Shared molecular and functional frameworks among five complex human disorders: a comparative study on interactomes linked to susceptibility genes

Do oral antidiabetic medications alter the risk of Parkinson's disease? An updated systematic review and meta-analysis

BACKGROUND

Diabetes mellitus is a known risk factor for Parkinson's disease (PD), but does this risk vary with antidiabetic medications is still unclear. This meta-analysis aims to compile evidence from the literature to assess the risk of idiopathic PD with various oral antidiabetic medications.

METHODS

Databases PubMed, CENTRAL, Scopus, Web of Science, and Embase were searched till 5th April 2023. Adjusted outcomes were pooled to generate a hazard ratio (HR) on the risk of PD with different antidiabetic medications.

RESULTS

Fifteen studies with 2,910,405 diabetic patients were eligible. Pooled analysis failed to show any significant difference in the risk of PD among users of metformin (HR: 1.05 95% CI: 0.91, 1.22 I2 = 81%), glitazones (HR: 0.84 95% CI: 0.68, 1.05 I2 = 91%), glucagon-like peptide-1 agonists (HR: 0.63 95% CI: 0.26, 1.55 I2 = 33%), and sulfonylureas (HR: 1.13 95% CI: 0.96, 1.32 I2 = 76%). However, a meta-analysis of four studies showed that dipeptidyl peptidase-4 inhibitor use was associated with reduced risk of PD in diabetics (HR: 0.69 95% CI: 0.56, 0.86 I2 = 46%). Insufficient data was available on sodium-glucose cotransporter-2 inhibitors, α-glucosidase inhibitors, and glinides.

CONCLUSIONS

Limited retrospective evidence indicates that DPP4i may reduce the risk of idiopathic PD in diabetics. Metformin, sulfonylureas, glucagon-like peptide-1 agonists, and glitazones were not associated with any change in the risk of PD. Further studies taking into confounding factors and using a common comparator group are needed to strengthen present evidence.

Association between diabetes mellitus, prediabetes and risk, disease progression of Parkinson's disease: A systematic review and meta-analysis

Background

Previous studies reported inconsistent results regarding association between diabetes mellitus (DM), prediabetes and risk, disease progression of Parkinson's disease (PD). The meta-analysis was made to investigate association between DM, prediabetes and risk, disease progression of PD.

Methods

Literatures investigating association between DM, prediabetes and risk, disease progression of PD were searched in these databases: PubMed and Web of Science. Included literatures were published before October 2022. STATA 12.0 software was used to compute odds ratios (ORs)/relative risks (RRs) or standard mean differences (SMDs).

Results

DM was associated with a higher risk of PD, compared to non-diabetic participants with a random effects model (OR/RR = 1.23, 95% CI 1.12–1.35, I2 = 90.4%, p < 0.001). PD with DM (PD-DM) was associated with a faster motor progression compared to PD without DM (PD-noDM) with a fixed effects model (RR = 1.85, 95% CI 1.47–2.34, I2 = 47.3%, p = 0.091). However, meta-analysis for comparison in change rate of United Rating Scale (UPDRS) III scores from baseline to follow-up time between PD-DM and PD-noDM reported no difference in motor progression between PD-DM and PD-noDM with a random effects model (SMD = 2.58, 95% CI = −3.11 to 8.27, I2 = 99.9%, p < 0.001). PD-DM was associated with a faster cognitive decline compared to PD-noDM with a fixed effects model (OR/RR = 1.92, 95% CI 1.45–2.55, I2 = 50.3%, p = 0.110).

Conclusions

In conclusion, DM was associated with a higher risk and faster disease decline of PD. More large-scale cohort studies should be adopted to evaluate the association between DM, prediabetes and PD.

