The role of the interactome in the maintenance of deleterious variability in human populations

A long non-coding RNA that harbors a SNP associated with type 2 diabetes regulates the expression of TGM2 gene in pancreatic beta cells

INTRODUCTION

Most of the disease-associated single nucleotide polymorphisms (SNPs) lie in non- coding regions of the human genome. Many of these variants have been predicted to impact the expression and function of long non-coding RNAs (lncRNA), but the contribution of these molecules to the development of complex diseases remains to be clarified.

METHODS

Here, we performed a genetic association study between a SNP located in a lncRNA known as LncTGM2 and the risk of developing type 2 diabetes (T2D), and analyzed its implication in disease pathogenesis at pancreatic beta cell level. Genetic association study was performed on human samples linking the rs2076380 polymorphism with T2D and glycemic traits. The pancreatic beta cell line EndoC-bH1 was employed for functional studies based on LncTGM2 silencing and overexpression experiments. Human pancreatic islets were used for eQTL analysis.

RESULTS

We have identified a genetic association between LncTGM2 and T2D risk. Functional characterization of the LncTGM2 revealed its implication in the transcriptional regulation of TGM2, coding for a transglutaminase. The T2Dassociated risk allele in LncTGM2 disrupts the secondary structure of this lncRNA, affecting its stability and the expression of TGM2 in pancreatic beta cells. Diminished LncTGM2 in human beta cells impairs glucose-stimulated insulin release.

CONCLUSIONS

These findings provide novel information on the molecular mechanisms by which T2D-associated SNPs in lncRNAs may contribute to disease, paving the way for the development of new therapies based on the modulation of lncRNAs.

Deleterious mutations and the rare allele burden on rice gene expression

Deleterious genetic variation is maintained in populations at low frequencies. Under a model of stabilizing selection, rare (and presumably deleterious) genetic variants are associated with increase or decrease in gene expression from some intermediate optimum. We investigate this phenomenon in a population of largely Oryza sativa ssp. indica rice landraces under normal unstressed wet and stressful drought field conditions. We include single nucleotide polymorphisms, insertion/deletion mutations, and structural variants in our analysis and find a stronger association between rare variants and gene expression outliers under the stress condition. We also show an association of the strength of this rare variant effect with linkage, gene expression levels, network connectivity, local recombination rate, and fitness consequence scores, consistent with the stabilizing selection model of gene expression.

Predicting functional consequences of mutations using molecular interaction network features

Variant interpretation remains a central challenge for precision medicine. Missense variants are particularly difficult to understand as they change only a single amino acid in a protein sequence yet can have large and varied effects on protein activity. Numerous tools have been developed to identify missense variants with putative disease consequences from protein sequence and structure. However, biological function arises through higher order interactions among proteins and molecules within cells. We therefore sought to capture information about the potential of missense mutations to perturb protein interaction networks by integrating protein structure and interaction data. We developed 16 network-based annotations for missense mutations that provide orthogonal information to features classically used to prioritize variants. We then evaluated them in the context of a proven machine-learning framework for variant effect prediction across multiple benchmark datasets to demonstrate their potential to improve variant classification. Interestingly, network features resulted in larger performance gains for classifying somatic mutations than for germline variants, possibly due to different constraints on what mutations are tolerated at the cellular versus organismal level. Our results suggest that modeling variant potential to perturb context-specific interactome networks is a fruitful strategy to advance in silico variant effect prediction.

Omics in a Digital World: The Role of Bioinformatics in Providing New Insights Into Human Aging

Background

Aging is a complex phenotype influenced by a combination of genetic and environmental factors. Although many studies addressed its cellular and physiological age-related changes, the molecular causes of aging remain undetermined. Considering the biological complexity and heterogeneity of the aging process, it is now clear that full understanding of mechanisms underlying aging can only be achieved through the integration of different data types and sources, and with new computational methods capable to achieve such integration.

