Team:UCL/Modeling/Bioinformatics

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<p class="major_title">WHAT CAUSES ALZHEIMER'S?</p>
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<p class="minor_title">Well, It's Debatable</p>
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<p class="major_title">A BIOINFORMATICS APPROACH</p>
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<p class="minor_title">Finding New Parts</p>
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There are many vying hypotheses that postulate how <a href="https://2013.igem.org/Team:UCL/Background/Alzheimers" target="_blank"> Alzheimer’s Disease (AD)</a> may arise. Of these, the most well known is the ‘Amyloid Hypothesis’. The Amyloid Hypothesis is centred around the ‘senile plaques’ that form in AD brains, and suggests that their removal could be key in halting the progression of the disease <a href="http://www.ncbi.nlm.nih.gov/pubmed/1763432?dopt=Abstract" target="_blank">(Hardy and Allsop 1991)</a>, though other hypotheses contradict this precept. So far, no single cause for the disease has been identified, though most AD research focuses around senile plaques. AD is generally accepted to cause three major histopathological changes in brain tissue.  
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Bioinformatics creates and enhances methods for storing, retrieving, organising and analysing biological data. We decided to take a completely new approach in our dry lab work and look into bioinformatic approaches to studying <a href="https://2013.igem.org/Team:UCL/Background/Alzheimers" target="_blank">Alzheimer’s disease (AD)</a>.
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<p class="minor_title">Histopathology</p>  
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Senile plaques are extracellular deposits of an abnormal form of the waste protein β-amyloid (Aβ), which tangle with cell matter in the brain <a href="http://www.ncbi.nlm.nih.gov/pubmed/11274343" target="_blank">(Selkoe 2001)</a>. These plaques are larger than cell bodies (15-25 um in diameter) and mature to become even denser. The tangling of the plaques, however, is not proportional to the amount of amyloid proteins in an area, and so the process by which the tangling and the plaques are created are still as yet unknown. Aβ is cleaved from a larger precursor protein - amyloid precursor protein (APP) <a href="http://www.ncbi.nlm.nih.gov/pubmed/16904243?dopt=Abstract" target="_blank">(Nistor et al. 2007)</a>. The Aβ peptide is predominantly cleaved to be 40 amino acids in length, that is, Aβ1-40. However, Aβ1-42 and Aβ1-43 nucleate more rapidly into amyloid fibrils than Aβ1-40 does, and are neurotoxic via unknown mechanisms. Strittmatter and Salvensen theorised that other proteins complexed with Aβ to exacerbate the plaque, mainly ApoE, which is another cerebrospinal fluid protein that has a high affinity for Aβ <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC46003/" target="_blank">(Srittmatter et al. 1993)</a>. The functional forms of ApoE aid protease-mediated degradation.
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The rationale behind this is simple. In order to make a genetic circuit in a synthetic biological construct as effective as possible in a medical application, we may need to target key dysfunctional genes within the problematic biological entity. There are many risk factors for AD and so predicting the key, ‘driver genes’, and the group of proteins with which they interact is invaluable in knowing what we want our construct to produce, in order to mitigate AD. The idea is that bioinformatics work can feed back into synthetic biology, and though we did not have the time to demonstrate this full circle, we feel bioinformatics can have a place in iGEM, helping teams to decide which dysfunctional genes to target in medical projects.</p>
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<a href="https://static.igem.org/mediawiki/2013/0/03/Human_interactome.jpg" data-lightbox="image-1" title="The Human Interactome">
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<p class="minor_title">Bioinformatics and Alzheimer’s Disease</p>  
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The second pathological feature is the collection of intraneuronal cytoskeletal filaments called neurofibrillary tangles, due to paired helical filaments <a href="http://www.ncbi.nlm.nih.gov/pubmed/11274343" target="_blank">(Selkoe 2001)</a>. These abnormal tangles are made up of poorly soluble, hyperphosphorylated isoforms of Tau, a microtubule-binding protein that is normally soluble. Functional Tau acts as a part of the cell’s ‘cytoskeleton’, which forms the structural support network of a cell. Its dysfunction disrupts the cytoskeleton, making it harder for the cell to carry out essential survival tasks, and engendering cell death. It is thought that tangle formation is aided and perhaps caused by the senile plaques <a href="http://www.ncbi.nlm.nih.gov/pubmed/12130773" target="_blank">(Hardy and Selkoe 2002)</a>. Plaques and tangles predominantly appear in brain areas involved in learning, memory and emotional behaviours.
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The third sign is cell death. Cell death is most apparent in the neocortex, limbic structures, hippocampus, amygdala, and some of the brainstem nuclei. It is cell death that directly causes the symptoms of AD.
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<p class="minor_title">Genetics</p>  
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Studying the genetics of AD has uncovered key genetic risk factors. While highly heritable, AD is genetically complicated, associated with multiple genetic factors, making genetic analysis difficult.  
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Recent progress in characterising AD has lead to the identification of dozens of highly interconnected genetic risk factors, yet it is likely that many more remain undiscovered <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3044851/" target="_blank">(Soler-Lopez et al. 2011)</a> and the elucidation of their roles in AD could prove pivotal in beating the condition. AD is genetically complex, linked with many defects both mutational or of susceptibility. These defects produce alterations in the molecular interactions of cellular pathways, the collective effect of which may be gauged through the structure of the protein network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>. In other words, there is a strong link between protein connectivity and the disease phenotype. AD arises from the downstream interplay between genetic and non-genetic alterations in the human protein interaction network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>.  
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Down’s Syndrome is caused by having three copies of chromosome 21, causing an extra copy of the APP gene <a href="http://www.ncbi.nlm.nih.gov/pubmed/16904243?dopt=Abstract" target="_blank">(Nistor et al. 2007)</a>. This may increase production of beta-amyloid, triggering the chain of biological events leading to AD. Early onset AD is a component of Down Syndrome, indicating that defects in chromosome 21 can lead to Alzheimer’s disease independently of Down’s syndrome. Heritable early-onset AD can also be caused by mutations in the genes presenilin 1 and presenilin 2, which modify how APP is processed. Late onset AD is not necessarily inherited, though relatives of those with AD are at greater risk. Again, why this should be is not fully understood.
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Recent progress in characterising AD has lead to the identification of dozens of highly interconnected genetic risk factors, yet it is likely that many more remain undiscovered <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3044851/" target="_blank">(Soler-Lopez et al. 2011)</a> and the elucidation of their roles in AD could prove pivotal in beating the condition. AD is genetically complex, linked with many defects both mutational or of susceptibility. These defects produce alterations in the molecular interactions of cellular pathways, the collective effect of which may be gauged through the structure of the protein network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>. In other words, there is a strong link between protein connectivity and the disease phenotype. AD arises from the downstream interplay between genetic and non-genetic alterations in the human protein interaction network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>.
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The gene ApoE has three versions called ‘alleles’, e2, e3 and e4. The frequency of e4 is five times higher in AD patients than in the general population <a href="http://www.ncbi.nlm.nih.gov/pubmed/17659183" target="_blank">(Ertekin-Taner 2007)</a>. Those that have two copies (homozygous) for this allele have as much as 8 times higher chance for developing AD. However, inheriting e4 does not cause AD, only increases its likelihood.