Cognitive Impairment in Parkinson's Disease: Epidemiology, Clinical Profile, Protective and Risk Factors

Cognitive impairment is a common non-motor symptom in Parkinson's Disease (PD) and an important source of patient disability and caregiver burden. The timing, profile and rate of cognitive decline varies widely among individuals with PD and can range from normal cognition to mild cognitive impairment (PD-MCI) and dementia (PDD). Beta-amyloid and tau brain accumulation, oxidative stress and neuroinflammation are reported risk factors for cognitive impairment. Traumatic brain injury and pesticide and tobacco exposure have also been described. Genetic risk factors including genes such as COMT, APOE, MAPT and BDNF may also play a role. Less is known about protective factors, although the Mediterranean diet and exercise may fall in this category. Nonetheless, there is conflicting evidence for most of the factors that have been studied. The use of inconsistent criteria and lack of comprehensive assessment in many studies are important methodological issues. Timing of exposure also plays a crucial role, although identification of the correct time window has been historically difficult in PD. Our understanding of the mechanism behind these factors, as well as the interactions between gene and environment as determinants of disease phenotype and the identification of modifiable risk factors will be paramount, as this will allow for potential interventions even in established PD.

Using Data Science to Understand Complexity and Quantify Heterogeneity in the Onset and Progression of Chronic Disease

Novel discoveries in genomics and other omics sciences are rapidly redefining our understanding of health and disease as well as advancing the development of targeted therapeutics for improving health outcomes. The scale of these findings, fueled by vast increases in computing power and new techniques in data analytics, easily supersedes that of phenomena observed using more traditional research approaches. Until recently, the classification and diagnosis of disease has involved rather subjective processes, whereby signs and late symptom patterns are linked with clinical outcomes. However, symptom patterns, disease trajectories, and health outcomes are complex entities characterized by a wide range of clinical manifestations and progression patterns. The burgeoning fields of data science and bioinformatics are opening opportunities for nurse scientists to quantify disease heterogeneity by defining and categorizing disease phenotypes and endotypes. Nurse scientists and clinicians can play a critical role in engaging patients and the larger scientific community in these efforts. The purpose of this article is to provide an introduction to concepts critical to understanding and quantifying heterogeneity in the onset and progression of chronic disease. To present and exemplify key concepts, we (1) discuss evidence for heterogeneity in the onset and progression of Type 1 diabetes, (2) link emerging research approaches in data science with principles in network science and systems biology to lay the groundwork for stratifying subclinical and advanced chronic disease, thus expanding the purview of symptom science, and (3) describe the computational skills needed to engage in these analyses.

Autoimmunity and autoinflammation: A systems view on signaling pathway dysregulation profiles

INTRODUCTION

Autoinflammatory and autoimmune disorders are characterized by aberrant changes in innate and adaptive immunity that may lead from an initial inflammatory state to an organ specific damage. These disorders possess heterogeneity in terms of affected organs and clinical phenotypes. However, despite the differences in etiology and phenotypic variations, they share genetic associations, treatment responses and clinical manifestations. The mechanisms involved in their initiation and development remain poorly understood, however the existence of some clear similarities between autoimmune and autoinflammatory disorders indicates variable degrees of interaction between immune-related mechanisms.

METHODS

Our study aims at contributing to a holistic, pathway-centered view on the inflammatory condition of autoimmune and autoinflammatory diseases. We have evaluated similarities and specificities of pathway activity changes in twelve autoimmune and autoinflammatory disorders by performing meta-analysis of publicly available gene expression datasets generated from peripheral blood mononuclear cells, using a bioinformatics pipeline that integrates Self Organizing Maps and Pathway Signal Flow algorithms along with KEGG pathway topologies.

RESULTS AND CONCLUSIONS

The results reveal that clinically divergent disease groups share common pathway perturbation profiles. We identified pathways, similarly perturbed in all the studied diseases, such as PI3K-Akt, Toll-like receptor, and NF-kappa B signaling, that serve as integrators of signals guiding immune cell polarization, migration, growth, survival and differentiation. Further, two clusters of diseases were identified based on specifically dysregulated pathways: one gathering mostly autoimmune and the other mainly autoinflammatory diseases. Cluster separation was driven not only by apparent involvement of pathways implicated in adaptive immunity in one case, and inflammation in the other, but also by processes not explicitly related to immune response, but rather representing various events related to the formation of specific pathophysiological environment. Thus, our data suggest that while all of the studied diseases are affected by activation of common inflammatory processes, disease-specific variations in their relative balance are also identified.

