Sean Maxwell

Gene, pathway and network frameworks to identify epistatic interactions of single nucleotide polymorphisms derived from GWAS data

BackgroundInteractions among genomic loci (also known as epistasis) have been suggested as one of the potential sources of missing heritability in single locus analysis of genome-wide association studies (GWAS). The computational burden of searching for interactions is compounded by the extremely low threshold for identifying significant p-values due to multiple hypothesis testing corrections. Utilizing prior biological knowledge to restrict the set of candidate SNP pairs to be tested can alleviate this problem, but systematic studies that investigate the relative merits of integrating different biological frameworks and GWAS data have not been conducted.ResultsWe developed four biologically based frameworks to identify pairwise interactions among candidate SNP pairs as follows: (1) for each human protein-coding gene, a set of SNPs associated with that gene was constructed providing a gene-based interaction model, (2) for each known biological pathway, a set of SNPs associated with the genes in the pathway was constructed providing a pathway-based interaction model, (3) a set of SNPs associated with genes in a disease-related subnetwork provides a network-based interaction model, and (4) a framework is based on the function of SNPs. The last approach uses expression SNPs (eSNPs or eQTLs), which are SNPs or loci that have defined effects on the abundance of transcripts of other genes. We constructed pairs of eSNPs and SNPs located in the target genes whose expression is regulated by eSNPs. For all four frameworks the SNP sets were exhaustively tested for pairwise interactions within the sets using a traditional logistic regression model after excluding genes that were previously identified to associate with the trait. Using previously published GWAS data for type 2 diabetes (T2D) and the biologically based pair-wise interaction modeling, we identify twelve genes not seen in the previous single locus analysis.ConclusionWe present four approaches to detect interactions associated with complex diseases. The results show our approaches outperform the traditional single locus approaches in detecting genes that previously did not reach significance; the results also provide novel drug targets and biomarkers relevant to the underlying mechanisms of disease.

Discovery of common sequences absent in the human reference genome using pooled samples from next generation sequencing

BackgroundSequences up to several megabases in length have been found to be present in individual genomes but absent in the human reference genome. These sequences may be common in populations, and their absence in the reference genome may indicate rare variants in the genomes of individuals who served as donors for the human genome project. As the reference genome is used in probe design for microarray technology and mapping short reads in next generation sequencing (NGS), this missing sequence could be a source of bias in functional genomic studies and variant analysis. One End Anchor (OEA) and/or orphan reads from paired-end sequencing have been used to identify novel sequences that are absent in reference genome. However, there is no study to investigate the distribution, evolution and functionality of those sequences in human populations.ResultsTo systematically identify and study the missing common sequences (micSeqs), we extended the previous method by pooling OEA reads from large number of individuals and applying strict filtering methods to remove false sequences. The pipeline was applied to data from phase 1 of the 1000 Genomes Project. We identified 309 micSeqs that are present in at least 1% of the human population, but absent in the reference genome. We confirmed 76% of these 309 micSeqs by comparison to other primate genomes, individual human genomes, and gene expression data. Furthermore, we randomly selected fifteen micSeqs and confirmed their presence using PCR validation in 38 additional individuals. Functional analysis using published RNA-seq and ChIP-seq data showed that eleven micSeqs are highly expressed in human brain and three micSeqs contain transcription factor (TF) binding regions, suggesting they are functional elements. In addition, the identified micSeqs are absent in non-primates and show dynamic acquisition during primate evolution culminating with most micSeqs being present in Africans, suggesting some micSeqs may be important sources of human diversity.Conclusions76% of micSeqs were confirmed by a comparative genomics approach. Fourteen micSeqs are expressed in human brain or contain TF binding regions. Some micSeqs are primate-specific, conserved and may play a role in the evolution of primates.

Proteome and Protein Network Analyses of Memory T Cells Find Altered Translation and Cell Stress Signaling in Treated Human Immunodeficiency Virus Patients Exhibiting Poor CD4 Recovery

