Protein-protein Interactome Network Research Articles

Molecular regulation of calcium-sensing receptor (CaSR)-mediated signaling.

Calcium-sensing receptor (CaSR), a family C G-protein-coupled receptor, plays a crucial role in regulating calcium homeostasis by sensing small concentration changes of extracellular Ca2+, Mg2+, amino acids (e.g., L-Trp and L-Phe), small peptides, anions (e.g., HCO3 - and PO4 3-), and pH. CaSR-mediated intracellular Ca2+ signaling regulates a diverse set of cellular processes including gene transcription, cell proliferation, differentiation, apoptosis, muscle contraction, and neuronal transmission. Dysfunction of CaSR with mutations results in diseases such as autosomal dominant hypocalcemia, familial hypocalciuric hypercalcemia, and neonatal severe hyperparathyroidism. CaSR also influences calciotropic disorders, such as osteoporosis, and noncalciotropic disorders, such as cancer, Alzheimer's disease, and pulmonary arterial hypertension. This study first reviews recent advances in biochemical and structural determination of the framework of CaSR and its interaction sites with natural ligands, as well as exogenous positive allosteric modulators and negative allosteric modulators. The establishment of the first CaSR protein-protein interactome network revealed 94 novel players involved in protein processing in endoplasmic reticulum, trafficking, cell surface expression, endocytosis, degradation, and signaling pathways. The roles of these proteins in Ca2+-dependent cellular physiological processes and in CaSR-dependent cellular signaling provide new insights into the molecular basis of diseases caused by CaSR mutations and dysregulated CaSR activity caused by its protein interactors and facilitate the design of therapeutic agents that target CaSR and other family C G-protein-coupled receptors.

Interpretable deep learning translation of GWAS and multi-omics findings to identify pathobiology and drug repurposing in Alzheimer's disease.

Translating human genetic findings (genome-wide association studies [GWAS]) to pathobiology and therapeutic discovery remains a major challenge for Alzheimer's disease (AD). We present a network topology-based deep learning framework to identify disease-associated genes (NETTAG). We leverage non-coding GWAS loci effects on quantitative trait loci, enhancers and CpG islands, promoter regions, open chromatin, and promoter flanking regions under the protein-protein interactome. Via NETTAG, we identified 156 AD-risk genes enriched in druggable targets. Combining network-based prediction and retrospective case-control observations with 10 million individuals, we identified that usage of four drugs (ibuprofen, gemfibrozil, cholecalciferol, and ceftriaxone) is associated with reduced likelihood of AD incidence. Gemfibrozil (an approved lipid regulator) is significantly associated with 43% reduced risk of AD compared with simvastatin using an active-comparator design (95% confidence interval 0.51-0.63, p<0.0001). In summary, NETTAG offers a deep learning methodology that utilizes GWAS and multi-genomic findings to identify pathobiology and drug repurposing in AD.

A comprehensive SARS-CoV-2-human protein-protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets.

Studying viral-host protein-protein interactions can facilitate the discovery of therapies for viral infection. We use high-throughput yeast two-hybrid experiments and mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of 739 high-confidence binary and co-complex interactions, validating 218 known SARS-CoV-2 host factors and revealing 361 novel ones. Our results show the highest overlap of interaction partners between published datasets and of genes differentially expressed in samples from COVID-19 patients. We identify an interaction between the viral protein ORF3a and the human transcription factor ZNF579, illustrating a direct viral impact on host transcription. We perform network-based screens of >2,900 FDA-approved or investigational drugs and identify 23 with significant network proximity to SARS-CoV-2 host factors. One of these drugs, carvedilol, shows clinical benefits for COVID-19 patients in an electronic health records analysis and antiviral properties in a human lung cell line infected with SARS-CoV-2. Our study demonstrates the value of network systems biology to understand human-virus interactions and provides hits for further research on COVID-19 therapeutics.

Abstract 12781: Network Medicine-Based Multimodal Omics Discovery and iPSC-Based Validation of Metformin for Potential Treatment of Atrial Fibrillation