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In all pathologies, the most common way to predict driver genes is to target commonly recurrent genes. However, this approach misses misses rare altered genes which comprise the majority of genetic defects leading to, for example, carcinogenesis and arguably AD. This is partly because alterations in a single protein module can lead to the same disease phenotype. Thus, identification may best be attempted on a modular level. Yet it is also important to note correlation events between modules. Simply put, many rare gene alterations that influence the module they belong to and co-altered modules can collectively generate the disease pathology (Gu et al. 2013).
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Not all cases of AD are thought to be genetic <a href="http://www.ncbi.nlm.nih.gov/pubmed/17659183" target="_blank">(Ertekin-Taner 2007)</a>. Both genetic and non-genetic AD cases could arise from the activation of a type of brain cell receptor, p75NTR, by local sources of a protein called pro-NGF. This can initiate what is known as ‘tetraploidisation’ - genetic information doubles as it must for cell division, even though the cell does not divide. This generally occurs when pro-NGF outcompetes a survival factor called BDNF does, in older brains where the brain cells are more stressed. It has been suggested that this process can increase the dosage of genes responsible for the onset of AD, and so heralds in the disease-state <a href="http://www.ncbi.nlm.nih.gov/pubmed/20436277" target="_blank">(Frade & Lopez-Sanchez 2010)</a>.
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<p class="minor_title">Our Programme</p>  
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Under the guidance and tutelage of <a href="http://bmm.cancerresearchuk.org/~cheng03/" target="_blank">Dr Tammy Cheng</a> from the <a href="http://bmm.cancerresearchuk.org/" target="_blank">Biomolecular Modelling (BMM) lab</a> at Cancer Research UK, team member <a href="https://2013.igem.org/Team:UCL/Team/Profile" target="_blank">Alexander Bates</a> coded in python a network analysis programme based on a method devised by Gu et al. and originally applied to the study of glioblastoma (brain cancer). The programme tries to reveal driver genes and co-altered functional modules for AD. The analysis procedure involves mapping altered genes (mutations, amplifications, repressions, etc.) in patient microRNA data to the protein interaction network (PIT), which currently accounts for 48,480 interactions between 10,982 human genes. This is termed the ‘AD altered network’, and is searched with the algorithm suggested by Gu et al. (which has been re-coded from scratch).
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<p class="minor_title">Amyloid Hypothesis</p>  
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It has been more than two decades since the first postulation that AD may be caused by deposition of Aβ  in plaques in brain tissue <a href="http://www.ncbi.nlm.nih.gov/pubmed/12130773" target="_blank">(Hardy and Selkoe 2002)</a>. The ‘Amyloid Hypothesis’ rose as a seemingly strong idea that threaded together genetic and histological data. It posits that concomitant signs and symptoms of the disease arise directly and indirectly due to plaques that appear in quantity in older brains and the genetically susceptible, due to an imbalance in Aβ deposition and clearance <a href="http://www.ncbi.nlm.nih.gov/pubmed/12130773" target="_blank">(Hardy and Selkoe 2002)</a>. Our project is mainly, but not completely, built around assuming the Amyloid Hypothesis is correct.
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The programme builds up gene sets, two at a time, starting from two seed genes. These sets are termed 'modules'. Pairs of  modules (‘G1’ and ‘G2’ in equation) are assumed to be co-altered if any gene within each module is altered in a proportion of AD sufferers, and genes between the modules are often altered together. For two modules, G1 and G2, we must calculate the probability, P, of observing than the number of the samples in the patient gene expression data that by chance simultaneously carry alterations in both gene sets. The gene expression data originates from post-mortem brain samples.
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This imbalance leads to plaque-formation and then a cascade of macromolecular and cellular events which eventually culminate in dementia. In the typical AD brain, there is 7 years worth of un-cleared amyloid production from a healthy person deposited around the brain.
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‘n’ is the total number of patient samples, ‘a’ is the number of patients with alterations in both G1 and G2, ‘b’ is the number of patients with alteration in just G1, ‘c’ is the number of patients with alterations in only G2, and ‘d’ is the number of patients with alterations in neither set. The co-altered score’ S, is defined below. A high score indicates that the two modules tend to be altered together in AD.
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Aβ is neurotoxic, and it has been suggested that this toxicity is due to diffusible forms of the protein permeating the areas around plaques, which are somewhere between the insoluble form and the non-pathological soluble form. They have subtle effects upon the chemical connections (synapses) between communicative brain cells called neurons, which impairs information flow <a href="http://www.ncbi.nlm.nih.gov/pubmed/12130773" target="_blank">(Hardy and Selkoe 2002)</a>. Neurons are further harmed by oxidative stress <a href="http://www.ncbi.nlm.nih.gov/pubmed/19075578" target="_blank">(Su et al. 2008)</a> and neuroinflammation. This is caused by plaques, because they produce oxygen free radicals and other stress agents. Neuroinflammation occurs because the resident immune cells of the brain, <a href="https://2013.igem.org/Team:UCL/Background/Microglia" target="_blank">mircoglial cells</a>, are attracted to plaques. It seems that early on in the disease they help clear amyloid and reduce symptoms, but at later stages the inflammation caused by their activation is far more harmful.
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The formation of intracellular protein (Tau) tangles is thought to occur downstream of plaque formation in the ‘amyloid cascade’ <a href="http://www.ncbi.nlm.nih.gov/pubmed/12130773" target="_blank">(Hardy and Selkoe 2002)</a>, though the two do not correlate in a linear fashion. Their ratios vary patient to patient. Tau tangles are also capable causing cell death on their own, as in frontotemporal lobe dementia, in which there are no protein deposits. In AD, deposits may help form tangles by altering neurons’ kinases and phosphatases (proteins that modify the functions of other proteins by taking away or adding a small  ‘phosphate’ group) activities, making Tau hyperphosphorylated.  
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Fig.1 depicts the searching algorithm. It searches and builds co-altered module pairs for the gene combinations within them that have the greatest co-alteration scores. In step 1, it  methodically choose two seed genes from the AD altered network. The ellipsoids in the diagram denote direct interaction partners for these genes. These are added to the seeds to make temporary module pairs. The dashed line represents co-alteration. In step 2, the co-alteration score for each temporary module pair is calculated. Only the pair with the maximal S score is retained for subsequent searching. This maximal group becomes the new seeds group in step 3. In step 4, temporary modules are again derived, this time from step 3, and the maximum score is kept. In step 5, it must determine whether or not this group of genes is going to continue to expand. Each new addition save for the original two starting seeds is removed and S is recalculated. If in one of these configurations S becomes smaller, we loop through steps 3 to 5 again. Otherwise, if all combinations equate to the S value of the gene groups chosen from step 4, the process stops, having assumed that we have reached maximal module size for the two starting seeds.
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This hypothesis suggests that clearing amyloid, reducing its production or stopping its aggregation ought to halt the progression of AD. However, several therapeutics purported to deplete Aβ production/aggregation have failed in Phase III clinical testing <a href="http://www.ncbi.nlm.nih.gov/pubmed/23178653" target="_blank">(Mullane and Williams 2013)</a>. Some treatments trialled in animal models appear to remove vast quantities of plaques without effect. This may be because AD is caused at a certain amyloid threshold and that excess plaques have no further effect  <a href="http://www.ncbi.nlm.nih.gov/pubmed/12130773" target="_blank">(Hardy and Selkoe 2002)</a>. Alternatively, it may be due to once the plaques have set AD in motion, it continues, and so plaques need to be removed at an earlier stage in the disease.  
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In other words, we try to build up gene sets within a module as large was we can, whilst with each new addition increasing the co-alteration score.
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<p class="minor_title">Other Hypotheses</p>
 
