Neurogenesis Unchanged by MTHFR Deficiency in Three-Week-Old Mice

doi: 10.22186/jyi.31.6.39-43

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

Abstract

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.

Introduction

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.

Figure 1. Partial diagram of the methionine/homocysteine and folate cycle outlining key contributing molecules to this research.

Figure 1. Partial diagram of the methionine/homocysteine and folate cycle outlining key contributing molecules to this research.

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

Animal Experimentation

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.

 Immunofluorescence

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.

Immunofluorescence Analysis

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.

Statistical Analysis

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.

Results

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.

Dentate Gyrus

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).

Figure 2. Combined images of the co-localization of PH3 and DAPI immunofluorescence staining in the dentate gyrus of a (A) wild-type MTHFR mouse (+/+) and (B) homozygous knockout MTHFR mouse (-/-). 200X magnification. Scale bar 50µm. Mean co-localization count in the dentate gyrus of MTHFR knockout mice and controls (C). No statistically significant differences were viewed (F(2,8) = 0.03, p = 0.974). Standard deviations are represented by the standard deviation bars attached to each column.

Figure 2. Combined images of the co-localization of PH3 and DAPI immunofluorescence staining in the dentate gyrus of a (A) wild-type MTHFR mouse (+/+) and (B) homozygous knockout MTHFR mouse (-/-). 200X magnification. Scale bar 50µm. Mean co-localization count in the dentate gyrus of MTHFR knockout mice and controls (C). No statistically significant differences were viewed (F(2,8) = 0.03, p = 0.974). Standard deviations are represented by the standard deviation bars attached to each column.

Cerebellum

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).

Figure 3. Combined images of the co-localization of PH3 and DAPI immunofluorescence staining in the cerebellum of (A) a wild-type MTHFR mouse (+/+) and (B) a homozygous knockout MTHFR mouse (-/-). 200X magnification. Scale bar 50µm. Mean co-localization count in the cerebellum of MTHFR knockout mice and controls (C). No statistically significant differences were viewed (F(2,13) = 1.29, p = 0.309). Standard deviations are represented in the figure by the standard deviation bars attached to each column.

Figure 3. Combined images of the co-localization of PH3 and DAPI immunofluorescence staining in the cerebellum of (A) a wild-type MTHFR mouse (+/+) and (B) a homozygous knockout MTHFR mouse (-/-). 200X magnification. Scale bar 50µm. Mean co-localization count in the cerebellum of MTHFR knockout mice and controls (C). No statistically significant differences were viewed (F(2,13) = 1.29, p = 0.309). Standard deviations are represented in the figure by the standard deviation bars attached to each column.

Cortex

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).

Figure 4. Combined images of the co-localization of PH3 and DAPI immunofluorescence staining in the cortex of (A) a wild-type MTHFR mouse (+/+) and (B) a homozygous knockout MTHFR mouse (-/-). 200X magnification. Scale bar 50µm. Mean co-localization count in the cortex of MTHFR knockout mice and controls (C). No statistically significant differences were viewed (F(2,10) = 0.26, p = .773). Standard deviations are represented in the figure by the standard deviation bars attached to each column.

Figure 4. Combined images of the co-localization of PH3 and DAPI immunofluorescence staining in the cortex of (A) a wild-type MTHFR mouse (+/+) and (B) a homozygous knockout MTHFR mouse (-/-). 200X magnification. Scale bar 50µm. Mean co-localization count in the cortex of MTHFR knockout mice and controls (C). No statistically significant differences were viewed (F(2,10) = 0.26, p = .773). Standard deviations are represented in the figure by the standard deviation bars attached to each column.

Discussion

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

  doi: 10.22186/jyi.31.6.44-50

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

Abstract

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. 

Introduction

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).

 

Figure 1. Aβ can aggregate into diverse structural polymorphs. (A) Microscopically, Aβ peptides arrange into β-structures by hydrogen bonds, and the aggregates form surfaces for more Aβ peptides to attach. (B) Structural polymorphs of Aβ40 solved by solid-state NMR. (i) 2-fold striated-ribbon Aβ9-40 prepared in vitro. (ii) 3-fold twisted Aβ9-40 prepared in vitro. (iii) AD patient-derived Aβ1-40 fibrils taken from brain tissue. We display different possible arrangements of the cross-β structures and flexible tails (i-iii), and the common 2- to 3-fold symmetry (i-ii). The accession codes for the relevant Protein Data Bank (PDB) structures used were noted.
Figure 1. Aβ can aggregate into diverse structural polymorphs. (A) Microscopically, Aβ peptides arrange into β-structures by hydrogen bonds, and the aggregates form surfaces for more Aβ peptides to attach. (B) Structural polymorphs of Aβ40 solved by solid-state NMR. (i) 2-fold striated-ribbon Aβ9-40 prepared in vitro. (ii) 3-fold twisted Aβ9-40 prepared in vitro. (iii) AD patient-derived Aβ1-40 fibrils taken from brain tissue. We display different possible arrangements of the cross-β structures and flexible tails (i-iii), and the common 2- to 3-fold symmetry (i-ii). The accession codes for the relevant Protein Data Bank (PDB) structures used were noted.

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).

Figure 2. Conventional and steered molecular dynamics (MD) simulations address different questions. (A) Conventional MD simulation involves calculation of energy, both bonded and non-bonded, under parameterization defined by force-fields as exemplified. Coupling with, for example, site-directed mutagenesis, it is geared to study biochemical properties of the aggregates along the time-scale at atomistic resolution. (B) Alternatively, steered MD simulations, coupled with umbrella sampling could be used to address the aggregative process, e.g., by considering free energy changes (ΔG) throughout the process. PDB entry 2BEG was illustrated here.

Figure 2. Conventional and steered molecular dynamics (MD) simulations address different questions. (A) Conventional MD simulation involves calculation of energy, both bonded and non-bonded, under parameterization defined by force-fields as exemplified. Coupling with, for example, site-directed mutagenesis, it is geared to study biochemical properties of the aggregates along the time-scale at atomistic resolution. (B) Alternatively, steered MD simulations, coupled with umbrella sampling could be used to address the aggregative process, e.g., by considering free energy changes (ΔG) throughout the process. PDB entry 2BEG was illustrated here.

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 3. Different spectroscopy and spectrometry techniques vary in the principles employed to capture intrinsic changes within molecules. (A) Both Infrared and Raman spectroscopy involve excitation of the molecule to a higher energy state. The former detects the absorption of infrared radiation when the molecule is excited to a higher vibrational energy state. The latter detects the inelastic scattering of electromagnetic radiation when the molecule returns from a higher virtual energy state while retaining some photon energy. This inelastic scattering is a very rare occurrence detected only through filtering out the elastic scatterings simultaneously. (B) For HDX-MS, when the peptides (blue and red) are still monomers, HDX (deuterium as green dots) occurs rapidly along the peptide backbone. As incubation progresses, peptides start to aggregate, and the core is protected from rapid HDX. MS analysis at these two time-points numerates differences. m/z, mass-charge ratio. (C) In AFM-SMSF, the cantilever beam tip serves as a probe to gauge the topography and to apply force to measure mechanical stability/elasticity of peptide aggregates.

Figure 3. Different spectroscopy and spectrometry techniques vary in the principles employed to capture intrinsic changes within molecules. (A) Both Infrared and Raman spectroscopy involve excitation of the molecule to a higher energy state. The former detects the absorption of infrared radiation when the molecule is excited to a higher vibrational energy state. The latter detects the inelastic scattering of electromagnetic radiation when the molecule returns from a higher virtual energy state while retaining some photon energy. This inelastic scattering is a very rare occurrence detected only through filtering out the elastic scatterings simultaneously. (B) For HDX-MS, when the peptides (blue and red) are still monomers, HDX (deuterium as green dots) occurs rapidly along the peptide backbone. As incubation progresses, peptides start to aggregate, and the core is protected from rapid HDX. MS analysis at these two time-points numerates differences. m/z, mass-charge ratio. (C) In AFM-SMSF, the cantilever beam tip serves as a probe to gauge the topography and to apply force to measure mechanical stability/elasticity of peptide aggregates.

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).

Figure 4. Computational and experimental approaches propose an intergrative model of amyloid aggregation dynamics. The dynamic process of amyloid aggregation is summarized from research utilizing techniques of ssNMR, MD simulation, EM, Fourier transform infrared spectroscopy, kinetic assays, radio-labelling and cell viability experiments. (i-iii) Aβ monomers grow by opportunistic reversible adhesion to each other into different sizes of oligomers. (iv) Weak oligomers might undergo structural rearrangement into more stabilized oligomeric aggregates or (v) commit to extendible fibril nucleation. (vi) Short monolayer protofibrils elongate and (vii) fragment upon instability. (viii) Repeated growth and fragmentation generate short regular fragments which enable fibril thickening into varying layers and morphology, e.g., two-layer striated-ribbons or three-layer twisted morphology as illustrated, depending on physiological conditions. (ix) These structures continue to elongate with minor unit detachment, and branch out by secondary nucleation. (x) Sustained growth gives massive fibril meshes.

