have generated insights into the dynamic properties of Aβ aggregates. We discuss their principles and usages and outline how a combination of experimental and computational methods contributes profound insights into Aβ aggregation. Both approaches should go hand in hand in order to understand, not only Aβ but critical peptides and proteins in human diseases.
Conventional Biophysical Techniques Only Give Snapshots of Aggregation
X-ray crystallography, NMR, and cryo-electron microscopy are established experimental methods to study protein structures and interaction at atomic resolution by capturing a particular instant of conformation. While they suffice to infer spatial arrangement and interactions, the study of time-dependent processes such as Aβ aggregation, which goes on for days, takes more than a few structural snapshots.
In the case of Aβ aggregation, solving a definitive structure is already a challenge. In its fibril state, Aβ becomes insoluble, and it is impossible to perform solution NMR or to grow crystals for typical X-ray diffraction analysis. It can at most be subjected to X-ray fibre diffraction at moderate resolution or solid-state NMR (ssNMR). Nevertheless, several sizes of Aβ oligomers can be soluble in sodium dodecyl sulphate (SDS), and such a sample had been subjected to solution NMR (Yu et al., 2009). These forms exhibit strong toxicity and neuropathogenicity (Ahmed et al., 2010). However, such solubilisation could shift the stabilization environment, possibly by adding detergent that does not properly reflect physiological situations. Moreover, the immense scale of Aβ aggregation complicates such inference (Robustelli, Stafford & Palmer, 2012).
Electron microscopy (EM) is one of the earliest approaches to studying amyloid fibrils (Shirahama & Cohen, 1967). Although EM has yielded insights into the basic structural features and might also capture several states of the aggregates, it is not meant to provide time-dependent data for analysis. In Aβ aggregation where the process is of utmost interest, EM, like NMR, is inadequate in understanding the dynamic details of Aβ fibril formation.
As shown in Figure 1B, amyloid fibrils can take on different morphology depending on the source. For example, in vitro-prepared fibrils can occur in varying layers. Although only one of the dozen conformations is displayed here, the remaining frames are merely a transient oscillation of flexible regions that highlight no key interaction events. Conventional methods thus leave us ignorant of processes that happened before and during aggregation, fragmentation, and secondary nucleation. As we are gradually convinced that Aβ aggregates can exist in any form, solving structures of each polymorph becomes less relevant and the spotlight shines on understanding and predicting how they are formed with molecular and biophysical details.
Molecular Dynamics Simulation can Computationally Model Aβ Aggregation
To overcome limitations of conventional biophysical techniques, computational MD simulations, provide a “time-lapse” dimension to the Aβ structure. Here we put the protein(s) of interest into a system surrounded with solvents and compute its/their movement by using Newton’s laws and sets of pre-defined parameters known as “force-fields”. These force-fields dictate the energy functions that model potential energies within bonded and non-bonded entities (Jernigan & Bahar, 1996). An all-atom conventional MD simulation of Aβ shows a trajectory from which inference can be drawn with regards to its stability and/or plasticity. Trajectories of wild-type and mutant protein can also be compared to identify critical residues in structure and function (Figure 2A). In the context of Aβ, a special variant of MD simulation known as steered MD (SMD) is particularly relevant. This is illustrated in Figure 2B, where the amyloid aggregate is taken as a control from which the fibril is pulled away. An “umbrella sampling” method samples conformations from varying distances of the pull to probe interaction across time (Lemkul & Bevan, 2010). This provides a detailed atomistic account of the reverse of aggregation, which is valuable in deciphering aggregation itself. Such framework can be enhanced to answer more specific biological questions. For example, robust free energy calculations performed on the SMD profile can quantify binding and aggregation of peptides (Perez, Morrone, Simmerling & Dill, 2016) (Figure 2B).
An early paper manipulated simulations to characterise the conformational heterogeneity of a short stretch Aβ16-22 mediated by electrostatic and hydrophobic interactions (Klimov & Thirumalai, 2003). Lemkul and Bevan (2010) revealed a critical role for the salt bridge between Asp23 and Lys28 of Aβ42. They observed that this interaction was maintained throughout trajectories of wild-type amyloid fibres, and free energy calculations confirmed their role in stabilizing amyloid aggregates. While they accounted for intrinsic molecular details behind plasticity, Aβ aggregation was not directly addressed.
Kahler, Sticht and Horn (2013) have contributed more in this area. Their simulations revealed that Aβ fibrils in larger aggregates twisted at larger angles, and large aggregates subsequently broke. Smaller fragments then emerged as seeds to propagate aggregation. Recent research revealed that water plays a critical role in driving fibril assembly in Aβ40-seeded growth (Schwierz, Frost, Geissler, & Zacharias, 2016). These studies offered many insights, but their reliability has always been a matter of debate. As we shall see later, intrinsic changes of the surroundings seem to play a role in Aβ aggregation, especially concerning water in Aβ fibrils. It remains hard to factor in delicate contexts into the simulation, and this is where experimental approaches come in to validate such bioinformatics insights.
Spectroscopic and Spectrometric Techniques can Manipulate Real-Time Aβ Aggregation
A variety of spectroscopic/spectrometric techniques have emerged for investigating Aβ and other amyloid aggregation, offering high-resolution details of transient intermediates. Firstly, infrared spectroscopy relies on the excited vibrations of atoms in a molecule upon infrared radiation absorption (Figure 3A). While it is highly sensitive for antiparallel β-sheets, which are abundant in amyloids (Bruker Optics Inc, 2013), isotope labelling is needed to comprehend its complex spectrum. Usually, one or two carbonyl residues are labelled at a time by 13C and 18O (Arnaud, 2009), and the shifts of their peaks due to motions of neighbouring charges are also monitored. Another useful vibrational spectroscopy method is Raman spectroscopy (Figure 3A), which excites molecules to a higher virtual energy state and measures inelastic scattering of light (Kurouski, Van Duyne & Lednev, 2015). These spectroscopies have provided valuable information on the role of water and the disruption of lipid membranes. 2D infrared spectroscopy identified water neighbouring the amide groups of the hydrophobic Leu17 and Leu34 (Kim, Liu, Axelsen & Hochstrasser, 2009), which warranted more kinetic experiments to study its role. Raman spectroscopy showed that Aβ40 peptide within anionic lipid bilayers changes from disordered or helical conformations to β-sheets over time (Kurouski et al., 2015). However, since they can only collect averaged signals from the entire population of Aβ monomers, it cannot characterize individual conformations in the heterogeneous population at equilibrium as in mass spectrometry.
Figure 3B shows a particularly relevant mass spectrometry for Aβ aggregation dynamics based on hydrogen-deuterium exchange (HDX-MS), in which backbone amide hydrogens, which are exposed to solvent comprising “heavy water” (deuterium oxide) as a result of protein unfolding or hydrogen-bond-breaking, are replaced by deuterium (Eyles & Kaltashov, 2004). Each protium-deuterium exchange will increase the protein mass by a single unit and be detected. Peptide digestion and liquid chromatography further characterize solvent-accessible protein sites. When applied to Aβ structural dynamics, HDX-MS readily differentiated between monomers, protofibrils, and fibrils (Kheterpal et al., 2003). This study revealed that 40% of the backbone amide hydrogens were located within the core of protofibrils and that this increased to 60% in mature fibrils (Kheterpal et al., 2003), indicating dynamic changes of the positioning of residues throughout fibril formation. The same technique also demonstrated how the core of Aβ42 (residues 20-35) “seeded” aggregation, while the hydrophobic C-terminus played a minor but indispensable role (Zhang et al., 2013).
Single molecule force spectroscopy (SMSF) offers a profound resolution to study the mechanical stability of Aβ secondary structures. It consists of the molecule-of-interest attached to a surface with a probe on each end. The probe exerting large force is a sharp tip at the free end of a cantilever-beam for atomic force microscopy (AFM-SMSF; Eghiaian & Rico, 2014; Figure 3C). Apart from measuring forces applied to the molecule, changes in reflection angle of a laser beam off the surface of the cantilever can give information on topography (Eghiaian & Rico, 2014). Of note, AFM-SMSF, coupled with other methods, was instrumental in showing nucleation as a critical step in fibril formation (Harper, Lieber & Lansbury, 1997). This technique showed that Aβ1-40 β-sheets attached to one another in a reversible zipping motion (Kellermayer et al., 2005). Currently, high-speed AFM is emerging and has already contributed to elucidating the formation of false fibril branching points (Milhiet et al., 2010).
Researchers are constantly refining the above techniques and combining them for optimization. For instance, HDX was coupled with Raman spectroscopy for determining the Aβ peptide psi dihedral angles, the nearby environment of aromatic amino acids, and overall core structure of the fibril (Kurouski et al., 2015). Nevertheless, the sophisticated set-up, accessibility, and requirement of training make these experimental techniques better candidates for validation than discovery.
Simulations and Experiments go Hand in Hand in Understanding Aβ Aggregation
It is evident now that simulations and experimental approaches are complementary to each other. One could benefit from the atomistic resolution that MD simulations propose for Aβ fibrillation and aggregation. The experimental evidence was solid for the biological relevance of these models. The idea of validations across different platforms is typically employed in refining experimentally solved structures (Raval, Piana, Eastwood, Dror & Shaw, 2012; Schröder, 2015). It has been extended by, for example, coupling modelling with NMR or small-angle X-ray scattering (SAXS) to understanding fluctuations of protein structures (Bernadó et al., 2010; Yang et al., 2007). In the Aβ field, the integration of in vitro and in silico approaches has offered meaningful insights into Aβ aggregation.
One contribution of combining the two approaches concerns the role of water. Water has been considered critical since early on as some drew parallelism of fibril formation with crystallization: In the latter process, escape of water molecules stands as a necessity (Thirumalai, Reddy & Straub, 2012), which resembles fibril formation where the landscape of solvation must change to accommodate aggregation. In Aβ fibrils, this happens early on (Tarus, Straub & Thirumalai, 2006) and is probably related to the establishment of hydrophobic interactions that organise aggregation. Many experimental and simulation studies have captured extensive conformational plasticity mediated by different contexts of water. Simulations exhibited discrepancies with respect to the amount of water within the pre-fibril nuclei (Krone et al., 2008; Tarus, Straub, & Thirumalai, 2006). The simulations of water channels, e.g., around the Asp23-Lys28 salt bridge (Lemkul & Bevan, 2010; Tarus et al., 2006), were validated by 2D infrared spectroscopy as previously discussed (Kim et al., 2009). This provides an example of integrating the two approaches: simulations described interesting interactions between different parts of Aβ with water, while experimental techniques addressed the discrepancies among in silico models.
Integrating in silico and in vitro approaches also contributed to understanding the influence of membrane lipid composition in Aβ42 embedment at the membrane, which modulates Aβ aggregation. This has first been primed by “lipidomics” strategies, characterising the compositions and prevalence of membrane lipid species across the human brain (Svennerholm, Boström, Jungbjer & Olsson, 1994). Contemporary large-scale shotgun MS-based lipid measurements (Han, 2010) added to this, by demonstrating that cholesterol redistribution is dependent upon aging and AD. Computational approaches contributed to the mechanistic explanation of these phenomena, e.g. in a study by Liguori, Nerenberg, Paul, and Head-Gordon (2013). They showed, with free energy calculations, that the abundant extracellular cholesterol favours extrusion of N-terminals of Aβ fibrils, probing it to oligomerise and aggregate. On the other hand, the helicity at the C-terminal of Aβ peptides contributed to retain themselves at the plasma membrane. Amongst other intrinsic and delicate components of the local environment which “omics” experiments have primed, MD simulations give fine resolution into their role for mediating Aβ association, folding, and oligomerization.
It is also interesting to look at the interplay of computational and experimental approaches in a broader, time-lapse view of Aβ research. Long before simulations were available, conventional structural biology had established that cross-β structures were common in aggregating amyloid fibrils. ssNMR provided direct evidence that the same peptide sequence could form a vast variety of polymorphs (Figure 1A). Although oligomers existed before protofibrils and fibrils, the rearrangement of weaker oligomer intermediates might be directed to either fibril formation or growth into various stable forms of oligomers, depending on the physical and chemical environment (Figure 4i-iv) as concluded in a ssNMR study (Tay, Huang, Rosenberry & Paravastu, 2013).
Once the oligomers are committed to forming protofibrils and fibrils, they start to elongate extensively. In MD-simulated seeded growth (Kahler et al., 2013), single layers of protofibrils attempted to elongate until stability broke and they dissociated into short protofibrils. Later, pairing occured and resulted in protofibril pairs and possibly triplets with varying molecular structures caused by subtle differences in growth conditions, as shown by EM and ssNMR (Petkova et al., 2005). The increase in β-sheet structures gave the fibril greater stability as revealed by Fourier transform infrared spectroscopy (Kodali, Williams, Chemuru & Wetzel, 2010). Additionally, this allowed further elongation into large fibrils (Figure 4v-x).
Subjected to the underlying molecular structures, these fibrils were polymorphic with unique physical properties. Nevertheless, all of these structures (including the oligomers) are in dynamic equilibrium with constant fragmentation and secondary nucleation which enable growing of new fibrils along the old ones, according to a series of kinetic, radio-labelling and cell viability experiments (Cohen et al., 2013). Altogether, this elaborate pathway of Aβ aggregation is the product of combining computational and experimental approaches in discovering biophysical basis at each step. Either one of these is indispensable in advancing mechanistic understanding of aggregation.
Conclusion and Future Perspectives
We presented an overview of the mechanism of Aβ aggregation and highlighted that computational and experimental approaches go hand in hand behind this. The synergy has contributed to identifying the distinct steps of aggregation as well as their biophysical underpinnings. This has allowed numerous attempts in identifying the so-called “wonder drugs”, which effectively antagonise aggregation and bring the least side effects (Karran et al., 2011). An understanding of the aggregative structural polymorphs can lead to possible strategies to target Aβ aggregation, e.g. by utilising the agents that disrupt salt bridges to destabilise the fibrils. For the theoretical biophysicists, the success of detailing the aggregative process also bears significant implications. It is this well-positioned biophysical problem that puts MD simulation to its best use, probing details that in vitro approaches often lack. The rise of simulation as a well-recognised approach to study biomolecular dynamics, as well as the excitement that the distributed computing project Folding@home (http://folding.stanford.edu/home) has brought to both the academic field and the media, bring protein folding and misfolding research to new heights. Further refinement, e.g., using patient-derived fibrils, would resolve ambiguities in this mechanism, considering the model in Figure 1B(iii) being the only, to our knowledge, published patient-derived Aβ fibril structure (Paravastu, Qahwash, Leapman, Meredith & Tycko, 2009). This would inspire further computational simulation and experimental validation to provide details on the basis of preferring one polymorph over another and their relevance to neuropathology. More broadly, combining different lines of investigations help greatly in understanding mechanistic aspects of many critical proteins in other diseases awaiting research efforts.
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Pulley Optimization for a Walking-Engine-Actuated Active Ankle-Foot Orthosis
Active orthotic devices for joint articulation have a vast number of applications that could benefit many people. Examples include joint articulation for people suffering from disabilities, increased load carrying capacity and walking distance for humans, and gait training. The main goal of this research is to help people with disabilities regain natural walking ability by replicating normal walking gait through the use of an active ankle-foot orthosis (AAFO). This research investigates the optimization of a pulley system for the primary actuator of an AAFO utilizing a high-efficiency pneumatic “Walking Engine.” In order to accurately replicate a healthy human gait, the AAFO device had to accurately reproduce the moment applied to the ankle during the gait cycle. The AAFO’s internal-combustion (IC) engine was characterized using a dual-combustion (limited-pressure) gas-power-cycle model. With the dual-combustion model, both a theoretical pressure-volume diagram and the thermodynamic engine efficiency were calculated. Using the calculated pressure output of the IC engine, the pulley system was optimized to best match the ankle moment of a healthy human gait, which was obtained from David Winter’s Biomechanics and Motor Control of Human Movement. The optimized pulley geometry is very complicated and additional research is necessary to utilize its design. The results of this research provide insight for the future development of untethered, lightweight, efficient AAFO devices.