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There are many other theories behind the causation of AD. They are not generally mutually exclusive and it may be that most are correct. The key problem is that it is hard to distinguish the primary cause from secondary effects. Here we give a brief overview of some of these theories:
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We should end up with modules that frequently exhibit significant co-alteration in AD patients, and their gene products are therefore likely to be biochemically significant in the disease state.
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<b>Neurotrophins</b> - Neurotrophins are chemicals that affect neurons. An imbalance in their activity could trigger AD. They activate two classes of receptors - tyrosine receptor kinases (Trks), whose activation supports the neuron, and p75NTR, which may activate an apoptotic pathway. Over-expression of p75NTR and pro-NGF may be caused by oxidative stress <a href="http://www.ncbi.nlm.nih.gov/pubmed/19075578" target="_blank">(Su et al. 2008)</a> in later life, possibly due to plaques. This leads to an increase in AD susceptibility gene dosage by initiating cell cycle re-entry without division. This generally occurs when pro-NGF signals to neurons more than another neurotrophin called BDNF does, in older brains where the brain cells are more stressed. It has been suggested that this process can increase the dosage of genes responsible for the onset of AD, and so heralds in the disease-state <a href="http://www.ncbi.nlm.nih.gov/pubmed/20436277" target="_blank">(Frade & Lopez-Sanchez 2010)</a>. BDNF acts to prevent apoptotic death, but this only means that these cells are fated to a slow death process and steady degeneration.
 
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<b>Neurotransmitters and signalling</b> - AD patients have lower levels of various neurotransmitters that are believed to influence intellectual functioning and behavior, such as acetylcholine <a href="http://www.ncbi.nlm.nih.gov/pubmed/10071091?dopt=Abstract" target="_blank">(Francis et al. 1999)</a>. The cause of this reduced production or something blocking their action may underlie a part of the AD pathology, for example, chemical imbalances or the great toxicity from heavy metals and homocysteine. AD may also be caused by dysfunction in ion channels proteins, which underlie the generation of suppression of electrical transmission in neurons <a href="http://www.ncbi.nlm.nih.gov/pubmed/10071091?dopt=Abstract" target="_blank">(Francis et al. 1999)</a>.
 
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<b>Blood supply</b> -  A poor blood supply in older brains could damage neurons and impair their functioning, leading to the formation of plaques and tangles.
 
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<b>Viral infections</b> -  Viruses have been known to cause brain disorders that symptomatically resemble AD. It is conceivable that an infection could cause the onset of AD by initiating an neuroinflammatory response that is detrimental for neurons, predisposing the brain to the disease-state <a href="http://www.ncbi.nlm.nih.gov/pubmed/18487848" target="_blank">(Itzhaki and Wozniak 2008)</a>.
 
