Role for protein-protein interaction databases in human genetics

Identification of high-risk non-synonymous SNPs (nsSNPs) in DNAH1 and DNAH17 genes associated with male infertility: a bioinformatics analysis

Male infertility is a significant reproductive issue affecting a considerable number of couples worldwide. While there are various causes of male infertility, genetic factors play a crucial role in its development. We focused on identifying and analyzing the high-risk nsSNPs in DNAH1 and DNAH17 genes, which encode proteins involved in sperm motility. A total of 20 nsSNPs for DNAH1 and 10 nsSNPs for DNAH17 were analyzed using various bioinformatics tools including SIFT, PolyPhen-2, CADD, PhD-SNPg, VEST-4, and MutPred2. As a result, V1287G, L2071R, R2356W, R3169C, R3229C, E3284K, R4096L, R4133C, and A4174T in DNAH1 gene and C1803Y, C1829Y, R1903C, and L3595P in DNAH17 gene were identified as high-risk nsSNPs. These nsSNPs were predicted to decrease protein stability, and almost all were found in highly conserved amino acid positions. Additionally, 4 nsSNPs were observed to alter post-translational modification status. Furthermore, the interaction network analysis revealed that DNAH1 and DNAH17 interact with DNAH2, DNAH3, DNAH5, DNAH7, DNAH8, DNAI2, DNAL1, CFAP70, DNAI3, DNAI4, ODAD1, and DNAI7, demonstrating the importance of DNAH1 and DNAH17 proteins in the overall functioning of the sperm motility machinery. Taken together, these findings revealed the detrimental effects of identified high-risk nsSNPs on protein structure and function and highlighted their potential relevance to male infertility. Further studies are warranted to validate these findings and to elucidate the underlying mechanisms.

Developing theragnostics for Alzheimer's disease: Insights from cancer treatment

The prevalence of Alzheimer's disease (AD) and its associated economic and societal burdens are on the rise, but there are no curative treatments for AD. Interestingly, this neurodegenerative disease shares several biological and pathophysiological features with cancer, including cell-cycle dysregulation, angiogenesis, mitochondrial dysfunction, protein misfolding, and DNA damage. However, the genetic factors contributing to the overlap in biological processes between cancer and AD have not been actively studied. In this review, we discuss the shared biological features of cancer and AD, the molecular targets of anticancer drugs, and therapeutic approaches. First, we outline the common biological features of cancer and AD. Second, we describe several anticancer drugs, their molecular targets, and their effects on AD pathology. Finally, we discuss how protein-protein interactions (PPIs), receptor inhibition, immunotherapy, and gene therapy can be exploited for the cure and management of both cancer and AD. Collectively, this review provides insights for the development of AD theragnostics based on cancer drugs and molecular targets.

Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context

At the heart of the cellular machinery through the regulation of cellular functions, protein-protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.

Inferring Gene Co-Expression Networks by Incorporating Prior Protein-Protein Interaction Networks

Inferring gene co-expression networks from high-throughput gene expression data is an important task in bioinformatics. Many gene networks often exhibit modular structures. Although several Gaussian graphical model-based methods have been developed to estimate gene co-expression networks by incorporating the modular structural prior, none of them takes into account the modular structures captured by the prior networks (e.g., protein interaction networks). In this study, we propose a novel prior network-dependent gene network inference (pGNI) method to estimate gene co-expression networks by integrating gene expression data and prior protein interaction network data. The underlying modular structure is learned from both sets of data. Through simulation studies, we demonstrate the feasibility and effectiveness of our method. We also apply our method to two real datasets. The modular structures in the networks estimated by our method are biological significant.

Application of proteomics in shrimp and shrimp aquaculture

Since proteins play an important role in the life of an organism, many researchers are now looking at how genes and proteins interact to form different proteins. It is anticipated that the creation of adequate tools for rapid analysis of proteins will accelerate the determination of functional aspects of these biomolecules and develop new biomarkers and therapeutic targets for the diagnosis and treatment of various diseases. Though shrimp contains high-quality marine proteins, there are reports about the heavy losses to the shrimp industry due to the poor quality of shrimp production and many times due to mass mortality also. Frequent outbreaks of diseases, water pollution, and quality of feed are some of the most recognized reasons for such losses. In the seafood export market, shrimp occupies the top position in currency earnings and strengthens the economy of many developing nations. Therefore, it is vital for shrimp-producing companies they produce healthy shrimp with high-quality protein. Though aquaculture is a very competitive market, global awareness regarding the use of scientific knowledge and emerging technologies to obtain better-farmed organisms through sustainable production has enhanced the importance of proteomics in seafood biology research. Proteomics, as a powerful tool, has therefore been increasingly used to address several issues in shrimp aquaculture. In the present paper, efforts have been made to address some of them, particularly the role of proteomics in reproduction, breeding and spawning, immunological responses and disease resistance capacity, nutrition and health, microbiome and probiotics, quality and safety of shrimp production, bioinformatics applications in proteomics, the discovery of protein biomarkers, and mitigating biotic and abiotic stresses. Future challenges and research directions on proteomics in shrimp aquaculture have also been discussed.