With increasing advancements in robotics technology, robotic systems, such as the Walking Engine, are continuously being implemented in new and exciting ways. As a result, humans experience ever increasing interactions with robotic systems on both a professional and personal level. On the personal level, these robotic systems can be used to assist with physical impairment, amplify normal physical capabilities, and even act as an extension of human abilities for the 35.2 million people suffering from physical functioning disabilities (Dollar & Herr, 2007). Some well-publicized examples include the systems being developed by Boston Dynamics for assisting military personnel in carrying equipment (Big Dog, Little Dog) and exoskeletons that provide walking assistance for those normally bound to wheel chairs for mobility (ReWalk, EKSO) (Dollar & Herr, 2007). The main goal of this research is to design an active ankle-foot orthosis (AAFO) that helps people with disabilities regain natural walking ability by replicating the normal walking gait of a human. This paper investigates the combination of robotic system technology and orthotics in the form of an AAFO to accomplish this goal.
Active Ankle-Foot Orthosis
Orthotics is a specialty within the medical field concerned with the design, manufacture, and application of an orthosis. An orthosis is an externally applied device used to modify the structural and functional characteristics of the neuromuscular and skeletal system (Lusardi, Jorge, & Nielson, 2013). Orthotic devices can be designed to control, guide, limit, or immobilize an extremity, joint, or body segment. Under the International Standard terminology, orthoses are classified by an acronym describing the anatomical joints that they contain (Lusardi et al., 2013). For example, an orthosis applied to the ankle and foot, such as the one investigated in this research, is an ankle-foot orthosis (AFO). AFOs are commonly used in the treatment of disorders that affect muscle function such as stroke, spinal cord injury, muscular dystrophy, cerebral palsy, polio, multiple sclerosis, and peripheral neuropathy by: providing artificial joint articulation, increasing load-carrying capabilities; and, assisting in gait-training procedures (i.e. people re-learning to walk from an injury or disability) (Lusardi et al., 2013).
Currently, ankle-foot orthoses fall into the two main categories of passive and active (powered) orthoses. Figure 1 shows a comparison of a passive and active AFO system. Passive AFOs rely on an external energy source (the user) and either utilize mechanical components, such as springs, to assist with movement, or are rigid and simply immobilize the joint (Lusardi et al., 2013). Passive devices are typically lightweight with simple designs, resulting in low cost and high robustness. However, despite their benefits, many problems exist in current passive AFO designs. While most patients see improvement with their ability to walk with the aid of these devices, the gait is labored and very unnatural (Lenhart & Sumarriva, 2008). AAFO devices seek to combat this limitation by replicating human gait with an integrated power source. AAFOs are typically powered using a pneumatic or electric power source (Lusardi et al., 2013). This integrated power source allows for more precise movement and complex functionality of the ankle joint. This complexity adds extra weight and size to the apparatus (Figure 1). One major shortcoming of current AAFO designs is this tradeoff between size and functionality. An ideal AAFO design must be compact and lightweight, in addition to having the capability for the complex functions needed to accurately replicate human gate. The issue of complexity versus weight can be addressed by using an internal-combustion (IC) engine, such as a “Walking Engine”, to power the device.
High Efficiency Pneumatic Walking Engine
It is understood that hydrocarbon fuels have a much higher specific energy than electric batteries or compressed air (Mitchell, Gallant, Thompson, & Shao-Horn, 2011). While liquid petroleum gases, such as butane, propane, or methane, have specific energies of around 45-50MJ/kg, electric batteries have specific energies of only around 9-10MJ/kg. Since a compact and lightweight design is required, an AAFO utilizing a small IC engine as the primary means of ankle articulation was chosen for this research. Propane, a common liquid petroleum gas, was selected as the IC engine’s fuel source due to its associated ease of implementation and high energy density.
To further meet the efficient and lightweight AAFO design requirements, an IC “Walking Engine” was chosen over a typical IC engine due to its increased thermodynamic and system efficiency. In a typical IC engine, a portion of the energy obtained from combustion is used to compress the engine’s pistons with the use of a flywheel. Consequently, this energy does not contribute to the forward motion of the vehicle. On the contrary, with a “Walking Engine” the user of the AAFO can be viewed as the flywheel. The kinetic energy from the forward motion of the user also supplies the energy to compress the pistons, providing much higher system efficiency. Thermodynamically speaking, a typical IC engine converts merely 25% of the applied fuel energy into usable energy, suffering losses from exhaust (40%), coolant (30%), and friction (5%) (Heywood, 1988). Conversely, a “Walking Engine” converts a substantial 55% of the applied fuel energy into usable energy. This is accomplished with the implementation of a passive cooling system and reusing recovered exhaust energy in a pneumatic system.
A design for an AAFO has been developed that utilizes a forced-induction, “Walking Engine” actuation system, as shown in Figure 2. The design employs two opposing (antagonistic), asymmetric piston actuators to provide the required power for locomotion. The primary actuator is driven by a forced-induction, propane-powered, IC “Walking Engine”. Exhaust energy from the combustion engine is captured to charge a pneumatic system that will drive the second actuator.
The two grey cylinders shown in Figure 2 are the two actuating pistons. On the left is the primary actuator, which is the IC engine and provides the power required for the plantarflexion phase of the gait cycle. The green object in the schematic represents the fuel canister, while the blue tube represents the surrounding air. The fuel and the air will be forced into the combustion chamber with a hydraulic bellows pump. The bellows pump is shown under the heel of the foot bed. For ignition, a spark plug device will be activated, creating a spark. The exhaust from the engine is shown on the diagram as the red area connecting the primary cylinder to the secondary cylinder. The energy from the engine exhaust is captured and used to pneumatically power the secondary actuator. The secondary actuator provides the power for the dorsiflexion phase of the gait cycle. This design represents the optimal compromise between size, weight, and power.
Materials and Methods
The main goal of this research is to help people with disabilities regain natural walking ability by replicating the normal walking gait of a human through the use of an AAFO device. We investigated the optimization of a pulley system for the primary actuator of an AAFO utilizing a high-efficiency pneumatic “Walking Engine”. To accurately replicate a healthy human gait, the AAFO device had to properly reproduce the moment applied to the ankle during that gait. Thus, the IC engine used on the AAFO had to be properly characterized. Once the IC engine’s performance was accurately modeled, a pulley system was optimized to best match the ankle moment of a healthy human gait.
Walking Engine Characterization
The IC engine used as the “Walking Engine” in the AAFO device was a pre-manufactured, or off-the-shelf (OTS), Bosch pneumatic actuator. The OTS actuator, having an inner chamber diameter of 25mm and a maximum piston extension of 50mm, was modified for use as an IC engine. Figure 3 shows a computer-aided design (CAD) model of the modified OTS Bosch actuator.
To convert the Bosch actuator into an operational IC engine, several modifications were necessary. First, a hole was drilled into the bottom of the actuator to allow for the insertion of the spark plug ignition source. A block extension was then added to the bottom of the actuator to ensure that the actuator could withstand the stresses generated from the 10:1 compression ratio combustion. A metal piston extension was also added to the bottom of the actuator’s piston to protect the rubber-sealing device from the flames generated during combustion. Finally, for experimental combustion testing, a needle valve was attached to the bottom actuator port to allow the fuel/air mixture in, hold the pressure during compression and combustion, and allow the exhaust gas to escape the engine chamber.
The first step in characterizing the IC engine used on the AAFO was to calculate the gas power cycle for the engine. A gas power cycle, or thermodynamic engine cycle, consists of a linked sequence of thermodynamic processes that involve the transfer of heat and work into and out of the engine system that eventually returns the system to its initial state (Heywood, 1988). Each thermodynamic engine cycle for the AAFO engine corresponds to one step of the leg equipped with the device during the gait cycle (i.e., one cycle per step by the leg that wears the device). A pressure-volume diagram is often used to represent the thermodynamic cycle of an engine. A dual-combustion (limited-pressure) pressure-volume diagram was chosen to model the gas power cycle of the IC engine used to power the AAFO.
A dual-combustion engine cycle is a combination of both the Otto and the Diesel engine cycle (Heywood, 1988). Energy from combustion is added as heat in two different stages, q1’ and q1”(Figure 4). Heat addition occurs first at a constant volume, similar to the Otto cycle, and then at a constant pressure, resembling the Diesel cycle. A dual-combustion cycle was chosen to represent the AAFO’s IC engine because this cycle is a closer approximation to the real life behavior of IC engines. In typical applications, the combustion process of an IC engine does not occur perfectly at a constant volume or a constant pressure, but rather in two separate stages as indicated by the dual cycle (Heywood, 1988).
When calculating the gas power cycle of the “Walking Engine” some important assumptions about the engine process were made in order to simplify the calculations. The first major assumption, which is assumed in most engine cycles, is that the working fluid (the air/fuel mixture) of the engine remains in a gaseous state throughout the cycle. It is also assumed that the working fluid can be modeled solely as air and can always be treated as an ideal gas. Furthermore, this research assumes an ideal engine cycle in which internal irreversibilities and other complexities, such as the combustion process and the exhaust of the products of combustion, are removed (Heywood, 1988).
In addition to these initial assumptions, the IC engine cycle was approximated with the following assumptions.
⋅ All of the engine processes listed in Figure 4 are internally reversible.
⋅ A dual-heat-addition process from an external source replaces the combustion process.
⋅ The lower heating value (LHV; 2044kJ/mol) of propane was used with an 80% burn efficiency during combustion calculations (Linstrom & Mallard, 2001).
⋅ A perfect stoichiometric air/fuel mixture was also used for the combustion calculations.
Thermodynamic Engine Efficiency
The second step in characterizing the IC engine used to power the AAFO was to calculate the thermodynamic efficiency of the engine. The thermodynamic efficiency of an engine is a dimensionless relationship between the total energy supplied to the engine—through the combustion of fuel—and the amount of energy available to perform useful work (Heywood, 1988). In other words, thermodynamic efficiency indicates how well an energy conversion process is accomplished. The thermodynamic efficiency of a dual-combustion engine cycle is represented by Equation 1:
where, as Figure 4 shows, q1’ is heat addition at a constant volume, q1” is heat addition at a constant pressure, and q2 is heat rejection during exhaust. By using the fact that an energy exchange can be modeled by heat capacity, q1’, q1”, and q2 can be rewritten as the following:
By substituting in these relations, Equation 1 can be rewritten in the following, more convenient form:
Thus, the thermodynamic efficiency of the IC engine used to power the AAFO can be calculated using Equation 3 and the temperatures found from the gas power cycle. It is interesting to note that because the dual-combustion engine cycle is a combination of the Otto and Diesel cycles, the efficiency of the dual-combustion cycle will always fall somewhere between the Otto and Diesel.
Pulley Optimization Process
In order to accurately replicate a healthy human gait, the AAFO device had to properly reproduce the moment applied to the ankle during that gait. This was done with a pulley system connected to the AAFO’s actuating pistons. Figure 5 shows a conceptual model of the pulley system used to power the AAFO.
By finding the pressures in the actuator chambers and multiplying them by the cross-sectional area of the actuators’ pistons, AC, the force output of the engine system can be determined. Dividing the optimal ankle moment by the engine’s force output results in the optimal pulley geometry. Equation 4 represents this procedure.
|Ankle Moment = (Pressure ※ Ac) ※ Pulley Radius||(Eq. 4)|
Since our AAFO design utilizes a dual piston configuration, a dual pulley configuration is also necessary. The primary actuator (IC engine), which is responsible for the plantarflexion phase of the gait cycle, requires different pulley radii than the secondary actuator (pneumatic exhaust recovery system), which is responsible for the dorsiflexion phase of the gait cycle, to match the ankle moment. A dual pulley configuration has been designed to allow for the optimization of both the inner pulley, the primary actuator, and the outer pulley for the secondary actuator (Figure 5). However, this research focuses only on the optimization of the primary actuator pulley.
There is no question that AAFOs have the potential to improve the quality of life for many people, but current AAFO designs lack a balance between functionality and compactness. The results of this research will be used to help further the development of the “Walking Engine” AAFO by addressing these shortcomings.
Characterized Walking Engine
As stated earlier, before the primary actuator pulley could be optimized, the AAFO’s IC engine had to be characterized. Figure 6 shows the calculated ideal dual-combustion pressure-volume diagram that was used to model the IC engine. Each step by the leg equipped with the AAFO corresponds to one thermodynamic engine cycle that can be broken into two main phases: the swing phase and the stance phase. The engine cycle begins at the start of the stance phase where the heel of the foot comes into contact with the ground (HCR), which is indicated by point 1 in Figure 6. At this time the fuel/air mixture is injected into the engine chamber (points 1-3) via a bellows pump. This fuel is compressed, ignited, and expanded to the actuators maximum extension, from points 3 to 7, to provide the locomotion power for the plantarflexion portion of gait. The remainder of the gait cycle, the swing phase, begins when the toe of the foot leaves contact with the ground (TOR) and is comprised of points 7, 8, and 1. The swing phase corresponds to the exhaust phase of the engine, which is used to power the dorsiflexion portion of gait with the help of the secondary pneumatic actuator.
Table 1 shows the volume of the engine chamber, pressure and temperature in the engine chamber, and the number of moles of working fluid in the engine at each of the indicated points in Figure 6. Using these values, the engine’s work output was calculated by finding the area inside the P-V curve. The work output of the AAFO engine was calculated to be 29.80J, which is more than enough energy to actuate the ankle joint.
By substituting the temperatures of the key points in the engine cycle into Equation 3, the theoretical thermodynamic engine efficiency of the modified OTS actuator was calculated: a thermodynamic engine efficiency of 0.61. The P-V diagram calculated in Figure 6 will be validated with experimental combustion testing data and altered to match actual engine performance if necessary.
Optimized Primary Actuator Pulley Configuration
Once the AAFO engine was properly characterized, the primary actuator pulley system could then be optimized. The first step in optimizing the AAFO pulley was to optimize the ankle moment data. The ankle moment data that was replicated comes from David Winter’s Biomechanics and Motor Control of Human Movement. As Figure 7 shows, eliminating the sudden jumps in the data and removing the negative moment values, which would have been impossible to replicate, resulted in an optimal ankle moment curve. This smoothed moment data allowed for a smooth and precise optimal pulley configuration to be calculated.
The optimal primary actuator pulley radius was calculated by finding the pressure in the engine as a function of time, from the pressure-volume diagram, and rearranging Equation 4 to solve for the pulley radius. It is important to note that while the pulley radius may change it is always assumed that the edge of the pulley and the AAFO engine are in line with one another. Figure 8 shows the optimal pulley radius as a function of time alongside several constant diameter pulleys. This parametric polar plot of the pulley configuration starts at the green dot and moves along the geometry line with time ending at the red dot.
The results of this research show that the modified OTS actuator, which has a theoretical work output of 29.80J with a thermodynamic engine efficiency of 0.61, is able to provide more than enough energy to activate the ankle joint, which makes it an acceptable power source for the AAFO. Using a theoretical P-V diagram (Figure 6), which will be validated with combustion testing, the optimal pulley radius for the plantarflexion phase of the gait cycle was determined. While the optimal pulley radius for the primary actuator was calculated (Figure 8), it has a very complicated geometry.