Recent Advances

In this review, we show that an omics vision of the age-dependent changes occurring as the individual ages can provide researchers with new opportunities to understand the mechanisms of aging. Combining results from single-cell analysis with systems biology tools would allow building interaction networks and investigate how these networks are perturbed during aging and disease. The development of high-throughput technologies such as next-generation sequencing, proteomics, metabolomics, able to investigate different biological markers and to monitor them simultaneously during the aging process with high accuracy and specificity, represents a unique opportunity offered to biogerontologists today.

Critical Issues

Although the capacity to produce big data drastically increased over the years, integration, interpretation and sharing of high-throughput data remain major challenges. In this paper we present a survey of the emerging omics approaches in aging research and provide a large collection of datasets and databases as a useful resource for the scientific community to identify causes of aging. We discuss their peculiarities, emphasizing the need for the development of methods focused on the integration of different data types.

Future Directions

We critically review the contribution of bioinformatics into the omics of aging research, and we propose a few recommendations to boost collaborations and produce new insights. We believe that significant advancements can be achieved by following major developments in bioinformatics, investing in diversity, data sharing and community-driven portable bioinformatics methods. We also argue in favor of more engagement and participation, and we highlight the benefits of new collaborations along these lines. This review aims at being a useful resource for many researchers in the field, and a call for new partnerships in aging research.

Using mechanistic models for the clinical interpretation of complex genomic variation

The sustained generation of genomic data in the last decade has increased the knowledge on the causal mutations of a large number of diseases, especially for highly penetrant Mendelian diseases, typically caused by a unique or a few genes. However, the discovery of causal genes in complex diseases has been far less successful. Many complex diseases are actually a consequence of the failure of complex biological modules, composed by interrelated proteins, which can happen in many different ways, which conferring a multigenic nature to the condition that can hardly be attributed to one or a few genes. We present a mechanistic model, Hipathia, implemented in a web server that allows estimating the effect that mutations, or changes in the expression of genes, have over the whole system of human signaling and the corresponding functional consequences. We show several use cases where we demonstrate how different the ultimate impact of mutations with similar loss-of-function potential can be and how the potential pathological role of a damaged gene can be inferred within the context of a signaling network. The use of systems biology-based approaches, such as mechanistic models, allows estimating the potential impact of loss-of-function mutations occurring in proteins that are part of complex biological interaction networks, such as signaling pathways. This holistic approach provides an elegant alternative to gene-centric approaches that can open new avenues in the interpretation of the genomic variability in complex diseases.

Association of a single nucleotide polymorphism in the ubxn6 gene with long-term non-progression phenotype in HIV-positive individuals

OBJECTIVES

The long-term non-progressors (LTNPs) are a heterogeneous group of HIV-positive individuals characterized by their ability to maintain high CD4⁺ T-cell counts and partially control viral replication for years in the absence of antiretroviral therapy. The present study aims to identify host single nucleotide polymorphisms (SNPs) associated with non-progression in a cohort of 352 individuals.

METHODS

DNA microarrays and exome sequencing were used for genotyping about 240 000 functional polymorphisms throughout more than 20 000 human genes. The allele frequencies of 85 LTNPs were compared with a control population. SNPs associated with LTNPs were confirmed in a population of typical progressors. Functional analyses in the affected gene were carried out through knockdown experiments in HeLa-P4, macrophages and dendritic cells.

RESULTS

Several SNPs located within the major histocompatibility complex region previously related to LTNPs were confirmed in this new cohort. The SNP rs1127888 (UBXN6) surpassed the statistical significance of these markers after Bonferroni correction (q = 2.11 × 10^-6). An uncommon allelic frequency of rs1127888 among LTNPs was confirmed by comparison with typical progressors and other publicly available populations. UBXN6 knockdown experiments caused an increase in CAV1 expression and its accumulation in the plasma membrane. In vitro infection of different cell types with HIV-1 replication-competent recombinant viruses caused a reduction of the viral replication capacity compared with their corresponding wild-type cells expressing UBXN6.