A determination of pan-pathogen antimicrobials?

While antimicrobial drug development has historically mitigated infectious diseases that are known, COVID-19 revealed a dearth of ‘in-advance’ therapeutics suitable for infections by pathogens that have not yet emerged. Such drugs must exhibit a property that is antithetical to the classical paradigm of antimicrobial development: the ability to treat infections by any pathogen. Characterisation of such ‘pan-pathogen’ antimicrobials requires consolidation of drug repositioning studies, a new and growing field of drug discovery. In this review, a previously-established system for evaluating repositioning studies is used to highlight 4 therapeutics which exhibit pan-pathogen properties, namely azithromycin, ivermectin, niclosamide, and nitazoxanide. Recognition of the pan-pathogen nature of these antimicrobials is the cornerstone of a novel paradigm of antimicrobial development that is not only anticipatory of pandemics and bioterrorist attacks, but cognisant of conserved anti-infective mechanisms within the host-pathogen interactome which are only now beginning to emerge. Ultimately, the discovery of pan-pathogen antimicrobials is concomitantly the discovery of a new class of antivirals, and begets significant implications for pandemic preparedness research in a world after COVID-19.

Aquaculture industry prospective from gut microbiome of fish and shellfish: An overview

The microbiome actually deals with micro-organisms that are associated with indigenous body parts and the entire gut system in all animals, including human beings. These microbes are linked with roles involving hereditary traits, defence against diseases and strengthening overall immunity, which determines the health status of an organism. Considerable efforts have been made to find out the microbiome diversity and their taxonomic identification in finfish and shellfish and its importance has been correlated with various physiological functions and activities. In recent past due to the availability of advanced molecular tools, some efforts have also been made on DNA sequencing of these microbes to understand the environmental impact and other stress factors on their genomic structural profile. There are reports on the use of next-generation sequencing (NGS) technology, including amplicon and shot-gun approaches, and associated bioinformatics tools to count and classify commensal microbiome at the species level. The microbiome present in the whole body, particularly in the gut systems of finfish and shellfish, not only contributes to digestion but also has an impact on nutrition, growth, reproduction, immune system and vulnerability of the host fish to diseases. Therefore, the study of such microbial communities is highly relevant for the development of new and innovative bio-products which will be a vital source to build bio and pharmaceutical industries, including aquaculture. In recent years, attempts have been made to discover the chemical ingredients present in these microbes in the form of biomolecules/bioactive compounds with their functions and usefulness for various health benefits, particularly for the treatment of different types of disorders in animals. Therefore, it has been speculated that microbiomes hold great promise not only as a cure for ailments but also as a preventive measure for the number of infectious diseases. This kind of exploration of new breeds of microbes with their miraculous ingredients will definitely help to accelerate the development of the drugs, pharmaceutical and other biological related industries. Probiotic research and bioinformatics skills will further escalate these opportunities in the sector. In the present review, efforts have been made to collect comprehensive information on the finfish and shellfish microbiome, their diversity and functional properties, relationship with diseases, health status, data on species-specific metagenomics, probiotic research and bioinformatics skills. Further, emphasis has also been made to carry out microbiome research on priority basis not only to keep healthy environment of the fish farming sector but also for the sustainable growth of biological related industries, including aquaculture.

The design and development of covalent protein-protein interaction inhibitors for cancer treatment

Protein-protein interactions (PPIs) are central to a variety of biological processes, and their dysfunction is implicated in the pathogenesis of a range of human diseases, including cancer. Hence, the inhibition of PPIs has attracted significant attention in drug discovery. Covalent inhibitors have been reported to achieve high efficiency through forming covalent bonds with cysteine or other nucleophilic residues in the target protein. Evidence suggests that there is a reduced risk for the development of drug resistance against covalent drugs, which is a major challenge in areas such as oncology and infectious diseases. Recent improvements in structural biology and chemical reactivity have enabled the design and development of potent and selective covalent PPI inhibitors. In this review, we will highlight the design and development of therapeutic agents targeting PPIs for cancer therapy.

An in-silico approach for discovery of microRNA-TF regulation of DISC1 interactome mediating neuronal migration

Neuronal migration constitutes an important step in corticogenesis; dysregulation of the molecular mechanisms mediating this crucial step in neurodevelopment may result in various neuropsychiatric disorders. By curating experimental data from published literature, we identified eight functional modules involving Disrupted-in-schizophrenia 1 (DISC1) and its interacting proteins that regulate neuronal migration. We then identified miRNAs and transcription factors (TFs) that form functional feedback loops and regulate gene expression of the DISC1 interactome. Using this curated data, we conducted in-silico modeling of the DISC1 interactome involved in neuronal migration and identified the proteins that either facilitate or inhibit neuronal migrational processes. We also studied the effect of perturbation of miRNAs and TFs in feedback loops on the DISC1 interactome. From these analyses, we discovered that STAT3, TCF3, and TAL1 (through feedback loop with miRNAs) play a critical role in the transcriptional control of DISC1 interactome thereby regulating neuronal migration. To the best of our knowledge, regulation of the DISC1 interactome mediating neuronal migration by these TFs has not been previously reported. These potentially important TFs can serve as targets for undertaking validation studies, which in turn can reveal the molecular processes that cause neuronal migration defects underlying neurodevelopmental disorders. This underscores the importance of the use of in-silico techniques in aiding the discovery of mechanistic evidence governing important molecular and cellular processes. The present work is one such step towards the discovery of regulatory factors of the DISC1 interactome that mediates neuronal migration.