This complicated pulley geometry stems from three main issues. The first issue is that during the gait cycle the ankle only rotates through a small 27° window. Because the ankle only rotates 27°, only 27° of the pulley perimeter—indicated by the red pie slice in Figure 8—is being used. This results in the squished pulley geometry that is shown. The second major issue is that during the gait cycle the ankle oscillates between negative and positive angle values, resulting in the pulley geometry doubling back on itself. This happens because each time the ankle rotates through a previous degree there is a different pressure in the AAFO engine and a different ankle moment that needs to be matched, resulting in different pulley radii at the same ankle angle. The final complication with the pulley geometry is the extreme pulley radii (2 to 15cm) that are experienced due to the relationship between the engine pressure and ankle moment. Because the ankle moment is such a small value during that beginning of the gait cycle, the radius necessary to match the moment is small, but when the moment reaches its larger values the radii necessary to reproduce this with the engine pressure are very large. It should also be noted that this pulley geometry is specific to the ideal P-V performance of the engine. The optimal pulley geometry will be subject to change when actual engine performance data becomes available through experimental combustion testing.
This paper introduced a unique AAFO design that utilizes a forced-induction, “Walking Engine” system to provide the power for locomotion. The implication of this research is that a modified OTS actuator used as an IC engine is a feasible power source for an AAFO. Providing 29.80J of work output and weighing less than 10oz, this design provides the balance between size and functionality that is not available with current designs. While the optimal pulley radius for the primary actuator was calculated, it has a very complicated geometry. Further research is necessary to determine a practical pulley system, such as a gear or chain torque converter, that allows for the successful implementation of the optimal pulley configuration. This research has laid necessary groundwork for future experiments to further the development of the “Walking Engine” AAFO. This work, characterizing the IC engine and optimizing the pulley radius, will be applied to future research used to construct a fully functional “Walking Engine” AAFO.
This project was funded by the National Science Foundation (NSF) and the Milwaukee School of Engineering (MSOE) Fluid Power Institute (FPI). A special thanks to Douglas Cook, Vito Gervasi, the Research Experience for Undergraduates (REU) staff, Milwaukee School of Engineering, The Center for Compact and Efficient Fluid Power, the Fluid Power Institute, and all other parties involved, because only with their help was this research possible.
This material is based upon work supported by the National Science Foundation under Grant No. EEC-0540834. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.
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Opportunity for Pharmaceutical Intervention in Lung Cancer: Selective Inhibition of JAK1/2 to Eliminate EMT-Derived Mesenchymal Cells
The Epithelial Mesenchymal Transition (EMT) has been implicated as a driving force behind the metastasis of epithelial derived cancers; it stimulates the acquisition of a migratory, drug resistant mesenchymal phenotype. Current proposals for targeting EMT-facilitated metastasis are ineffective, as a majority focus on the inhibition of EMT-initiating signals. Instead, this study used a novel approach aimed at directly inhibiting the mesenchymal phenotype by targeting mesenchymal survival pathways post mapping at specifically up-regulated points in the mesenchymal state. In vitro EMT models of three lung adenocarcinoma cell lines (A549, HCC827, HCC4006) were each treated with three concentrations (50nM, 0.5µM, and 5µM) of AZD1480, BAY87-2243, MK-2206, and GDC0994 at 48 hours, which targeted the JAK/STAT, PI3K-AKT, and MAPK pathways, respectively. MTT assays were used to quantify cell death and determine cell viability. Across all assays, the JAK 1/2 inhibitor AZD1480 resulted in the greatest elimination of EMT-xderived mesenchymal cells. 5µM AZD1480 significantly reduced cell viability in mesenchymal populations treated with AZD1480, supporting JAK1/2 as a potential therapeutic target in lung cancer. Future investigations include testing AZD1480’s effectiveness in decreasing cell viability among alternative epithelial-derived cell lines in vitro and its effectiveness as a suppressor of metastasis in vivo.
Lung cancer is currently the most prevalent form of cancer in the United States, causing over 158,000 deaths each year (American Lung Association, 2016). More than 90% of these deaths and other cancer-associated mortality can be attributed to metastasis (Ray & Jablons, 2009). An immense clinical need exists for novel treatments either targeting or preventing metastasis, especially in early stage cancer patients.
In order for distant metastasis to occur, primary tumor cells must disseminate through the blood vessels and invade a distant organ site (Brabletz, 2012). A biological phenomenon known as the Epithelial-Mesenchymal Transition (EMT) is a key facilitator of this process. EMT involves a series of changes which allow a cancer cell to transition from a stationary epithelial phenotype to a migratory, drug resistant mesenchymal phenotype. This process includes the disassembly of epithelial cell-junctions and a loss of epithelial polarity in exchange for mesenchymal characteristics, such as a fibroblast-like morphology and increased invasiveness (Thomson et al., 2010). A full EMT, defined as a complete transition from epithelial cell surface markers, such as E-cadherin and Occludin, to mesenchymal markers, such as Vimentin and N-cadherin, often requires ten days or more (Cao, Xu, Liu, Wan, & Lai, 2015; Rai & Ahmed, 2014).
After EMT has transpired, the newly formed mesenchymal cells enter the bloodstream through the process of intravasation (invasion of cancer cells through the basement membrane) and disseminate throughout the body, eventually invading a distant organ site, through the process of extravasation (cancer cells exit the capillaries and invade an organ; Fig 1). At this distant organ site, the cancer cells undergo a Mesenchymal-Epithelial Transition (MET), the reverse process of EMT, in order to colonize and form a secondary epithelial tumor. By reverting to their original epithelial phenotype, the cells regain the ability to proliferate rapidly and form cell-cell junctions, two characteristics that are necessary for successful colonization. These characteristics were lost during the original EMT, when the cells acquired the ability to metastasize (Kalluri & Weinberg, 2009). After MET has occurred and a second epithelial tumor is established, the process of metastasis is complete. Thus, EMT is considered an important process during the early stages of metastasis, while MET is considered an important process during the later stages of metastasis (Brabletz, 2012).
EMT induction primarily occurs through the activation of phosphorylation cascades by various cytokines and growth factors present within the tumor microenvironment, including Transforming Growth Factor Beta (TGFß), Hepatocyte Growth Factor (HGF), Epidermal Growth Factor (EGF), and Platelet Derived Growth Factor (PDGF), among others. As the cascades are activated, the expression of E-cadherin, an important cell-adhesion molecule which maintains the epithelial state, is repressed, and EMT ensues (Cao, Xu, Liu, Wan, & Lai, 2015). This repression is facilitated by the transcriptional activity of four EMT-initiating transcription factors: Snail, Slug, Zeb, and Twist. Snail, Slug, and Zeb repress E-cadherin through direct binding to the promoter region of CDH1, the gene which codes for E-cadherin. Twist, on the other hand, functions in a different mechanism and represses E-cadherin expression through association with other proteins, most notably SET8, which binds to the promoter region of CDH1 and promotes the transcription of N-cadherin through activation of CDH2, the gene which codes for N-cadherin (Lamouille, Xu & Derynck, 2014; Yang et al., 2011).
Targeting EMT-Mediated Metastasis
Four strategies have been proposed to target EMT-mediated metastasis (Fig 2). Current research has predominantly focused on targeting EMT-induction through the inhibition of EMT-initiating signals, such as TGFß and EGF, by targeting their respective receptors (Buonato & Lazzara, 2013; Halder, Beauchamp & Datta, 2005; Wendt & Schiemann, 2009). In a clinical setting, individual targeting of pathways which initiate EMT and MET is implausible due to the significant toxicity of such a treatment. Yet, there is currently no effective method to eliminate EMT-derived mesenchymal cells. They are resistant to both chemotherapy and radiation treatments, which allows them to drive metastasis and cause tumor reoccurrence even after standard treatment has been administered (Bosco, Kenworthy, Zorio & Sang, 2015; Creighton et al., 2009; Gupta et al., 2009; Thomson, 2005).
Objective: Targeting Mesenchymal Survival Pathways
Because of the important role EMT-derived mesenchymal cells play in driving the metastatic process and their resistance to standard treatment, the overarching goal was to develop an effective strategy to eliminate EMT-derived mesenchymal cells. A novel approach was taken by identifying survival pathways within the mesenchymal state and testing the effects of several small molecule inhibitors on cell viability of differential epithelial and mesenchymal populations in vitro.
Small molecule inhibitors were selected based on their potency towards their intended target. Inhibitors that demonstrated a lower IC50 (half maximal inhibitory concentration), which indicates the amount of drug necessary to inhibit the intended biological component by half, were considered more potent and were chosen for use in this study. These inhibitors include GDC-0994, MK-2206, AZD1480, BAY 87-2243. GDC-0994 and AZD1480 inhibit the Extracellular Signal–Regulated Kinase (ERK1/2) and the Janus-Kinase 1/2 (JAK1/2), respectively, in an ATP-competitive manner (Ioannidis et al., 2011; Robarge et al., 2014). MK-2206 acts as an allosteric inhibitor of Protein Kinase B (AKT). It is equally potent against both AKT1 and AKT2 but approximately five-fold less potent against AKT3 (Hirai et al., 2010). Meanwhile, BAY 87-224 targets Hypoxia Inducible Factor 1 alpha (HIF1A) through inhibition of mitochondrial complex I (Ellinghaus et al., 2013).
Materials and Methods
Cytoscape computes survival pathways of the mesenchymal context by visualizing, modeling, and analyzing genetic interaction networks (Cline et al., 2007). Using information from the CytoKegg plugin in Cytoscape and the Kegg Pathway Database, survival pathways were mapped in Cell Designer. These pathways were color-coded based on RNA sequence data previously generated by Dr. John Haley of the Stony Brook Proteomics Center. Nodes which were up-regulated in a two-fold manner, at minimum, were selected as targets for drug inhibition.
A549, HCC4006, and HCC827 adenocarcinoma cell lines were purchased from the ATCC; two plates of cells were generated per cell line (+/- TGFß). 5ng/ml TGFß was added to each TGFß+ plate in order to induce EMT. In preparation for future assays, a target concentration of 5×103 cells/100µl was achieved for each plate. Cells were maintained in RPMI Medium 1640 (1x) in a 37°C/5% CO2 environment; Glutamine, Pyruvate, Penicillin Streptomycin (Pen Strep), HEPES, and Fetal Bovine Serum (FBS) were added to the RPMI media. Cell counting was performed every two days using a Hausser Scientific hemocytometer.
Verification of EMT with Light Microscopy
After a ten-day period, images of all cell lines (+/- TGFß) were taken at 60x using a Nikon Eclipse TS100 light microscope.
Verification of EMT with Immunofluorescence
A549 cells (+/- TGFß) were plated on Lab-Tek® II Chamber Slides. Preparation of slides for immunofluorescence was performed following the protocol included in the Life Technologies Image-iT® Fixation/Permeabilization Kit. Mouse Anti E-cadherin (Santa Cruz Biotechnology Catalog #21791) and Mouse Anti-Vimentin (BD Biosciences Catalog #550513) were used as primary antibodies. Alexa Fluor 488 goat anti-mouse (Molecular Probes by Life Technologies Ref. A11001), Alexa Fluor 555 goat anti-mouse (Molecular Probes by Life Technologies Ref. A21422), and Vimentin (D21H3) XP® Rabbit mAb (Alexa Fluor® 555 Conjugate; Cell Signaling Technology #9855) were used as secondary antibodies. All cells were stained with DAPI (Molecular Probes by Life Technologies Ref. D3571). After antibody incubation, ProLong Diamond Anti-fade Mountant (Molecular Probes by Life Technologies Ref. P36965) was applied to prolong fluorescence signal after initial exposure to light. Fluorescence images were captured at 200x and 630x with a Zeiss Fluorescence Microscope and quantified with Image J (Schneider, Rasband, & Eliceiri, 2012).
Verification of EMT with Western Blot
Cell lysates were created from all cell lines (+/- TGFß) using ThermoScientific Pierce RIPA Buffer with protease inhibitors. 3×106 cells were lysed at the conclusion of the ten-day period in which EMT occurred. To prepare loading samples, 25µl of NuPAGE® LDS Sample Buffer (4x) was added to each lysate. ThermoScientific NuPAGE® Bis-Tris Precast Gels were used. Along with the loading samples, 25µl of Invitrogen Novel Sharp Pre-stained Protein Standards was loaded into one well, acting as a protein ladder. After gel electrophoresis, samples were transferred to nitrocellulose membrane using a LKB Electrophoretic Transfer Kit and NuPAGE® Transfer Buffer (2x). The membrane was blocked with 5% milk with TBST. Mouse Anti E-cadherin (Santa Cruz Biotechnology Catalog #21791) and Mouse Anti-Vimentin (BD Biosciences Catalog #550513) were used as primary antibodies. Mouse Anti E-cadherin was diluted 1:500, while Mouse Anti-Vimentin was diluted 1:100. Peroxidase Conjugated Goat Anti-Mouse (ThermoScientific Catalog #32430) was used as the secondary antibody; it was diluted 1:100. After multiple washes with TBST, Enhanced Chemiluminescence (ECL) was performed, with Super Signal Pico (ThermoScientific) acting as the ECL (Mruk & Cheng, 2011). Quantification was performed with Image J.
Preparation of Drugs
Small molecule inhibitors (GDC-0994, MK-2206 2HCl, AZD1480, BAY 87-2243, Bl2536) were purchased from Selleck Chemicals (Houston, TX). Experimental target concentrations for GDC-0994 (ERK1/2 inhibitor), MK-2206 2HCl (Pan-AKT inhibitor), AZD1480 (JAK1/2 Inhibitor), and BAY 87-224 (HIF1A Inhibitor) were 50nM, 0.5µM, and 5µM. The target concentration for Bl2536 (PLK inhibitor) was 1µM.
All cell lines (+/- TGFß) were plated in Costar White 96 Well Plates; 5×103 cells were plated in each well. Experimental groups were set up on each plate; all cell types were treated with the three distinct target concentrations of the experimental drugs. After addition of drugs, plates were left in a humidified incubator at 37°C and 5% CO2 for a 48-hour period. MTT assays were then performed to quantify cell death. Absorbance was measured using a Molecular Devices SpectraMax M2. Absorbance was measured in Optical Density (O.D.) units.
In each cell line, every experimental condition was performed in triplicate. A549 and HCC827 trials were performed twice. For each data set, average deviation was represented by positive y-error bars, which were graphed using Graph Pad Prism 6. Statistical Analysis was performed using a Student’s T-Test in Microsoft Excel. Significance was set at p<0.05.
Identification of Survival Pathways and Drug Targets
Using Cytoscape and Cell Designer, survival pathways within the mesenchymal state were identified and illustrated (Fig 3). These pathways were focused on due to the presence of multiple proteins which were specifically up-regulated during the transition from epithelial to mesenchymal state. Based on this map, targets for drug inhibition were selected. Because of their specific up-regulation, JAK1/2, AKT1/2/3, and ERK1/2 were chosen as targets. Additionally, HIF1A was selected due to its role in downstream Vascular Endothelial Growth Factor (VEGF) signaling, an important transcriptional regulator of angiogenesis (Karar & Maity, 2011).
Light Microscopy Analysis
The first test used to verify TGFß induces EMT was a qualitative analysis of cell characteristics using light microscopy. TGFß- cells remain colonized and display a significant proliferative capacity (Fig 4A, 4B, 4C). These characteristics are in line with characteristics expected of epithelial cells. Thus, based on the light microscopy analysis, we concluded that TGFß- cells remained in the epithelial state. Meanwhile, TGFß+ cells were no longer colonized and displayed an individualized, spindle-like morphology (Fig 4D, 4E, 4F). These characteristics are distinctive of cells in the mesenchymal state.