Comparative analysis of SNP candidates in disparate milk yielding river buffaloes using targeted sequencing

River buffalo (Bubalus bubalis) milk plays an important role in economy and nutritious diet in several developing countries. However, reliable milk-yield genomic markers and their functional insights remain unexposed. Here, we have used a target capture sequencing approach in three economically important buffalo breeds namely: Banni, Jafrabadi and Mehsani, belonging to either high or low milk-yield group. Blood samples were collected from the milk-yield/breed balanced group of 12 buffaloes, and whole exome sequencing was performed using Roche 454 GS-FLX Titanium sequencer. Using an innovative approach namely, MultiCom; we have identified high-quality SNPs specific for high and low-milk yield buffaloes. Almost 70% of the reported genes in QTL regions of milk-yield and milk-fat in cattle were present among the buffalo milk-yield gene candidates. Functional analysis highlighted transcriptional regulation category in the low milk-yield group, and several new pathways in the two groups. Further, the discovered SNP candidates may account for more than half of mammary transcriptome changes in high versus low-milk yielding cattle. Thus, starting from the design of a reliable strategy, we identified reliable genomic markers specific for high and low-milk yield buffalo breeds and addressed possible downstream effects.

Functional genomics of candidate genes derived from genome-wide association studies for five common neurological diseases

AIM

Recent genome-wide association studies (GWAS) are identifying novel candidate genes for several neurological diseases (NDs). However, a global functional analysis of those genes derived from GWAS for NDs is missing. We explored the genomic and functional features of novel candidate genes for five common NDs: Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, stroke and migraine.

MATERIALS AND METHODS

A functional enrichment analysis was performed for GWAS-derived genes, for categories such as Kyoto Encyclopedia of Genes and Genomes pathways, gene expression, InterPro domains, transcription factor binding sites, gene ontology (GO) terms and microRNA (miRNA) targets. An analysis of protein-protein interactions was carried out.

RESULTS

Six hundred and forty-two unique single nucleotide polymorphisms (SNPs) were identified for the five NDs and 2.3% of them were non-synonymous SNPs. There were no common SNPs for all five NDs and eight genes were associated with more than one ND. The enrichment analysis showed significant values for several GO categories, such as cell-cell adhesion and location in neurites and for expression in prefrontal cortex. An analysis of protein-protein interactions showed the evidence of a large component. Fifty-one of these GWAS-derived genes are known to be potentially druggable and twelve are known to harbor mutations for neuropsychiatric disorders.

CONCLUSIONS

Our results suggest that there is little overlap between the genes identified in GWAS for the five common NDs. Identification of functional categories in the GWAS-derived candidate genes for common NDs could lead to a better understanding of their functional consequences and could be useful for the future discovery of additional genetic risk factors for those diseases.

Neurological Diseases from a Systems Medicine Point of View

The difficulty to understand, diagnose, and treat neurological disorders stems from the great complexity of the central nervous system on different levels of physiological granularity. The individual components, their interactions, and dynamics involved in brain development and function can be represented as molecular, cellular, or functional networks, where diseases are perturbations of networks. These networks can become a useful research tool in investigating neurological disorders if they are properly tailored to reflect corresponding mechanisms. Here, we review approaches to construct networks specific for neurological disorders describing disease-related pathology on different scales: the molecular, cellular, and brain level. We also briefly discuss cross-scale network analysis as a necessary integrator of these scales.