CONCLUSIONS

A higher prevalence of Ala31Thr in UBXN6 was found among LTNPs within its N-terminal region, which is crucial for UBXN6/VCP protein complex formation. UBXN6 knockdown affected CAV1 turnover and HIV-1 replication capacity.

Integrating molecular networks with genetic variant interpretation for precision medicine

More reliable and cheaper sequencing technologies have revealed the vast mutational landscapes characteristic of many phenotypes. The analysis of such genetic variants has led to successful identification of altered proteins underlying many Mendelian disorders. Nevertheless the simple one‐variant one‐phenotype model valid for many monogenic diseases does not capture the complexity of polygenic traits and disorders. Although experimental and computational approaches have improved detection of functionally deleterious variants and important interactions between gene products, the development of comprehensive models relating genotype and phenotypes remains a challenge in the field of genomic medicine. In this context, a new view of the pathologic state as significant perturbation of the network of interactions between biomolecules is crucial for the identification of biochemical pathways associated with complex phenotypes. Seminal studies in systems biology combined the analysis of genetic variation with protein–protein interaction networks to demonstrate that even as biological systems evolve to be robust to genetic variation, their topologies create disease vulnerabilities. More recent analyses model the impact of genetic variants as changes to the “wiring” of the interactome to better capture heterogeneity in genotype–phenotype relationships. These studies lay the foundation for using networks to predict variant effects at scale using machine‐learning or algorithmic approaches. A wealth of databases and resources for the annotation of genotype–phenotype relationships have been developed to support developments in this area. This overview describes how study of the molecular interactome has generated insights linking the organization of biological systems to disease mechanism, and how this information can enable precision medicine.

This article is categorized under:

Translational, Genomic, and Systems Medicine > Translational Medicine

Biological Mechanisms > Cell Signaling

Models of Systems Properties and Processes > Mechanistic Models

Analytical and Computational Methods > Computational Methods

Graph-theoretical comparison of normal and tumor networks in identifying BRCA genes

BACKGROUND

Identification of driver genes related to certain types of cancer is an important research topic. Several systems biology approaches have been suggested, in particular for the identification of breast cancer (BRCA) related genes. Such approaches usually rely on differential gene expression and/or mutational landscape data. In some cases interaction network data is also integrated to identify cancer-related modules computationally.

RESULTS

We provide a framework for the comparative graph-theoretical analysis of networks integrating the relevant gene expression, mutations, and potein-protein interaction network data. The comparisons involve a graph-theoretical analysis of normal and tumor network pairs across all instances of a given set of breast cancer samples. The network measures under consideration are based on appropriate formulations of various centrality measures: betweenness, clustering coefficients, degree centrality, random walk distances, graph-theoretical distances, and Jaccard index centrality.

CONCLUSIONS

Among all the studied centrality-based graph-theoretical properties, we show that a betweenness-based measure differentiates BRCA genes across all normal versus tumor network pairs, than the rest of the popular centrality-based measures. The AUROC and AUPR values of the gene lists ordered with respect to the measures under study as compared to NCBI BioSystems pathway and the COSMIC database of cancer genes are the largest with the betweenness-based differentiation, followed by the measure based on degree centrality. In order to test the robustness of the suggested measures in prioritizing cancer genes, we further tested the two most promising measures, those based on betweenness and degree centralities, on randomly rewired networks. We show that both measures are quite resilient to noise in the input interaction network. We also compared the same measures against a state-of-the-art alternative disease gene prioritization method, MUFFFINN. We show that both our graph-theoretical measures outperform MUFFINN prioritizations in terms of ROC and precions/recall analysis. Finally, we filter the ordered list of the best measure, the betweenness-based differentiation, via a maximum-weight independent set formulation and investigate the top 50 genes in regards to literature verification. We show that almost all genes in the list are verified by the breast cancer literature and three genes are presented as novel genes that may potentialy be BRCA-related but missing in literature.