Figure 4. Computational and experimental approaches propose an intergrative model of amyloid aggregation dynamics. The dynamic process of amyloid aggregation is summarized from research utilizing techniques of ssNMR, MD simulation, EM, Fourier transform infrared spectroscopy, kinetic assays, radio-labelling and cell viability experiments. (i-iii) Aβ monomers grow by opportunistic reversible adhesion to each other into different sizes of oligomers. (iv) Weak oligomers might undergo structural rearrangement into more stabilized oligomeric aggregates or (v) commit to extendible fibril nucleation. (vi) Short monolayer protofibrils elongate and (vii) fragment upon instability. (viii) Repeated growth and fragmentation generate short regular fragments which enable fibril thickening into varying layers and morphology, e.g., two-layer striated-ribbons or three-layer twisted morphology as illustrated, depending on physiological conditions. (ix) These structures continue to elongate with minor unit detachment, and branch out by secondary nucleation. (x) Sustained growth gives massive fibril meshes.

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

doi: 10.22186/jyi.31.5.32-38 

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

References |PDF

Abstract

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. 

Introduction

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.

Figure 1: Passive vs. Active Ankle-Foot Orthosis. This figure shows a comparison of a passive (A) and active (B) AFO. Passive AFOs simply immobilize the ankle and foot while active AFOs assist the ankle and foot in replicating human gait (Blackwell, Lucas, & Clarke, 2014).

Figure 1: Passive vs. Active Ankle-Foot Orthosis. This figure shows a comparison of a passive (A) and active (B) AFO. Passive AFOs simply immobilize the ankle and foot while active AFOs assist the ankle and foot in replicating human gait (Blackwell, Lucas, & Clarke, 2014).

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.

Figure 2: Walking-Engine-Actuated Active Ankle-Foot Orthosis. A conceptual schematic of a Walking Engine AAFO is presented. The AAFO is powered by an internal-combustion engine using two opposing (antagonistic), asymmetric, piston actuators to provide the required power for locomotion.

Figure 2: Walking-Engine-Actuated Active Ankle-Foot Orthosis. A conceptual schematic of a Walking Engine AAFO is presented. The AAFO is powered by an internal-combustion engine using two opposing (antagonistic), asymmetric, piston actuators to provide the required power for locomotion.

 

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 (Bosch, Stuttgart, Germany). 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.

Figure 3: Walking Engine. This figure shows a CAD model of the modified Bosch pneumatic actuator used to power the AAFO. By adding a fuel line (needle valve) and ignition source (spark plug), an off-the-self pneumatic actuator was turned into a small IC engine.

Figure 3: Walking Engine. This figure shows a CAD model of the modified Bosch pneumatic actuator used to power the AAFO. By adding a fuel line (needle valve) and ignition source (spark plug), an off-the-self pneumatic actuator was turned into a small IC engine.

 

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.

Figure 4: Dual-Combustion Engine Cycle. This figure shows the theoretical thermodynamic engine cycle (dual-combustion cycle) used to model the AAFO’s internal-combustion engine. A dual-combustion engine cycle is a combination of both the Otto and the Diesel engine cycle (Heywood, 1988).

Figure 4: Dual-Combustion Engine Cycle. This figure shows the theoretical thermodynamic engine cycle (dual-combustion cycle) used to model the AAFO’s internal-combustion engine. A dual-combustion engine cycle is a combination of both the Otto and the Diesel engine cycle (Heywood, 1988).

Gas-Power-Cycle Calculation

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:

                                                                       

         Eq 1                                                                                   (Eq. 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:

                                                     

    Eq 2                                (Eq. 2)

 

By substituting in these relations, Equation 1 can be rewritten in the following, more convenient form:

          Eq 3                                                                (Eq. 3)  

                                                                                                                                    

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.

Figure 5: AAFO Pulley System. The AAFO pulley system is represented. The primary actuator is responsible for the plantarflexion phase of the gait cycle, while the secondary actuator (pneumatic exhaust recovery system) is responsible for the dorsiflexion phase of the gait cycle. This dual pulley design allows for the pulley radii of both phases of the gait cycle to be fully optimized.

Figure 5: AAFO Pulley System. The AAFO pulley system is represented. The primary actuator is responsible for the plantarflexion phase of the gait cycle, while the secondary actuator (pneumatic exhaust recovery system) is responsible for the dorsiflexion phase of the gait cycle. This dual pulley design allows for the pulley radii of both phases of the gait cycle to be fully optimized.

 

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.

Results

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.

Figure 6: Walking Engine Gas Power Cycle. This figure shows the calculated dual-combustion pressure-volume diagram used to model the AAFO’s IC engine. The work output of the engine was calculated to be ~30 joules of energy. This P-V diagram will be validated with experimental combustion testing data and altered to match actual engine performance if necessary.

Figure 6: Walking Engine Gas Power Cycle. This figure shows the calculated dual-combustion pressure-volume diagram used to model the AAFO’s IC engine. The work output of the engine was calculated to be ~30 joules of energy. This P-V diagram will be validated with experimental combustion testing data and altered to match actual engine performance if necessary.

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.

Table 1. Walking Engine Gas Power Cycle. This table shows the volume, pressure, temperature, and number of moles at key points during the thermodynamic engine cycle of the “Walking Engine.”

Table 1. Walking Engine Gas Power Cycle. This table shows the volume, pressure, temperature, and number of moles at key points during the thermodynamic engine cycle of the “Walking Engine.”

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 (Winter, 2009).  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.

Figure 7: Ankle Moment. This figure shows the exact moment experience at the ankle during normal gait, obtained from David Winter’s Biomechanics and Motor Control of Human Movement, and the optimal moment experienced at the ankle during normal gait (Winter, 2009). The optimal moment is obtained by eliminating the sudden jumps and removing the negative moment values in the exact moment data.

Figure 7: Ankle Moment. This figure shows the exact moment experience at the ankle during normal gait, obtained from David Winter’s Biomechanics and Motor Control of Human Movement, and the optimal moment experienced at the ankle during normal gait (Winter, 2009). The optimal moment is obtained by eliminating the sudden jumps and removing the negative moment values in the exact moment data.

 

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.

 

Discussion

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.

Figure 8: Optimal Pulley Geometry. This figure shows the optimal pulley geometry as a function of time for the primary actuator of the AAFO.

Figure 8: Optimal Pulley Geometry. This figure shows the optimal pulley geometry as a function of time for the primary actuator of the AAFO.

 

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. 

Acknowledgements

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.

References

Blackwell, D. L., Lucas, J. W., & Clarke, T. C. (2014). Summary health statistics for US adults: national health interview survey, 2012. Vital and health statistics. Series 10, Data from the National Health Survey, (260), 1-161.

Dollar, A. M., & Herr, H. (2007). Active orthoses for the lower-limbs: challenges and state of the art. In 2007 IEEE 10th International Conference on Rehabilitation Robotics (pp. 968-977). IEEE. doi:10.1109/ICORR.2007.4428541

Heywood, J. (1988). Internal combustion engine fundamentals. New York: McGraw-Hill.

Lenhart, R. L., & Sumarriva, N. (2008). Design of Improved Ankle-Foot Orthosis. University of Tennessee Honors Thesis Projects.

Linstrom, P. J., & Mallard, W. G. (2001). The NIST Chemistry WebBook: A chemical data resource on the internet. Journal of Chemical & Engineering Data, 46(5), 1059-1063. doi:10.1021/je000236i 

Lusardi, M. M., Jorge, M., & Nielsen, C. C. (2013). Orthotics and prosthetics in rehabilitation. Elsevier Health Sciences. doi:10.1016/b978-1-4377-1936-9.09990-2 

Mitchell, R. R., Gallant, B. M., Thompson, C. V., & Shao-Horn, Y. (2011). All-carbon-nanofiber electrodes for high-energy rechargeable Li–O 2 batteries. Energy & Environmental Science4(8), 2952-2958. doi:10.1039/c1ee01496j

Winter, D. A. (2009). Biomechanics and motor control of human movement. John Wiley & Sons. doi:10.1002/9780470549148

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Opportunity for Pharmaceutical Intervention in Lung Cancer: Selective Inhibition of JAK1/2 to Eliminate EMT-Derived Mesenchymal Cells

doi:10.22186/jyi.31.5.17-24

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

Abstract 

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. 

Introduction

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). 

EMT-Mediated Metastasis 

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).

Figure 1. EMT as a facilitator of the metastatic process. The progression from normal epithelium to invasive carcinoma occurs through several phases. As tumorigenesis transpires, epithelial cells rapidly begin to proliferate, forming a primary carcinoma in situ, which consists of epithelial cancer cells. As EMT occurs, these epithelial cancer cells shed their cell polarity and detach from the basement membrane, while transitioning into an invasive/migratory mesenchymal phenotype. EMT is complete upon the degradation of the basement membrane. Once EMT is complete, the mesenchymal cells migrate away from the primary tumor body and into the blood stream, allowing them to disseminate throughout the body and invade distant organ sites. Upon reaching a secondary site, the mesenchymal cells undergo MET, the reverse process of EMT, and revert to their original epithelial phenotype. Consequently, metastasis ensues and a second epithelial tumor is established (created by student researcher; adapted from Kalluri & Weinberg, 2009).