Identification of shared and unique susceptibility pathways among cancers of the lung, breast, and prostate from genome-wide association studies and tissue-specific protein interactions

Results from genome-wide association studies (GWAS) have indicated that strong single-gene effects are the exception, not the rule, for most diseases. We assessed the joint effects of germline genetic variations through a pathway-based approach that considers the tissue-specific contexts of GWAS findings. From GWAS meta-analyses of lung cancer (12 160 cases/16 838 controls), breast cancer (15 748 cases/18 084 controls) and prostate cancer (14 160 cases/12 724 controls) in individuals of European ancestry, we determined the tissue-specific interaction networks of proteins expressed from genes that are likely to be affected by disease-associated variants. Reactome pathways exhibiting enrichment of proteins from each network were compared across the cancers. Our results show that pathways associated with all three cancers tend to be broad cellular processes required for growth and survival. Significant examples include the nerve growth factor (P = 7.86 × 10(-33)), epidermal growth factor (P = 1.18 × 10(-31)) and fibroblast growth factor (P = 2.47 × 10(-31)) signaling pathways. However, within these shared pathways, the genes that influence risk largely differ by cancer. Pathways found to be unique for a single cancer focus on more specific cellular functions, such as interleukin signaling in lung cancer (P = 1.69 × 10(-15)), apoptosis initiation by Bad in breast cancer (P = 3.14 × 10(-9)) and cellular responses to hypoxia in prostate cancer (P = 2.14 × 10(-9)). We present the largest comparative cross-cancer pathway analysis of GWAS to date. Our approach can also be applied to the study of inherited mechanisms underlying risk across multiple diseases in general.

Network analysis identifies SOD2 mRNA as a potential biomarker for Parkinson's disease

Increasing evidence indicates that Parkinson's disease (PD) and type 2 diabetes (T2DM) share dysregulated molecular networks. We identified 84 genes shared between PD and T2DM from curated disease-gene databases. Nitric oxide biosynthesis, lipid and carbohydrate metabolism, insulin secretion and inflammation were identified as common dysregulated pathways. A network prioritization approach was implemented to rank genes according to their distance to seed genes and their involvement in common biological pathways. Quantitative polymerase chain reaction assays revealed that a highly ranked gene, superoxide dismutase 2 (SOD2), is upregulated in PD patients compared to healthy controls in 192 whole blood samples from two independent clinical trials, the Harvard Biomarker Study (HBS) and the Diagnostic and Prognostic Biomarkers in Parkinson's disease (PROBE). The results from this study reinforce the idea that shared molecular networks between PD and T2DM provides an additional source of biologically meaningful biomarkers. Evaluation of this biomarker in de novo PD patients and in a larger prospective longitudinal study is warranted.

System-based approaches to decode the molecular links in Parkinson's disease and diabetes

A growing body of evidence indicates an increased risk for developing Parkinson's disease (PD) among people with type 2 diabetes (T2DM). The relationship between the etiology and development of both chronic diseases is beginning to be uncovered and recent studies show that PD and T2DM share remarkably similar dysregulated pathways. It has been proposed that a cascade of events including mitochondrial dysfunction, impaired insulin signaling, and metabolic inflammation trigger neurodegeneration in T2DM models. Network-based approaches have elucidated a potential molecular framework linking both diseases. Further, transcriptional signatures that modulate the neurodegenerative phenotype in T2DM have been identified. Here we contextualize the current experimental approaches to dissect the mechanisms underlying the association between PD and T2DM and discuss the existing challenges toward the understanding of the coexistence of these devastating aging diseases.

Identification of shared genes and pathways: a comparative study of multiple sclerosis susceptibility, severity and response to interferon beta treatment