Associations of Mitochondrial and Nuclear Mitochondrial Variants and Genes with Seven Metabolic Traits

Mitochondria (MT), the major site of cellular energy production, are under dual genetic control by 37 mitochondrial DNA (mtDNA) genes and numerous nuclear genes (MT-nDNA). In the CHARGEmtDNA+ Consortium, we studied genetic associations of mtDNA and MT-nDNA associations with body mass index (BMI), waist-hip-ratio (WHR), glucose, insulin, HOMA-B, HOMA-IR, and HbA1c. This 45-cohort collaboration comprised 70,775 (insulin) to 170,202 (BMI) pan-ancestry individuals. Validation and imputation of mtDNA variants was followed by single-variant and gene-based association testing. We report two significant common variants, one in MT-ATP6 associated (p ≤ 5E-04) with WHR and one in the D-loop with glucose. Five rare variants in MT-ATP6, MT-ND5, and MT-ND6 associated with BMI, WHR, or insulin. Gene-based meta-analysis identified MT-ND3 associated with BMI (p ≤ 1E-03). We considered 2,282 MT-nDNA candidate gene associations compiled from online summary results for our traits (20 unique studies with 31 dataset consortia's genome-wide associations [GWASs]). Of these, 109 genes associated (p ≤ 1E-06) with at least 1 of our 7 traits. We assessed regulatory features of variants in the 109 genes, cis- and trans-gene expression regulation, and performed enrichment and protein-protein interactions analyses. Of the identified mtDNA and MT-nDNA genes, 79 associated with adipose measures, 49 with glucose/insulin, 13 with risk for type 2 diabetes, and 18 with cardiovascular disease, indicating for pleiotropic effects with health implications. Additionally, 21 genes related to cholesterol, suggesting additional important roles for the genes identified. Our results suggest that mtDNA and MT-nDNA genes and variants reported make important contributions to glucose and insulin metabolism, adipocyte regulation, diabetes, and cardiovascular disease.

A Review on Methods for Detecting SNP Interactions in High-Dimensional Genomic Data

In this era of genome-wide association studies (GWAS), the quest for understanding the genetic architecture of complex diseases is rapidly increasing more than ever before. The development of high throughput genotyping and next generation sequencing technologies enables genetic epidemiological analysis of large scale data. These advances have led to the identification of a number of single nucleotide polymorphisms (SNPs) responsible for disease susceptibility. The interactions between SNPs associated with complex diseases are increasingly being explored in the current literature. These interaction studies are mathematically challenging and computationally complex. These challenges have been addressed by a number of data mining and machine learning approaches. This paper reviews the current methods and the related software packages to detect the SNP interactions that contribute to diseases. The issues that need to be considered when developing these models are addressed in this review. The paper also reviews the achievements in data simulation to evaluate the performance of these models. Further, it discusses the future of SNP interaction analysis.

Benchmarking selected computational gene network growing tools in context of virus-host interactions

Several available online tools provide network growing functions where an algorithm utilizing different data sources suggests additional genes/proteins that should connect an input gene set into functionally meaningful networks. Using the well-studied system of influenza host interactions, we compare the network growing function of two free tools GeneMANIA and STRING and the commercial IPA for their performance of recovering known influenza A virus host factors previously identified from siRNA screens. The result showed that given small (~30 genes) or medium (~150 genes) input sets all three network growing tools detect significantly more known host factors than random human genes with STRING overall performing strongest. Extending the networks with all the three tools significantly improved the detection of GO biological processes of known host factors compared to not growing networks. Interestingly, the rate of identification of true host factors using computational network growing is equal or better to doing another experimental siRNA screening study which could also be true and applied to other biological pathways/processes.

Pathway Analysis Incorporating Protein-Protein Interaction Networks Identified Candidate Pathways for the Seven Common Diseases

Pathway analysis has become popular as a secondary analysis strategy for genome-wide association studies (GWAS). Most of the current pathway analysis methods aggregate signals from the main effects of single nucleotide polymorphisms (SNPs) in genes within a pathway without considering the effects of gene-gene interactions. However, gene-gene interactions can also have critical effects on complex diseases. Protein-protein interaction (PPI) networks have been used to define gene pairs for the gene-gene interaction tests. Incorporating the PPI information to define gene pairs for interaction tests within pathways can increase the power for pathway-based association tests. We propose a pathway association test, which aggregates the interaction signals in PPI networks within a pathway, for GWAS with case-control samples. Gene size is properly considered in the test so that genes do not contribute more to the test statistic simply due to their size. Simulation studies were performed to verify that the method is a valid test and can have more power than other pathway association tests in the presence of gene-gene interactions within a pathway under different scenarios. We applied the test to the Wellcome Trust Case Control Consortium GWAS datasets for seven common diseases. The most significant pathway is the chaperones modulate interferon signaling pathway for Crohn’s disease (p-value = 0.0003). The pathway modulates interferon gamma, which induces the JAK/STAT pathway that is involved in Crohn’s disease. Several other pathways that have functional implications for the seven diseases were also identified. The proposed test based on gene-gene interaction signals in PPI networks can be used as a complementary tool to the current existing pathway analysis methods focusing on main effects of genes. An efficient software implementing the method is freely available at http://puppi.sourceforge.net.