Figure 1. EMT as a facilitator of the metastatic process. The progression from normal epithelium to invasive carcinoma occurs through several phases. As tumorigenesis transpires, epithelial cells rapidly begin to proliferate, forming a primary carcinoma in situ, which consists of epithelial cancer cells. As EMT occurs, these epithelial cancer cells shed their cell polarity and detach from the basement membrane, while transitioning into an invasive/migratory mesenchymal phenotype. EMT is complete upon the degradation of the basement membrane. Once EMT is complete, the mesenchymal cells migrate away from the primary tumor body and into the blood stream, allowing them to disseminate throughout the body and invade distant organ sites. Upon reaching a secondary site, the mesenchymal cells undergo MET, the reverse process of EMT, and revert to their original epithelial phenotype. Consequently, metastasis ensues and a second epithelial tumor is established (created by student researcher; adapted from Kalluri & Weinberg, 2009).

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).

 

Figure 2. Potential therapeutic strategies for targeting EMT-induced metastasis. These strategies consist of: (A) Inhibiting EMT-inducing signals from the tumor microenvironment, such as growth factors. (B) Blocking signal transduction pathways, such as MAPK, that relay EMT-initiating signals to the nucleus. (C) Targeting cells while in the mesenchymal state to prevent cell dissemination. (D) Blocking MET to prevent colonization at a distant tumor site (created by student researcher; adapted from Davis, Stewart, Thompson, & Monteith, 2014).

Figure 2. Potential therapeutic strategies for targeting EMT-induced metastasis. These strategies consist of: (A) Inhibiting EMT-inducing signals from the tumor microenvironment, such as growth factors. (B) Blocking signal transduction pathways, such as MAPK, that relay EMT-initiating signals to the nucleus. (C) Targeting cells while in the mesenchymal state to prevent cell dissemination. (D) Blocking MET to prevent colonization at a distant tumor site (created by student researcher; adapted from Davis, Stewart, Thompson, & Monteith, 2014).

 

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

Bioinformatics 

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. 

Cell Culture  

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. 

MTT  

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.

Statistics 

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.

Results 

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).

 

Figure 3. Survival pathways in the mesenchymal state. Pathway identification was completed using a bioinformatics analysis of RNA sequence data. Complete pathway mapping was completed using information from the Kegg Pathway databases. Up-regulated proteins in the mesenchymal state are color-coded red. Down-regulated proteins in the mesenchymal state are color-coded green. Proteins which maintained stable expression are color-coded yellow. Cellular effects of the illustrated pathways are color-coded purple. Included in the map are the JAK-STAT pathway, on the top, the PI3K-AKT pathway, in the middle, and the MAPK pathway, on the bottom.

Figure 3. Survival pathways in the mesenchymal state. Pathway identification was completed using a bioinformatics analysis of RNA sequence data. Complete pathway mapping was completed using information from the Kegg Pathway databases. Up-regulated proteins in the mesenchymal state are color-coded red. Down-regulated proteins in the mesenchymal state are color-coded green. Proteins which maintained stable expression are color-coded yellow. Cellular effects of the illustrated pathways are color-coded purple. Included in the map are the JAK-STAT pathway, on the top, the PI3K-AKT pathway, in the middle, and the MAPK pathway, on the bottom.

 

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. 

 

Figure 4. Nikon TS100 light microscopy of in vitro cell models. (A) TGFß- A549 cells (B) TGFß- HCC827 cells (C) TGFß- HCC4006 cells (D) TGFß+ A549 cells (E) TGFß+ HCC827 cells (F) TGFß+ HCC4006 cells. Based on a qualitative analysis of cell characteristics, it was determined that TGFß- cells in groups A, B, and C did not undergo EMT and remained in the epithelial state. Meanwhile, it was determined that TGFß+ cells in groups D, E, and F underwent EMT and are mesenchymal in nature.

Figure 4. Nikon TS100 light microscopy of in vitro cell models. (A) TGFß- A549 cells (B) TGFß- HCC827 cells (C) TGFß- HCC4006 cells (D) TGFß+ A549 cells (E) TGFß+ HCC827 cells (F) TGFß+ HCC4006 cells. Based on a qualitative analysis of cell characteristics, it was determined that TGFß- cells in groups A, B, and C did not undergo EMT and remained in the epithelial state. Meanwhile, it was determined that TGFß+ cells in groups D, E, and F underwent EMT and are mesenchymal in nature.

 

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.

Figure 5. Zeiss Immunofluorescent staining of TGFß- cells. (A) TGFß- A549 cells at 630x (B) TGFß- A549 cells at 200x (C) TGFß+ A549 cells at 200x. All cells were stained with E-cadherin antibody (red) and DAPI (blue). The expression of E-cadherin in images A and B demonstrate TGFß- cells are in the epithelial state. The lack of Vimentin expression in image C demonstrate TGFß+ cells are in the mesenchymal state.

Figure 5. Zeiss Immunofluorescent staining of TGFß- cells. (A) TGFß- A549 cells at 630x (B) TGFß- A549 cells at 200x (C) TGFß+ A549 cells at 200x. All cells were stained with E-cadherin antibody (red) and DAPI (blue). The expression of E-cadherin in images A and B demonstrate TGFß- cells are in the epithelial state. The lack of Vimentin expression in image C demonstrate TGFß+ cells are in the mesenchymal state.

 

Figure 6. Zeiss Immunofluorescent staining of TGFß+ cells. (A) TGFß+ A549 cells at 630x (B) TGFß+ A549 cells at 200x (C) TGFß- A549 cells at 200x. All cells were stained with Vimentin antibody (red) and DAPI (blue). The expression of Vimentin in images A and B demonstrate that the TGFß+ cells are in the mesenchymal state. The lack of E-cadherin expression in image C demonstrate that the TGFß- cells are in the epithelial state.

Figure 6. Zeiss Immunofluorescent staining of TGFß+ cells. (A) TGFß+ A549 cells at 630x (B) TGFß+ A549 cells at 200x (C) TGFß- A549 cells at 200x. All cells were stained with Vimentin antibody (red) and DAPI (blue). The expression of Vimentin in images A and B demonstrate that the TGFß+ cells are in the mesenchymal state. The lack of E-cadherin expression in image C demonstrate that the TGFß- cells are in the epithelial state.

 

Figure 7. Western Blotting of TGFß+ and TGFß- cells. Anti E-cadherin was used to determine the expression levels of E-cadherin. The TGFß- cells express a greater level of E-cadherin than the TGFß+ cells, demonstrating that the TGFß- cells are in the epithelial state, while the TGFß+ cells are in the mesenchymal state.

Figure 7. Western Blotting of TGFß+ and TGFß- cells. Anti E-cadherin was used to determine the expression levels of E-cadherin. The TGFß- cells express a greater level of E-cadherin than the TGFß+ cells, demonstrating that the TGFß- cells are in the epithelial state, while the TGFß+ cells are 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).

Figure 8. A549 Assay. (A) Inhibition activity of BAY 87-2243 (HIF1A) (B) Inhibition Activity of MK-2206 (AKT 1/2/3) (C) Inhibition activity of AZD1480 (Jak 1/2) (D) Inhibition activity of GDC-0994 (Erk 1/2). Each experiment was performed in triplicate (5x103 cells per well) over a 48-hour inhibition period. After 48 hours, cell death was quantified using MTT; absorbance was read at 590nm. Because the greatest percent decrease occurred between the DMSO mesenchymal population and the 5µM mesenchymal population, AZD1480 (Jak 1/2) was most effective in this trial. *: p<0.05, **: p<0.01

Figure 8. A549 Assay. (A) Inhibition activity of BAY 87-2243 (HIF1A) (B) Inhibition Activity of MK-2206 (AKT 1/2/3) (C) Inhibition activity of AZD1480 (Jak 1/2) (D) Inhibition activity of GDC-0994 (Erk 1/2). Each experiment was performed in triplicate (5×103 cells per well) over a 48-hour inhibition period. After 48 hours, cell death was quantified using MTT; absorbance was read at 590nm. Because the greatest percent decrease occurred between the DMSO mesenchymal population and the 5µM mesenchymal population, AZD1480 (Jak 1/2) was most effective in this trial. *: p<0.05, **: p<0.01

 

Figure 9. HCC4006 Assay. (A) Inhibition activity of BAY 87-2243 (HIF1A) (B) Inhibition Activity of MK-2206 (AKT 1/2/3) (C) Inhibition activity of AZD1480 (Jak 1/2) (D) Inhibition activity of GDC-0994 (Erk 1/2). Each experiment was performed in triplicate (5x103 cells per well) over a 48-hour inhibition period. After 48 hours, cell death was quantified using MTT; absorbance was read at 590nm. Because it shows the greatest percent decrease between the DMSO mesenchymal population and the 5µM mesenchymal population, AZD1480 (Jak 1/2) was most effective in this trial. *: p<0.05