Analysis of Protein Expression with Immunofluorescence
In order to validate the results of the light microscopy, protein expression of TGFß- and TGFß+ cells were analyzed using immunofluorescence. TGFß- cells expressed over five times as much E-cadherin than Vimentin when quantified using fluorescent microscopy at both 200x and 630x magnification (Fig 5A, 5B, 5C). On the other hand, TGFß+ cells expressed over six times as much Vimentin as E-cadherin when quantified using fluorescent microscopy at both 200x and 630x magnification (Fig 6A, 6B, 6C).
Because the TGFß- cells demonstrate a high E-cadherin expression and low Vimentin expression, we concluded that the cells were in the epithelial state. Conversely, the TGFß+ cells demonstrate a high Vimentin expression with low E-cadherin expression and were thus in the mesenchymal state.
Analysis of Protein Expression with Western Blot
Finally, EMT was verified by Western blot. Unlike the immunofluorescence analysis in which the expression of two distinct protein markers was assessed, only expression of E-cadherin was examined in this test. Because cancer cells experience reduced E-cadherin expression as they undergo EMT and transition from an epithelial to mesenchymal state, this analysis tested for a decrease in E-cadherin expression between the TGFß- and TGFß+ cells of the same cell line. Because lanes 1 and 3 demonstrate a more expression of E-cadherin than lanes 2 and 4 (35% vs. 20% and 17% vs. 5%, respectively), we concluded that EMT occurred (Fig 7). Thus, the TGFß- cells expressing a higher level of E-cadherin were in the epithelial state, while the TGFß+ cells expressing a lower level of E-cadherin were in the mesenchymal state.
Cell Viability Assays
Cell viability assays were performed on three lung adenocarcinoma cell lines after treatment: A549, HCC827, and HCC4006. In each cell line, the effect of BAY 87-2243 (HIF1A inhibitor), MK-2206 (AKT inhibitor), AZD1480 (JAK1/2 inhibitor), and GDC-0944 (ERK1/2 inhibitor) at 50nM, 0.5µM, and 5µM, respectively, on cell viability were tested on differential epithelial (TGFß-) and mesenchymal (TGFß+) populations. DMSO and BI2536, a PLK inhibitor that is a known suppressor of tumor growth, served as control groups (Steegmaier et al., 2007). Drug effectiveness was determined by comparing cell viability in mesenchymal populations treated with the drug and cell viability in the DMSO control. The drug which demonstrated a consistent decrease in viability of the drug-treated mesenchymal population compared to the DMSO control was considered the most effective drug in eliminating EMT-derived mesenchymal cells.
In the A549 assay, AZD1480 effectively decreased cell viability among the mesenchymal populations, with greatest effect achieved at 5µM (Fig 8C). Other inhibitors had limited effectiveness in decreasing cell viability (Fig 8A, 8B, 8D). Although the results weren’t significant for most of the HCC827 and HCC4006 assays, a similar trend was observed, with AZD1480 continuing to serve as the most effective inhibitor (Fig 9C, 10C).
To date, a very limited amount of research has been conducted that focuses on direct elimination of EMT-derived mesenchymal cells. This study used the novel approach of targeting specific survival pathways within the mesenchymal state as a strategy to eliminate EMT-derived mesenchymal cells. Results demonstrate that AZD1480, a potent ATP-competitive inhibitor of JAK1/2, induced the greatest decrease in cell viability within mesenchymal populations among inhibitors tested. This suggests JAK1/2 is a key modulator of the mesenchymal state and a plausible target of inhibition within EMT-derived mesenchymal cells. JAK1/2 plays an important role in regulating the activation of EMT-initiating transcription factors.
Because of the regulation by JAK1/2, the cell is maintained in a distinctly mesenchymal state as long as JAK1/2 is constitutively expressed, making JAK1/2 a promising drug target. Targeting the JAK-STAT pathway is feasible because of its relative simplicity compared to other pathways (Harrison, 2012). Additionally, JAK1/2 is a preferable target for inhibition because the phosphorylation of STAT3 is one of its primary functions, thus making it less likely that its inhibition will have significant effects on non-malignant cells (Levy & Lee, 2012).
As an ATP-competitive inhibitor, AZD1480 inhibits the ATP-binding pocket of the kinase domain, thus preventing the phosphorylation of STAT3 by JAK1/2 (Hedvat et al., 2009). Consequently, STAT3’s constitutive activation of EMT-transcription factors is prevented. This is the hypothesized mechanism by which AZD1480 eliminates EMT-derived mesenchymal cells (Fig 11).
In the future, continued study of AZD1480’s effectiveness as an anti-metastatic drug is warranted. This includes testing AZD1480’s effect on JAK protein expression at 48, 72, and 96 hours post treatment to ensure there is no protein rebound at a particular time point. Further studies include testing the effectiveness of AZD1480 in eliminating mesenchymal cells derived from other epithelial cell lines (e.g., breast, renal, colon) in vitro in order to potentially broaden the applications. Additionally, studies can be conducted in immune-deficient mice to test the in vivo efficacy of AZD1480.
Besides metastasis, EMT-derived mesenchymal cells have been implicated in numerous other malignancies. For example, because these cells are innately resistant to traditional therapeutics, they remain after the initial rounds of therapy and cause cancer reoccurrence in the future (Guffanti F, 2014). Furthermore, mesenchymal cells have been shown to not only be resistant to therapy themselves, but also to induce chemo-resistance within the surrounding cells. Thus, this strategy acts not only as an anti-metastatic treatment method, but also as a preventative measure for cancer reoccurrence and as a method to combat chemo-resistance. With this advancement, it may become possible for a patient to adhere to a low toxicity, yet effective treatment method. Such a combination has eluded science thus far.
The author would like to thank Dr. John Haley and Dr. Serena McCalla for their guidance and support in making this research possible.
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Effects of High Fructose/Glucose on Nlrp3/Il1β Inflammatory Pathway
The Nod-Like Receptor Protein-3/Interleukin-1β (NLRP3/IL-1β) inflammatory pathway activation is associated with autoimmune diseases including gout, Muckle-Wells syndrome, familial Mediterranean fever and multiple sclerosis. Type 2 diabetes and insulin resistance have been associated with chronic inflammation; however, the mechanism by which the NLRP3 inflammatory pathway participates in this condition remains unclear. Past research shows that fructose could induce production of reactive oxygen species (ROS), which are involved in NLRP3 complex assembly and secretion of IL-1β. In this study, the activation of NLRP3 inflammatory pathway was investigated in mouse macrophage cells (J774A1) treated with fructose and glucose (25, 50 and 100mM) for 24 hours. The cell lysates were analyzed using western blot analysis for inflammatory protein components NLRP3, IL-1β, and caspase 1, as well as antioxidant enzymes including peroxiredoxins (PRXSO3, PRX1), superoxide dismutase (SOD) and catalase. Mitochondria-derived ROS and mitochondrial permeability were assessed by MitoSOX red staining and JC-1 assay respectively. Results suggest that the intracellular levels of pro-IL-1β, antioxidant proteins (PRXSO3, SOD) and ROS were more elevated in cells treated with fructose and glucose at 25, 50 and 100mM when compared to untreated cells. Although pro-IL-1β accumulation was observed, no extracellular IL-1β could be detected using enzyme-linked immunosorbent assay (ELISA).
Western culture has adopted a diet rich in energy-loaded carbohydrates. This increased consumption of high-energy foods has been accompanied by reliance on mechanical technology to do work, reducing necessary physical activity (Popkin, 2001). The ratio of energy consumed to energy spent is imbalanced in favor of consumption, which results in storage of fat cells as adipose tissue and uncontrolled deposition of fats could lead to an individual carrying an excess amount of weight, referred to as being overweight or obese. This condition can be defined using the body mass index (BMI) of an individual (Finucane et al, 2011). Higher BMIs correspond to excess weight and obesity. Studies (Finucane et al, 2011) show that the mean BMI worldwide has increased over the years and so has the rate of obesity. In 2008, over 500 million people worldwide were considered obese and about 1.46 billion were overweight (Finucane et al, 2011).
In obese individuals, enlarged fat cells secrete fatty acids and cytokine factors such as tumor necrosis factor-α (TNF-α), that are capable of causing a wide range of downstream effects such as having higher risks for a variety of diseases including coronary artery disease, hypertension, metabolic syndrome, gall bladder disease, cancer, osteoarthritis and type 2 diabetes (Rodriguez-Hernandez, Simental-Mendia, Rodriguez-Ramirez, & Reyes-Romero, 2013). Among the obesity related diseases, type 2 diabetes has recently been classified an autoimmune disease involving inflammation through NLRP3 activation (Bray, 2004; Gunter & Leitzmann, 2006; Hajer, Haeften, & Visseren 2008). Studies have investigated the relationship between type 2 diabetes, insulin resistance and IL-1β expression (Gao et al, 2014; Larsen et al, 2007). Larsen et al.’s experimental results (2007) showed that blockade of IL-1β expression in patients with type 2 diabetes improved β-cell function and promoted glycemic control while Goa et al.’s results (2014) showed that IL-1β presence in human adipocytes significantly reduced the gene expression of insulin signaling molecules and its absence improved insulin sensitivity. The secretion of IL-1β is regulated by the Nod-Like Receptor protein 3 (NLRP3) inflammasome. IL-1β secretion is carried out in two steps. The first signal, also known as the priming step, consists of activation of Nod-Like Receptor protein 3 (NLRP3) coupled with accumulation of pro- IL-1β – the inactivated precursor protein for IL-1β. Upon accumulation of the precursor, a second signal is needed to recruit the NLRP3 inflammasome complex, consisting of (NLRP3), adaptor protein apoptosis speck-like Protein (ASC) and activated caspase 1, consequently responsible for cleavage of pro-IL-1β to secretion as Il-1β (Figure 1). When NLRP3/IL-1β pathway is activated, ROS production is also observed (Jo, Kim, Shin, & Sasakawa et al, 2016). An article (Tschopp & Schroder, 2010) suggested that mitochondrial ROS is not only correlated with NLRP3 activation, but is involved in assembling the NLRP3 inflammasome complex.
Mitochondria are considered the main source of ROS in normal and altered metabolism, and are the site of aerobic carbohydrate metabolism (Lenaz, 2001). After consumption, carbohydrates are broken down to sugars, which are metabolized to yield acetyl-CoA, the primary substrate of the tricarboxylic acid cycle (TCA) taking place in the mitochondria. During the TCA cycle, Acetyl-CoA goes though series of oxidation steps carried out by co-enzymes NAD+ and FAD, which are reduced to NADH and FADH2 respectively. These energy rich hydrogen atoms are supplied to the electron transport chain (ETC), where they are re-oxidized simultaneously with the reduction of oxygen to water by complexes on the mitochondrial membrane, yielding ATP (adenosine triphosphate), the primary form of energy in cells. This process results in 1-2% of consumed oxygen being partially reduced to generate superoxide anion (O2●-) (Thannickal & Fanburgh, 2000). Antioxidants usually act collectively to suppress ROS and other free radicals by reducing them to a less reactive species as in the multistep reduction of superoxide to water. Superoxide dismutase (SOD) reduces O2●- to hydrogen peroxide (H2O2), which is further reduced by catalase (CAT) or Glutathione peroxidase (GPX) to water (Devasagayam et al., 2004). Peroxiredoxins (PRX) are able to directly reduce H2O2 to water, while GPX works to reduce H2O2 by using Glutathione (GSH), a thiol-based antioxidant, as a substrate. In an adverse physiological state referred to as oxidative stress, the body is overwhelmed with production of ROS resulting in an imbalance of ROS/antioxidant ratio (Nordberg & Arner 2001). Oxidative stress has been associated with several pathological conditions such as cancer, diabetes (Ceriello & Motz, 2004) and chronic inflammation as well as other autoimmune diseases (Reuter et al, 2010). Among the inflammatory pathways that are associated with chronic inflammation and autoimmune diseases is the NLRP3 Inflammasome/IL-1β Inflammatory pathway (Jo et al, 2016).
ROS direct involvement with NLRP3 inflammasome activation has not been completely elucidated. The purpose of this study was to investigate whether or not ROS production is proportional to the sugar concentration in macrophage cell culture and if this higher input of glucose and fructose would contribute to an inflammatory response through the NLRP3 inflammasome. Our findings will give insight on the impact of a sugar-rich diet on oxidative and inflammatory pathways, as well as provide a perspective on macrophage cellular processes involved with obesity-associated inflammation for other scientists and researchers to expand on.
Materials and Methods
Chemicals and Reagents
3, 3’, 5, 5’-tetramethylbenzidine (TMB) reagent, Antimycin A (AA) and β-actin antibody were obtained from Sigma. Mitochondrial superoxide indicator (MitoSOX red, was purchased from Molecular Probes® (InvitrogenTM). NLRP3 antibody, caspase 1 antibody and IL-1β antibody was obtained from Santa Cruz. PRX1 and PRXSO3 antibodies were obtained from Abcam. The secondary IgG peroxidase-linked mouse antibody, and rabbit antibodies were purchased from GE Healthcare. All reagents were of analytical grade.
Cell Culture and Experimental Conditions
Mouse monocyte (macrophage) cell line J774A1 was obtained from American type culture collection (ATCC). Cells were seeded at 1 x 105 cells/mL using RPMI 1640 medium supplemented with 10% FBS, penicillin, streptomycin and L-glutamine, and incubated at 37°C in a 5% CO2-supplemented atmosphere for at least 24 hours before treatments. The cells were treated with glucose or fructose at concentrations 25mM, 50mM and 100mM for 24 hours. Treatments were performed in triplicates.
Cell Harvesting and Lysates Preparation
After respective treatments, tissue culture plates were placed on ice and the attached cells were rinsed once with cold phosphate buffered saline (PBS) and lysed using total lysis buffer (50mM Tris, pH 7.4, 150mM Sodium Chloride (NaCl), 2mM ethylenediaminetetraacetic acid (EDTA), 0.2% TritonTM X-100, 0.3% IGEPAL®, and protease inhibitor cocktail). Lysates were removed from the plate, transferred to microcentrifuge tubes, and immediately frozen in liquid nitrogen to prevent protein degradation and enhance cell lysis.
Protein Concentration Determination: Bradford assay
The J774A1 lysates were thawed at room temperature. 10µL of bovine serum albumin (BSA) standards or cell lysates samples were transferred into separate clean tubes and 1000µL of the 1:4 diluted dye (Bradford) was added to the respective tubes. The tubes were incubated for five minutes at room temperature before their content was transferred into a cuvette. Absorbance was measured at 595nm and the protein content in the samples was calculated using a standard curve with a series of BSA standards to 1500 – 100mg/ml. The amount of protein in each condition was determined and adjusted to 0.5mg/ml by adding PBS buffer.
Sodium Dodecyl Sulfate Polyacrylamide Gel Electrophoresis (SDS-PAGE) and Immunoblot
Leamli SDS buffer was added to the lysate and 30µL of the mixture per condition was loaded on to polyacrylamide gels. The proteins were separated on 10% SDS-PAGE and transferred from the gel to a nitrocellulose membrane. The membrane was blocked for one hour at RT with 3% milk in PBS containing 0.05% Tween®20 (PBST) and then incubated with respective primary antibodies overnight at 4oC on a rotating platform. The membranes were washed three times with PBST for five minutes each and incubated with respective horseradish peroxidase-conjugated secondary antibodies. Western blots were developed utilizing, 3, 3’, 5, 5’ TMB liquid substrate system for membranes according manufacturer’s instructions.