Improving analytical methods for protein-protein interaction through implementation of chemically inducible dimerization

When investigating interactions between two proteins with complementary reporter tags in yeast two-hybrid or split GFP assays, it remains troublesome to discriminate true- from false-negative results and challenging to compare the level of interaction across experiments. This leads to decreased sensitivity and renders analysis of weak or transient interactions difficult to perform. In this work, we describe the development of reporters that can be chemically induced to dimerize independently of the investigated interactions and thus alleviate these issues. We incorporated our reporters into the widely used split ubiquitin-, bimolecular fluorescence complementation (BiFC)- and Förster resonance energy transfer (FRET)- based methods and investigated different protein-protein interactions in yeast and plants. We demonstrate the functionality of this concept by the analysis of weakly interacting proteins from specialized metabolism in the model plant Arabidopsis thaliana. Our results illustrate that chemically induced dimerization can function as a built-in control for split-based systems that is easily implemented and allows for direct evaluation of functionality.

Epistasis analysis using artificial intelligence

Here we introduce artificial intelligence (AI) methodology for detecting and characterizing epistasis in genetic association studies. The ultimate goal of our AI strategy is to analyze genome-wide genetics data as a human would using sources of expert knowledge as a guide. The methodology presented here is based on computational evolution, which is a type of genetic programming. The ability to generate interesting solutions while at the same time learning how to solve the problem at hand distinguishes computational evolution from other genetic programming approaches. We provide a general overview of this approach and then present a few examples of its application to real data.

Mis-sesnse mutations in Tafazzin (TAZ) that escort to mild clinical symptoms of Barth syndrome is owed to the minimal inhibitory effect of the mutations on the enzyme function: In-silico evidence

Tafazzin (EC 2.3.1.23) is a Phospholipid Transacylase involved in Cardiolipin remodeling on mitochondrial membrane and coded by TAZ gene (Cytogenetic Location: Xq28) in human. Its mutations cause Barth syndrome (MIM ID: #302060)/3-Methyl Glutaconyl Aciduria Type II, an inborn error of metabolism often leading to foetal or infantile fatality. Nevertheless, some mis-sense mutations result in mild clinical symptoms. To evaluate the rationale of mild symptoms and for an insight of Tafazzin active site, sequence based and structure based ramifications of wild and mutant Tafazzins were compared in-silico. Sequence based domain predictions, surface accessibilities on substitution & conserved catalytic sites with statistical drifts, as well as thermal stability changes for the mutations and the interaction analysis of Tafazzin were performed. Crystal structure of Tafazzin is not yet resolved experimentally, therefore 3D coordinates of Tafazzin and its mutants were spawned through homology modeling. Energetically minimized and structurally validated models were used for comparative docking simulations. We analyzed active site geometry of the models in addition to calculating overall substrate binding efficiencies for each of the enzyme-ligand complex deduced from binding energies instead of comparing only the docking scores. Also, individual binding energies of catalytic residues on conserved HX4D motif of Acyltransferase superfamily present in Tafazzins were estimated. This work elucidates the basis of mild symptoms in patients with mis-sense mutations, identifies the most pathogenic mutant among others in the study and also divulges the critical role of HX4D domain towards successful transacylation by Taffazin. The in-silico observations are in complete agreement with clinical findings reported for the patients with mutations.

A network-based approach to dissect the cilia/centrosome complex interactome

BACKGROUND

Cilia are microtubule-based organelles protruding from almost all mammalian cells which, when dysfunctional, result in genetic disorders called "ciliopathies". High-throughput studies have revealed that cilia are composed of thousands of proteins. However, despite many efforts, much remains to be determined regarding the biological functions of this increasingly important complex organelle.

RESULTS

We have derived an online tool, from a systematic network-based approach to dissect the cilia/centrosome complex interactome (CCCI). The tool integrates all current available data into a model which provides an "interaction" perspective on ciliary function. We generated a network of interactions between human proteins organized into functionally relevant "communities", which can be defined as groups of genes that are both highly inter-connected and strongly co-expressed. We then combined sequence and co-expression data in order to identify the transcription factors responsible for regulating genes within their respective communities. Our analyses have discovered communities significantly specialized for delegating specific biological functions such as mRNA processing, protein translation, folding and degradation processes that had never been associated with ciliary proteins until now.

CONCLUSIONS

CCCI will allow us to clarify the roles of previously unknown ciliary functions, elucidate the molecular mechanisms underlying ciliary-associated phenotypes, and apply our knowledge of the functional roles of relatively uncharacterized molecular entities to disease phenotypes and new clinical applications.