Figure 9. HCC4006 Assay. (A) Inhibition activity of BAY 87-2243 (HIF1A) (B) Inhibition Activity of MK-2206 (AKT 1/2/3) (C) Inhibition activity of AZD1480 (Jak 1/2) (D) Inhibition activity of GDC-0994 (Erk 1/2). Each experiment was performed in triplicate (5×103 cells per well) over a 48-hour inhibition period. After 48 hours, cell death was quantified using MTT; absorbance was read at 590nm. Because it shows the greatest percent decrease between the DMSO mesenchymal population and the 5µM mesenchymal population, AZD1480 (Jak 1/2) was most effective in this trial. *: p<0.05

 

Figure 10. HCC827 Assay. (A) Inhibition activity of BAY 87-2243 (HIF1A) (B) Inhibition Activity of MK-2206 (AKT 1/2/3) (C) Inhibition activity of AZD1480 (Jak 1/2) (D) Inhibition activity of GDC-0994 (Erk 1/2). Each data set was performed in triplicate (5x103 cells per well) over a 48-hour inhibition period. After 48 hours, cell death was quantified using MTT; absorbance was read at 590nm. Because it shows the greatest percent decrease between the DMSO mesenchymal population and the 5µM mesenchymal population, AZD1480 (Jak 1/2) was most effective in this trial. *: p<0.05

Figure 10. HCC827 Assay. (A) Inhibition activity of BAY 87-2243 (HIF1A) (B) Inhibition Activity of MK-2206 (AKT 1/2/3) (C) Inhibition activity of AZD1480 (Jak 1/2) (D) Inhibition activity of GDC-0994 (Erk 1/2). Each data set was performed in triplicate (5×103 cells per well) over a 48-hour inhibition period. After 48 hours, cell death was quantified using MTT; absorbance was read at 590nm. Because it shows the greatest percent decrease between the DMSO mesenchymal population and the 5µM mesenchymal population, AZD1480 (Jak 1/2) was most effective in this trial. *: p<0.05

 

Discussion

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.

Figure 11. Hypothesized Mechanism of AZD1480 Action in Eliminating EMT-Derived Mesenchymal Cells. By preventing the phosphorylation of STAT3, AZD1480 blocks the activation of EMT-initiating transcription factors which are necessary for cell survival.

Figure 11. Hypothesized Mechanism of AZD1480 Action in Eliminating EMT-Derived Mesenchymal Cells. By preventing the phosphorylation of STAT3, AZD1480 blocks the activation of EMT-initiating transcription factors which are necessary for cell survival.

Acknowledgements 

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

doi: 10.22186/jyi.31.5.25-30 

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

Abstract

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).

Introduction

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. 

Figure 1. Diagram summarizing the NLRP3 inflammasome activation and IL-1β secretion. Signal 1 is the priming step, where inactive precursor, pro- IL-1β, is accumulated. Signal 2 involves the assembly of an inflammasome complex consisting of NLRP3, ASC and Caspase-1, which is responsible for cleavage of pro-IL-1β into the active form IL-1β, for further secretion by the cells.

Figure 1. Diagram summarizing the NLRP3 inflammasome activation and IL-1β secretion. Signal 1 is the priming step, where inactive precursor, pro- IL-1β, is accumulated. Signal 2 involves the assembly of an inflammasome complex consisting of NLRP3, ASC and Caspase-1, which is responsible for cleavage of pro-IL-1β into the active form IL-1β, for further secretion by the cells.

 

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. 

Results

Mitochondria-Derived ROS 

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). 

Figure 2. Mitochondria-derived ROS. J774A1 cells were treated with increased concentrations, 25mM, 50mM and 100mM, of fructose or glucose for 24h, and then incubated for ten minutes with MitoSOX red. Images (Figure 2a) were taken using Olympus BX53 fluorescence microscope. Amount of mitochondrial ROS was indicated by intensity of red fluorescence. The numbers on the top right of each panel indicate the fluorescence intensity (arbitrary units) analyzed by ImageJ image analysis software. The bar chart below (Figure 2b) depicts the fluorescence intensities as shown in the panels.

Figure 2. Mitochondria-derived ROS. J774A1 cells were treated with increased concentrations, 25mM, 50mM and 100mM, of fructose or glucose for 24h, and then incubated for ten minutes with MitoSOX red. Images (Figure 2a) were taken using Olympus BX53 fluorescence microscope. Amount of mitochondrial ROS was indicated by intensity of red fluorescence. The numbers on the top right of each panel indicate the fluorescence intensity (arbitrary units) analyzed by ImageJ image analysis software. The bar chart below (Figure 2b) depicts the fluorescence intensities as shown in the panels.

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).

 

Figure 3: Mitochondrial permeability. J774A1 cells were treated with increased concentrations, 25mM, 50mM and 100mM, of fructose or glucose for 24h, and then incubated with JC-1 dye for 15 minutes. The pictures (Figure 3a) of live cells were taken using the Olympus BX53 fluorescence microscope at 540/570 nm (red) for healthy cells and 485/535 nm (green) for unhealthy cells. An increase in red/green fluorescence intensity indicates healthy or hyperpolarized mitochondria, illustrated on the graph on the bottom (Figure 3b). The red/green ratio was calculated using integrated intensity of red and green channels measured by ImageJ software.

Figure 3: Mitochondrial permeability. J774A1 cells were treated with increased concentrations, 25mM, 50mM and 100mM, of fructose or glucose for 24h, and then incubated with JC-1 dye for 15 minutes. The pictures (Figure 3a) of live cells were taken using the Olympus BX53 fluorescence microscope at 540/570 nm (red) for healthy cells and 485/535 nm (green) for unhealthy cells. An increase in red/green fluorescence intensity indicates healthy or hyperpolarized mitochondria, illustrated on the graph on the bottom (Figure 3b). The red/green ratio was calculated using integrated intensity of red and green channels measured by ImageJ software.

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).

Figure 4: Antioxidant enzymes. J774A1 cells were treated with fructose or glucose at 25mM, 50mM and 100mM for 24h after which they were lysed using lysis buffer. Total amount of antioxidant enzymes, catalase (60KDa), peroxiredoxin 1 (PRX1, 23KDa), the oxidized from of peroxiredoxins (PRXSO3) ranging from 27-20KDa and superoxide dismutase 1 (SOD1, 14KDa), were analyzed by western blotting. β-actin (50KDa) was used as loading control for total protein content.

Figure 4: Antioxidant enzymes. J774A1 cells were treated with fructose or glucose at 25mM, 50mM and 100mM for 24h after which they were lysed using lysis buffer. Total amount of antioxidant enzymes, catalase (60KDa), peroxiredoxin 1 (PRX1, 23KDa), the oxidized from of peroxiredoxins (PRXSO3) ranging from 27-20KDa and superoxide dismutase 1 (SOD1, 14KDa), were analyzed by western blotting. β-actin (50KDa) was used as loading control for total protein content.

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).

Figure 5: NLRP3 inflammasome complex proteins. J774A1 cells were treated with fructose or glucose at 25mM, 50mM and 100mM for 24h after which they were lysed using lysis buffer. Total amount of intracellular components of the NLRP3 inflammasome complex proteins, NLRP3 (115KDa), caspase 1 (50KDa) and pro-1L-1β (37KDa) were detected by western blotting. β- actin (50KDa) was used as loading control for total protein content.

Figure 5: NLRP3 inflammasome complex proteins. J774A1 cells were treated with fructose or glucose at 25mM, 50mM and 100mM for 24h after which they were lysed using lysis buffer. Total amount of intracellular components of the NLRP3 inflammasome complex proteins, NLRP3 (115KDa), caspase 1 (50KDa) and pro-1L-1β (37KDa) were detected by western blotting. β- actin (50KDa) was used as loading control for total protein content.

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.

Acknowledgements

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

doi:10.22186/jyi.31.4.13-16

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

Abstract

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.

Introduction

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).           

                                                    TEC 1

(1)

 

 

Where:

 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.

Methods

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).

                                                                   TEC 2

(2)

 

 

Where:

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.

Results

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.

Figure 1. Images of heat maps of TEC over the 100 closest receivers to the epicenter of the earthquake on the day of the earthquake (March 11, 2011) for four different time points. These times show enhanced TEC (A), a sudden depletion (B), and the large fluctuations following (C and D) The X- and Y-axes represents the longitude and latitude, respectively.

Figure 1. Images of heat maps of TEC over the 100 closest receivers to the epicenter of the earthquake on the day of the earthquake (March 11, 2011) for four different time points. These times show enhanced TEC (A), a sudden depletion (B), and the large fluctuations following (C and D) The X- and Y-axes represents the longitude and latitude, respectively.

 

Figure 2. Images of simulations of TEC over the 100 closest receivers to the epicenter of the earthquake a month before the earthquake (February 11, 2011) for four different time points. These points are from the same times of day as the enhancement (A), depletion (B), and fluctuations (C and D). The X- and Y-axes represents the longitude and latitude, respectively.

Figure 2. Images of simulations of TEC over the 100 closest receivers to the epicenter of the earthquake a month before the earthquake (February 11, 2011) for four different time points. These points are from the same times of day as the enhancement (A), depletion (B), and fluctuations (C and D). The X- and Y-axes represents the longitude and latitude, respectively.

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).