Detection of Mitochondria-Derived ROS
Mitochondria-derived ROS, was detected using the mitochondrial superoxide indicator, MitoSOX red, a cationic dihydroethidium modified to target the mitochondria. According to the manufacturer, MitoSOX red is a cell-permeable dye that reacts with ROS to form ethidium, which upon binding to nucleic acids gives a bright red fluorescence. Briefly, cells were seeded onto eight well CultureSlides (BD Falcon™) and treated as described above. At the end of the respective treatments, the cells were rinsed with PBS and loaded with MitoSOX red (2.5μM) for ten minutes. The medium containing fluorescent probes was removed, and the cells were rinsed with PBS, and observed in an Olympus BX53 Fluorescence Microscope coupled to an Olympus DP73 digital camera.
Detection of Mitochondrial Membrane Potential (∆ψm)
Cells (5 x 105 cells/mL) were seeded in 12-well plates and 24 hours later were treated as indicated. After washing with PBS, cells were incubated in fresh medium containing 5,5′,6,6′-tetrachloro-1,1′,3,3′-tetraethylbenzimidazol-carbocyanine iodide (JC-1) for 15min. The dye was then removed; and cells were washed with PBS and fresh medium was added. Then live cells were immediately observed in an Olympus BX53 Fluorescence Microscope coupled to Olympus DP73 digital camera. Healthy cells, mainly JC-1 aggregates were observed at 540/570nm excitation/emission and the apoptotic or unhealthy cells with mainly JC-1 monomers at 485/535nm excitation/emission.
As ROS are a by-product of fructose and glucose metabolism, it was anticipated that an increase in substrate would cause an increase in ROS production. This increase in ROS could promote an environment suitable for NLRP3 inflammasome activation. In the vicinity of ROS, MitoSOX red produces a bright red fluorescence, which varies in intensity with amount of ROS present. Antimycin A was used as the positive control as it is known to be an inhibitor of complex III in the ETC and leads to robust ROS production and mitochondrial damage. J774A1 cells appeared to produce more red fluorescence when treated with fructose and glucose in contrast to the control, untreated cells (Figure 2a). In addition, fructose treatment elicits a more intense fluorescence than glucose at concentrations 25 and 50mM, as demonstrated by fluorescence intensity analysis (Figure 2b).
Mitochondrial Membrane Potential (∆ψm)
When the mitochondrion is constantly hyperactive, it has the tendency to go into a dysfunctional state evidenced by leaks in the mitochondrial membrane. These leaks could cause it to become depolarized (Tsujimoto & Shimizu, 2007). The JC-1 dye kit can be used to detect the overall health of the mitochondria. In hyperpolarized mitochondria the dye emits a red fluorescence while in depolarized mitochondria it emits green. Hence, the red fluorescence indicates healthier mitochondria while green indicates a disturbed mitochondrial function. In cells treated with glucose (25mM) and fructose (25mM, 50mM), there is a higher red intensity compared with control, indicating that the mitochondrion is polarized. However, as treatment concentration is increased, there is a decrease in red and increase in green fluorescence in glucose treatment, indicating mitochondrial depolarization occurs. It appears that the decrease in red and increase in green was more intense in fructose treated cells than glucose treated cells at 100mM concentration (Figure 3a & b).
Western Blot analysis for Antioxidant and Inflammatory Proteins
Western blot analysis was used to detect the presence of antioxidant and inflammatory proteins. Comparing the intensity and thickness of the bands can give insight on the amounts of proteins present across experimental conditions. The Bradford assay was used to measure the total amounts of protein in each sample and ensure the total protein loaded on to gels were constant throughout the treatment conditions. Increased expression of antioxidant protein is a marker of oxidative stress. As a response to counteract ROS, antioxidant enzymes are produced through stress response mechanisms to prevent oxidative damage (Nguyen et al, 2009). Compared with the control experiment, PRX1 levels did not seem to vary throughout the conditions, however, as implied by the prominent bands observed, the amounts of its oxidized form (PRXSO3) and catalase appear to be increased, in particularly with fructose treatment. SOD1 expression showed to be slightly increased in both fructose and glucose treatments (Figure 4).
The presence and amount of precursor proteins of NLRP3 inflammatory pathway can be used to assess NLRP3 inflammasome activation and determine the extent of inflammatory response in macrophage cells. The precursor protein, Pro-IL-1β, appeared to be increased only in fructose and glucose-treated cells. In addition, it seems to be expressed in higher amounts in fructose treatments compared to glucose treatments. The cleaved products of caspase 1, seen as the multiple lower bands, are also observed majorly in fructose condition, indicating some level of caspase 1 activation. NLRP3 protein, however, seems to remain constant throughout the conditions (Figure 5).
Discussion and Conclusions
The results appear to be consistent with the presumption that sugars can induce oxidative stress through an over active mitochondria and promote an inflammatory response in J774A1 macrophage cells. The MitoSOX red assay possibly indicates an increased ROS production in treated cells. In lower concentrations (25mM, 50mM), fructose seems to invoke more ROS production than glucose, however in the highest concentration, fructose and glucose treatments produced comparable amounts of ROS, perhaps because the antioxidant systems were increased to compensate the potentially higher amounts of ROS.
The mitochondrial permeability assay suggests that the lower concentrations of fructose (25mM, 50mM) and glucose (25mM) treatments actually cause the cells to become relatively healthy compared to the control. However, as the treatment concentrations increased, the membrane was depolarized, indicating an unhealthy cell. One of the key events during apoptosis and necrosis, or cell death, is an increase in mitochondrial membrane permeability by opening of permeability transition pore proteins, which are ion channels. This leak in the membrane leads to a decrease in membrane potential of the mitochondria (Tsujimoto & Shimizu, 2007). Another study (Russell et al., 2002) also found a dose-dependent increase in ROS production and mitochondria depolarization in neurons treated with glucose. This study (Russell et al., 2002) observed that the mitochondrial membrane goes through hyperpolarization before it is depolarized suggesting that depolarization might be time dependent. Future studies could investigate this by measuring membrane potential at varied time points.
As ROS levels appeared to increase with treatment, the intracellular antioxidant protein amounts were investigated. It appeared that the amounts of some antioxidant proteins (Catalase and PRXSO3) were increased in treated cells, indicating an upregulation of antioxidant protein expression and implying an increased level of oxidative stress. We found that fructose had a more observable effect on this response. This could be due to the differences in fructose and glucose metabolism. According to Cha et al (2008) glucose metabolism is more regulated than that of fructose. In the glycolytic pathway, fructose is able to bypass the rate-limiting step catalyzed by phosphofructokinase, an important control enzyme for the rate of glucose metabolism, allowing fructose to be metabolized at a faster rate. In fructose fed mice, it was found that fructose metabolic pathway, and not glucose, was essential to recruitment of macrophages and production of pro-inflammatory mediators including TNF-α in the visceral adipose tissue (Marek et al, 2015).
Although the results indicate the precursor to IL-1β – pro-IL-1β, was accumulated intracellularly, extracellular IL-1β remained undetected. We speculate that this might be due to the duration of incubation. A time course experiment in our research group (Sunasee et al, 2015) showed that pro-IL-1β accumulation is also time dependent. In the experiment using cellulose nanocrystals (CNCs) cationic derivatives, seven hours of treatment was the peak time for pro-IL-1β detection in J774A1 cells. Treatments at other time points did not seem to show as much pro-IL-1β accumulation. In the future, we would vary the lengths of treatments and investigate if this is a factor affecting pro- IL-1β accumulation and IL-1β secretion in fructose and glucose treatments. We also would like to investigate the effect of adding antioxidants in the current treatments and possibly, including lipids in treatments to create a more type 2-diabetic/metabolic-syndrome environment.
Future studies should focus on quantifying the cell counts and analyzing the MitoSox and JC-1 fluorescence imaging data statistically. Overall, our results corroborate with the existing information regarding the effects of excess sugar on ROS production by the mitochondria, and the potential link with activation of NLRP3 inflammasome pathway. By understanding the behavior of this pathway, researchers will be able to better understand autoimmune diseases and possibly create new ideas on mitochondria-targeted therapies.
I am extremely grateful to my mentor, Dr. Karina Ckless, whose continuous guidance and encouragement made it possible to execute this study. In addition, I would like to thank the SUNY Plattsburgh Redcay Honors center for their approval and sponsorship of the project.
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Total Electron Content (TEC) Variations and Correlation with Seismic Activity over Japan
Earthquakes are extremely dangerous physical phenomena. The ability to properly forecast them would go a long way in reducing the damage they cause. One earthquake forecasting method being researched uses the ionospheric Total Electron Content (TEC). Our investigation used TEC data from 2011 during certain days near and on the date of the earthquake off the coast of Tōhoku, Japan. We took advantage of the large amounts of GPS records obtained by the GPS Earth Observation Network (GEONET) of Japan which contained the TEC data needed. This data was used to visualize the TEC over the course of the day of the Tōhoku earthquake. The video produced abnormalities consistent with the predicted effect an earthquake has on the ionosphere. These abnormalities were shown to not be caused by solar and geomagnetic activity. These results suggest that detectable ionospheric activity precedes earthquakes. Ionospheric disturbances are also known to be caused by other confounding factors such as solar and geomagnetic activity. Careful analysis is included in this paper to exclude this class of disturbances from those that are seen to occur due to seismic and pre-earthquake activities. The hope is that the potential correlation between seismic and pre-earthquake activities may be used as an earthquake precursor towards the development of an earthquake forecasting method.
Recently, research has focused on the relationship between the state of the ionosphere and seismic activity (Heki & Ping, 2005; Oyama, Kakinami, Liu, Abdu, & Cheng, 2011). One ionospheric parameter that has been investigated is the Total Electron Content (TEC). TEC is defined as the number of electrons along a path between a receiver (rx) on the surface of the earth and a GPS satellite (st) in orbit. The TEC can be computed with the line integral from a GPS satellite and a receiver as described in (1).
r = is range or radial position (meters)
Θ = latitude (degrees)
Φ = longitude (degrees)
t = time (seconds)
With regards to the correlations between TEC and pre-earthquake and seismic activities, the TEC is an important parameter of study because it has the potential for showing the changes in the ionosphere due to these activities. It is because seismic and pre-earthquake activities create stress in rocks in the earth’s crust. These stresses are known to positively charge the rocks on the earth’s crust. As the positive charges accumulate at the rocks outer surfaces, they create a difference in potential which in turn creates a flow of charges that can travel fast and far from their point of origin. As the charges travel upward under the influence of the electric field lines between the surface of earth and the bottom of the ionosphere, they reach the bottom of the ionosphere, disturbing the equilibrium of the electrons in the ionosphere (Freund, Takeuchi & Lau, 2006). These disturbances can be seen in the TEC which makes TEC a potential candidate as an earthquake precursor. If TEC disturbances could be used as an earthquake precursor, tracking those disturbances could be used as part of an earthquake forecasting system which would improve earthquake warning systems, in turn saving countless lives.
This study uses TEC data from Japan and current knowledge of the Tōhoku Japan earthquake to determine whether pre-earthquake and seismic activities correlate with TEC changes around the time of the earthquake.
TEC disturbances can be observed using GPS signals. It is because the ionosphere creates a phase delay in the electromagnetic signals, sent from a receiver on earth to a GPS satellite in orbit. The phase delays change based on several variables: the frequency of the emitted signals, the path from the receiver to the satellite and the associated electron density along the path. The phase delay can be used to estimate the distance (called pseudo-range) from the GPS satellite and the receiver. More specifically, since the ionosphere is a dispersive medium (i.e., varies with frequency), using two signals with known GPS frequencies, we can measure the phase delay between the returned signals to estimate the TEC as shown in (2).
Prs = The differential pseudo-range measurements
f1,f2 = The GPS measurement frequencies: 1,575.42 and 1,227.60 MHz
TEC = The Total Electron Content (m/s2)
DCB = The biases in the measurements
From these measurements, we can test if seismic activity causes detectable disturbances in the ionosphere’s TEC.
The visualization of TEC data over Japan required GPS readings from the GPS Earth Observation Network (GEONET) of Japan (http://datahouse1.gsi.go.jp/terras/terras_english.html). From these readings, TEC values were obtained using the relationship in (2), resulting in multiple data points for each receiver over time. The GPS data was contained in receiver independent exchange format (RINEX) observation data files. These files contain phase delay measurements for GPS signals, as well as the time at which these measurements were made. These files were processed to extract TEC data over time, which was done using a FORTRAN program. The FORTRAN program was originally developed by Professor Kosuke Heki of Hokkaido University in Japan. The TEC data over time was then input into MATLAB (The Mathworks Inc., Natick, MA, 2013) for analysis and visualization. We averaged the data points at each site and time, resulting in one data point for each site every 30 seconds (the sampling period of the GPS receivers). This data was interpolated using a linear triangulation method, resulting in one grid of interpolated data over Japan every 30 seconds. We turned these grids into contour plots, each plot creating a frame for the video file produced.
Since disturbances in the ionosphere are not necessarily caused by seismic activity, we considered other possible sources of disturbances in our analysis. We obtained measurements of 10.7 cm solar flux (F10.7), the sunspot number (SSN), and earth’s geomagnetic storm activity (Kp index) over the month of March in 2011. This data was provided by the National Oceanic and Atmospheric Administration’s Space Physics Interactive Data Resource (SPIDR) (http://spidr.ionosonde.net/spidr/). This data was compared to other research (Hasbi et al., 2009) (Ouzounov et al., 2011) to determine whether any ionospheric irregularities could have been caused by solar or geomagnetic sources.
The video of TEC levels produced for February 11th and March 11th show the behavior of the ionosphere throughout the day. From the video on March 11th, after an initial enhancement (Figure 1A), a sudden depletion of roughly 30 TECU in 15 minutes was observed from 1:35 PM JST to 1:50 PM JST (Figure 1B). Large fluctuations were also observed, with a significant example from 4:25 PM JST (Figure 1C) to 5:00 PM JST (Figure 1D) showing levels decreasing by 50 TECU in 45 minutes. For comparison, plots from February 11th (one month before the earthquake) were taken at the same times (Figure 2). Data from the same times of day show no enhancements, depletions, or fluctuations.
Sunspot number data for March 2011 shows a sunspot count of roughly 100 on March 8, coming down to 60 on March 11th (Figure 3A). Geomagnetic storm activity data for March 2011 shows a Kp of roughly 5.3 on March 11th, with relatively low numbers before March 11th (Figure 3B). Solar flux (F10.7) data for March 2011 shows heavy activity on March 7th with measurements of more than 900 watt per square meter-hertz, with relatively calm activity for the rest of the month (Figure 3C).
It has been proposed that TEC anomalies can be caused by seismic activity. Our study looked at GPS data during the 2011 Tōhoku Earthquake to investigate the correlation between TEC and seismic activity. The investigation resulted in evidence supporting detectable TEC disturbances caused by pre-seismic activity.
TEC measurements taken over the course of the earthquake showed significant TEC anomalies during seismic activity compared to a time period with no seismic activity. The TEC on the day of the earthquake shows an initial enhancement, followed by a rapid depletion and large fluctuations. Comparatively, TEC levels on a day with no seismic activity behave smoothly.
While these results provide strong evidence indicative of seismic activity causing TEC disturbances, it is necessary to make sure no other source caused these enhancements, depletions, and fluctuations. While the values are relatively high for the sunspot number (SSN) and magnetic storm activity (Kp) (Figure 3A-B) compared to expected levels, prior work has shown these values alone are not significant to cause a disturbance of this magnitude in the ionosphere (Hasbi et al., 2009). The solar flux peaked for the time frame analyzed on March 7th, four days before the earthquake (Figure 3C). Based on previous studies, the solar flux around the day of the earthquake is likely not the cause of the TEC disturbances on the day of the earthquake due to the timing of the maximum flux (Ouzounov et al., 2011).