Introduction: Atrial Fibrillation (AF) is the most prevalent cardiac arrhythmia in the United States. Effective, safe, and durable therapeutic interventions are lacking. Thus, it is imperative to improve clinical management by utilizing novel approaches for drug repurposing. To date, multimodal omics analysis of genomics, transcriptomics, and protein-protein interactions [PPIs] have not been fully exploited for AF drug repurposing. Hypothesis: We hypothesize that using Network Medicine-based multimodal omics analysis for drug prioritization and functional observations using induced pluripotent stem cell (iPSC) models can help us identify effective therapeutic strategies and elucidate drug’s mechanisms-of-action for restoring normal sinus rhythm. Methods: Here, we used RNA-sequencing data from a biobank of left atrial tissues obtained from cardiac surgery patients to identify testable AF disease modules under the human protein-protein interactome network model. We systematically prioritized repurposable drug candidates for AF using both network proximity and gene-set enrichment analysis approaches by leveraging AF disease module findings, drug-target network, and drug-induced gene signatures in human cell lines. RNAseq was used to assess the transcriptomic impact of a top drug candidate, metformin, on gene expression of atrial myocytes differentiated from human iPSCs (a-iCMs). Results: Via network-based screening of 2,891 FDA-approved or clinical drugs, we found nine putative drug candidates (including Metformin, Furosemide, and Rofecoxib) using both network proximity z-score and GSEA-based enrichment scores. RNA-seq analysis of metformin-treated a-iCMs identified 238 differentially expressed genes following multiple test correction (P&lt; 2.96E-06). Several key cardiovascular or drug-related markers were differentially expressed in Metformin treated a-EHTs. Of note NMRK2 and PCK2 were upregulated, and TGFB1 and TXNIP were downregulated, suggesting an improved energy utilization following metformin treatment. Conclusions: In summary, this study presented state-of-the-art network medicine methodologies for AF drug repurposing and identified metformin as a candidate treatment for AF patients.

Metabolic endophenotype associated with right ventricular glucose uptake in pulmonary hypertension.

Alterations in metabolism and bioenergetics are hypothesized in the mechanisms leading to pulmonary vascular remodeling and heart failure in pulmonary hypertension (PH). To test this, we performed metabolomic analyses on 30 PH individuals and 12 controls. Furthermore, using 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography, we dichotomized PH patients into metabolic phenotypes of high and low right ventricle (RV) glucose uptake and followed them longitudinally. In support of metabolic alterations in PH and its progression, the high RV glucose group had higher RV systolic pressure (p < 0.001), worse RV function as measured by RV fractional area change and peak global longitudinal strain (both p < 0.05) and may be associated with poorer outcomes (33% death or transplantation in the high glucose RV uptake group compared to 7% in the low RV glucose uptake group at five years follow-up, log-ranked p = 0.07). Pathway enrichment analysis identified key metabolic pathways including fructose catabolism, arginine-nitric oxide metabolism, tricarboxylic acid cycle, and ketones metabolism. Integrative human protein-protein interactome network analysis of metabolomic and transcriptomic data identified key pathobiological pathways: arginine biosynthesis, tricarboxylic acid cycle, purine metabolism, hypoxia-inducible factor 1, and apelin signaling. These findings identify a PH metabolomic endophenotype, and for the first time link this to disease severity and outcomes.

Towards a global investigation of transcriptomic signatures through co-expression networks and pathway knowledge for the identification of disease mechanisms.

We attempt to address a key question in the joint analysis of transcriptomic data: can we correlate the patterns we observe in transcriptomic datasets to known interactions and pathway knowledge to broaden our understanding of disease pathophysiology? We present a systematic approach that sheds light on the patterns observed in hundreds of transcriptomic datasets from over sixty indications by using pathways and molecular interactions as a template. Our analysis employs transcriptomic datasets to construct dozens of disease specific co-expression networks, alongside a human protein-protein interactome network. Leveraging the interoperability between these two network templates, we explore patterns both common and particular to these diseases on three different levels. Firstly, at the node-level, we identify most and least common proteins across diseases and evaluate their consistency against the interactome as a proxy for their prevalence in the scientific literature. Secondly, we overlay both network templates to analyze common correlations and interactions across diseases at the edge-level. Thirdly, we explore the similarity between patterns observed at the disease-level and pathway knowledge to identify signatures associated with specific diseases and indication areas. Finally, we present a case scenario in schizophrenia, where we show how our approach can be used to investigate disease pathophysiology.

Identifying miRNAs in multiple sclerosis gray matter lesions that correlate with atrophy measures.