Figure 3. Histograms of potential confounding parameters of TEC: A) Sunspot, B) 10.7 cm solar flux and C) kp, analyzed over a one-month period (March 2 – April 1, 2011) around the earthquake. Yellow highlight indicates the day of the earthquake.

Figure 3. Histograms of potential confounding parameters of TEC. A) Sunspot, B) 10.7 cm solar flux and C) kp, analyzed over a one-month period (March 2 – April 1, 2011) around the earthquake. Yellow highlight indicates the day of the earthquake.

Discussion

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.       

Conclusion

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.

References

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

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Chemical Reduction and Deposition of Nanostructured Pt–Au Alloy

doi:10.22186/jyi.31.4.7-11

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

Abstract

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.

Introduction

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

Materials

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, H2PtCland 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.

Results

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).

Figure 1. SEM images of Pt–Au NMA products in varying ratios. (a) 1:1 (b) 1:2 and (c) 1:3. SE2 represents the signal making up of secondary electrons generated from backscattered electrons. EHT (extra high tension) voltage; WD and Mag; working distance and magnification respectively.

Figure 1. SEM images of Pt–Au NMA products in varying ratios. (a) 1:1 (b) 1:2 and (c) 1:3. SE2 represents the signal making up of secondary electrons generated from backscattered electrons. EHT (extra high tension) voltage; WD and Mag; working distance and magnification respectively.

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.

Figure 2: XPS spectra of NMA of various Pt:Au mass loading ratios. The spectra is plotted with the signal intensity in arbitrary units (A.U.) and binding energy in eV.

Figure 2. XPS spectra of NMA of various Pt:Au mass loading ratios. The spectra is plotted with the signal intensity in arbitrary units (A.U.) and binding energy in eV.

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.

Figure 3: 15th cyclic voltammogram of (a) control Pt and Pt–Au NMA products with ratios (b) 1:1, (c) 1:2 and (d) 1:3. The voltage is measured against the saturated calomel electrode (SCE). The positions of If and Ib peaks are indicated.

Figure 3. 15th cyclic voltammogram of (a) control Pt and Pt–Au NMA products with ratios (b) 1:1, (c) 1:2 and (d) 1:3. The voltage is measured against the saturated calomel electrode (SCE). The positions of If and Ib peaks are indicated.

 

 

Screenshot 2016-10-01 10.25.40

Table 1. If/Ib ratios for control Pt and NMA of various Pt:Au mass loading ratios.

Discussion

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):

2Pt+CH3OH→Pt-CH2OH+Pt-H                                               (1a) 
 2Pt+Pt-CH3OH→Pt2-CHOH+Pt-H                                        (1b) 
2Pt+Pt2-CHOH→Pt3-COH+Pt-H                                             (1c) 

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):

Pt3+COH→Pt2-CO+Pt+H++e-                                         (2a) 
 Pt2-CO+Pt-OH→Pt-CO2H+Pt                                        (2b) 
Pt-CO2H+Pt-OH→Pt-CO2+H2O                                     (2c) 
 
Some unreacted adsorbed CO can re-arrange to form double bonds with Pt sites in the NMA catalyst, causing catalytic poisoning (Manoharan & Goodenough, 1992). These double bonds can be destroyed by reversing the bias to remove adsorbed OH species and to free up Pt sites (Manoharan & Goodenough, 1992; Liu, Ling, Su, & Lee, 2004). During the backward sweep, the presence of CO motivates the conversion of adsorbed OH species into H2O, which liberates electrons that contribute to the Ib peak:
 
Pt-OH+H++e-→Pt+H2O                                                     (3) 
 
With more free Pt sites, adsorbed CO doubly bonded to Pt will be converted back to Pt2−CO, and CO will be oxidised in later forward sweeps in Equations (2b) and (2c). The presence of peaks demonstrated the catalysts’ ability to oxidize CH3OH. Also, the number and positions of peaks indicated that chemisorption activities on NMA are attributed to Pt rather than Au, due to the poor OH species’ adsorption ability of Au (Xu, Zhao, Yeng, & Shen, 2010; Ye et al., 2011).

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.

Acknowledgements

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

doi:10.22186/jyi.31.4.1-6

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

Abstract

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.

Introduction

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).

Methods

Calculations

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 Screenshot 2016-10-01 10.53.50 is

 

 Equation 1

(1)    

Since the potential function V(r) does not depend time, we may write the wave function as

 

 Equation 2

(2)    

where E is the energy of the state and Screenshot 2016-10-01 10.54.47 satisfies the time-independent Schrödinger equation:

 

 Equation 3

(3)    

We now introduce the coefficient  Screenshot 2016-10-01 10.56.54 and use the model of a particle in a box, where V = 0 inside the box and is infinite outside. [Eq. (3)] can now be written as the Helmholtz equation:

 

 Equation 4

(4)    

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 ab, and c, all with Dirichlet boundary condition (i.e., Screenshot 2016-10-01 10.58.49 on the boundaries), has the series

 

 Equation 5

(5)    

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,

 

 Equation 6

(6)    

where Screenshot 2016-10-01 10.59.28 is the density function and j is the current vector. Note that both Screenshot 2016-10-01 11.00.38 and j are time-independent because of [Eq. (2)]. From [Eq. (6)] and [Eq. (3)], one obtains the following expression for the probability current:

 

 Equation 7

(7)    

This expression is evaluated numerically by a finite-difference approximation of the gradients. We choose units such that Screenshot 2016-10-01 11.01.49.

Visualization

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.,

 

 Equation 8

(8)    

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.

Results

Neumann Boundary Condition

We begin with a state under Neumann boundary condition

 

 Equation 9

(9)    

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 (xb(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).

Figure 1. Nodal lines and probability current for [Eq. (9)]. Nodal lines and a part of the probability current for the state in [Eq. (9)].

Figure 1. Nodal lines and probability current for [Eq. (9)]. Nodal lines and a part of the probability current for the state in [Eq. (9)].

 

Figure 2. Nodal surfaces and probability current for [Eq. (9)]. (A) Nodal surfaces for the state in [Eq. (9)] with Neumann boundary condition are shown. The horizontal plane is for the real part and the vertical are for the imaginary. (B-D) Probability current viewed from three different directions where the x-, y-, and z-axes show the special dimensions of the box.

Figure 2. Nodal surfaces and probability current for [Eq. (9)]. (A) Nodal surfaces for the state in [Eq. (9)] with Neumann boundary condition are shown. The horizontal plane is for the real part and the vertical are for the imaginary. (B-D) Probability current viewed from three different directions where the x-, y-, and z-axes show the special dimensions of the box.

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

 

 Equation 10

(10)               

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 (xbn/4, c/2) with = 1,2,3 and similarly in the y-direction. The current propagates in vortices around these lines (Figure 3B-D).

Figure 3. Nodal surfaces and probability current for [Eq. (10)]. (A) Nodal surfaces for the state in [Eq. (10)] with Dirichlet boundary condition. (B-D) Obtained probability current is viewed from three different directions.

Figure 3. Nodal surfaces and probability current for [Eq. (10)]. (A) Nodal surfaces for the state in [Eq. (10)] with Dirichlet boundary condition. (B-D) Obtained probability current is viewed from three different directions.

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

 

 Equation 11

(11)   

and the nodal surfaces and current are shown in (Figure 4A-D).

Figure 4. Nodal surfaces and probability current for [Eq. (11)]. (A) Nodal surfaces for the state in [Eq. (11)] with Dirichlet boundary condition are displayed. (B-D) Obtained probability current is viewed from three different directions.

Figure 4. Nodal surfaces and probability current for [Eq. (11)]. (A) Nodal surfaces for the state in [Eq. (11)] with Dirichlet boundary condition are displayed. (B-D) Obtained probability current is viewed from three different directions.

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

 

 Equation 12

(12)   

is with Neumann boundary condition and

 

 Equation 13

(13)   

is with Dirichlet boundary condition. The last state is with mixed conditions.

 

 Equation 14

(14)   

Screenshot 2016-10-01 11.20.17

Figure 5. Different examples of nodal surfaces. Nodal surfaces for the states in (A) Equation (12), (B) Equation (13) and (C) Equation (14) are shown. For this simple case with only two terms in the Fourier series, the higher excitation in a direction is observed as more sheets in that direction.

Using more terms in the linear combination the surfaces becomes more complex. For example:

 

 Equation 15

(15)   

For this state, the nodal surfaces are visualized both as animated figures and 3D-printed models (Figure 6A-D).

Figure 6. Simulated and 3D printed complex nodal surfaces.  Nodal surfaces for the state in [Eq. (15)] are shown. (A) is the real part and (B) is the imaginary part. (C-D) 3D-printed versions of the same surfaces are displayed.

Figure 6. Simulated and 3D printed complex nodal surfaces. Nodal surfaces for the state in [Eq. (15)] are shown. (A) is the real part and (B) is the imaginary part. (C-D) 3D-printed versions of the same surfaces are displayed.

Particle Separation

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

 

 Equation 16

(16)   

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.

Figure 7. Simulation of particle sorting. (A) Current used in the simulation of particle separation is shown. This current corresponds to the state in [Eq. (16)]. (B) Simulation of particles with mass ratio 1:10 demonstrates that particles begin at the same point but end up at different points.