This study had limitations with certain systemic sources of error in the measurements of TEC, as reflected in the DCB term shown in (2). The data analyzed did not take into account the angle through which the GPS signals passed through the ionosphere. This leads to the GPS signals passing through a larger section of the ionosphere when the satellite is not directly above the receiver. Another source of error that should be considered is certain biases introduced from the GPS satellites and receivers, as well as the troposphere. These biases are introduced due to many factors, such as satellite ephemeris inaccuracies, clock errors, as well as atmospheric conditions and measuring equipment temperature. These errors were out of the scope of this study, and should be looked into further.
It is highly probable that the earthquake caused the enhancements, depletions, and fluctuations in the TEC. However, more detailed work is needed to help determine the exact cause and nature of TEC events surrounding seismic activity. Future investigations should focus on tracking and monitoring TEC for longer periods of time surrounding an earthquake to better characterize the beginning and length of disturbances. In addition, further research should be performed to determine how solar flux, magnetic storms, or other external events affect TEC disturbances.
The pattern of TEC enhancements, depletions, and fluctuations expected by previous works (Heki & Ping, 2005) were noticed in TEC measurements near the time of the earthquake. These TEC behaviors are quite different from the average expected behaviors resulting from a smooth ionosphere. These TEC behaviors are also seen not to result from or be correlated with other outside phenomena such as solar flux and geomagnetic storms. Our results support the hypothesis that seismic activity increases disturbances in TEC. Future research should focus on developing methods to isolate the effects of seismic activities from confounding factors and to remove errors from TEC measurements.
Freund, F. T., Takeuchi, A., & Lau, B. W. (2006). Electric currents streaming out of stressed igneous rocks – A step towards understanding pre-earthquake low frequency EM emissions. Physics and Chemistry of the Earth, 31(4-9), 389-396. doi:10.1016/j.pce.2006.02.027
Garner, T., Gaussiran II, T., B.W., T., R.B., H., Calfas, R., & Gallagher, H. (2008). Total electron content measurements in ionospheric physics. Advances in Space Research, 42, 720-726. doi:10.1016/j.asr.2008.02.025
Hasbi, A. M., Momani, M. A., Mohd Ali, M. A., Misran, N., Shiokawa, K., Otsuka, Y., & Yumoto, K. (2009). Ionospheric and geomagnetic disturbances during the 2005 Sumatran earthquakes. Journal of Atmospheric and Solar-Terrestrial Physics, 71(17-18), 1992-2005. doi:10.1016/j.jastp.2009.09.004
Heki, K., & Ping, J. (2005). Directivity and apparent velocity of the coseismic ionospheric disturbances observed with a dense GPS array. Earth and Planetary Science Letters, 236, 845-855. doi:10.1016/j.epsl.2005.06.010
Ouzounov, D., Pulinets, S., Romanov, A., Romanov, A., Tsybulya, K., Davidenko, D., . . . Taylor, P. (2011). Atmosphere-Ionosphere Response to the M9 Tohoku Earthquake Revealed by Joined Satellite and Ground Observations. Preliminary results. Earthquake Science, 24(6), 557-564. doi:10.1007/s11589-011-0817-z
Oyama, K., Kakinami, Y., Liu, J. Y., Abdu, M. A., & Cheng, C. Z. (2011). Latitudinal distribution of anomalous ion density as a precursor of a large earthquake. Journal of Geophysical Research, 116(A4). doi:10.1029/2010JA015948
Chemical Reduction and Deposition of Nanostructured Pt–Au Alloy
Nanostructured metal alloys made up of Pt and another metal are more efficient in catalysing reactions than pure Pt nanoparticles. However, few studies have investigated low heat, solvent-free chemical deposition techniques of nanostructured metal alloys. This paper investigates the deposition of Pt–Au nanostructured metal alloy on fluorine-doped tin oxide glass via the low heat, solvent-free polyol reduction and the effect of Pt:Au mass loading ratio on the catalytic performance. The deposition process involves drop-casting the metal precursors, H2PtCl6 and HAuCl4, on the glass substrates and reducing the precursors with vaporised ethylene glycol at 170°C for 15 minutes. The scanning electron microscope revealed that the structure of Pt–Au alloy changes from three-dimensional globular nanostructures to two-dimensional triangular and hexagonal nanoplates and to three-dimensional nanocrystals as the Au concentration increases. The X-ray photoelectron spectroscopy confirmed that the precursors on glass substrates were reduced to metallic Pt and Au. The electrocatalysis of CH3OH, with the Pt–Au glass substrates as work electrodes, showed that Pt–Au alloys have better catalytic performances than those of pure Pt and the catalytic rate peaks at a certain Pt:Au mass loading ratio.
Platinum (Pt) nanoparticles act as catalysts in proton exchange membrane (PEM) fuel cells powering machinery (Bing, Liu, Zhang, Ghosh, & Zhang, 2010; Ouyang & Cho, 2011). Using H2 or liquid fuels like CH3OH, PEM fuel cells, made up of acid-soaked PEM placed in between the anode and cathode catalyst, oxidize the fuel at the cathode and reduce the oxygen entering the cell. This creates a potential difference, V, that drives an electric current. Said electric current can be used to power a variety of applications. The fuel cell could use a Pt plate or Pt coated substrate as either the anode or cathode catalysts. It has been reported in studies that by combining Pt with other metals to form nanostructured metal alloys (NMA), the adsorption of carbonaceous poisoning species like CO is suppressed (Ren et al., 2010). Such poisoning species tend to permanently bind themselves to the catalyst, leaving less sites for the oxidation and reduction of chemical species responsible for driving the electric current. Less adsorption of poisoning species in NMA catalysts can lead to enhanced catalytic performance.
NMA can be chemically deposited on substrates in the same way as pure metal nanoparticles do. There are several deposition methods, of which the chemical reduction method is the most popular method (Herricks, Chen & Xia, 2004; Ouyang & Cho, 2011; Skrabalak, Wiley, Kim, Formo, & Xia, 2008). The chemical reduction method uses a reducing agent, such as NaBH4 and LiBEt3H, to reduce metal precursors to their pure metallic form (Gonsalvesa, Rangarajan & Wang, 2000). By controlling the experimental conditions, like the pH and precursor concentration, the shape, size and composition of NMA are well-controlled, making this method the most popular (Ouyang & Cho, 2011).
Most studies employ complex chemical reduction methods to produce NMA with high catalytic capabilities. They include heating at high temperatures over 300°C (Ganesan, Freemantle, & Obare, 2007; Jana, Dutta, Bera, & Koner, 2008) or using complicated postnanoparticle immobilisation processes like layer-by-layer deposition (Ouyang & Cho, 2011). A convenient chemical reduction method by Ouyang & Cho (2011) is the low heat, solvent-free polyol reduction. The reducing agent used is ethylene glycol (EG), which is vaporized under low heat (below 200°C) so that the vapour will reduce metal precursors. EG is then oxidized to aldehydes and carboxylic acids. Gaseous products from this reaction will escape into the air, leaving the NMA end-product free of any liquid organic compounds. The formed NMA will have well-defined shapes and good adhesion to glass substrates. Thus, no additional steps of mixing metal precursors with surfactants and additives, which control the shapes, are required.
Here we investigate the deposition of Pt–Au NMA of varying Pt:Au mass loading ratios on fluorine-doped tin oxide (FTO) glass substrates using the low heat, solvent-free polyol reduction by Ouyang & Cho (2011). We evaluate the hypothesis that the low heat, solvent-free polyol reduction method is able to produce the Pt–Au NMA that has better catalytic ability than that of pure Pt. We also investigate the role that the Pt:Au ratio plays in determining the catalytic capability of Pt–Au NMA.
Materials and Methods
An FTO glass sheet was cut into 1cm×1cm pieces for the SEM imaging and 1cm×6cm pieces for CV. They were then sterilized by subjecting them to sonication in an ultrasonic bath using distilled water and isopropyl alcohol. The chemicals used were EG, chloroplatinic acid hexahydrate (H2PtCl6.6H2O) and gold (III) chloride trihydrate (HAuCl4.3H2O) obtained from Sigma-Aldrich. The respective acid salts were then each dissolved in 90% ethanol to form 0.01M solution.
Deposition of Pt–Au NMA
NMA were deposited by reducing the metal precursors, H2PtCl6 and HAuCl4, with EG vapor. NMA of varying Pt:Au mass loading ratios were to be investigated. Hence, three 40ml solutions with different metal precursor mixing ratios were prepared. The mixing ratios were such that once they are reduced, they produce the required Pt:Au ratio (1:1, 1:2 and 1:3) on the glass substrates. A 40ml solution consisting of only H2PtCl6 without HAuCl4 was also prepared.
In a typical experiment, 10ml of the prepared solution was drop-casted onto the glass substrate. After drying the metal precursor layer at room temperature for 15 minutes, the substrate was placed into a large petri dish containing a small petri dish filled with EG. The large petri dish was then placed on a hot plate at 170°C for 15 minutes. The large petri dish was covered with a glass lid so as to create a closed environment, which allowed the vaporised EG to be contained within the dish and to effectively reduce the metal precursors. After 15 minutes, the glass cover was removed and the heating continued for another ten minutes in order to dry the substrate.
Characterization of Pt–Au NMA
To study the morphologies of NMA, SEM images of NMA were taken with a Zeiss Supra 40 field emission scanning electron microscope, with the extra high tension voltage fixed at 5kV under the secondary electron imaging mode. The XPS spectra of NMA, which are used to confirm if the metal precursors are successfully reduced to their metallic forms, were acquired using an Axis Ultra DLD X-ray photoelectron spectrometer equipped with an Al Ka1 X-ray source of 1486.6eV.
Electrochemical Catalysis of Pt–Au NMA
The CV was used to investigate the oxidation of CH3OH with the NMA-doped glass as the catalyst. The NMA glass substrate, a saturated calomel electrode and a Pt plate were used as the work, reference and counter electrode respectively. The electrolyte consisted of 0.5M H2SO4 and 0.5M CH3OH. The potential scan rate was 20mVs-1.
Pt–Au NMA Characterization Using SEM
Differing morphologies are observed under the SEM, for the three NMA of different Pt:Au mass loading ratios (Figure 1). For the 1:1–NMA, three-dimensional (3D) globular nanostructures were observed (Figure 1a). For the 1:2–NMA, 2D triangular and hexagonal nanoplates covered with mesh-like structures were observed (Figure 1b). For the 1:3–NMA, 3D nanocrystals were observed (Figure 1c).
Pt–Au NMA Characterization Using XPS
The three XPS spectra of the three NMA display four sets of peaks located around 71.0, 74.2, 83.7 and 87.5eV (Figure 2). The corresponding binding energies of the peaks will be compared with those of Pt and Au in existing literature to determine if the NMA are made up of metallic Pt and Au.
Electrochemical Catalytic Performance of Pt–Au NMA
From the oxidation of CH3OH on the Pt–Au NMA catalyst, the varying oxidation current shown in the cyclic voltammogram can determine the NMA’s catalytic performance. The 15th cyclic voltammograms of the NMA-FTO glass of different Pt:Au ratios, as well as the control FTO glass doped with only Pt, display two distinct current peaks (Figure 3). The right peak, If, which was produced by the forward sweep and the left peak, Ib, which was produced by the backward sweep when the potentials are ~ 0.7V and ~ 0.5V respectively. The If and Ib values for the Pt and various Pt–Au NMA catalysts are listed in Table 1.
Pt–Au NMA Characterization Using SEM
The differing morphologies exhibited by different NMA can be partially explained by existing literature and the specific surface energies of Pt and Au. Existing studies have shown that Au nanostructures are usually 2D (Cho, Mei, & Ouyang, 2012), while Pt nanostructures are 3D (Cho & Ouyang, 2011; Shen et al., 2008) when grown on substrates. By referring to their lowest surface energy crystallographic plane (111), given that Pt and Au have face-centered cubic structures, the specific surface energies of Pt and Au are 2.299 J m–2 and 1.283 J m–2 respectively (Vitos, Ruban, Skriver, & Kollar, 1998). The high surface energy of Pt causes Pt nanoparticles to cluster together, in order to minimize the surface area of Pt, for maximum stabilization, leading to the formation of 3D (rather than 2D) nanostructures. In contrast, the formation of 2D Au nanoplates (which have wide surface areas) is energetically feasible due to the low surface energy of Au. Thus, the highest concentration of Pt nanoparticles in the 1:1–NMA, compared to other NMA, can explain the observed overall 3D globular nanostructures dominated by Pt in the 1:1–NMA. Similarly, the more Au nanoparticles in the 1:2–NMA compared to the 1:1–NMA led to overall visible growths of 2D nanoplates dominated by Au in the 1:2–NMA but not in the 1:1–NMA.
However, no existing studies provide insight on the formation of 3D nanocrystals in the 1:3–NMA, which has more Au nanoparticles than that of 1:2–NMA. The transformation from 2D nanoplates to 3D nanocrystals may suggest that with increasing Au concentration, it is not energetic feasible for 2D nanoplates to exist. The higher total surface energy of Pt–Au system results in Pt and Au nanoparticles to cluster together to form crystalline structures for stabilization. Nevertheless, further research is necessary to explicate the transition from 2D nanoplates to 3D nanocrystals above the threshold Au concentration for Pt–Au NMA. Since the morphology controls the chemical reaction sites governing catalytic activity, understanding the morphological change can perhaps explain the different catalytic performances of Pt–Au NMA of varying ratios when used in fuel cells (Bing et al., 2010).
Pt–Au NMA Characterization Using XPS
As seen in the results section, for all three NMA, the binding energies associated with four sets of peaks are close to those reported in existing literature (Cho et al., 2012; Cho & Ouyang, 2011; Ye et al., 2011). These studies report that for metallic Pt and Au, the Pt4f bands lie around 71 and 74eV, and the Au4f bands lie around 84 and 88eV. Thus, from the spectra, the binding energies of the first two peaks (71.0 and 74.2eV) belong to Pt4f bands and the last two peaks (83.7 and 87.5eV) belong to Au4f bands. This indicates that the metal precursors were successfully reduced to their metallic forms in all NMA.
Electrochemical Catalytic Performance of Pt–Au NMA
From the voltammograms, the If peak was attributed to the three-step adsorption of CH3OH on Pt sites of NMA (Bagotzky, Vassilliey, & Khazova, 1977; Manoharan & Goodenough, 1992):
The adsorbed H species will subsequently be lost into the solution as H+ ions, while liberating electrons that contribute to the If peak. In addition, OH species will be adsorbed throughout the three-step oxidation process, leading to the oxidation of adsorbed carbonaceous species in Equation 1 (CH2OH, CHOH and COH) to form CH2O, HCOOH and CO2 (Bagotzky, Vassilliey, & Khazova, 1977; Manoharan & Goodenough, 1992). Should the adsorbed carbonaceous species fail to be oxidised by OH species, at higher voltages, adsorbed COH species will be oxidised to CO and CO2, thereby liberating electrons that contribute to the If peak as well according to Equation (2a) (Manoharan & Goodenough, 1992):
The catalyst’s performance can be determined by calculating the ratio of If/Ib. A lower ratio indicates a poorer catalytic performance. This is because a lower ratio corresponds to a higher Ib peak, which implies that more CO molecules are adsorbed to the catalyst and greater CO oxidation rate. The tabulated ratios in Table 1 show that the FTO glass doped with only Pt has the lowest ratio. Also, modifying the Pt:Au ratio would result in different tolerance performances, which the Pt:Au ratio of 1:2 yields the highest If/Ib ratio and best catalytic performance.