Recent genome-wide association studies (GWAS) have successfully identified several gene loci associated with multiple sclerosis (MS) susceptibility, severity or interferon-beta (IFN-ß) response. However, due to the nature of these studies, the functional relevance of these loci is not yet fully understood. We have utilized a systems biology based approach to explore the genetic interactomes of these MS related traits. We hypothesised that genes and pathways associated with the 3 MS related phenotypes might interact collectively to influence the heterogeneity and unpredictable clinical outcomes observed. Individual genetic interactomes for each trait were constructed and compared, followed by prioritization of common interactors based on their frequencies. Pathway enrichment analyses were performed to highlight shared functional pathways. Biologically relevant genes ABL1, GRB2, INPP5D, KIF1B, PIK3R1, PLCG1, PRKCD, SRC, TUBA1A and TUBA4A were identified as common to all 3 MS phenotypes. We observed that the highest number of first degree interactors were shared between MS susceptibility and MS severity (p = 1.34×10⁻⁷⁹) with UBC as the most prominent first degree interactor for this phenotype pair from the prioritisation analysis. As expected, pairwise comparisons showed that MS susceptibility and severity interactomes shared the highest number of pathways. Pathways from signalling molecules and interaction, and signal transduction categories were found to be highest shared pathways between 3 phenotypes. Finally, FYN was the most common first degree interactor in the MS drugs-gene network. By applying the systems biology based approach, additional significant information can be extracted from GWAS. Results of our interactome analyses are complementary to what is already known in the literature and also highlight some novel interactions which await further experimental validation. Overall, this study illustrates the potential of using a systems biology based approach in an attempt to unravel the biological significance of gene loci identified in large GWAS.

Early life nutrient restriction impairs blood-brain metabolic profile and neurobehavior predisposing to Alzheimer's disease with aging

Prenatal nutrient restriction (NR) culminating in intra-uterine growth restriction (IUGR) with postnatal catch up growth leads to diabesity. In contrast, postnatal NR with growth restriction (PNGR) superimposed on IUGR (IPGR) protects young and aging adults from this phenotype. We hypothesized that PNGR/IPGR will compromise the blood-brain metabolic profile impairing neurobehavior and predisposing to Alzheimer's disease (AD). NR (50%) in late gestation followed by cross-fostering of rat pups to either ad lib fed (CON) or NR (50%) lactating mothers generated CON, IUGR, PNGR and IPGR male (M) and female (F) offspring that were examined through the life span. In PNGR/IPGR plasma/CSF glucose and lactate decreased while ketones increased in (M) and (F) (PN21, PN50). In addition increased brain glucose transporters, Glut1 & Glut3, greater brain derived neurotrophic factor (BDNF), reduced Glut4, with unchanged serotonin transporter concentrations were noted in (F) (PN50-60). While (F) displayed more hyperactivity, both (F) and (M) exhibited anxiety although socially and cognitively unimpaired (PN25-28&50). Aging (15-17 m) (F) not (M), expressed low plasma insulin, reduced brain IRS-2, pAkt, and pGSK-3β(Ser9), unchanged pPDK1, pTau or lipoprotein receptor related protein 1 (LRP1), higher glial fibrillary acidic protein (GFAP) and spinophilin but a 10-fold increased amyloid-β42. We conclude that therapeutically superimposing PNGR on IUGR (IPGR) should be carefully weighed in light of unintended consequences related to perturbed neurobehavior and potential predilection for AD.

Insights on personalized medicine from Ayurveda

The "omics" era of research has provided vital information on the genetic and biochemical diversity of individuals. This has lead to the emergence of "personalized medicine," wherein one aims to design specific drugs for individual patients or subtypes of patients. Indeed, the ongoing patent wars on this matter, suggest that personalized medicine represents a major goal for today's pharmaceutical industries. Although the concept of personalized medicine is new to modern medicine, it is a well-established concept in Ayurveda, the traditional system of Indian medicine that is still being practiced. Therefore, this article discusses topics that are crucial for the advancement of modern personalized medicine. These topics include disease susceptibility, disease subtypes, and Ayurvedic therapeutics. First, we explain how Ayurveda, Traditional Chinese Medicine, and Traditional Korean medicine or Sasang Constitutional medicine; conceptualize disease susceptibility and disease subtypes. Next, we focus on conceptual similarities between molecular medicine and Ayurvedic concepts of disease susceptibility and disease subtypes. For each topic, we explain the relevant experimental evidence reported in the literature. We also propose new hypotheses and suggest experimental approaches for their testing and validation.

What have we learned from six years of GWAS in autoimmune diseases, and what is next?