Detecting gene subnetworks under selection in biological pathways

Advances in high throughput sequencing technologies have created a gap between data production and functional data analysis. Indeed, phenotypes result from interactions between numerous genes, but traditional methods treat loci independently, missing important knowledge brought by network-level emerging properties. Therefore, detecting selection acting on multiple genes affecting the evolution of complex traits remains challenging. In this context, gene network analysis provides a powerful framework to study the evolution of adaptive traits and facilitates the interpretation of genome-wide data. We developed a method to analyse gene networks that is suitable to evidence polygenic selection. The general idea is to search biological pathways for subnetworks of genes that directly interact with each other and that present unusual evolutionary features. Subnetwork search is a typical combinatorial optimization problem that we solve using a simulated annealing approach. We have applied our methodology to find signals of adaptation to high-altitude in human populations. We show that this adaptation has a clear polygenic basis and is influenced by many genetic components. Our approach, implemented in the R package signet, improves on gene-level classical tests for selection by identifying both new candidate genes and new biological processes involved in adaptation to altitude.

Accumulation of Deleterious Mutations During Bacterial Range Expansions

Recent theory predicts that the fitness of pioneer populations can decline when species expand their range, due to high rates of genetic drift on wave fronts making selection less efficient at purging deleterious variants. To test these predictions, we studied the fate of mutator bacteria expanding their range for 1650 generations on agar plates. In agreement with theory, we find that growth abilities of strains with a high mutation rate (HMR lines) decreased significantly over time, unlike strains with a lower mutation rate (LMR lines) that present three to four times fewer mutations. Estimation of the distribution of fitness effect under a spatially explicit model reveals a mean negative effect for new mutations (-0.38%), but it suggests that both advantageous and deleterious mutations have accumulated during the experiment. Furthermore, the fitness of HMR lines measured in different environments has decreased relative to the ancestor strain, whereas that of LMR lines remained unchanged. Contrastingly, strains with a HMR evolving in a well-mixed environment accumulated less mutations than agar-evolved strains and showed an increased fitness relative to the ancestor. Our results suggest that spatially expanding species are affected by deleterious mutations, leading to a drastic impairment of their evolutionary potential.

A new correlation clustering method for cancer mutation analysis

MOTIVATION

Cancer genomes exhibit a large number of different alterations that affect many genes in a diverse manner. An improved understanding of the generative mechanisms behind the mutation rules and their influence on gene community behavior is of great importance for the study of cancer.

RESULTS

To expand our capability to analyze combinatorial patterns of cancer alterations, we developed a rigorous methodology for cancer mutation pattern discovery based on a new, constrained form of correlation clustering. Our new algorithm, named C³ (Cancer Correlation Clustering), leverages mutual exclusivity of mutations, patient coverage and driver network concentration principles. To test C³, we performed a detailed analysis on TCGA breast cancer and glioblastoma data and showed that our algorithm outperforms the state-of-the-art CoMEt method in terms of discovering mutually exclusive gene modules and identifying biologically relevant driver genes. The proposed agnostic clustering method represents a unique tool for efficient and reliable identification of mutation patterns and driver pathways in large-scale cancer genomics studies, and it may also be used for other clustering problems on biological graphs.

AVAILABILITY AND IMPLEMENTATION

The source code for the C³ method can be found at https://github.com/jackhou2/C3 CONTACTS: jianma@cs.cmu.edu or milenkov@illinois.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Network biology concepts in complex disease comorbidities

The co-occurrence of diseases can inform the underlying network biology of shared and multifunctional genes and pathways. In addition, comorbidities help to elucidate the effects of external exposures, such as diet, lifestyle and patient care. With worldwide health transaction data now often being collected electronically, disease co-occurrences are starting to be quantitatively characterized. Linking network dynamics to the real-life, non-ideal patient in whom diseases co-occur and interact provides a valuable basis for generating hypotheses on molecular disease mechanisms, and provides knowledge that can facilitate drug repurposing and the development of targeted therapeutic strategies.