Comparing between Pt-doped catalyst with only Pt and Pt–Au NMA catalysts, Au plays a key role in enhancing the catalytic performance of Pt as described by Choi et al. (2006). It is because Au is responsible for increasing the oxidation rate of HCOOH by inducing a major oxidation reaction pathway, which reduces the formation of CO. Thus, lower amount of CO accumulates in Pt–Au catalysts, which reduce catalytic poisoning and lead to lower Ib peak in NMA compared to Pt-doped catalyst.
While the incorporation of small amount of Au leads to better catalytic performance, based on the current study that investigates three different Pt:Au ratios (1:1, 1:2 and 1:3), higher Au concentration above a threshold Pt:Au ratio of 1:2 can instead reduce the catalytic performance. According to Wang et al. (2010) and Ye et al. (2011), since the Fermi energies of Pt and Au are different, Au modifies the electronic structure of Pt–Au NMA, resulting in electrons tending to flow from Pt to Au. At high Au concentration, electrons are less available in Pt for adsorption of OH species that oxidize CO in Equations (2b) and (2c). In addition, as Pt (but not Au) is primarily responsible for the chemisorption activities, excessively high Au concentration in Pt–Au NMA implies that less Pt sites are available for adsorption of OH species. It is possible that less adsorbed OH species lead to more incomplete oxidation of CH3OH, more accumulation of CO and higher Ib peak. Hence, these explanations justify the peak catalytic performance at the Pt:Au ratio of 1:2.
In conclusion, this investigation had successfully deposited Pt–Au NMA on the FTO glass via the low heat, solvent-free polyol reduction. From SEM imaging, different Pt:Au loading ratios resulted in different morphologies, as attributed to differing total surface energies of Pt–Au systems. The XPS spectra confirmed that the precursors were reduced to metallic Pt and Au. Studying the electrochemical catalysis of methanol confirmed the hypothesis that the NMA-doped glass had a better catalytic performance than the Pt-doped glass. Also, another hypothesis that was verified is varying the Pt:Au ratio affects the catalytic performance, which its performance peaks at the Pt:Au ratio of 1:2. The investigated method provides a convenient alternative for producing high-performing catalysts compared to the high heat, solvent-based methods that are currently employed in industries. Future research can explain the changing morphologies of Pt–Au NMA with respect to varying Pt:Au ratio, which can assist other research seeking formulate new methods of controlling the morphology of Pt–Au NMA during the NMA production in order to develop optimally-performing NMA. Also, Pt–Au NMA of other Pt:Au ratios, apart from the three currently studied ratios (1:1, 1:2 and 1:3), can be characterized by other research to understand the catalytic performance change by increasing the Pt:Au ratio. This has important implications on situations where there is limited Pt or Au and there is a need to produce Pt–Au NMA of maximal catalytic capability with limited resources.
This project is conducted under the 2013 Undergraduate Research Opportunities Programme supported by the Faculty of Engineering at National University of Singapore. The author is grateful to his advisors, Dr. Karimbintharikkal G. Nishanthand Prof. Ouyang Jianyong for providing the necessary guidance for this project, as well as the Laboratory Office of Department of Materials Science and Engineering for providing the materials and equipment for this project.
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Probability Current and a Simulation of Particle Separation
The structure of scattered wave fields and currents is of interest in a variety of fields within physics such as quantum mechanics and optics. Traditionally two-dimensional structures have been investigated; here we focus on three-dimensional structures. We make a generic study of three dimensional quantum box cavities, and our main objective is to visualize the probability current. Visualizations are achieved for complex linear combinations of wave functions with different excitations and with boundary conditions: Dirichlet, Neumann, and mixed. By using different boundary conditions, the results reported here are relevant to many different wave analogues such as microwave billiards and acoustic cavities. Visualization was mainly done through animated images, but a chaotic state was visualized by 3D printing. Our results suggest that if the state of excitation is the same in the different boundary conditions, the current is the same, except at the boundaries of the box. Application to sort nanoparticles in acoustic cavities is considered.
Mapping and understanding the structure of wave fields and currents is relevant to a variety of structures such as quantum structures, optical and acoustic cavities, microwave billiards and water waves in a tank (Berggren & Ljung, 2009; Berggren, Yakimenko, & Hakanen, 2010; Blümel, Davidson, Reinhardt, Lin, & Sharnoff, 1992; Stöckmann , 1999; Chen, Liu, Su, Lu, Chen, & Huang, 2007; Ohlin & Berggren, 2016; Panda & Hazra, 2014). Here, cavities and billiards stand for systems with hard walls such that a particle or a wave only scatter from the walls and there is no scattering within the system. Acoustic cavities are metal enclosed cavities where a microphone is used to emit the waves. The underlying physics of all these systems is seemingly different. For example, in acoustic cavities, the waves are pressure differences in the air. In quantum cavities, the waves are the particles themselves. Nevertheless, the wave nature of these phenomena is often equivalent because it is governed by the Helmholtz equation (Stöckmann, 1999). Although there has been much progress within the fabrication of heterostructures, where quantum mechanical phenomena can be directly observed and measured, they are still difficult to manipulate. The different wave analogues are therefore important supplements for the experimental realization of quantum structures since they are easier to handle. Optical and laser cavities have recently been used for studying the concept of space-time reflection symmetry (Brandstetter et al., 2013; Liertzer et al., 2012) developed by Bender, Boettcher and Meisinger (Bender & Boettcher, 1998; Bender, Boettcher, & Meisinger, 1999) as an extension of Hermitian quantum mechanics. Acoustic and microwave cavities have been used to study exceptional points (Dembowski, et al., 2001; Ding, Ma, Xiao, Zhang, & Chan, 2016) which are points where the eigenvalues of two states are equal and their corresponding eigenvectors just differ by a phase (Rotter, 2009). Microwave cavities have also been important in the development of quantum chaos (Sadreev & Berggren, 2005; Stöckmann, 1999); the study of quantum systems that in the classical limit is chaotic i.e. a change in the initial conditions leads to exponential divergence of the trajectories in phase space.
Earlier studies, especially of quantum structures, have focused on two-dimensional fields because they are easier to study experimentally. However, a more realistic theoretical model would need to take all three dimensions into account (Ferry, Goodnick, & Bird, 2009). In this paper, we model three-dimensional quantum billiards with different boundary conditions and study the structure of the probability current for states of different excitations. Because different boundary conditions in the modeling were employed, the results are also applicable to the other wave analogues. The probability current is a three-dimensional vector field, which is difficult to visualize. Therefore, we also study the nodal surfaces and nodal lines. Nodal surfaces are surfaces where either the real or the imaginary part of the wave function is zero and the nodal lines are the intersection between the nodal surfaces (i.e., where both the real and imaginary parts are zero). The current will create vortices around these lines (Dirac, 1931; Wyatt, 2005). If the distribution of vortices is known, the overall structure of the current is known. The appearance and location of vortices have been directly connected to minima in the conductance i.e. the transmission through two-dimensional quantum structures (Lundberg, Sjöqvist, & Berggren, 1998) and the vortex distribution can be used to determine whether a system is chaotic (Saichev, Berggren, & Sadreev, 2001).
To map the probability current, a quasi-analytic method based on separation of variables and the finite difference method (FDM) is used. The ordinary time-dependent Schrödinger equation (Merzbacher, 1998) for the wave function is
Since the potential function V(r) does not depend time, we may write the wave function as
The solutions can be expressed as Fourier series. Depending on the boundary conditions, the series will contain sine, cosine or both. For example, a box with side lengths a, b, and c, all with Dirichlet boundary condition (i.e., on the boundaries), has the series
where the square root is due to normalization and are Fourier coefficients. If the boundary condition is changed to Neumann in one direction (i.e., instead of putting the wave function to zero at the boundaries in this direction, we set the derivative of the wave function to zero) the sine factor in this direction is changed to cosine.
Conservation of electrical charge entails that if the amount of charge in a system is changed there must exist a current responsible for this change. Similarly, probability current is obtained due to the conservation of probability; if the probability density of the system is changed there must exist a probability current mediating this change. For both probability and charge, the continuity equation reads,
where is the density function and j is the current vector. Note that both and j are time-independent because of [Eq. (2)]. From [Eq. (6)] and [Eq. (3)], one obtains the following expression for the probability current:
The Fourier series in [Eq. (5)] with sine and/or cosine terms can express the solution for different boundary conditions. From this expression, the nodal surfaces are easily found by standard routines (iso-surface) in MATLAB (MATLAB, 2012). Because of the simplicity of calculating the nodal surfaces, we will mostly visualize them throughout this work. Finding the nodal lines, which are more directly connected to the current, is a much more difficult problem. However, it is analytically solvable for some simple cases. One of the easiest cases arises when the Fourier series only contains two terms, one real and one imaginary, such that one of the terms contains two trigonometric factors in the ground state and only one excited factor and vice versa for the other term. That means, for the case with Dirichlet boundary condition, that two of the numbers (say n and l) is equal to one while m > 1, and n, l > 1 and m = 1 for the other, i.e.,
With Neumann boundary condition all the numbers are decreased by one. This case is solvable because the nodal surfaces will be orthogonal sheets with straight orthogonal intersections (Figures 1 and 2). The position of these lines can be found directly by reading the wave function. For more complicated cases the nodal lines can be found by an algorithm presented by Ljung and Ynnerman (2003). To obtain a clearer picture of the structure of the nodal surfaces, we 3D-printed the surfaces for a chaotic wave function.
Neumann Boundary Condition
We begin with a state under Neumann boundary condition
This represents one of the cases previously mentioned in which the nodal lines can be found directly because of the simple structure of the nodal surfaces (Figure 1A). There are six nodal lines to this state: three in the x-direction and three in the y-direction (Figure 2). The nodal lines in the x-direction are parameterized by (x, b(1+2n)/6, c/2) where n = 0,1,2. The nodal lines in the y-direction are parameterized similarly. Vortices are observed around these lines (Figure 2B-D).
Dirichlet and Mixed Boundary Conditions
Here we study states corresponding to the same state of excitation as the state in [Eq. (9)] but now with Dirichlet and mixed boundary condition. The corresponding state with Dirichlet boundary condition is
Note that because of the boundary condition, the wave function is zero on every side of the box. For the sake of visualization, these parts of the nodal surfaces have been removed (Figure 3A). The nodal lines for this state in the x-direction are parameterized by (x, bn/4, c/2) with n = 1,2,3 and similarly in the y-direction. The current propagates in vortices around these lines (Figure 3B-D).
We choose the mixed boundary condition such that it is Dirichlet in the x- and y-directions and Neumann in the z-direction. This state is given by
and the nodal surfaces and current are shown in (Figure 4A-D).
To represent the structure of higher order states, we visualize three states with higher excitations (Figure 5A-C). These states are given by [Eq. (12)], [Eq. (13)] and [Eq. (14)] respectively and each has one of the studied boundary conditions. The state given by
is with Neumann boundary condition and
is with Dirichlet boundary condition. The last state is with mixed conditions.
Using more terms in the linear combination the surfaces becomes more complex. For example:
For this state, the nodal surfaces are visualized both as animated figures and 3D-printed models (Figure 6A-D).
We simulate particle separation in an acoustic cavity for which the appropriate boundary condition is Neumann (Morse, 1948). To do the simulation, a very simple state with only one nodal line (Figure 7A) is employed
We now place two particles with mass ratio 1:10 at the top of the box near the nodal line. They are affected both by the gravitational force and a pressure force obtained by the current. The trajectories of the two particles are clearly different (Figure 7B). The heavy particle is much less affected by the current. Thus, in a scenario with many particles, the heavy particles would be found near the center of the box while the lighter particles would be located at the boundaries.
Discussion and Conclusion
The main objective of this work was to map the three-dimensional structure of probability currents and nodal surfaces in box cavities. By using different boundary conditions, the results obtained here are not only relevant for quantum cavities but also for microwave and acoustic cavities. A chaotic state was visualized by 3D printing. We suggest that these currents can be used for particle separation in acoustic cavities.
Three states with the same order of excitation [Eq. (9)], [Eq. (10)], and [Eq. (11)] were studied. We conclude that although different boundary conditions are used, the structure of the current is similar (Figures 2, 3, and 4). The obtained current for Neumann boundary condition (Figure 2) shows full agreement with the results in the recent experimental study by Ohlin and Berggren (2016). The findings could be used to simplify the modeling of complex structures in quantum transport calculations. When calculating the transmission through a quantum system one should in principle take an infinite number of states into account. This is numerically not possible so the number of states must be truncated. For transmission calculations using an effective non-Hermitian Hamiltonian it has been shown that only the case with Neumann boundary condition is stable with a finite number of states (Pichugin, Schanz, & Seba, 2001). If the physical importance of the boundary conditions can be relaxed, effective non-Hermitian Hamiltonians could be used with Dirichlet boundary condition for cases when Neumann seems like the natural boundary condition (Lee & Reichl, 2010).
We simulated particle separation in an acoustic cavity and observed a clear separation of particles with mass ratio 1:10. However, the current model is only conceptual and in need of improvements. Future research should take into account the shape and spin of the particles. One should also consider that the particles are present in the cavity and hence affect the structure of the current. These observations should be included in the Navier-Stokes equations and thus a fluid dynamic description of the system is obtained.
We would like to thank Magnus Sethson and David Beuger at the Department of Management and Engineering at Linköping University for providing access to their 3D-printers.
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A Wear Evaluation of Ultra High Molecular Weight Polyethylene (UHMWPE) against Nanostructured Diamond-Coated Ti-6Al-4V Alloy
Reducing the wear of joint replacements would increase the lifespans of both the replacement and the patient. In this study, the wear of ultra-high molecular weight polyethylene (UHMWPE) against nanostructured diamond (NSD)-coated titanium alloy (Ti-6Al-4V) and uncoated cobalt-chromium alloy (CoCr) hemi-cylinders was compared to determine if the NSD coating results in less volumetric wear of polyethylene (PE). A wear simulator was used with a gait cycle to mimic the knee joint, but with an axial force ranging from 30N to 700N during the cycle. Both tests ran for 1.5 million cycles while immersed in bovine serum. The roughness and volumetric wear of the NSD-coated alloy were greater than the non-coated control sample. No significant differences in the PE surface structure hardness were observed for either wear couple, as measured using Raman spectroscopy, X-ray diffraction, and nanoindentation. Although the roughness of the PE (worn by NSD-coated alloy) decreased faster than that of the control PE surface (worn by CoCr), the as-deposited surface roughness of the NSD coating was about three to four times higher than that of the starting CoCr surface. These results suggest that a much higher abrasive wear occurred for the NSD-PE couple due to the high NSD surface roughness, which also accounted for its inferior wear performance. Due to the higher initial surface roughness of the NSD-coated hemi-cylinder, the wear of the NSD-PE couple is greater than the non-coated couple, indicating that this combination would not increase the lifespan of a replacement joint.
The number of total knee replacements in the US has doubled in the last ten years (Salomon et al., 2010; Weinstein et al., 2013). In addition, the average age of patients receiving a total knee replacement (TKR) is decreasing (Weinstein et al., 2013). It is becoming increasingly important to determine how to reduce wear and degradation of implanted joints in order to improve their long-term performance (Smith, Dieppe, Porter, & Blom, 2012). Most TKRs contain an ultra-high molecular weight polyethylene (UHMWPE) tibial component that articulates against a metallic alloy femoral component (Ritter, 2009). These pieces are often attached to the bone with bone cement but this method of fixation has the potential to create inflammatory debris and wear-induced periprosthetic osteolysis (Ritter, 2009).