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<b>Neuroinflammation</b>  - It is thought that inflammatory processes could increase the production of waste products such as amyloid in cells, leading to the formation of plaques, then indirectly causing tangle formation <a href="http://www.ncbi.nlm.nih.gov/pubmed/16859761" target="_blank">(Streit 2006)</a>. Anti-inflammatory medication given to patients with conditions other than AD has resulted in a lower contraction of AD amongst these patients. Inflammation also damages cells directly. The microglial cells recruited to the plaques in AD may actually cause cell death by becoming activated and inflaming an area <a href="http://www.ncbi.nlm.nih.gov/pubmed/11801334?dopt=Abstract" target="_blank">(Hensley 2010)</a>, while the plaques themselves have a comparatively minor toxic effect. Producing <a href="https://2013.igem.org/Team:UCL/Project" target="_blank">‘vasoactive intestinal peptide’</a> in our circuit will act to de-active microglia around the plaque, subsequently reducing neuroinflammation.
 
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<b>Tau</b> - Tau tangles could be the direct cause of cell death <a href="http://www.ncbi.nlm.nih.gov/pubmed/11801334?dopt=Abstract" target="_blank">(Mudher and Lovestone 2002)</a>. Plaque build up could be the secondary event, with a comparatively minor toxic effect.
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<b>More Soluble Amyloid</b> - More soluble, smaller abnormal pieces of amyloid could cause AD, while the plaques themselves may actually serve a protective role, by bundling excess amyloid together in discrete locations <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2907530/" target="_blank">(Castellani et al. 2010)</a> where, in early stages of the disease they can be removed by microglia. The plaques may even have an antioxidant, neuroprotective role. They could be constructs typical of end-stage downstream processes triggered by oxidative stress, cell cycle re-entry, inflammation, etc. MMP-9, the protease in our <a href="https://2013.igem.org/Team:UCL/Project" target="_blank">circuit</a>, degrades soluble and insoluble amyloid.
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A complete understanding of the underlying AD molecular mechanisms is vital for the creation of novel treatments able to modify the disease-state biology and efficiently combat the increase of AD with age in global society’s increasing life expectancy. Therefore, a much more effective system than the potential one we propose under the <a href="https://2013.igem.org/Team:UCL/Project" target="_blank">'circuit overview'</a> section of this website could be achieved with an entirely new suite of genes, as more light is shone on the pathogenesis of AD.
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<p class="minor_title">Results</p>  
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Originally we planned, as previously suggested, to use the entirety of the human interactome to create an AD interactome and then run our programme in such a way as to build modules from this interactome. However, the estimated run time of the programme over-shot the iGEM 'wiki freeze' deadline. Therefore, we used the expression data for 311 hub genes, whose proteins are points of high connectivity in the human interactome, across 62 modules defined by Zhang et al., and searched for the hub genes combinations that produced the greatest co-alteration scores. The 62 modules are named after colours.  
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<b>Module groups: </b> <a href="https://static.igem.org/mediawiki/2013/e/ec/AlzModules.txt" target="_blank">AlzModules.py</a>
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<b>Hub expression data:</b> <a href="https://static.igem.org/mediawiki/2013/7/7a/ALzData2.txt" target="_blank">AlzData.py</a>
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<b>Module matrix:</b> <a href="https://static.igem.org/mediawiki/2013/5/5f/AlzList.txt" target="_blank">AlzMatrix.py</a>
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The code for our network analysis programme can be found <a href="https://static.igem.org/mediawiki/2013/4/40/Alex4.txt" target="_blank">here</a>. It needs to be converted to a .py file to be used. Please note that the output is given as a set of numbers that as assigned to genes. For example, the final output for the data we ran can be found <a href="https://static.igem.org/mediawiki/2013/0/0f/AlzFinal.txt" target="_blank">here</a>.
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<th><p class="citation_text">Fig.1 Histogram showing the frequency of gene sets by co-alteration score.</p></th>