267 Spanish Exomes Reveal Population-Specific Differences in Disease-Related Genetic Variation

Recent results from large-scale genomic projects suggest that allele frequencies, which are highly relevant for medical purposes, differ considerably across different populations. The need for a detailed catalog of local variability motivated the whole-exome sequencing of 267 unrelated individuals, representative of the healthy Spanish population. Like in other studies, a considerable number of rare variants were found (almost one-third of the described variants). There were also relevant differences in allelic frequencies in polymorphic variants, including ∼10,000 polymorphisms private to the Spanish population. The allelic frequencies of variants conferring susceptibility to complex diseases (including cancer, schizophrenia, Alzheimer disease, type 2 diabetes, and other pathologies) were overall similar to those of other populations. However, the trend is the opposite for variants linked to Mendelian and rare diseases (including several retinal degenerative dystrophies and cardiomyopathies) that show marked frequency differences between populations. Interestingly, a correspondence between differences in allelic frequencies and disease prevalence was found, highlighting the relevance of frequency differences in disease risk. These differences are also observed in variants that disrupt known drug binding sites, suggesting an important role for local variability in population-specific drug resistances or adverse effects. We have made the Spanish population variant server web page that contains population frequency information for the complete list of 170,888 variant positions we found publicly available (http://spv.babelomics.org/), We show that it if fundamental to determine population-specific variant frequencies to distinguish real disease associations from population-specific polymorphisms.

Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing

Characterizing the behavior of disease genes in the context of biological networks has the potential to shed light on disease mechanisms, and to reveal both new candidate disease genes and therapeutic targets. Previous studies addressing the network properties of disease genes have produced contradictory results. Here we have explored the causes of these discrepancies and assessed the relationship between the network roles of disease genes and their tolerance to deleterious germline variants in human populations leveraging on: the abundance of interactome resources, a comprehensive catalog of disease genes and exome variation data. We found that the most salient network features of disease genes are driven by cancer genes and that genes related to different types of diseases play network roles whose centrality is inversely correlated to their tolerance to likely deleterious germline mutations. This proved to be a multiscale signature, including global, mesoscopic and local network centrality features. Cancer driver genes, the most sensitive to deleterious variants, occupy the most central positions, followed by dominant disease genes and then by recessive disease genes, which are tolerant to variants and isolated within their network modules.

Improving the management of Inherited Retinal Dystrophies by targeted sequencing of a population-specific gene panel

Next-generation sequencing (NGS) has overcome important limitations to the molecular diagnosis of Inherited Retinal Dystrophies (IRD) such as the high clinical and genetic heterogeneity and the overlapping phenotypes. The purpose of this study was the identification of the genetic defect in 32 Spanish families with different forms of IRD. With that aim, we implemented a custom NGS panel comprising 64 IRD-associated genes in our population, and three disease-associated intronic regions. A total of 37 pathogenic mutations (14 novels) were found in 73% of IRD patients ranging from 50% for autosomal dominant cases, 75% for syndromic cases, 83% for autosomal recessive cases, and 100% for X-linked cases. Additionally, unexpected phenotype-genotype correlations were found in 6 probands, which led to the refinement of their clinical diagnoses. Furthermore, intra- and interfamilial phenotypic variability was observed in two cases. Moreover, two cases unsuccessfully analysed by exome sequencing were resolved by applying this panel. Our results demonstrate that this hypothesis-free approach based on frequently mutated, population-specific loci is highly cost-efficient for the routine diagnosis of this heterogeneous condition and allows the unbiased analysis of a miscellaneous cohort. The molecular information found here has aid clinical diagnosis and has improved genetic counselling and patient management.