Genome-wide association studies (GWAS) have discovered hundreds of common genetic variants that predispose humans to autoimmune diseases, opening up unprecedented potential for elucidating the pathways and processes of disease. To understand the role of these variants in susceptibility, we need to derive mechanistic insight by integration of genetic results with other biological data types and also with careful functional studies. In many cases, such studies have highlighted coherent biological processes at a high level and elucidated specific mechanisms that contribute to autoimmunity and inflammation. The understanding of the genetic component of autoimmune etiology will become more complete as fine-mapping and sequencing data become readily available. A comprehensive catalog of human immune phenotypes could provide a functional basis for assessing genetic influence on immune function and variation in response to therapeutic interventions, as well as for rationally designing new targeted therapeutics.

An arrayed human genomic library constructed in the PAC shuttle vector pJCPAC-Mam2 for genome-wide association studies and gene therapy

The various iterations of the HapMap Project and many genome-wide association studies (GWAS) have identified hundreds of potential genes involved in monogenic and multifactorial traits. We constructed an arrayed 115,000-member human genomic library in the PAC shuttle vector pJCPAC-Mam2 that can be propagated in both bacterial and human cells. The library appears to represent a two-fold coverage of the human genome. Transient transfection of a p53-containing PAC clone into p53-null Saos-2 human osteosarcoma cells demonstrated that both p53 mRNA and protein were produced. Additionally, expression of the p53 protein triggered apoptosis in a subset of the Saos-2 cells. This library should serve as a valuable resource to validate potential disease genes identified by GWAS in human cell lines and in animal models. Also, individual library members could potentially be used for gene therapy trials for a variety of recessive disorders.

Mechanisms underlying insulin deficiency-induced acceleration of β-amyloidosis in a mouse model of Alzheimer's disease

Although evidence is accumulating that diabetes mellitus is an important risk factor for sporadic Alzheimer's disease (AD), the mechanisms by which defects in insulin signaling may lead to the acceleration of AD progression remain unclear. In this study, we applied streptozotocin (STZ) to induce experimental diabetes in AD transgenic mice (5XFAD model) and investigated how insulin deficiency affects the β-amyloidogenic processing of amyloid precursor protein (APP). Two and half months after 5XFAD mice were treated with STZ (90 mg/kg, i.p., once daily for two consecutive days), they showed significant reductions in brain insulin levels without changes in insulin receptor expression. Concentrations of cerebral amyloid-β peptides (Aβ40 and Aβ42) were significantly increased in STZ-treated 5XFAD mice as compared with vehicle-treated 5XFAD controls. Importantly, STZ-induced insulin deficiency upregulated levels of both β-site APP cleaving enzyme 1 (BACE1) and full-length APP in 5XFAD mouse brains, which was accompanied by dramatic elevations in the β-cleaved C-terminal fragment (C99). Interestingly, BACE1 mRNA levels were not affected, whereas phosphorylation of the translation initiation factor eIF2α, a mechanism proposed to mediate the post-transcriptional upregulation of BACE1, was significantly elevated in STZ-treated 5XFAD mice. Meanwhile, levels of GGA3, an adapter protein responsible for sorting BACE1 to lysosomal degradation, are indistinguishable between STZ- and vehicle-treated 5XFAD mice. Moreover, STZ treatments did not affect levels of Aβ-degrading enzymes such as neprilysin and insulin-degrading enzyme (IDE) in 5XFAD brains. Taken together, our findings provide a mechanistic foundation for a link between diabetes and AD by demonstrating that insulin deficiency may change APP processing to favor β-amyloidogenesis via the translational upregulation of BACE1 in combination with elevations in its substrate, APP.