Bioinformatic prediction of WSSV-host protein-protein interaction

WSSV is one of the most dangerous pathogens in shrimp aquaculture. However, the molecular mechanism of how WSSV interacts with shrimp is still not very clear. In the present study, bioinformatic approaches were used to predict interactions between proteins from WSSV and shrimp. The genome data of WSSV (NC_003225.1) and the constructed transcriptome data of F. chinensis were used to screen potentially interacting proteins by searching in protein interaction databases, including STRING, Reactome, and DIP. Forty-four pairs of proteins were suggested to have interactions between WSSV and the shrimp. Gene ontology analysis revealed that 6 pairs of these interacting proteins were classified into “extracellular region” or “receptor complex” GO-terms. KEGG pathway analysis showed that they were involved in the “ECM-receptor interaction pathway.” In the 6 pairs of interacting proteins, an envelope protein called “collagen-like protein” (WSSV-CLP) encoded by an early virus gene “wsv001” in WSSV interacted with 6 deduced proteins from the shrimp, including three integrin alpha (ITGA), two integrin beta (ITGB), and one syndecan (SDC). Sequence analysis on WSSV-CLP, ITGA, ITGB, and SDC revealed that they possessed the sequence features for protein-protein interactions. This study might provide new insights into the interaction mechanisms between WSSV and shrimp.

Analysis of serum metabolites for the discovery of amino acid biomarkers and the effect of galangin on cerebral ischemia

Ischemic stroke, a devastating disease with a complex pathophysiology, is a leading cause of death and disability worldwide. In our previous study, we reported that galangin provided direct protection against ischemic injury and acted as a potential neuroprotective agent. However, its associated neuroprotective mechanism has not yet been clarified. In this paper, we explored the potential AA biomarkers in the acute phase of cerebral ischemia and the effect of galangin on those potential biomarkers. In our study, 12 AAs were quantified in rat serum and found to be impaired by middle cerebral artery occlusion (MCAO)-induced focal cerebral ischemia. Using partial least squares discriminate analysis (PLS-DA), we identified the following amino acids as potential biomarkers of cerebral ischemia: glutamic acid (Glu), homocysteine (Hcy), methionine (Met), tryptophan (Trp), aspartic acid (Asp), alanine (Ala) and tyrosine (Tyr). Moreover, four amino acids (Hcy, Met, Glu and Trp) showed significant change in galangin-treated (100 and 50 mg kg(-1)) groups compared to vehicle groups. Furthermore, we identified three pathway-related enzymes tyrosine aminotransferase (TAT), glutamine synthetase (GLUL) and monocarboxylate transporter (SLC16A10) by multiplex interactions with Glu and Hcy, which have been previously reported to be closely related to cerebral ischemia. Through an analysis of the metabolite-protein network analysis, we identified 16 proteins that were associated with two amino acids by multiple interactions with three enzymes; five of them may become potential biomarkers of galangin for acute ischemic stroke as the result of molecule docking. Our results may help develop novel strategies to explore the mechanism of cerebral ischemia, discover potential targets for drug candidates and elucidate the related regulatory signal network.

Integrated neurobiology of bipolar disorder

From a neurobiological perspective there is no such thing as bipolar disorder. Rather, it is almost certainly the case that many somewhat similar, but subtly different, pathological conditions produce a disease state that we currently diagnose as bipolarity. This heterogeneity - reflected in the lack of synergy between our current diagnostic schema and our rapidly advancing scientific understanding of the condition - limits attempts to articulate an integrated perspective on bipolar disorder. However, despite these challenges, scientific findings in recent years are beginning to offer a provisional "unified field theory" of the disease. This theory sees bipolar disorder as a suite of related neurodevelopmental conditions with interconnected functional abnormalities that often appear early in life and worsen over time. In addition to accelerated loss of volume in brain areas known to be essential for mood regulation and cognitive function, consistent findings have emerged at a cellular level, providing evidence that bipolar disorder is reliably associated with dysregulation of glial-neuronal interactions. Among these glial elements are microglia - the brain's primary immune elements, which appear to be overactive in the context of bipolarity. Multiple studies now indicate that inflammation is also increased in the periphery of the body in both the depressive and manic phases of the illness, with at least some return to normality in the euthymic state. These findings are consistent with changes in the hypothalamic-pituitary-adrenal axis, which are known to drive inflammatory activation. In summary, the very fact that no single gene, pathway, or brain abnormality is likely to ever account for the condition is itself an extremely important first step in better articulating an integrated perspective on both its ontological status and pathogenesis. Whether this perspective will translate into the discovery of innumerable more homogeneous forms of bipolarity is one of the great questions facing the field and one that is likely to have profound treatment implications, given that fact that such a discovery would greatly increase our ability to individualize - and by extension, enhance - treatment.