The Mutational Landscape of Acute Promyelocytic Leukemia Reveals an Interacting Network of Co-Occurrences and Recurrent Mutations

Preliminary Acute Promyelocytic Leukemia (APL) whole exome sequencing (WES) studies have identified a huge number of somatic mutations affecting more than a hundred different genes mainly in a non-recurrent manner, suggesting that APL is a heterogeneous disease with secondary relevant changes not yet defined. To extend our knowledge of subtle genetic alterations involved in APL that might cooperate with PML/RARA in the leukemogenic process, we performed a comprehensive analysis of somatic mutations in APL combining WES with sequencing of a custom panel of targeted genes by next-generation sequencing. To select a reduced subset of high confidence candidate driver genes, further in silico analysis were carried out. After prioritization and network analysis we found recurrent deleterious mutations in 8 individual genes (STAG2, U2AF1, SMC1A, USP9X, IKZF1, LYN, MYCBP2 and PTPN11) with a strong potential of being involved in APL pathogenesis. Our network analysis of multiple mutations provides a reliable approach to prioritize genes for additional analysis, improving our knowledge of the leukemogenesis interactome. Additionally, we have defined a functional module in the interactome of APL. The hypothesis is that the number, or the specific combinations, of mutations harbored in each patient might not be as important as the disturbance caused in biological key functions, triggered by several not necessarily recurrent mutations.

Forward Individualized Medicine from Personal Genomes to Interactomes

When considering the variation in the genome, transcriptome, proteome and metabolome, and their interaction with the environment, every individual can be rightfully considered as a unique biological entity. Individualized medicine promises to take this uniqueness into account to optimize disease treatment and thereby improve health benefits for every patient. The success of individualized medicine relies on a precise understanding of the genotype-phenotype relationship. Although omics technologies advance rapidly, there are several challenges that need to be overcome: Next generation sequencing can efficiently decipher genomic sequences, epigenetic changes, and transcriptomic variation in patients, but it does not automatically indicate how or whether the identified variation will cause pathological changes. This is likely due to the inability to account for (1) the consequences of gene-gene and gene-environment interactions, and (2) (post)transcriptional as well as (post)translational processes that eventually determine the concentration of key metabolites. The technologies to accurately measure changes in these latter layers are still under development, and such measurements in humans are also mainly restricted to blood and circulating cells. Despite these challenges, it is already possible to track dynamic changes in the human interactome in healthy and diseased states by using the integration of multi-omics data. In this review, we evaluate the potential value of current major bioinformatics and systems biology-based approaches, including genome wide association studies, epigenetics, gene regulatory and protein-protein interaction networks, and genome-scale metabolic modeling. Moreover, we address the question whether integrative analysis of personal multi-omics data will help understanding of personal genotype-phenotype relationships.

Reads meet rotamers: structural biology in the age of deep sequencing

Structure has traditionally been interrelated with sequence, usually in the framework of comparing sequences across species sharing a common fold. However, the nature of information within the sequence and structure databases is evolving, changing the type of comparisons possible. In particular, we now have a vast amount of personal genome sequences from human populations and a greater fraction of new structures contain interacting proteins within large complexes. Consequently, we have to recast our conception of sequence conservation and its relation to structure-for example, focusing more on selection within the human population. Moreover, within structural biology there is less emphasis on the discovery of novel folds and more on relating structures to networks of protein interactions. We cover this changing mindset here.

Exome sequencing reveals a high genetic heterogeneity on familial Hirschsprung disease

Hirschsprung disease (HSCR; OMIM 142623) is a developmental disorder characterized by aganglionosis along variable lengths of the distal gastrointestinal tract, which results in intestinal obstruction. Interactions among known HSCR genes and/or unknown disease susceptibility loci lead to variable severity of phenotype. Neither linkage nor genome-wide association studies have efficiently contributed to completely dissect the genetic pathways underlying this complex genetic disorder. We have performed whole exome sequencing of 16 HSCR patients from 8 unrelated families with SOLID platform. Variants shared by affected relatives were validated by Sanger sequencing. We searched for genes recurrently mutated across families. Only variations in the FAT3 gene were significantly enriched in five families. Within-family analysis identified compound heterozygotes for AHNAK and several genes (N = 23) with heterozygous variants that co-segregated with the phenotype. Network and pathway analyses facilitated the discovery of polygenic inheritance involving FAT3, HSCR known genes and their gene partners. Altogether, our approach has facilitated the detection of more than one damaging variant in biologically plausible genes that could jointly contribute to the phenotype. Our data may contribute to the understanding of the complex interactions that occur during enteric nervous system development and the etiopathology of familial HSCR.