Background. Human immunodeficiency virus (HIV) patients who experience poor CD4 T-cell recovery despite viral suppression during antiretroviral therapy (ART) are known as immunological nonresponders. The molecular mechanism(s) underlying incomplete immune restoration during ART is not fully understood. Methods. Label-free quantitative proteomics on single-cell type central memory T cells were used to reveal relative protein abundance changes between nonresponder, responder (good CD4 recovery during ART), and healthy individuals. Proteome changes were analyzed by protein pathway and network analyses and verified by selected reaction monitoring mass spectrometry. Results. Proteomic analysis across groups detected 155 significant proteins from 1500 nonredundant proteins. Pathway and network analyses revealed dysregulation in mammalian target of rapamycin and protein translation-related proteins and decreases in stress response-related proteins for nonresponder subjects compared with responders and controls. Actin cytoskeleton signaling was increased for HIV responders and nonresponders alike. Conclusions. Memory T cells from immunologic nonresponders have increases in proteins related to motility and protein translation and decreases in proteins capable of responding to cellular stresses compared with responders and controls. The potential for T cells to manage stress and modulate metabolism may contribute to their capacity to reconstitute a lymphopenic host.

Efficiently Enumerating All Connected Induced Subgraphs of a Large Molecular Network

In systems biology, the solution space for a broad range of problems is composed of sets of functionally associated biomolecules. Since connectivity in molecular interaction networks is an indicator of functional association, such sets can be identified from connected induced subgraphs of molecular interaction networks. Applications typically quantify the relevance (e.g., modularity, conservation, disease association) of connected subnetworks using an objective function and use a search algorithm to identify sets of subnetworks that maximize this objective function. Efficient enumeration of connected subgraphs of a large graph is therefore useful for these applications, and many existing search algorithms can be used for this purpose. However, there is a lack of non-heuristic algorithms that minimize the total number of subgraphs evaluated during the search for subgraphs that maximize the objective function. Here, we propose and evaluate an algorithm that reduces the computations necessary to enumerate subgraphs that maximize an objective function given a monotonically decreasing bounding function.

Proteomics and Network Analyses Reveal Inhibition of Akt-mTOR Signaling in CD4+ T Cells by Mycobacterium tuberculosis Mannose-Capped Lipoarabinomannan

Mycobacterium tuberculosis (Mtb) cell wall glycolipid mannose‐capped lipoarabinomannan (ManLAM) inhibits CD4+ T‐cell activation by inhibiting proximal T‐cell receptor (TCR) signaling when activated by anti‐CD3. To understand the impact of ManLAM on CD4+ T‐cell function when both the TCR–CD3 complex and major costimulator CD28 are engaged, we performed label‐free quantitative MS and network analysis. Mixed‐effect model analysis of peptide intensity identified 149 unique peptides representing 131 proteins that were differentially regulated by ManLAM in anti‐CD3‐ and anti‐CD28‐activated CD4+ T cells. Crosstalker, a novel network analysis tool identified dysregulated translation, TCA cycle, and RNA metabolism network modules. PCNA, Akt, mTOR, and UBC were found to be bridge node proteins connecting these modules of dysregulated proteins. Altered PCNA expression and cell cycle analysis showed arrest at the G2M phase. Western blot confirmed that ManLAM inhibited Akt and mTOR phosphorylation, and decreased expression of deubiquitinating enzymes Usp9x and Otub1. Decreased NF‐κB phosphorylation suggested interference with CD28 signaling through inhibition of the Usp9x‐Akt‐mTOR pathway. Thus, ManLAM induced global changes in the CD4+ T‐cell proteome by affecting Akt‐mTOR signaling, resulting in broad functional impairment of CD4+ T‐cell activation beyond inhibition of proximal TCR–CD3 signaling.

Linearity of network proximity measures: implications for set-based queries and significance testing

Motivation: In recent years, various network proximity measures have been proposed to facilitate the use of biomolecular interaction data in a broad range of applications. These applications include functional annotation, disease gene prioritization, comparative analysis of biological systems and prediction of new interactions. In such applications, a major task is the scoring or ranking of the nodes in the network in terms of their proximity to a given set of ‘seed’ nodes (e.g. a group of proteins that are identified to be associated with a disease, or are deferentially expressed in a certain condition). Many different network proximity measures are utilized for this purpose, and these measures are quite diverse in terms of the benefits they offer. Results: We propose a unifying framework for characterizing network proximity measures for set‐based queries. We observe that many existing measures are linear, in that the proximity of a node to a set of nodes can be represented as an aggregation of its proximity to the individual nodes in the set. Based on this observation, we propose methods for processing of set‐based proximity queries that take advantage of sparse local proximity information. In addition, we provide an analytical framework for characterizing the distribution of proximity scores based on reference models that accurately capture the characteristics of the seed set (e.g. degree distribution and biological function). The resulting framework facilitates computation of exact figures for the statistical significance of network proximity scores, enabling assessment of the accuracy of Monte Carlo simulation based estimation methods. Availability and Implementation: Implementations of the methods in this paper are available at https://bioengine.case.edu/crosstalker which includes a robust visualization for results viewing. Contact : stm@case.edu or mxk331@case.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Phosphoproteomics Profiling of Nonsmall Cell Lung Cancer Cells Treated with a Novel Phosphatase Activator