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We used the output of our programme to produce a histogram, which shows that the frequency of gene combinations falls exponentially with increasing co-alteration score This suggests that a significant few combinations are regularly co-altered in Alzheimer's disease, in modules that may help drive the disease state. Because we are only looking at which hub genes within modules, we are most interested in what modules are co-altered in the high score end of the histogram, and not the hub genes specifically.</p>
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<p class="body_text">
 +
Below, Fig.2 shows the twenty gene set pairs between two modules, which yielded the greatest co-alteration score. The module pair with the highest score, and that recurs most frequently in the top twenty, are the 'Khaki' and 'Honey Dew' modules. The most enriched functional category of the khaki module is the biosynthesis of a neurotransmitter called GABA. GABA is responsible for neuronal excitability and muscle tone. The Honey Dew module is primarily involved in muscle contraction, though the hub genes AHCYL1 and C9orf61 are thought to be involved in inositol signaling and are possibly associated with another brain condition, bi-polar disorder. However, since the gene expression data is from generally older patients, given the profile of AD, these muscle associated modules may be altered together because of changing muscle usage with age (there is no muscle in the brain but this may represent brain cell structural integrity). Both of these modules have almost 100% of their total brain gene expression in the prefrontal cortex, and area known to be heavily impacted in AD, causing cognitive and intellectual damage. This suggests that our genetic circuit could be adapted to target signaling mechanisms in this area.</p>
 +
 +
<div class="gap"></div>
 +
<div class="gap"></div>
 +
 +
<table>
 +
<th><p class="citation_text">Fig.2 Table of the top 20 gene combinations and their modules by co-alteration score.</p></th>
 +
</table>
 +
<table>
 +
<tr>
 +
<th>Module Name and Gene Set</th>
 +
<th>Module Name and Gene Set</th>
 +
<th>Co-alteration Score</th>
 +
</tr>
 +
<tr>
 +
<td></td>
 +
</tr>
 +
<td><b>Khaki</b></td>
 +
<td><b>Honey Dew</b></td>
 +
<td>20.39 </td>
 +
<tr>
 +
<td>SLC15A2, FXYD1</td>
 +
<td>AHCYL1, C9orf61</td>
 +
</tr>
 +
<tr>
 +
<td></td>
 +
</tr>
 +
<td><b>Khaki</b></td>
 +
<td><b>Honey Dew</b></td>
 +
<td>19.73 </td>
 +
<tr>
 +
<td>GJA1, FXYD1</td>
 +
<td>RFX4, AHCYL1, C9orf61</td>
 +
</tr>
 +
<tr>
 +
<td></td>
 +
</tr>
 +
<td><b>Khaki</b></td>
 +
<td><b>Honey Dew</b></td>
 +
<td>19.37  </td>
 +
<tr>
 +
<td>GJA1, FXYD1, ATP13A4</td>
 +
<td>C20orf141, RFX4, AHCYL1, DGCR6</td>
 +
</tr>
 +
<tr>
 +
<td></td>
 +
</tr>
 +
<td><b>Turquoise</b></td>
 +
<td><b>Cyan</b></td>
 +
<td>18.99 </td>
 +
<tr>
 +
<td>DYNC2LI1, CIRBP, ACRC, RBM4</td>
 +
<td>Contig47252_RC, IFITM2, CDK2</td>
 +
</tr>
 +
<tr>
 +
<td></td>
 +
</tr>
 +
<td><b>Turquoise</b></td>
 +
<td><b>Cyan</b></td>
 +
<td>18.81 </td>
 +
<tr>
 +
<td>DYNC2LI1, CIRBP, ACRC, RBM4</td>
 +
<td>ENST00000289005, Contig47252_RC, IFITM2, CDK2</td>
 +
</tr>
 +
<tr>
 +
<td></td>
 +
</tr>
 +
<td><b>Khaki</b></td>
 +
<td><b>Honey Dew</b></td>
 +
<td>17.69 </td>
 +
<tr>
 +
<td>GJA1, FXYD1, SLC15A2</td>
 +
<td>RFX4, AHCYL1, C9orf61</td>
 +
</tr>
 +
<tr>
 +
<td></td>
 +
</tr>
 +
<td><b>Green 4</b></td>
 +
<td><b>Yellow 3</b></td>
 +
<td>17.57 </td>
 +
<tr>
 +
<td>RRM2, NM_022346, FAM64A</td>
 +
<td>OR4F5, GRAP, XM_166973</td>
 +
</tr>
 +
<tr>
 +
<td></td>
 +
</tr>
 +
<td><b>Turquoise</b></td>
 +
<td><b>Wheat</b></td>
 +
<td>17.49 </td>
 +
<tr>
 +
<td>DYNC2LI1, RBM4</td>
 +
<td>AF087999</td>
 +
</tr>
 +
<tr>
 +
<td></td>
 +
</tr>
 +
<td><b>Green 4</b></td>
 +
<td><b>Yellow 3</b></td>
 +
<td>16.95 </td>
 +
<tr>
 +
<td>HMMR</td>
 +
<td>OR4F5, GRAP</td>
 +
</tr>
 +
<tr>
 +
<td></td>
 +
</tr>
 +
<td><b>Green 4</b></td>
 +
<td><b>Yellow 3</b></td>
 +
<td>16.95 </td>
 +
<tr>
 +
<td>HMMR</td>
 +
<td>OR4F5, GRAP, CRYBA2</td>
 +
</tr>
 +
<tr>
 +
<td></td>
 +
</tr>
 +
<td><b>Turquoise</b></td>
 +
<td><b>Wheat</b></td>
 +
<td>16.78 </td>
 +
<tr>
 +
<td>CIRBP, RBM4</td>
 +
<td>AF087999</td>
 +
</tr>
 +
<tr>
 +
<td></td>
 +
</tr>
 +
<td><b>Green 4</b></td>
 +
<td><b>Yellow 3</b></td>
 +
<td>16.64 </td>
 +
<tr>
 +
<td>RRM2, NMMR, FAM64A</td>
 +
<td>KRTHB4, GRAP, XM_166973</td>
 +
</tr>
 +
<tr>
 +
<td></td>
 +
</tr>
 +
<td><b>Turquoise</b></td>
 +
<td><b>Cyan</b></td>
 +
<td>16.47 </td>
 +
<tr>
 +
<td>DYNC2LI1, CIRBP, ACRC, RCC1, RBM4</td>
 +
<td>Contig47252_RC, IFITM2</td>
 +
</tr>
 +
<tr>
 +
<td></td>
 +
</tr>
 +
<td><b>Turquoise</b></td>
 +
<td><b>Cyan</b></td>
 +
<td>16.46 </td>
 +
<tr>
 +
<td>DYNC2LI1, CIRBP, ACRC, RCC1, RBM4</td>
 +
<td>Contig47252_RC, IFITM2, CDK2</td>
 +
</tr>           
 +
<tr>
 +
<td></td>
 +
</tr>
 +
<td><b>Forestgreen</b></td>
 +
<td><b>Cyan</b></td>
 +
<td>16.43 </td>
 +
<tr>
 +
<td>IFITM3, CSDA</td>
 +
<td>CSDA</td>
 +