Figure 7. Simulation of particle sorting. (A) Current used in the simulation of particle separation is shown. This current corresponds to the state in [Eq. (16)]. (B) Simulation of particles with mass ratio 1:10 demonstrates that particles begin at the same point but end up at different points.

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.

Acknowledgements

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

doi:10.22186/jyi.31.3.21-26

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

Abstract

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.

Introduction

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.

Methods

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.

Wear-Evaluation-Figure  (1)

Figure 1. Experimental Setup. A) UHMWPE sample with wear from CoCr counterface, B) CoCr counterface, C) UHMWPE sample with wear from diamond coated counterface, D) diamond coated counterface, E) in situ experiment in bovine serum.

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.

Weighing Samples

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.

Volumetric 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

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

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.

Raman Spectroscopy

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

Results

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.

Wear-Evaluation-Figure  (2)

Figure 2. Volumetric Wear of Samples 1 and 2. Sample 1 (Worn by CoCr): The starting wear is around 1.2mm3 while the ending wear is 3.7mm3. The R2 value of a parabolic line of fit is 0.8476. The rate of wear decreases as the number of cycles increases. Sample 2 (worn by Ti alloy): The starting wear is around 6.3mm3 while the ending wear is 18mm3. The R2 value of a linear line of fit is 0.9991. The rate of wear seems to stay consistent as the number of cycles increases.

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 30µm(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.

Figure 3. Average Roughness of PE Sample 1 and 2. The starting roughness for both samples at areas of 10µm2 and 30µm2 is higher than the final roughness. The Sample 1 roughness value on approximately 750 thousand cycles is larger than expected. However, the error is very large as well. Four Samples per data point yields errors bars that are less than 1 standard deviation (SD). Sample 1: Data from 250k (10µm2), 500k (30µm2), and 750k (30µm2) cycles had a roughness range greater than 1 SD but less than 2 SDs. Sample 2: Data from 250k (30µm2) cycles had a roughness range greater than 1 SD.

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).

Wear-Evaluation-Figure  (4)

Figure 4. Average Roughness of CoCr and Titanium Alloy Hemi-cylinders. Both the non-coated CoCr and diamond-coated Titanium alloy generally show that, as the area being imaged increases, so does the average roughness. Both “before wear” and “after wear” show this same trend. For the non-coated CoCr hemi-cylinder, the “before wear” error bars (10um2 and 80um2) ranged greater than 1 SD.

 

Wear-Evaluation-Figure  (5)

Figure 5. Raman Spectroscopy of PE Samples 1 and 2. Raman Spectroscopy peaks remained unchanged for both PE samples.

Discussion

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).

Acknowledgements

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.

References 

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Amstutz, H., Campbell, P., Kossovsky, N., & Clarke, I. (1992). Mechanism and Clinical Significance of Wear Debris-Induced Osteolysis. Clinical Orthopaedics and Related Research. 276, 7-18. http://journals.lww.com/corr/Abstract/1992/03000/ Mechanism_and_ Clinical_Significance_of_Wear.3.aspx

ASTM F732-00. Standard test method for wear testing of polymeric materials used in total joint prostheses. (2011). ASTM International, West Conshohocken, PA. www.astm.org

Bhatt, H., & Goswami, T. Implant wear mechanisms—basic approach. (2008). Biomedical Materials, 3(4), 109-109. http://dx.doi.org/10.1088/1748-6041/3/4/042001

Daniilidis, K., & Tibesku, C. O. (2012). Frontal plane alignment after total knee arthroplasty using patient-specific instruments. International Orthopaedics, 37(1), 45-50. http://dx.doi.org/10.1007/s00264-012-1732-1

Davidson, J. (1993). Characteristics of Metal and Ceramic Total Hip Bearing Surfaces and Their Effect on Long-Term Ultra High Molecular Weight Polyethylene Wear. Clinical Orthopaedics and Related Research294, 361-378. Retrieved from http://journals.lww.com/corr/Abstract/1993/09000/Characteristics_of_Metal_and_Ceramic_ Total_Hip.53.aspx

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AIRE Deficiency Exposes Inefficiencies of Peripheral Tolerance Leading to Variable APECED Phenotypes

doi:10.22186/jyi.31.3.15-20

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

Abstract

Autoimmune polyendocrinopathy-candidiasis-ectodermal dystrophy (APECED) is a rare, recessive disease caused by mutations in the autoimmune regulator (AIRE) gene. A loss of function at the AIRE locus is widely known to induce autoimmune activation against host tissues due to lack of central tolerance during thymic T cell development. Failure to delete autoreactive T cell clones allows their release into the periphery, where they may proliferate and initiate an autoimmune response. While APECED is a monogenic disorder, disruption of AIRE function can have diverse implications: similar mutations in AIRE can lead to a myriad of phenotypes and symptoms. By investigating the multiple ways AIRE function can be compromised, recent research has uncovered the steadfast mechanisms explaining how AIRE is expressed in mTECs, how AIRE transactivates tissue-specific antigens (TSAs), and how those TSAs are presented to T cells by both medullary thymic epithelial cells (mTECs) and bone marrow-derived antigen-presenting cells. However, the stochastic nature of APECED symptoms remains. Therefore, new approaches to APECED therapy should investigate the intersection of pragmatism and randomness inherent in the relationship between central and peripheral tolerance.

Introduction

T cells provide capable, targeted defense against foreign antigens through their receptor specificity. The vast repertoire of T cell receptors allows the immune system to mount a response against most foreign invaders. Generation of receptor diversity is accomplished mainly through gene rearrangement at the alpha and beta chain loci.

Positive selection in the thymic cortex is able to expand T cell clones with receptors that bind major histocompatibility complex (MHC)/self-peptide complexes with at least moderate affinity (De Martino et al., 2013). However, cells that pass positive selection may still have a strong affinity for self-peptides presented on MHC molecules. In order to eliminate these autoreactive T cells from escaping from the thymus into the periphery, T cell clones positively selected for in the thymic cortex undergo negative selection in the thymic medulla. During the negative selection process, T cells are presented with medullary thymic epithelial cell (mTEC)-expressed tissue-specific antigens (TSAs) in the medulla (Derbinski, Schulte, Kyewski, & Klein, 2001; Kyewski & Derbinski, 2004). T cells that show strong affinity for these self-peptide/MHC complexes are deleted by activation-induced apoptosis. The deletion of autoreactive T cell clones through thymic-expressed TSAs is known as central tolerance.  

The discrepancy between antigens expressed and presented by cortical thymic epithelial cells (cTECs) versus mTECs has been termed the alternate peptide hypothesis. This hypothesis can partially explain how autoreactive T cells survive positive selection in the cortex but fail to pass negative selection in the medulla (Marrack, McCormack, & Kappler, 1989). In order to express TSAs, mTECs must transactivate genes that are not normally expressed in the thymus through a process called promiscuous gene expression (PGE; De Martino et al., 2013; Kyewski & Derbinski, 2004; Laan & Peterson, 2013; Metzger & Anderson, 2011; Tykocinski, Sinemus, & Kyewski, 2008). PGE is dependent upon the transcription of DNA in chromatin states often associated with inhibited expression (Abramson, Giraud, Benoist, & Mathis, 2010; Tykocinski et al., 2010; Ucar & Rattay, 2015; Žumer, Saksela, & Peterlin, 2013). The autoimmune regulator (AIRE) protein expressed in mTECs is a transcription factor that facilitates this process.

Loss of AIRE function limits TSA tolerance, leading to organ-specific autoimmunity and autoantibody production (Kisand & Peterson, 2015; Laan & Peterson, 2013; Metzger & Anderson, 2011). Autoimmune polyendocrinopathy candidiasis ectodermal dystrophy (APECED) is the monogenic disorder caused by mutations at the AIRE locus. However, APECED may be considered a syndrome because symptoms can also stem from indirect disruptions of AIRE function (De Martino et al., 2013). While APECED cases may feature some similar symptoms such as mucocutaneous candidiasis, Addison’s disease, and hypoparathyroidism, AIRE’s role in maintaining central tolerance to most TSAs makes APECED patients susceptible to further autoimmune responses against a number of host tissues including the eyes, liver, pancreas, kidney, and sex organs (Kisand & Peterson, 2015; Kyewski & Derbinski, 2004). Therefore, even APECED patients with similar mutations at the AIRE locus may have dissimilar symptoms due to variation in specific self-antigen tolerance (De Martino et al., 2013).

Because disruption of AIRE function can occur in numerous ways, this review will discuss how disruptions in AIRE expression, PGE promotion, and TSA presentation can all instigate autoimmunity. Furthermore, this review will explain how the interplay between central and peripheral tolerance contributes to the variation seen in APECED phenotypes and symptoms.   

mTEC Development, Epigenetic Profile, and miRNA Govern AIRE Expression

Expression of AIRE is vital to expression of self-antigens in the thymus. AIRE is predominantly expressed in mTECs, although other cell types in the periphery and thymus have been shown to express AIRE at low levels (Derbinski et al., 2001; Metzger & Anderson, 2011). While mTEC lineage cells are primarily responsible for PGE, only 1-5% of mTEC cells express TSAs at a given time (Gallegos & Bevan, 2004). It is likely that factors unlinked to AIRE expression levels, such as changes in signaling states within the medullary microenvironment, alter PGE in AIRE+ mTECs. Nonetheless, because AIRE plays a direct role in PGE, disruption of AIRE expression leads to autoimmune phenotypes.