ObjectiveMultiple sclerosis (MS) is an inflammatory, demyelinating and neurodegenerative disease of the central nervous system (CNS). Though MS was initially considered to be a white matter demyelinating disease, myelin loss in cortical gray matter has been reported in all disease stages. We previously identified microRNAs (miRNAs) in white matter lesions (WMLs) that are detected in serum from MS patients. However, miRNA expression profiles in gray matter lesions (GMLs) from progressive MS brains are understudied.MethodsWe used a combination of global miRNAs and gene expression profiling of GMLs and independent validation using real‐time quantitative polymerase chain reaction (RT‐qPCR), immuno‐in situ hybridization, and immunohistochemistry.ResultsCompared to matched myelinated gray matter (GM) regions, we identified 82 miRNAs in GMLs, of which 10 were significantly upregulated and 17 were significantly downregulated. Among these 82 miRNAs, 13 were also detected in serum and importantly were associated with brain atrophy in MS patients. The predicted target mRNAs of these miRNAs belonged to pathways associated with axonal guidance, TGF‐β signaling, and FOXO signaling. Further, using state‐of‐the‐art human protein–protein interactome network analysis, we mapped the four key GM atrophy‐associated miRNAs (hsa‐miR‐149*, hsa‐miR‐20a, hsa‐miR‐29c, and hsa‐miR‐25) to their target mRNAs that were also changed in GMLs.InterpretationOur study identifies miRNAs altered in GMLs in progressive MS brains that correlate with atrophy measures. As these miRNAs were also detected in sera of MS patients, these could act as markers of GML demyelination in MS.

Network-Based Analysis of Cognitive Impairment and Memory Deficits from Transcriptome Data.

Aging is an inevitable process that negatively affects all living organisms and their vital functions. The brain is one of the most important organs in living beings and is primarily impacted by aging. The molecular mechanisms of learning, memory and cognition are altered over time, and the impairment in these mechanisms can lead to neurodegenerative diseases. Transcriptomics can be used to study these impairments to acquire more detailed information on the affected molecular mechanisms. Here we analyzed learning- and memory-related transcriptome data by mapping it on the organism-specific protein-protein interactome network. Subnetwork discovery algorithms were applied to discover highly dysregulated subnetworks, which were complemented with co-expression-based interactions. The functional analysis shows that the identified subnetworks are enriched with genes having roles in synaptic plasticity, gliogenesis, neurogenesis and cognition, which are reported to be related to memory and learning. With a detailed analysis, we show that the results from different subnetwork discovery algorithms or from different transcriptomic datasets can be successfully reconciled, leading to a memory-learning network that sheds light on the molecular mechanisms behind aging and memory-related impairments.

Multiple-genotypes transcriptional analysis revealed candidates genes and nucleotide variants for improvement of quality characteristics in tea (Camellia sinensis (L.) O. Kuntze)

Tea quality is a polygenic trait that exhibits tremendous genetic variability due to accumulation of array of secondary metabolites. To elucidate global molecular insights controlling quality attributes, metabolite profiling and transcriptome sequencing of twelve diverse tea cultivars was performed in tea shoots harvested during quality season. RP-HPLC-DAD analysis of quality parameters revealed significant difference in catechins, theanine and caffeine contents. Transcriptome sequencing resulted into 50,107 non-redundant transcripts with functional annotations of 81.6% (40,847) of the transcripts. Interestingly, 2872 differentially expressed transcripts exhibited significant enrichment in 38 pathways (FDR ≤ 0.05) including secondary metabolism, amino acid and carbon metabolism. Thirty-eight key candidates reportedly involved in biosynthesis of fatty acid derived volatiles, volatile terpenes, glycoside hydrolysis and key quality related pathways (flavonoid, caffeine and theanine-biosynthesis) were highly expressed in catechins-rich tea cultivars. Furthermore, enrichment of candidates involved in flavonoid biosynthesis, transcriptional regulation, volatile terpene and biosynthesis of fatty acid derived volatile in Protein-Protein Interactome network revealed well-coordinated regulation of quality characteristics in tea. Additionally, ascertainment of 23,649 non-synonymous SNPs and validation of candidate SNPs present in quality related genes suggests their potential utility in genome-wide mapping and marker development for expediting breeding of elite compound-rich tea cultivars.

The Epidemiological and Mechanistic Understanding of the Neurological Manifestations of COVID-19: A Comprehensive Meta-Analysis and a Network Medicine Observation