</tr>
 +
<tr>
 +
<td></td>
 +
</tr>
 +
<td><b>Turquoise</b></td>
 +
<td><b>Cyan</b></td>
 +
<td>16.38 </td>
 +
<tr>
 +
<td>DYNC2LI1, CIRBP, ACRC, RCC1, RBM4</td>
 +
<td>ENST00000289005, Contig47252_RC, IFITM2</td>
 +
</tr>
 +
<tr>
 +
<td></td>
 +
</tr>
 +
<td><b>Khaki</b></td>
 +
<td><b>Honey Dew</b></td>
 +
<td>16.27 </td>
 +
<tr>
 +
<td>FXYD1, ATP13A4, SLC15A2</td>
 +
<td>AHCYL1, C9orf61</td>
 +
</tr>
 +
<tr>
 +
<td></td>
 +
</tr>
 +
<td><b>Khaki</b></td>
 +
<td><b>Honey Dew</b></td>
 +
<td>16.25 </td>
 +
<tr>
 +
<td>FXYD1, ATP13A4</td>
 +
<td>DGCR6, AHCYL1, C20orf141, C9orf61</td>
 +
</tr>
 +
<tr>
 +
<td></td>
 +
</tr>
 +
<td><b>Gold 2</b></td>
 +
<td><b>Honey Dew</b></td>
 +
<td>16.21 </td>
 +
<tr>
 +
<td>TUBB2B, NM_178525</td>
 +
<td>AHCYL1, C9orf61</td>
 +
</tr>
 +
<tr>
 +
<td></td>
 +
</tr>
 +
<td><b>Khaki</b></td>
 +
<td><b>Honey Dew</b></td>
 +
<td>16.04 </td>
 +
<tr>
 +
<td>SPON1, FXYD1, SLC15A2</td>
 +
<td>AHCYL1, C9orf61</td>
 +
</tr>
 +
</table>
 +
 +
</p>
 +
<p class="minor_title">Analysis and Feedback into Circuit</p>
 +
<p class="body_text">
 +
The second highest scoring module pair,  and the second most frequent in the top twenty, are 'Turquoise' and 'Cyan'. The former is primarily involved with NAD(P) homeostasis, and so is significant in cells' metabolism, while the genes in the later mainly play a role in vasculature development. This suggests that co-alteration in genes involved within these two modules could impact cell vitality and trophic support and help cause AD. This suggests that our circuit could be improved by being adapted to help maintain general cell health and energy supply in the brain. </p>
 +
<p class="body_text">
 +
The third highest scoring module pair, and the third most frequent in the top twenty, are 'Green 4' and 'Yellow 3'. Green 4 is involved in cell cycle regulation, and area that has already been targeted by our circuit, which produces <b>BDNF</b> to help avoid chromosomal division in the neurons of AD patients. Yellow 3 is associated with the peripheral nervous system. Co-alteration here may again be indicative of gene expression changes with age, and its link with Green 4 may suggest that this is to do with a deficiency in cell division, regeneration and growth, but this is not directly related to AD, although hub genes like GRAP do play a role in cytoplasmic signaling in cells including neurons and glia,  This suggests that our circuit could be improved by being adapted to help maintain general cell health and energy supply in the brain. </p>
 +
<p class="body_text">
 +
Other module pairs that feature in the top twenty include 'Wheat' and 'Turqouise', 'Forestgreen' and 'Cyan' and 'Gold 2' and 'Honey Dew'. Wheat is involved in protein folding and responses to unfolded and mis-folded protein. This is significant because incorrectly formed and folded amyloid is strongly associated with the progression of AD. This is something out circuit already seeks to address, but by targeting elements of the 'Wheat' module and similar modules it could aim to avoid mis-creation in the first place, and the nucleation of other mis-folded proteins. Forestgreen is involved in immune functions, which implicates microglia and the cellular response to inflammation in neurons - factors our circuit already tries to help address by acting to prevent neuroinflammation. Its association with Cyan could imply that negative inflammatory effects may be inked with brain vasculature in AD. Gold 2 is associated with the cytoskeleton and axonal cytoskeletal control.In AD, the formation of plaques and protein tangles disrupts the cytoskeleton and perturb axonal connections, engendering cell death. Our circuit tries to target this already by removing the plaques, but perhaps a future improvement should to be to create an element capable to supporting a healthy cytoskeleton or able to remove cytoskeletal protein tangles. Its association with Honey Dew, however, could point to unusual gene expression in this module being due to the lessened use of muscle in old age.</p>
 +
 +
<p class="body_text">
 +
The main addition that we made from the <b>Bioinformatics</b> was related to the third data set: <b>Cell Division</b> and <b>Peripheral Nervous System</b>. The degradation associated with these systems highlighted the need for an additional part for our circuit. We decided on <b>BDNF</b> as a part to promote neuronal growth, which could counter the negative implication from these synergistic genes, which in theory could make our project more marketable, and also more viable for use in clinical trials if the project was progressed. </p>
<!-- END CONTENT ------------------------------------------------------------------------------------------------------>
<!-- END CONTENT ------------------------------------------------------------------------------------------------------>