It is essential that TKRs be made to last longer than they do now, especially given the fact that the average human lifespan is projected to increase significantly over the next 100 years (Weinstein et al., 2013). In a 15-year survivorship study (Ranawat et al., 1993), only 70.6% of patients who weighed more than 80kg survived. If implant wear can be decreased, the life expectancy of people weighing over 80kg could potentially increase. Assuming humans continue getting TKRs around the age of 65 (Daniilidis & Tibesku, 2012), the knee replacement must last at least 20 years, ideally 30. To enhance the lifespan of the knee replacement, the current focus has been on improving the UHMWPE spacer, which simulates the articular cartilage that allows for smooth movement of the femur and the tibia. It has been shown that polyethylene (PE) wear particles (Amstutz, Campbell, Kossovsky, & Clarke, 1992; Ritter, 2009; Teeter, Parikh, Taylor, Sprague, & Naudie, 2007) generated from the PE spacer causes osteolysis and possible loosening of the tibial plate (Willert, Bertram, & Buchhorn, 1990). The purpose of this study is to reduce this wear.
Third-body wear (the introduction of hard particles in the space between two articulating members), has been shown to increase the roughness of the cobalt-chromium (CoCr) femoral component and PE wear (Davidson, 1993; Lawson, Catledge, & Vohra, 2005; Pierannunzii, Fischer, & D’Imporzano, 2008; Wang & Essner, 2001). A study done by Wang and Essner (2001) showed that loose poly-methyl-methacrylate (PMMA) bone cement particles in the lubricant, in excess of 5g/L, adhere to CoCr femoral heads and lead to accelerated wear of the acetabular cups. In contrast, the attachment of PMMA particles to ceramic heads was much reduced, resulting in an UHMWPE wear rate that was independent of the concentration of the PMMA particles.
To minimize third-body and other mechanisms of wear from shortening the lifespan of a knee replacement, a nanostructured diamond (NSD) or amorphous carbon coating on the femoral component can be used (Amaral et al., 2007). The tribological benefits may be attributed to: (1) superior lubricating properties (more wettable, hence better able to maintain lubricant on the surface), (2) high hardness and (3) relative inertness of the material. These characteristics can provide a decrease in the coefficient of friction at the bearing surface, with less susceptibility to third-body wear and scratching, as well as less biological response to any debris generated by ceramic wear particles (Lawson et al., 2005; Zietz, Bergschmidt, Lange, Mittelmeier, & Bader, 2013). Recently, it has been shown that the lubricity of amorphous carbon arises from shear induced strain localization, which dominates the shearing process (Pierannunzii et al., 2008). This lubricity is characterized by covalent bond reorientation, phase transformation and structural ordering in a localized tribolayer region. A transition in movement from stick-slip friction to continuous slipping, with ultra-low friction, is observed due to gradual clustering and layering of graphitic sheets in the tribolayer. This enhanced lubricity potentially offered by a variety of carbon-containing coatings may reduce wear in total joint prostheses.
In a previous pin-on-disk study (Hill et al., 2008), we showed that a NSD-coated Ti-6Al-4V disk surface resulted in a factor of two less wear of the PE pin when compared to a non-coated CoCr disk. However, this study was not representative of the gait cycle experienced by the knee joint. In the present study, we compare the effects of wear of an NSD-coated titanium alloy hemi-cylinder to a non-coated CoCr control using a multi-axis wear simulator to more closely mimic the knee joint. We hypothesize that the NSD-coated hemi-cylinder will cause less wear compared with the non-coated hemi-cylinder due to more favorable lubricity and reduced friction conditions.
For wear-testing, an AMTI Force 5 machine (Advanced Mechanical Technology, Inc. Watertown, MA) was used by applying a cyclic vertical load ranging from 30N to 700N on the flat PE samples shown in Figure 1. The tests followed ISO (International Organization for Standardization) standard 14243-3 (ISO, 2014). However, the axial force was scaled down during the gait cycle from a maximum of 2600N (as stated in the standard) to 700N, due to limitations of our load cell capacity. Flexion movement (rotation of flexion arm around a horizontal axis), anterior and posterior movement, and rotation of the stage were incorporated into the cyclic waveform representative of the knee gait cycle, according to the ISO standard. Approximately 1.5 million cycles at 1Hz were performed on each PE sample using the uncoated CoCr and the NSD-coated hemi-cylinder. For the uncoated upper piece, the CoCr hemi-cylinder was attached to a rotating flexion arm using a thin layer of bone cement. For the NSD-coated piece, a titanium hemi-cylinder of the same dimensions was used as the substrate for coating. This hemi-cylinder was attached to the flexion arm using two stainless steel bolts.
To determine the effect of the wear testing, four PE samples were measured: two controls (soaked for an equivalent time in bovine serum but not wear-tested) and two wear-test samples. The wear-test samples were imaged using atomic force microscopy (AFM) and weighed every 250 thousand cycles. All samples were kept completely submerged in a bovine serum mixture at 37°C for the duration of the 1.5 million cycles. The serum mixture (consisting of 0.2%w/v of Sodium Azide (0.8 g), 20mM of EDTA (2.98mL), 100mL of bovine serum, and 300mL of deionized water) was replaced every 750 thousand cycles. To counteract evaporation, 100mL of deionized water was added every day to the machine reservoir. The control samples were kept in a sealed container, without adjusting volume.
To obtain the weight of the wear-test samples, a cleaning procedure was followed based on ASTM (American Society for Testing and Materials) F732 “Standard Test Method for Wear Testing of Polymeric Materials Used in Total Joint Prostheses” (ASTM 2011). The samples were retrieved from the machine and rinsed with water. Extraneous particles were removed with lens paper. Next, samples were sonicated for 15 minutes in 100mL of tap water and 1mL of liquid cleaner. After rinsing in deionized water, sonication procedure was repeated for an additional five minutes in 100mL of deionized water. Following sonication in deionized water, the samples were soaked in methanol bath for three minutes. Next, the samples were placed in a desiccator for 30 minutes. Finally, samples were weighed and imaged to determine changes in mass and surface roughness.
Atomic Force Microscopy (AFM)
AFM imaging was done to determine how the surface roughness of the PE samples was changing over the 1.5 million cycles. Imaging was performed using close-contact mode at a scan rate of 0.45Hz with 256 points per line. Four AFM images were taken in various regions of the PE sample with scan areas of 10um2 and 30um2. Surface roughness values were obtained using Scanning Probe Image Processor (SPIP) 5.1.1 (Image Metrology A/S, Hørsholm, Denmark) and MS Excel. Optical microscope images of the wear samples were also taken at the start and end of the 1.5 million cycles. The wear-test samples and control samples were weighed at the same intervals to account for mass gain from serum absorption. The overall change in roughness of the PE samples was recorded. AFM was also done on the surface of the hemi-cylinders before and after wear.
In order to calculate volumetric wear, the mass of the wear-test sample was corrected by subtracting the mass absorption of the control sample from that of the wear-test sample measured at the same interval. The initial mass measurement (M0) was subtracted from each consequent measurement (Mm) to acquire the mass difference (Mc). The change observed, true mass (Mt), of the control sample was then subtracted from the change in the mass of wear-test sample. The wear volume (mm3) was calculated from Mt using the density of UHMWPE (0.9363kg/cm3).
Nanoindentation was performed on the PE samples using a Berkovich diamond tip (nominal radius 50nm) to a depth of one µm. Indentation was done before and after wear testing to detect any changes in surface hardness or elastic modulus, potentially as a result of structural transformations.
X-ray diffraction (λ = 1.54154Å) was used on the PE sample surfaces to further examine potential effects of wear, such as phase transformations or texturing, from articulation against the NSD-coated and non-coated alloy.
Laser Raman spectroscopy (λ = 514.5nm) was performed on the control and wear-test samples to evaluate possible structural transformation (such as disordering of carbon bonds), as measured in the wavenumber range from 1000 to 1800cm-1.
In this study, we evaluated two samples: a control UHMWPE sample worn against CoCr (Sample 1) and a test UHMWPE sample worn against NSD-coated Ti-6Al-4V alloy (Sample 2). Nanoindentation performed on both samples before and after wear did not show a significant change in either hardness or Young’s modulus. Compared to the UHMWPE/CoCr couple, the NSD/UHMWPE couple produced higher volumetric wear. The volumetric wear of Sample one was 1.20mm3 after 250k cycles and reached a value of 3.72mm3 at the end of the 1.5 million cycles (Fig. 2). By comparison, the wear of Sample 2 after 250k cycles was 6.29mm3, which was already almost twice as much as the maximum wear volume of Sample 1. Note that the volumetric wear trends of these two samples are different. Sample 1’s volumes tapered off in the final measurement intervals whereas Sample 2 showed a consistent amount of wear occurring at each interval. Using a parabolic curve fit, the best fit gives an R2 value of 0.8476 for Sample 1. The linear fit of Sample 2 has an R2 value of 0.9945. Overall, the volumetric wear of Sample 2 is higher and more consistent than the wear of Sample 1.
The initial average roughness value of Sample 1 (PE worn by CoCr) was lower than the initial average roughness of Sample 2 (PE worn by the NSD-coated alloy) (Fig. 3). Sample 1’s initial roughness was 177.21 ± 42.23nm while Sample 2 had the initial roughness of 260.64 ± 69.74nm for an AFM scan area of 30µm2 (Fig. 3). The final roughness of Samples 1 and 2 were 27.32 ± 5.67nm and 11.13 ± 3.42nm, respectively. The overall roughness of Sample 1 decreased by 85% while the overall roughness of Sample 2 decreased by 96%. For an AFM scan area of 10µm2, Sample 1 had a decrease of 80% in overall roughness, which is 8% lower than that of Sample 2. The roughness of both PE samples dropped by more than 90% within the first 500 thousand cycles (as measured from an AFM scan area of 30µm2). The outlier at approximately 1 million cycles for the roughness of Sample 1 had an average value of 128.16nm with a relatively large standard deviation obtained from four separate measurements, the least of which was 51nm.
No measurably significant change in surface roughness was detected from either the uncoated CoCr or NSD-coated hemi-cylinders before and after wear (Fig. 4). However, it should be noted that all roughness values for the CoCr hemi-cylinder are below 8nm, while those of the NSD-coated alloy are about a factor of three larger. Finally, Raman spectroscopy revealed that no significant change in PE carbon bonding occurred after wear (Fig.5).
With the number of people receiving total knee replacements growing, as well as an overall increase in lifespan, the need for a longer-lasting total knee replacement is becoming more urgent. Out results indicated that the wear effect of an NSD-coated titanium hemi-cylinder on a PE sample was greater than the wear from a non-coated CoCr on a PE sample. The UHMWPE/NSD resulted in a smoother surface on the PE sample.
The volumetric wear and AFM roughness data both showed that more wear occurred for the UHMWPE/NSD couple compared to the control UHMWPE/CoCr couple. Given that the starting surface roughness of the NSD coating was three to four times higher than that of the CoCr surface, it suggested that excessive abrasive wear occurred for the UHMWPE/NSD wear couple. Our result showed the opposite result from a previous pin-on-disk study (Hill et al., 2008), which found that that less wear occurred for the UHMWPE/NSD couple compared to an UHMWPE/CoCr couple. In that study, the NSD coating was smoother by a factor of five and the loads/displacements were much less clinically relevant. In order to test the potential benefits of an NSD-coated counterface against UHMWPE for a total joint replacement, future efforts will need to be directed toward decreasing the NSD surface roughness to a value at least as small as the starting bare alloy. Otherwise, abrasive wear may dominate the wear mechanisms.
Figure 2 shows that, if extrapolated, the curved trend seen in the volumetric wear of Sample 1 can be expected to continue increasing at a slower rate. However, when observing the volumetric wear of Sample 2, no such trend is seen. Instead, a nearly linear trend is observed along with a high R2 value of 0.9991. If this trend were continued, it would most likely increase linearly, as it did for the past 1.5 million cycles.
Based on the results of this study, the NSD-coated alloy, with its relatively high surface roughness compared to the CoCr alloy, would cause unacceptable wear of the PE inserted in an artificial joint, leading to a high probability of early implant failure. As shown in Figure 2, the volumetric wear of the control PE (Sample 1) appears to drop off near the end of the 1.5 million cycles. This trend is not seen with the test PE (Sample 2). Instead, the wear rate is consistent for the entire 1.5 million cycles. The average roughness for PE Sample 2 (Figure 3) shows that the roughness rapidly decreases within the first 500 thousand cycles and remains nearly unchanged after that. Therefore, we cannot assume that the mass loss is proportional to a decrease in surface roughness. Instead, it is more likely that abrasive wear caused by the NSD surface initially removed asperities from the UHMWPE (whose initial roughness was several hundred nanometers) and then continued to wear this surface at a constant rate. Since the NSD surface is a factor of three to four rougher than the CoCr, one would expect abrasive wear to be higher for this wear couple. For the UHMWPE/CoCr couple, the apparent plateau in wear may be explained by improved lubricity at the metal/polymer interface as the polymer surface roughness drops. Though the roughness changed greatly throughout the wear testing, both Raman spectroscopy and x-ray diffraction (XRD) showed no significant changes between the control and test PE samples.
An outlier in measured surface roughness from Sample 1 was observed at the 4th measurement interval (near 1 million cycles). At this point in the wear-test, the roughness is not uniform throughout the contact surface areas. One of these less-smooth areas could have been chosen for collecting these data rather than the typical smoother locations used in the other experiments. The four average roughness values obtained for this measurement had a very large range. It is not clear why these measurements resulted in such large spread. Since the surfaces of the alloys were not of the same roughness at the beginning, this could have also affected how much polyethylene was worn during the 1.5 million cycles. To maintain consistency, both alloy surfaces should be either polished or chosen to have the same roughness. Given the limitations of this study, the overall wear of polyethylene from the non-coated alloy was nearly a factor of five less than that of the NSD-coated alloy.
The NSD-coated hemi-cylinder resulted in nearly five times more PE wear than that from the non-coated CoCr hemi-cylinder while the average PE roughness decreased much faster for Sample 2 (involving the NSD counterface) than for Sample 1 (involving the non-coated CoCr counterface). This suggests a more aggressive abrasive wear-in period in the beginning due to the higher surface roughness of NSD. To determine how significant the wear rate is, more PE samples should be tested. This will help ensure reproducibility as well as reduce random errors.
The surface of the NSD-coated hemi-cylinder itself showed no appreciable changes in average roughness before and after wear. For both control and test PE samples, no change in mechanical properties (hardness or elastic modulus as measured by nanoindentation) or in carbon bond structure (as measured by Raman spectroscopy) were detected. Both XRD and nanoindentation should be done on the sample worn by the NSD-coated hemi-cylinder in the future. These techniques could help detect changes in the PE crystallinity as well as possible alignment of the PE fibers. Although this data suggests that an UHMWPE/NSD couple is inferior, the influence of starting surface roughness must be considered as a limiting factor. All counterface surfaces should have similar and low initial roughness values to ensure reproducibility. This could be achieved through polishing. We hope to achieve a similar surface roughness for both counterfaces by improving the NSD coating process. The NSD-coating needs to be have reduced roughness and improved consistency over relatively large surface areas. In the future, reduction of NSD coating surface roughness should be a primary goal for a better comparison to the control wear couple.
Although this study evaluated an UHMWPE/NSD couple, an alternative couple that could lead to more promising results would involve NSD/NSD. In this way, the NSD-coating may be expected to minimize abrasion while acting as a barrier to prevent leaching of potentially toxic heavy metal ions from the substrate. The applications for this coating are more suitable for artificial hip joints (which also employ hard-on-hard bearings). Perhaps, for a ceramic-on-ceramic hip replacement, an NSD coating could even further increase the longevity of implants (Bhatt & Goswami, 2008; Hill et al., 2008; Ranawat et al., 1993).
The authors greatly appreciate the support provided by the UAB Research Experiences for Undergraduates (REU) program funded by the National Science Foundation Grant DMR 1460392.
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