Addressing the Challenges of Detecting Epistasis in Genome-Wide Association Studies of Common Human Diseases Using Biological Expert Knowledge

Recent technological developments in the field of genetics have given rise to an abundance of research tools, such as genome-wide genotyping, that allow researchers to conduct genome-wide association studies (GWAS) for detecting genetic variants that confer increased or decreased susceptibility to disease. However, discovering epistatic, or gene-gene, interactions in high dimensional datasets is a problem due to the computational complexity that results from the analysis of all possible combinations of single-nucleotide polymorphisms (SNPs). A recently explored approach to this problem employs biological expert knowledge, such as pathway or protein-protein interaction information, to guide an analysis by the selection or weighting of SNPs based on this knowledge. Narrowing the evaluation to gene combinations that have been shown to interact experimentally provides a biologically concise reason why those two genes may be detected together statistically. This chapter discusses the challenges of discovering epistatic interactions in GWAS and how biological expert knowledge can be used to facilitate genome-wide genetic studies.

Using volcano plots and regularized-chi statistics in genetic association studies

Labor intensive experiments are typically required to identify the causal disease variants from a list of disease associated variants in the genome. For designing such experiments, candidate variants are ranked by their strength of genetic association with the disease. However, the two commonly used measures of genetic association, the odds-ratio (OR) and p-value may rank variants in different order. To integrate these two measures into a single analysis, here we transfer the volcano plot methodology from gene expression analysis to genetic association studies. In its original setting, volcano plots are scatter plots of fold-change and t-test statistic (or -log of the p-value), with the latter being more sensitive to sample size. In genetic association studies, the OR and Pearson's chi-square statistic (or equivalently its square root, chi; or the standardized log(OR)) can be analogously used in a volcano plot, allowing for their visual inspection. Moreover, the geometric interpretation of these plots leads to an intuitive method for filtering results by a combination of both OR and chi-square statistic, which we term "regularized-chi". This method selects associated markers by a smooth curve in the volcano plot instead of the right-angled lines which corresponds to independent cutoffs for OR and chi-square statistic. The regularized-chi incorporates relatively more signals from variants with lower minor-allele-frequencies than chi-square test statistic. As rare variants tend to have stronger functional effects, regularized-chi is better suited to the task of prioritization of candidate genes.

Genetic association studies in lumbar disc degeneration: a systematic review

Objective

Low back pain is associated with lumbar disc degeneration, which is mainly due to genetic predisposition. The objective of this study was to perform a systematic review to evaluate genetic association studies in lumbar disc degeneration as defined on magnetic resonance imaging (MRI) in humans.

Methods

A systematic literature search was conducted in MEDLINE, MEDLINE In-Process, SCOPUS, ISI Web of Science, The Genetic Association Database and The Human Genome Epidemiology Network for information published between 1990–2011 addressing genes and lumbar disc degeneration. Two investigators independently identified studies to determine inclusion, after which they performed data extraction and analysis. The level of cumulative genetic association evidence was analyzed according to The HuGENet Working Group guidelines.

Results

Fifty-two studies were included for review. Forty-eight studies reported at least one positive association between a genetic marker and lumbar disc degeneration. The phenotype definition of lumbar disc degeneration was highly variable between the studies and replications were inconsistent. Most of the associations presented with a weak level of evidence. The level of evidence was moderate for ASPN (D-repeat), COL11A1 (rs1676486), GDF5 (rs143383), SKT (rs16924573), THBS2 (rs9406328) and MMP9 (rs17576).

Conclusions

Based on this first extensive systematic review on the topic, the credibility of reported genetic associations is mostly weak. Clear definition of lumbar disc degeneration phenotypes and large population-based cohorts are needed. An international consortium is needed to standardize genetic association studies in relation to disc degeneration.

Integration of biological networks and pathways with genetic association studies

Millions of genetic variants have been assessed for their effects on the trait of interest in genome-wide association studies (GWAS). The complex traits are affected by a set of inter-related genes. However, the typical GWAS only examine the association of a single genetic variant at a time. The individual effects of a complex trait are usually small, and the simple sum of these individual effects may not reflect the holistic effect of the genetic system. High-throughput methods enable genomic studies to produce a large amount of data to expand the knowledge base of the biological systems. Biological networks and pathways are built to represent the functional or physical connectivity among genes. Integrated with GWAS data, the network- and pathway-based methods complement the approach of single genetic variant analysis, and may improve the power to identify trait-associated genes. Taking advantage of the biological knowledge, these approaches are valuable to interpret the functional role of the genetic variants, and to further understand the molecular mechanism influencing the traits. The network- and pathway-based methods have demonstrated their utilities, and will be increasingly important to address a number of challenges facing the mainstream GWAS.