Latest revision as of 03:40, 5 October 2013

A BIOINFORMATICS APPROACH

Finding New Parts

Bioinformatics creates and enhances methods for storing, retrieving, organising and analysing biological data. We decided to take a completely new approach in our dry lab work and look into bioinformatic approaches to studying Alzheimer’s disease (AD).

The rationale behind this is simple. In order to make a genetic circuit in a synthetic biological construct as effective as possible in a medical application, we may need to target key dysfunctional genes within the problematic biological entity. There are many risk factors for AD and so predicting the key, ‘driver genes’, and the group of proteins with which they interact is invaluable in knowing what we want our construct to produce, in order to mitigate AD. The idea is that bioinformatics work can feed back into synthetic biology, and though we did not have the time to demonstrate this full circle, we feel bioinformatics can have a place in iGEM, helping teams to decide which dysfunctional genes to target in medical projects.

Bioinformatics and Alzheimer’s Disease

Recent progress in characterising AD has lead to the identification of dozens of highly interconnected genetic risk factors, yet it is likely that many more remain undiscovered (Soler-Lopez et al. 2011) and the elucidation of their roles in AD could prove pivotal in beating the condition. AD is genetically complex, linked with many defects both mutational or of susceptibility. These defects produce alterations in the molecular interactions of cellular pathways, the collective effect of which may be gauged through the structure of the protein network (Zhang et al. 2013). In other words, there is a strong link between protein connectivity and the disease phenotype. AD arises from the downstream interplay between genetic and non-genetic alterations in the human protein interaction network (Zhang et al. 2013).

Recent progress in characterising AD has lead to the identification of dozens of highly interconnected genetic risk factors, yet it is likely that many more remain undiscovered (Soler-Lopez et al. 2011) and the elucidation of their roles in AD could prove pivotal in beating the condition. AD is genetically complex, linked with many defects both mutational or of susceptibility. These defects produce alterations in the molecular interactions of cellular pathways, the collective effect of which may be gauged through the structure of the protein network (Zhang et al. 2013). In other words, there is a strong link between protein connectivity and the disease phenotype. AD arises from the downstream interplay between genetic and non-genetic alterations in the human protein interaction network (Zhang et al. 2013).

In all pathologies, the most common way to predict driver genes is to target commonly recurrent genes. However, this approach misses misses rare altered genes which comprise the majority of genetic defects leading to, for example, carcinogenesis and arguably AD. This is partly because alterations in a single protein module can lead to the same disease phenotype. Thus, identification may best be attempted on a modular level. Yet it is also important to note correlation events between modules. Simply put, many rare gene alterations that influence the module they belong to and co-altered modules can collectively generate the disease pathology (Gu et al. 2013).

Our Programme

Under the guidance and tutelage of Dr Tammy Cheng from the Biomolecular Modelling (BMM) lab at Cancer Research UK, team member Alexander Bates coded in python a network analysis programme based on a method devised by Gu et al. and originally applied to the study of glioblastoma (brain cancer). The programme tries to reveal driver genes and co-altered functional modules for AD. The analysis procedure involves mapping altered genes (mutations, amplifications, repressions, etc.) in patient microRNA data to the protein interaction network (PIT), which currently accounts for 48,480 interactions between 10,982 human genes. This is termed the ‘AD altered network’, and is searched with the algorithm suggested by Gu et al. (which has been re-coded from scratch).

The programme builds up gene sets, two at a time, starting from two seed genes. These sets are termed 'modules'. Pairs of modules (‘G1’ and ‘G2’ in equation) are assumed to be co-altered if any gene within each module is altered in a proportion of AD sufferers, and genes between the modules are often altered together. For two modules, G1 and G2, we must calculate the probability, P, of observing than the number of the samples in the patient gene expression data that by chance simultaneously carry alterations in both gene sets. The gene expression data originates from post-mortem brain samples.

‘n’ is the total number of patient samples, ‘a’ is the number of patients with alterations in both G1 and G2, ‘b’ is the number of patients with alteration in just G1, ‘c’ is the number of patients with alterations in only G2, and ‘d’ is the number of patients with alterations in neither set. The co-altered score’ S, is defined below. A high score indicates that the two modules tend to be altered together in AD.

Fig.1 depicts the searching algorithm. It searches and builds co-altered module pairs for the gene combinations within them that have the greatest co-alteration scores. In step 1, it methodically choose two seed genes from the AD altered network. The ellipsoids in the diagram denote direct interaction partners for these genes. These are added to the seeds to make temporary module pairs. The dashed line represents co-alteration. In step 2, the co-alteration score for each temporary module pair is calculated. Only the pair with the maximal S score is retained for subsequent searching. This maximal group becomes the new seeds group in step 3. In step 4, temporary modules are again derived, this time from step 3, and the maximum score is kept. In step 5, it must determine whether or not this group of genes is going to continue to expand. Each new addition save for the original two starting seeds is removed and S is recalculated. If in one of these configurations S becomes smaller, we loop through steps 3 to 5 again. Otherwise, if all combinations equate to the S value of the gene groups chosen from step 4, the process stops, having assumed that we have reached maximal module size for the two starting seeds.

In other words, we try to build up gene sets within a module as large was we can, whilst with each new addition increasing the co-alteration score.

We should end up with modules that frequently exhibit significant co-alteration in AD patients, and their gene products are therefore likely to be biochemically significant in the disease state.

Results

Originally we planned, as previously suggested, to use the entirety of the human interactome to create an AD interactome and then run our programme in such a way as to build modules from this interactome. However, the estimated run time of the programme over-shot the iGEM 'wiki freeze' deadline. Therefore, we used the expression data for 311 hub genes, whose proteins are points of high connectivity in the human interactome, across 62 modules defined by Zhang et al., and searched for the hub genes combinations that produced the greatest co-alteration scores. The 62 modules are named after colours.

Module groups: AlzModules.py

Hub expression data: AlzData.py

Module matrix: AlzMatrix.py

The code for our network analysis programme can be found here. It needs to be converted to a .py file to be used. Please note that the output is given as a set of numbers that as assigned to genes. For example, the final output for the data we ran can be found here.

Fig.1 Histogram showing the frequency of gene sets by co-alteration score.

We used the output of our programme to produce a histogram, which shows that the frequency of gene combinations falls exponentially with increasing co-alteration score This suggests that a significant few combinations are regularly co-altered in Alzheimer's disease, in modules that may help drive the disease state. Because we are only looking at which hub genes within modules, we are most interested in what modules are co-altered in the high score end of the histogram, and not the hub genes specifically.