Problems with AIRE expression in mTECs may arise from impediments to mature mTEC development. Immature mTECs begin at the MHC IIlow, CD80low, AIRE- stage and mature to an MHC IIhigh, CD80high, AIRE- stage and then an MHC IIhigh, CD80high, AIRE+ stage, at which point AIRE-dependent and AIRE-independent antigens can be expressed (Metzger & Anderson, 2011). While this three-step maturation process represents the development needed for mTECs to activate PGE, mTECs have recently been shown to lose their AIREphenotype during a fourth, and final, maturation stage. This loss of AIRE expression is coupled with a loss of TSA expression in these mature mTECs (Laan & Peterson, 2013; Yano et al., 2008). Therefore, while the paucity of TSA expression by the total mTEC population likely hinges on many factors, one possible contributor may be the finite timeframe in which AIRE is expressed by maturing mTECs.

While AIRE propels mTEC maturation, disruptions to early mTEC development stunt AIRE expression. AIRE-deficient mice produce malformed thymi, which illustrates the important role AIRE plays in thymic formation and mTEC development (Yano et al., 2008). However, failure of immature mTECs to pass proper developmental stages can have a substantial impact on AIRE expression and PGE in the thymus. For example, Rossi et al. (2007) show that RANK signaling from CD4+, CD3- cells facilitates mTEC development and promotes AIREphenotypes. Absence of RANK signaling was shown to incite autoimmunity (Rossi et al., 2007). Later experiments in vivo uncovered that RANK signaling regulates AIRE function by promoting its accumulation within chromatin-associated nuclear bodies (Ferguson et al., 2008).

Failure to express AIRE may also stem from improper epigenetic markers at the AIRE locus. One epigenetic marker that influences gene expression is methylation of DNA. Hypermethylation of DNA can occur in contiguous regions, such as CpG islands, and is associated with low expression rates. Bisulfate sequencing of CpG islands near the AIRE promoter revealed hypomethylation surrounding the AIRE promoter in AIREmTECS. However, these hypomethylation markers were also found in AIRE- immature (MHC IIlow) mTECs and cTECs, illustrating that AIRE promoter methylation likely has little effect on AIRE expression (Kont et al., 2011; Ucar & Rattay, 2015).

Alterations in the packing and chemical modification of chromatin can also dictate levels of gene expression. DNA is packed into nucleosomes, which contain DNA looped around octamers of histone proteins, similar to beads on a string. Tightly packed chromatin (heterochromatin) can sterically hinder transcriptional machinery from accessing promoters and other DNA sequences, making heterochromatin states unreceptive to gene expression. Conversely, loosely packed chromatin (euchromatin) is permissive of transcriptional machinery and gene expression. Furthermore, methylation of lysine residues on individual histone proteins can also promote or repress transcription. Histone profiling at the AIRE promoter in AIRE+ mTECs showed increased amounts of transcriptionally active histone marks (H3K4me3) and lower amounts of repressive histone marks (H3K27me3) than other cell types (Kont et al., 2011). These epigenetic patterns illustrate that AIRE expression is correlated with histone modifications at the promoter region. Failure to properly mark specific histone residues at the AIRE promoter may cause epigenetic silencing of AIRE, leading to decreased PGE and autoimmune phenotypes.

Expression of AIRE may be further regulated by miRNA interactions. Research by Ucar, Tykocinski, Dooley, Liston, and Kyewski (2013). revealed that miRNAs are tightly regulated in developing mTECs. In addition, mice lacking Dicer function showed loss of AIRE expression and reduced PGE, which demonstrates that miRNA regulates AIRE expression and function (Ucar et al., 2013). Therefore, loss of AIRE function may stem from an inability of miRNA to regulate AIRE activity.

Disruptions of AIRE or Other Promiscuous Gene Expression Mediators Yield Autoimmunity

AIRE contributes to central tolerance by enabling the expression of self-antigens within mTECs through PGE. Promoting TSA expression is a complex process, and AIRE is able to facilitate PGE through its unique protein domains, which allow for subcellular localization and interaction with other proteins that assist in the transcription and processing of TSAs (Abramson et al., 2010; De Martino et al., 2013; Gallo et al., 2013; Ramsey, Bukrinsky, & Peltonen, 2002). Taken together, mutations in the AIRE locus compromise the function of AIRE protein domains and lead to nonfunctional PGE.

In order to facilitate the transcription of TSAs not canonically expressed in the thymus, AIRE must localize to genes that are epigenetically repressed. AIRE protein domains allow it to access repressive chromatin states and transactivate TSA expression. For example, a dominant missense mutation in the SAND domain inhibited PGE in heterozygous mice by impeding localization of AIRE proteins encoded by both alleles to nuclear bodies. This mutation was sufficient to prompt an autoimmune phenotype (Su et al., 2008). Mutations in the CARD domain limited AIRE homodimerization and nuclear localization in vitro (Ferguson et al., 2008; Metzger & Anderson, 2011). Mutations leading to elimination of the AIRE C-terminus barred TSA expression by preventing AIRE from interacting with positive transcription elongation factor B (P-TEFb; Žumer, Plemenitaš, Saksela, & Peterlin, 2011). Synthetic mutations in the PHD domain revealed that the BHC80 region of AIRE’s PHD1 domain is vital for localization to nucleosomes. The PHD1 domain is a protein-binding zinc finger, which can bind hypomethylated H3K4, a traditionally repressive histone mark, in order to allow transcription within regions of heterochromatin (Anderson & Su, 2016). While AIRE binding of hypomethylated histone H3 tails was necessary for PGE, overexpression of H3K4-demethylase did not increase PGE, indicating that AIRE’s targets other epigenetic modifications (Koh, Kingston, Benoist, & Mathis, 2010). This hypothesis was supported by Waterfield et al. (2014)who used a screening approach to demonstrate that AIRE interacts with MBD1on its SAND domain. MDB1 is able to bind methylated CpG dinucleotides, which allows AIRE to localize to genes located within hypermethylated CpG islands (Waterfield et al., 2014).

Subcellular localization of AIRE to epigenetically-repressed sites via its protein domains is necessary for AIRE to facilitate the transcription of TSA genes. However, further protein-protein interactions also contribute to TSA transactivation. Because of AIRE’s integral role in facilitating a process that breaks conventional guidelines of gene regulation, it may be assumed that AIRE acts as a “pioneer protein,” which recruits RNA Polymerase II to TSA loci amidst a jumble of heterochromatin and other repressive epigenetic marks. However, Giraud et al.  (2012) showed that the absence of AIRE did not inhibit expression of the first exon in AIRE-targeted genes. This illustrates that AIRE is not necessary for RNA Polymerase II to access epigenetically-repressed loci. Instead, RNA Polymerase II can be recruited to these sites by DNA-Dependent Protein Kinase (DNA-PK) in response to double stranded breaks caused by Topoisomerase II activity. RNA Polymerase II is then able to begin transcription of the first exon, but elongation is halted by negative elongation factors. AIRE also interacts with DNA-PK, which allows it to co-localize with RNA Polymerase II. After co-localization, AIRE’s interaction with P-TEFb prompts RNA polymerase II phosphorylation and transcriptional elongation (Žumer et al., 2013). Therefore, instead of initiating transcription at TSA loci, AIRE works to promote TSA transcription by unleashing RNA Polymerase II in order to transcribe downstream exons (Giraud et al., 2012). AIRE localization to double stranded break repair sites via DNA-PK provides a viable explanation to how AIRE accesses epigenetically-repressed TSA loci. However, as illustrated above, mutations to multiple AIRE protein domains have also been shown to inhibit subcellular localization and provoke autoimmune phenotypes. Further research will need to define whether these various methods of TSA localization work in tandem or in isolation to induce PGE.