High-order SNP combinations associated with complex diseases: efficient discovery, statistical power and functional interactions

There has been increased interest in discovering combinations of single-nucleotide polymorphisms (SNPs) that are strongly associated with a phenotype even if each SNP has little individual effect. Efficient approaches have been proposed for searching two-locus combinations from genome-wide datasets. However, for high-order combinations, existing methods either adopt a brute-force search which only handles a small number of SNPs (up to few hundreds), or use heuristic search that may miss informative combinations. In addition, existing approaches lack statistical power because of the use of statistics with high degrees-of-freedom and the huge number of hypotheses tested during combinatorial search. Due to these challenges, functional interactions in high-order combinations have not been systematically explored. We leverage discriminative-pattern-mining algorithms from the data-mining community to search for high-order combinations in case-control datasets. The substantially improved efficiency and scalability demonstrated on synthetic and real datasets with several thousands of SNPs allows the study of several important mathematical and statistical properties of SNP combinations with order as high as eleven. We further explore functional interactions in high-order combinations and reveal a general connection between the increase in discriminative power of a combination over its subsets and the functional coherence among the genes comprising the combination, supported by multiple datasets. Finally, we study several significant high-order combinations discovered from a lung-cancer dataset and a kidney-transplant-rejection dataset in detail to provide novel insights on the complex diseases. Interestingly, many of these associations involve combinations of common variations that occur in small fractions of population. Thus, our approach is an alternative methodology for exploring the genetics of rare diseases for which the current focus is on individually rare variations.

Protein-protein interactions: principles, techniques, and their potential role in new drug development

A vast network of genes is inter-linked through protein-protein interactions and is critical component of almost every biological process under physiological conditions. Any disruption of the biologically essential network leads to pathological conditions resulting into related diseases. Therefore, proper understanding of biological functions warrants a comprehensive knowledge of protein-protein interactions and the molecular mechanisms that govern such processes. The importance of protein-protein interaction process is highlighted by the fact that a number of powerful techniques/methods have been developed to understand how such interactions take place under various physiological and pathological conditions. Many of the key protein-protein interactions are known to participate in disease-associated signaling pathways, and represent novel targets for therapeutic intervention. Thus, controlling protein-protein interactions offers a rich dividend for the discovery of new drug targets. Availability of various tools to study and the knowledge of human genome have put us in a unique position to understand highly complex biological network, and the mechanisms involved therein. In this review article, we have summarized protein-protein interaction networks, techniques/methods of their binding/kinetic parameters, and the role of these interactions in the development of potential tools for drug designing.

An organogenesis network-based comparative transcriptome analysis for understanding early human development in vivo and in vitro

Background

Integrated networks hold great promise in a variety of contexts. In a recent study, we have combined expression and interaction data to identify a putative network underlying early human organogenesis that contains two modules, the stemness-relevant module (hStemModule) and the differentiation-relevant module (hDiffModule). However, owing to its hypothetical nature, it remains unclear whether this network allows for comparative transcriptome analysis to advance our understanding of early human development, both in vivo and in vitro.

Results

Based on this integrated network, we here report comparisons with the context-dependent transcriptome data from a variety of sources. By viewing the network and its two modules as gene sets and conducting gene set enrichment analysis, we demonstrate the network's utility as a quantitative monitor of the stem potential versus the differentiation potential. During early human organogenesis, the hStemModule reflects the generality of a gradual loss of the stem potential. The hDiffModule indicates the stage-specific differentiation potential and is therefore not suitable for depicting an extended developmental window. Processing of cultured cells of different types further revealed that the hStemModule is a general indicator that distinguishes different cell types in terms of their stem potential. In contrast, the hDiffModule cannot distinguish between differentiated cells of different types but is able to predict differences in the differentiation potential of pluripotent cells of different origins. We also observed a significant positive correlation between each of these two modules and early embryoid bodies (EBs), which are used as in vitro differentiation models. Despite this, the network-oriented comparisons showed considerable differences between the developing embryos and the EBs that were cultured in vitro over time to try to mimic in vivo processes.

Conclusions

We strongly recommend the use of these two modules either when pluripotent cell types of different origins are involved or when the comparisons made are constrained to the in vivo embryos during early human organogenesis (and an equivalent in vitro differentiation models). Network-based comparative transcriptome analysis will contribute to an increase in knowledge about human embryogenesis, particularly when only transcriptome data are currently available. These advances will add an extra dimension to network applications.

Intercellular interactomics of human brain endothelial cells and th17 lymphocytes: a novel strategy for identifying therapeutic targets of CNS inflammation

Leukocyte infiltration across an activated brain endothelium contributes to the neuroinflammation seen in many neurological disorders. Recent evidence shows that IL-17-producing T-lymphocytes (e.g., Th17 cells) possess brain-homing capability and contribute to the pathogenesis of multiple sclerosis and cerebral ischemia. The leukocyte transmigration across the endothelium is a highly regulated, multistep process involving intercellular communications and interactions between the leukocytes and endothelial cells. The molecules involved in the process are attractive therapeutic targets for inhibiting leukocyte brain migration. We hypothesized and have been successful in demonstrating that molecules of potential therapeutic significance involved in Th17-brain endothelial cell (BEC) communications and interactions can be discovered through the combination of advanced membrane/submembrane proteomic and interactomic methods. We describe elements of this strategy and preliminary results obtained in method and approach development. The Th17-BEC interaction network provides new insights into the complexity of the transmigration process mediated by well-organized, subcellularly localized molecular interactions. These molecules and interactions are potential diagnostic, therapeutic, or theranostic targets for treatment of neurological conditions accompanied or caused by leukocyte infiltration.