Below, Fig.2 shows the twenty gene set pairs between two modules, which yielded the greatest co-alteration score. The module pair with the highest score, and that recurs most frequently in the top twenty, are the 'Khaki' and 'Honey Dew' modules. The most enriched functional category of the khaki module is the biosynthesis of a neurotransmitter called GABA. GABA is responsible for neuronal excitability and muscle tone. The Honey Dew module is primarily involved in muscle contraction, though the hub genes AHCYL1 and C9orf61 are thought to be involved in inositol signaling and are possibly associated with another brain condition, bi-polar disorder. However, since the gene expression data is from generally older patients, given the profile of AD, these muscle associated modules may be altered together because of changing muscle usage with age (there is no muscle in the brain but this may represent brain cell structural integrity). Both of these modules have almost 100% of their total brain gene expression in the prefrontal cortex, and area known to be heavily impacted in AD, causing cognitive and intellectual damage. This suggests that our genetic circuit could be adapted to target signaling mechanisms in this area.

Fig.2 Table of the top 20 gene combinations and their modules by co-alteration score.

Module Name and Gene Set Module Name and Gene Set Co-alteration Score
Khaki Honey Dew 20.39
SLC15A2, FXYD1 AHCYL1, C9orf61
Khaki Honey Dew 19.73
GJA1, FXYD1 RFX4, AHCYL1, C9orf61
Khaki Honey Dew 19.37
GJA1, FXYD1, ATP13A4 C20orf141, RFX4, AHCYL1, DGCR6
Turquoise Cyan 18.99
DYNC2LI1, CIRBP, ACRC, RBM4 Contig47252_RC, IFITM2, CDK2
Turquoise Cyan 18.81
DYNC2LI1, CIRBP, ACRC, RBM4 ENST00000289005, Contig47252_RC, IFITM2, CDK2
Khaki Honey Dew 17.69
GJA1, FXYD1, SLC15A2 RFX4, AHCYL1, C9orf61
Green 4 Yellow 3 17.57
RRM2, NM_022346, FAM64A OR4F5, GRAP, XM_166973
Turquoise Wheat 17.49
DYNC2LI1, RBM4 AF087999
Green 4 Yellow 3 16.95
HMMR OR4F5, GRAP
Green 4 Yellow 3 16.95
HMMR OR4F5, GRAP, CRYBA2
Turquoise Wheat 16.78
CIRBP, RBM4 AF087999
Green 4 Yellow 3 16.64
RRM2, NMMR, FAM64A KRTHB4, GRAP, XM_166973
Turquoise Cyan 16.47
DYNC2LI1, CIRBP, ACRC, RCC1, RBM4 Contig47252_RC, IFITM2
Turquoise Cyan 16.46
DYNC2LI1, CIRBP, ACRC, RCC1, RBM4 Contig47252_RC, IFITM2, CDK2
Forestgreen Cyan 16.43
IFITM3, CSDA CSDA
Turquoise Cyan 16.38
DYNC2LI1, CIRBP, ACRC, RCC1, RBM4 ENST00000289005, Contig47252_RC, IFITM2
Khaki Honey Dew 16.27
FXYD1, ATP13A4, SLC15A2 AHCYL1, C9orf61
Khaki Honey Dew 16.25
FXYD1, ATP13A4 DGCR6, AHCYL1, C20orf141, C9orf61
Gold 2 Honey Dew 16.21
TUBB2B, NM_178525 AHCYL1, C9orf61
Khaki Honey Dew 16.04
SPON1, FXYD1, SLC15A2 AHCYL1, C9orf61

Analysis and Feedback into Circuit

The second highest scoring module pair, and the second most frequent in the top twenty, are 'Turquoise' and 'Cyan'. The former is primarily involved with NAD(P) homeostasis, and so is significant in cells' metabolism, while the genes in the later mainly play a role in vasculature development. This suggests that co-alteration in genes involved within these two modules could impact cell vitality and trophic support and help cause AD. This suggests that our circuit could be improved by being adapted to help maintain general cell health and energy supply in the brain.

The third highest scoring module pair, and the third most frequent in the top twenty, are 'Green 4' and 'Yellow 3'. Green 4 is involved in cell cycle regulation, and area that has already been targeted by our circuit, which produces BDNF to help avoid chromosomal division in the neurons of AD patients. Yellow 3 is associated with the peripheral nervous system. Co-alteration here may again be indicative of gene expression changes with age, and its link with Green 4 may suggest that this is to do with a deficiency in cell division, regeneration and growth, but this is not directly related to AD, although hub genes like GRAP do play a role in cytoplasmic signaling in cells including neurons and glia, This suggests that our circuit could be improved by being adapted to help maintain general cell health and energy supply in the brain.

Other module pairs that feature in the top twenty include 'Wheat' and 'Turqouise', 'Forestgreen' and 'Cyan' and 'Gold 2' and 'Honey Dew'. Wheat is involved in protein folding and responses to unfolded and mis-folded protein. This is significant because incorrectly formed and folded amyloid is strongly associated with the progression of AD. This is something out circuit already seeks to address, but by targeting elements of the 'Wheat' module and similar modules it could aim to avoid mis-creation in the first place, and the nucleation of other mis-folded proteins. Forestgreen is involved in immune functions, which implicates microglia and the cellular response to inflammation in neurons - factors our circuit already tries to help address by acting to prevent neuroinflammation. Its association with Cyan could imply that negative inflammatory effects may be inked with brain vasculature in AD. Gold 2 is associated with the cytoskeleton and axonal cytoskeletal control.In AD, the formation of plaques and protein tangles disrupts the cytoskeleton and perturb axonal connections, engendering cell death. Our circuit tries to target this already by removing the plaques, but perhaps a future improvement should to be to create an element capable to supporting a healthy cytoskeleton or able to remove cytoskeletal protein tangles. Its association with Honey Dew, however, could point to unusual gene expression in this module being due to the lessened use of muscle in old age.

The main addition that we made from the Bioinformatics was related to the third data set: Cell Division and Peripheral Nervous System. The degradation associated with these systems highlighted the need for an additional part for our circuit. We decided on BDNF as a part to promote neuronal growth, which could counter the negative implication from these synergistic genes, which in theory could make our project more marketable, and also more viable for use in clinical trials if the project was progressed.