Activation of protein phosphatase 2A (PP2A) is a promising anticancer therapeutic strategy, as this tumor suppressor has the ability to coordinately downregulate multiple pathways involved in the regulation of cellular growth and proliferation. In order to understand the systems‐level perturbations mediated by PP2A activation, we carried out mass spectrometry‐based phosphoproteomic analysis of two KRAS mutated non‐small cell lung cancer (NSCLC) cell lines (A549 and H358) treated with a novel small molecule activator of PP2A (SMAP). Overall, this permitted quantification of differential signaling across over 1600 phosphoproteins and 3000 phosphosites. Kinase activity assessment and pathway enrichment implicate collective downregulation of RAS and cell cycle kinases in the case of both cell lines upon PP2A activation. However, the effects on RAS‐related signaling are attenuated for A549 compared to H358, while the effects on cell cycle‐related kinases are noticeably more prominent in A549. Network‐based analyses and validation experiments confirm these detailed differences in signaling. These studies reveal the power of phosphoproteomics studies, coupled to computational systems biology, to elucidate global patterns of phosphatase activation and understand the variations in response to PP2A activation across genetically similar NSCLC cell lines.

CoPhosK: A Method for Comprehensive Kinase Substrate Annotation Using Co-phosphorylation Analysis

We present CoPhosK to predict kinase-substrate associations for phosphopeptide substrates detected by mass spectrometry (MS). The tool utilizes a Naïve Bayes framework with priors of known kinase-substrate associations (KSAs). Through the mining of MS data for the collective dynamic signatures of the kinases’ substrates, as revealed by correlation analysis of phosphopeptide intensity data, the tool infers KSAs in the data for the considerable body of substrates lacking such annotations. We benchmarked the tool against existing approaches for predicting KSAs that rely on static information (e.g. sequences, structures and interactions) using publically available MS data, including breast and ovarian cancer models. The benchmarking reveals that co-phosphorylation analysis can improve prediction performance when static information is available (about 35% of sites) while providing reliable predictions for the remainder, tripling the KSAs available from the experimental MS data to comprehensively and reliably characterize the landscape of kinase-substrate interactions well beyond current limitations.

Integrated microRNA and mRNA network analysis of the human myometrial transcriptome in the transition from quiescence to labor

Abstract We conducted integrated transcriptomics network analyses of miRNA and mRNA interactions in human myometrium to identify novel molecular candidates potentially involved in human parturition. Myometrial biopsies were collected from women undergoing primary Cesarean deliveries in well-characterized clinical scenarios: (1) spontaneous term labor (TL, n = 5); (2) term nonlabor (TNL, n = 5); (3) spontaneous preterm birth (PTB) with histologic chorioamnionitis (PTB-HCA, n = 5); and (4) indicated PTB nonlabor (PTB-NL, n = 5). RNAs were profiled using RNA sequencing, and miRNA-target interaction networks were mined for key discriminatory subnetworks. Forty miRNAs differed between TL and TNL myometrium, while seven miRNAs differed between PTB-HCA vs. PTB-NL specimens; six of these were cross-validated using quantitative PCR. Based on the combined sequencing data, unsupervised clustering revealed two nonoverlapping cohorts that differed primarily by absence or presence of uterine quiescence, rather than gestational age or original clinical cohort. The intersection of differentially expressed miRNAs and their targets predicted 22 subnetworks with enriched representation of miR-146b-5p, miR-223-3p, and miR-150-5p among miRNAs, and of myocyte enhancer factor-2C (MEF2C) among mRNAs. Of four known MEF2 transcription factors, decreased MEF2A and MEF2C expression in women with uterine nonquiescence was observed in the sequencing data, and validated in a second cohort by quantitative PCR. Immunohistochemistry localized MEF2A and MEF2C to myometrial smooth muscle cells and confirmed decreased abundance with labor. Collectively, these results suggest altered MEF2 expression may represent a previously unrecognized process through which miRNAs contribute to the phenotypic switch from quiescence to labor in human myometrium. Summary Sentence Integrated miRNA–mRNA study in human myometrium.