The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored

An essential prerequisite for any systems-level understanding of cellular functions is to correctly uncover and annotate all functional interactions among proteins in the cell. Toward this goal, remarkable progress has been made in recent years, both in terms of experimental measurements and computational prediction techniques. However, public efforts to collect and present protein interaction information have struggled to keep up with the pace of interaction discovery, partly because protein-protein interaction information can be error-prone and require considerable effort to annotate. Here, we present an update on the online database resource Search Tool for the Retrieval of Interacting Genes (STRING); it provides uniquely comprehensive coverage and ease of access to both experimental as well as predicted interaction information. Interactions in STRING are provided with a confidence score, and accessory information such as protein domains and 3D structures is made available, all within a stable and consistent identifier space. New features in STRING include an interactive network viewer that can cluster networks on demand, updated on-screen previews of structural information including homology models, extensive data updates and strongly improved connectivity and integration with third-party resources. Version 9.0 of STRING covers more than 1100 completely sequenced organisms; the resource can be reached at http://string-db.org.

Layers of epistasis: genome-wide regulatory networks and network approaches to genome-wide association studies

The conceptual foundation of the genome-wide association study (GWAS) has advanced unchecked since its conception. A revision might seem premature as the potential of GWAS has not been fully realized. Multiple technical and practical limitations need to be overcome before GWAS can be fairly criticized. But with the completion of hundreds of studies and a deeper understanding of the genetic architecture of disease, warnings are being raised. The results compiled to date indicate that risk-associated variants lie predominantly in noncoding regions of the genome. Additionally, alternative methodologies are uncovering large and heterogeneous sets of rare variants underlying disease. The fear is that, even in its fulfillment, the current GWAS paradigm might be incapable of dissecting all kinds of phenotypes. In the following text, we review several initiatives that aim to overcome these limitations. The overarching theme of these studies is the inclusion of biological knowledge to both the analysis and interpretation of genotyping data. GWAS is uninformed of biology by design and although there is some virtue in its simplicity, it is also its most conspicuous deficiency. We propose a framework in which to integrate these novel approaches, both empirical and theoretical, in the form of a genome-wide regulatory network (GWRN). By processing experimental data into networks, emerging data types based on chromatin immunoprecipitation are made computationally tractable. This will give GWAS re-analysis efforts the most current and relevant substrates, and root them firmly on our knowledge of human disease.

Identification of epistatic effects using a protein-protein interaction database

Epistasis (i.e. gene-gene interaction) has long been recognized as an important mechanism underlying the complexity of the genetic architecture of human traits. Definitions of epistasis range from the purely molecular to the traditional statistical measures of interaction. The statistical detection of epistasis usually does not map onto or easily relate to the biological interactions between genetic variations through their combined influence on gene expression or through their interactions at the gene product (i.e. protein) or DNA level. Recently, greater high-dimensional data on protein-protein interaction (PPI) and gene expression profiles have been collected that enumerates sets of biological interactions. To better align statistical and molecular models of epistasis, we present an example of how to incorporate the PPI information into the statistical analysis of interactions between copy number variations (CNVs). Among the 23 640 pairs of known human PPIs and the 1141 common CNVs detected among HapMap samples, we identified 37 pairs of CNVs overlapping with both genes of a PPI pair. Two CNV pairs provided sufficient genotype variation to search for epistatic effects on gene expression. Using 47 294 probe-specific gene expression levels as the outcomes, five epistatic effects were identified with P-value less than 10(-6). We found a CNV-CNV interaction significantly associated with gene expression of TP53TG3 (P-value of 2 × 10(-20)). The proteins associated with the CNV pair also bind TP53 which regulates the transcription of TP53TG3. This study demonstrates that using PPI data can assist in targeting statistical hypothesis testing to biological plausible epistatic interaction that reflects molecular mechanisms.

Bioinformatics challenges for genome-wide association studies

Motivation: The sequencing of the human genome has made it possible to identify an informative set of >1 million single nucleotide polymorphisms (SNPs) across the genome that can be used to carry out genome-wide association studies (GWASs). The availability of massive amounts of GWAS data has necessitated the development of new biostatistical methods for quality control, imputation and analysis issues including multiple testing. This work has been successful and has enabled the discovery of new associations that have been replicated in multiple studies. However, it is now recognized that most SNPs discovered via GWAS have small effects on disease susceptibility and thus may not be suitable for improving health care through genetic testing. One likely explanation for the mixed results of GWAS is that the current biostatistical analysis paradigm is by design agnostic or unbiased in that it ignores all prior knowledge about disease pathobiology. Further, the linear modeling framework that is employed in GWAS often considers only one SNP at a time thus ignoring their genomic and environmental context. There is now a shift away from the biostatistical approach toward a more holistic approach that recognizes the complexity of the genotype–phenotype relationship that is characterized by significant heterogeneity and gene–gene and gene–environment interaction. We argue here that bioinformatics has an important role to play in addressing the complexity of the underlying genetic basis of common human diseases. The goal of this review is to identify and discuss those GWAS challenges that will require computational methods.

Contact:jason.h.moore@dartmouth.edu