AIRE also regulates TSA output via post-translational mRNA splicing (Kyewski & Derbinski, 2004; Žumer et al., 2011). mTECs had the greatest amount of alternatively spliced isoforms compared to any other cell type (Keane, Ceredig, & Seoighe, 2015). AIRE is thought to recruit splicing machinery in multiple ways. For example, splice factor snRNP is known to localize to nuclear bodies, (Sleeman & Lamond, 1999) where AIRE is also recruited via its SAND domain (Ramsey et al., 2002). Furthermore, Zumer et al. (2011) showed that snRNP subunit U5 was recruited by AIRE to the 3’ end of TSA transcripts. Therefore, AIRE utilizes co-localization with RNA Polymerase II to promote mRNA splicing (Žumer et al., 2011). AIRE is thought to perform mRNA splicing in order to tolerize autoreactive T cells specific to particular TSA isoforms, thus increasing the breadth of clonal deletion in the thymus (Keane et al., 2015; Kyewski & Derbinski, 2004). Therefore, disruption of mRNA splicing mechanics may limit the breadth of PGE expression, leading to autoimmunity of specific self-peptide isoforms.

mTECs and Thymic Dendritic Cells Present Tissue-Specific Antigens to Induce Tolerance

While TSA expression in mTECs is necessary for negative selection, central tolerance can be accomplished only if those TSAs are presented to T cells via MHC molecules. Therefore, failure to regulate TSA presentation on thymic cell types may undermine AIRE function and cause autoimmune phenotypes. Although mTECs have the proper surface molecules to initiate activation-induced apoptosis in both CD4+ and CD8+ T cells (Laan & Peterson, 2013), mTECs share these presenting responsibilities with thymic dendritic cells. These dendritic cells can be recruited to the thymus through the XC-chemokine ligand 1 (XCL1), a protein that is expressed by AIREmTECs (Anderson & Su, 2016). It should be noted that thymic dendritic cells do not express AIRE and do not perform PGE (Derbinski et al., 2001). Therefore, mTECs serve as TSA reservoirs, and can selectively pass off PGE products to thymic dendritic cells for presentation (Gallegos & Bevan, 2004; Hubert et al., 2011; Metzger & Anderson, 2011).

By regulating thymic and bone marrow expression of ovalbumin (OVA) peptide and MHC I/II, respectively, in mouse models, researchers have investigated whether mTECs are self-sufficient at inducing autoreactive CD4+ and/or CD8+ T cell deletion through TSA presentation. TSAs produced by mTECs are intracellular proteins, and should therefore be canonically presented by MHC I to CD8+ T cells; mTEC presentation to CD4+ T cells would require cross-presentation of intracellular TSAs to MHC II. Gallegos and Bevan hypothesized that because mTECs were insufficient at antigen presentation, thymic dendritic cells were responsible for presentation to CD4+ and CD8+ T cells. Their results indicated that mTECs self-sufficiently induced CD8+ T cell tolerance to mOVA, but bone-marrow derived thymic dendritic cells were necessary for tolerance of mOVA-specific CD4+ T cells (Gallegos & Bevan, 2004). However, recent evidence has qualified those findings, asserting that mTECs are responsible for some TSA antigen via MHC II, but induction of CD4+ OVA tolerance is greatly diminished in mice with MHC II-deficient bone marrow (Hubert et al., 2011).

While inducing thymic expression of OVA through knock-in experiments spotlights the presentation responsibilities between mTECs and thymic dendritic cells for one non-self peptide, how presentation of numerous, specific TSAs is delineated between mTECs and thymic dendritic cells for comprehensive tolerance induction remains unknown. Research by Zhang et al. (2003) showed that soluble hen egg lysozyme(HEL) expression in the thymus produced more efficient negative selection of CD4+ thymocytes than membrane-bound HEL, suggesting mTEC secretion of peptides to thymic dendritic cells is important for tolerance induction. However, because autoreactive T cells are prone to interact with membrane-bound molecules on the surface of tissues, uncovering how shared presentation responsibilities ensure full tolerance to all self-peptides is an important step to uncovering more about negative selection mechanics.

Peripheral Tolerance Drives Variability in APECED Symptomatology

The disparities found in APECED symptomatology stem from the limited power of peripheral tolerance. Mechanisms of peripheral tolerance inactivate autoreactive lymphocytes that have escaped central tolerance during T lymphocyte development. For example, immature dendritic cells in the periphery are responsible for induction of tolerance to self-antigens under steady-state conditions (Hawiger et al., 2001; Mueller, 2010). Dendritic cells in both lymph nodes and the spleen can process, load, and present self-antigens from the periphery to T cells. Thus, expression of certain antigens in the periphery is sufficient to induce tolerance of those antigens (Derbinski et al., 2001). Additionally, certain dendritic cells express limited amounts of AIRE. These extra-thymic AIRE-expressing cells (eTACs) may provide additional tolerance in basal conditions by presenting AIRE-dependent self-antigens in the periphery (Metzger & Anderson, 2011; Mueller, 2010). eTACs lack costimulatory molecules CD80/86, which may induce anergy in T cells that recognize eTAC-presented peptides (Metzger & Anderson, 2011). However, it is likely that eTAC levels are minimal in APECED patients.

Peripheral tolerance is also formed by regulatory T cells (CD4+, FOXP3+, CD25+), which induce anergy to helper and cytotoxic T cells through direct interaction, releasing anti-inflammatory signals, and expending cytokines that potentiate T cell activation and proliferation. T cells in the thymus may be pushed to the thymic regulatory T cell lineage if they bind MHC/self-peptide complexes with strong affinity during negative selection in the thymus (Jordan et al., 2001). Induced regulatory T cells may be induced to undergo lineage commitment in the periphery through receptor activation and epigenetic change at the FOXP3 locus (Ohkura et al., 2012). Because AIRE deficiency hinders negative selection through dysfunctional PGE, APECED patients possess limited regulatory T cell populations, likely due to the inability to facilitate thymic regulatory T cell lineage commitment (Kekäläinen et al., 2007; Perry et al., 2014). However, induced regulatory T cells may play a role in muffling the autoimmune response in some tissues.

The role of peripheral tolerance mechanisms to silence autoimmunity in host tissues causes the variability of APECED phenotypes. The stochastic nature of T cell receptor gene rearrangement leads to a diverse potential of peripheral autoreactive T cells in AIRE-deficient individuals (Kisand & Peterson, 2015). Peripheral tolerance serves to filter out those autoreactive T cell responses, but because the peripheral filter is imperfect, the list of specific autoreactive T cell clones left unconstrained is unpredictable (Figure 1). For example, the self-peptides available for dendritic cells to uptake and present may depend on random circumstance, leaving the peripheral tolerance of specific tissues up to chance. Furthermore, variability in the activation and recruitment of specific induced regulatory T cell clones further confounds which autoreactive helper and cytotoxic T cells will cause host tissue damage. Still other factors, such as the amount of costimulatory molecules and activation-inducing cytokines present in a given tissue, play further roles (Klein & Kyewski, 2000). Therefore, while dysfunctional PGE in the thymus is sufficient to promote a myriad of autoreactive T cells in the periphery of APECED patients, the variable phenotypes associated with the disorder result from the stochastic mechanisms of peripheral tolerance used to neutralize autoreactive activity.

Figure 1

Figure 1. The relationship between central and peripheral tolerance determines the profile of autoreactive T cells in the periphery. Positive selection expands T cell clones that garner a signal from MHC/self-peptide complexes. Negative selection filters out autoreactive T cell clones that bind with high affinity to MHC/tissue-specific antigen complexes. Inability to perform negative selection permits autoreactive T cell clones into the periphery. Peripheral tolerance suppresses a limited number of autoreactive T cell responses and, in cases of APECED, dictates the specific autoimmune symptoms of the patient.

Conclusions and Future Directions

Because of AIRE’s central role in facilitating PGE, lack of central tolerance is intrinsic in every APECED phenotype (Figure 1). However, current treatment options do not remedy issues with central tolerance. Instead, treatments of APECED focus on maintaining tissue function and suppressing immune system responses through anti-inflammatory drugs. These treatments are often ineffective in limiting the autoimmune responses (Kisand & Peterson, 2015). While successful constitution of central tolerance in APECED patients would cure their symptoms, the complexity of the mechanisms involved in AIRE expression, PGE, and TSA presentation poses significant obstacles to targeting central tolerance therapeutically. Therefore, treatments for APECED patients could instead utilize the suppressive mechanisms of peripheral tolerance.

Peripheral tolerance is an effective suppressor of autoimmune responses. Despite the diversity of autoreactive T cells in the periphery of APECED patients, typical patients experience autoimmune responses to only a limited number of tissues (Figure 1; Kisand & Peterson, 2015). This is because peripheral tolerance is responsible for suppressing the activation, proliferation, and activity of autoreactive T cells. As such, an autoimmune response to any self-antigen can be thought of as a failure of peripheral tolerance to protect that antigen from immune targeting.

Identifying the autoimmune responses in each APECED patient inherently identifies the limits of peripheral tolerance in that individual. Therefore, new therapeutic efforts for APECED could address breaches in peripheral tolerance in a symptom-specific manner: patients would receive therapy that would induce peripheral tolerance to the tissues under attack. This treatment might be accomplished by introducing the self-antigens of interest to secondary lymphoid organs, where immature dendritic cells may tolerize peripheral T cells specific to those antigens; a 2012 study showed that mice injected with microparticles decorated with a specific antigen induced long term tolerance of T cells specific to that antigen (Getts et al., 2012). Additionally, transplanting tissue-specific regulatory T cells into the periphery may promote anergy to a given tissue. Many regulatory T cell-based therapeutic studies are currently in clinical trials, and future studies may utilize specific MHC/peptide combinations to isolate and expand antigen-specific regulatory T cells (Khor, 2016). While these methods of inducing tissue-specific peripheral tolerance are far from developed, they provide the potential to overcome the variability associated with both the causes and symptoms of APECED.

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Access Full PDF: AIRE Deficiency Exposes Inefficiencies of Peripheral Tolerance Leading to Variable APECED Phenotypes

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