A Pan-Cancer Catalogue of Cancer Driver Protein Interaction Interfaces

Despite their importance in maintaining the integrity of all cellular pathways, the role of mutations on protein-protein interaction (PPI) interfaces as cancer drivers has not been systematically studied. Here we analyzed the mutation patterns of the PPI interfaces from 10,028 proteins in a pan-cancer cohort of 5,989 tumors from 23 projects of The Cancer Genome Atlas (TCGA) to find interfaces enriched in somatic missense mutations. To that end we use e-Driver, an algorithm to analyze the mutation distribution of specific protein functional regions. We identified 103 PPI interfaces enriched in somatic cancer mutations. 32 of these interfaces are found in proteins coded by known cancer driver genes. The remaining 71 interfaces are found in proteins that have not been previously identified as cancer drivers even that, in most cases, there is an extensive literature suggesting they play an important role in cancer. Finally, we integrate these findings with clinical information to show how tumors apparently driven by the same gene have different behaviors, including patient outcomes, depending on which specific interfaces are mutated.

Until now, most efforts in cancer genomics have focused on identifying genes and pathways driving tumor development. Although this has been unquestionably a success, as evidenced by the fact that we now have an extensive catalogue of cancer driver genes and pathways, there is still a poor understanding of why patients with the same affected driver genes may have different disease outcomes or drug responses. This is precisely the aim of this work-to show how by considering proteins as multifunctional factories instead of monolithic black boxes, it is possible to identify novel cancer driver genes and propose molecular hypotheses to explain such heterogeneity. To that end we have mapped the mutation profiles of 5,989 cancer patients from TCGA to more than 10,000 protein structures, leading us to identify 103 protein interaction interfaces enriched in somatic mutations. Finally, we have integrated clinical annotations as well as proteomics data to show how tumors apparently driven by the same gene can display different behaviors, including patient outcomes, depending on which specific interfaces are mutated.

A Rich-Club Organization in Brain Ischemia Protein Interaction Network

Ischemic stroke involves multiple pathophysiological mechanisms with complex interactions. Efforts to decipher those mechanisms and understand the evolution of cerebral injury is key for developing successful interventions. In an innovative approach, we use literature mining, natural language processing and systems biology tools to construct, annotate and curate a brain ischemia interactome. The curated interactome includes proteins that are deregulated after cerebral ischemia in human and experimental stroke. Network analysis of the interactome revealed a rich-club organization indicating the presence of a densely interconnected hub structure of prominent contributors to disease pathogenesis. Functional annotation of the interactome uncovered prominent pathways and highlighted the critical role of the complement and coagulation cascade in the initiation and amplification of injury starting by activation of the rich-club. We performed an in-silico screen for putative interventions that have pleiotropic effects on rich-club components and we identified estrogen as a prominent candidate. Our findings show that complex network analysis of disease related interactomes may lead to a better understanding of pathogenic mechanisms and provide cost-effective and mechanism-based discovery of candidate therapeutics.

Personalized biochemistry and biophysics

Whole human genome sequencing of individuals is becoming rapid and inexpensive, enabling new strategies for using personal genome information to help diagnose, treat, and even prevent human disorders for which genetic variations are causative or are known to be risk factors. Many of the exploding number of newly discovered genetic variations alter the structure, function, dynamics, stability, and/or interactions of specific proteins and RNA molecules. Accordingly, there are a host of opportunities for biochemists and biophysicists to participate in (1) developing tools to allow accurate and sometimes medically actionable assessment of the potential pathogenicity of individual variations and (2) establishing the mechanistic linkage between pathogenic variations and their physiological consequences, providing a rational basis for treatment or preventive care. In this review, we provide an overview of these opportunities and their associated challenges in light of the current status of genomic science and personalized medicine, the latter often termed precision medicine.