Alzheimer's Disease: APP, Gamma Secretase, APOE, CLU, CR1, PICALM, ABCA7, BIN1, CD2AP, CD33, EPHA1, and MS4A2, and Their Relationships with Herpes Simplex, C. Pneumoniae, Other Suspect Pathogens, and the Immune System

Alzheimer's disease susceptibility genes, APP and gamma-secretase, are involved in the herpes simplex life cycle, and that of other suspect pathogens (C. pneumoniae, H. pylori, C. neoformans, B. burgdorferri, P. gingivalis) or immune defence. Such pathogens promote beta-amyloid deposition and tau phosphorylation and may thus be causative agents, whose effects are conditioned by genes. The antimicrobial effects of beta-amyloid, the localisation of APP/gamma-secretase in immunocompetent dendritic cells, and gamma secretase cleavage of numerous pathogen receptors suggest that this network is concerned with pathogen disposal, effects which may be abrogated by the presence of beta-amyloid autoantibodies in the elderly. These autoantibodies, as well as those to nerve growth factor and tau, also observed in Alzheimer's disease, may well be antibodies to pathogens, due to homology between human autoantigens and pathogen proteins. NGF or tau antibodies promote beta-amyloid deposition, neurofibrillary tangles, or cholinergic neuronal loss, and, with other autoantibodies, such as anti-ATPase, are potential agents of destruction, whose formation is dictated by sequence homology between pathogen and human proteins, and thus by pathogen strain and human genes. Pathogen elimination in the ageing population and removal of culpable autoantibodies might reduce the incidence and offer hope for a cure in this affliction.

Evidence for more than one Parkinson's disease-associated variant within the HLA region

Parkinson's disease (PD) was recently found to be associated with HLA in a genome-wide association study (GWAS). Follow-up GWAS's replicated the PD-HLA association but their top hits differ. Do the different hits tag the same locus or is there more than one PD-associated variant within HLA? We show that the top GWAS hits are not correlated with each other (0.00≤r(2)≤0.15). Using our GWAS (2000 cases, 1986 controls) we conducted step-wise conditional analysis on 107 SNPs with P<10(-3) for PD-association; 103 dropped-out, four remained significant. Each SNP, when conditioned on the other three, yielded P(SNP1) = 5×10(-4), P(SNP2) = 5×10(-4), P(SNP3) = 4×10(-3) and P(SNP4) = 0.025. The four SNPs were not correlated (0.01≤r(2)≤0.20). Haplotype analysis (excluding rare SNP2) revealed increasing PD risk with increasing risk alleles from OR = 1.27, P = 5×10(-3) for one risk allele to OR = 1.65, P = 4×10(-8) for three. Using additional 843 cases and 856 controls we replicated the independent effects of SNP1 (P(conditioned-on-SNP4) = 0.04) and SNP4 (P(conditioned-on-SNP1) = 0.04); SNP2 and SNP3 could not be replicated. In pooled GWAS and replication, SNP1 had OR(conditioned-on-SNP4) = 1.23, P(conditioned-on-SNP4) = 6×10(-7); SNP4 had OR(conditioned-on-SNP1) = 1.18, P(conditioned-on-SNP1) = 3×10(-3); and the haplotype with both risk alleles had OR = 1.48, P = 2×10(-12). Genotypic OR increased with the number of risk alleles an individual possessed up to OR = 1.94, P = 2×10(-11) for individuals who were homozygous for the risk allele at both SNP1 and SNP4. SNP1 is a variant in HLA-DRA and is associated with HLA-DRA, DRB5 and DQA2 gene expression. SNP4 is correlated (r(2) = 0.95) with variants that are associated with HLA-DQA2 expression, and with the top HLA SNP from the IPDGC GWAS (r(2) = 0.60). Our findings suggest more than one PD-HLA association; either different alleles of the same gene, or separate loci.

Allotment of carbon is responsible for disorders in proteins

Sequence stretches in proteins that do not fold into a form are referred as disordered regions. Databases like Disport describe disordered regions in proteins and web servers like PrDOS and DisEMBL, facilitate the prediction of disordered regions. These studies are often based on residue level features. Here, we describe proteins with disordered regions using carbon content and distributions. The distribution pattern for proteins with disordered regions is different from those that do not show disordered regions.