Background and Purpose: The clinical characteristics and biological effects on the nervous system of infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remain poorly understood. The aim of this study is to advance epidemiological and mechanistic understanding of the neurological manifestations of coronavirus disease 2019 (COVID-19) using stroke as a case study.Methods: We performed a meta-analysis of clinical studies reporting stroke history, intensive inflammatory response and procoagulant state (C-reactive protein (CRP), Procalcitonin (PCT) and coagulation indicator (D-dimer)) in patients with COVID-19. Via network-based analysis of SARS-CoV-2 host genes and stroke-associated genes in the human protein-protein interactome, we inspected the underlying inflammatory endophenotypes between COVID-19 and stroke. Finally, we further verified the network medicine-based findings using large-scale single-cell RNA-sequencing data generated from the lymphocytic choriomeningitis virus (LCMV) infected mouse model.Results: The overall pooled prevalence of stroke history was 2.98% (95% CI, 1.89 - 4.68; I2=69.2%) in the COVID-19 population. Notably, the severe group had a higher prevalence of stroke (6.06%; 95% CI 3.80 - 9.52; I2 = 42.6%) compare to the non-severe group (1.1%, 95% CI 0.72 - 1.71; I2 = 0.0%). In addition, there were increased CRP, PCT and D-dimer in severe illness, and the pooled mean difference was 40.7mg/L (95% CI, 24.3 - 57.1), 0.07mg/L (95% CI, 0.04 - 0.10) and 0.63mg/L (95% CI, 0.28 - 0.97) respectively. Vascular cell adhesion molecule 1 (VCAM-1), dimethylarginine dimethylaminohydrolase-1 (DDAH-1), CD59 and NOTCH-3 were selected from the results of the protein-protein interactome (PPI) network analysis. Among them, VCAM-1, one of the leukocyte adhesion molecules, is suspected to play a vital role in a holistic perspective. Even though the verification using LCMV data revealed no significant change could be observed in part of virus-host genes and stroke-associated genes, suggesting the relative importance of inflammatory responses in SARS-CoV-2 infection-associated neurological manifestations.Conclusion: In this study, we identified increased vulnerability of those with a history of stroke to severe COVID-19, suggesting monotonic relationships, thus implicating causality. Some candidates underlying inflammatory responses (i.e., VCAM-1) and procoagulant pathways were preliminarily identified for neurologists in caring COVID-19 patients with cerebrovascular disease, which requires future clinical and functional validation studies.Funding Statement: This work was supported by the National Heart, Lung, and Blood Institute (R00HL138272) and the National Institute of Aging (R01AG066707).Declaration of Interests: The authors declare that they have no conflict of interest.

Comprehensive analysis of genomic alterations, clinical outcomes, putative functions and potential therapeutic value of MMP11 in human breast cancer

The aim of the current study is to comprehend the prognostic and therapeutic value of matrix metalloproteinase (MMP11) in breast cancer using computational analyses. Gene expression-based Outcome for Breast cancer Online (GOBO) analysis revealed that MMP11 expression was increased in tumor when compared to normal. Among the subtypes, human epidermal growth factor receptor 2 (HER2) enriched and estrogen receptor (ER) positive showed higher expression of MMP11. The results were independently validated with cBioPortal and Oncomine databases. Tumors with high MMP11 expression showed poor overall survival and distant metastasis free survival. Further, protein-protein interactome network was constructed and molecular pathway analysis using Reactome database identified its putative role in diverse cellular processes. In addition, UALCAN web portal revealed hypomethylation in the promoter region of MMP11 in breast tumors. Molecular docking using iGEMDOCK and Autodock Vina were performed to identify natural compounds and repurposed drugs interaction with MMP11. Our study confirms the prognostic significance of MMP11 in breast cancer and their intrinsic subtypes. Twelve bio-compounds which were predominantly flavonoids and anti-psychotic drug pimozide showed significant docking scores with MMP11. Further, potential of these compounds in inhibiting MMP11 related molecular pathways and in targeting breast cancer cells are yet to be studied.

OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine

Breast cancer (BC) is the leading cause of cancer-related death among women and the most commonly diagnosed cancer worldwide. Although in recent years large-scale efforts have focused on identifying new therapeutic targets, a better understanding of BC molecular processes is required. Here we focused on elucidating the molecular hallmarks of BC heterogeneity and the oncogenic mutations involved in precision medicine that remains poorly defined. To fill this gap, we established an OncoOmics strategy that consists of analyzing genomic alterations, signaling pathways, protein-protein interactome network, protein expression, dependency maps in cell lines and patient-derived xenografts in 230 previously prioritized genes to reveal essential genes in breast cancer. As results, the OncoOmics BC essential genes were rationally filtered to 140. mRNA up-regulation was the most prevalent genomic alteration. The most altered signaling pathways were associated with basal-like and Her2-enriched molecular subtypes. RAC1, AKT1, CCND1, PIK3CA, ERBB2, CDH1, MAPK14, TP53, MAPK1, SRC, RAC3, BCL2, CTNNB1, EGFR, CDK2, GRB2, MED1 and GATA3 were essential genes in at least three OncoOmics approaches. Drugs with the highest amount of clinical trials in phases 3 and 4 were paclitaxel, docetaxel, trastuzumab, tamoxifen and doxorubicin. Lastly, we collected ~3,500 somatic and germline oncogenic variants associated with 50 essential genes, which in turn had therapeutic connectivity with 73 drugs. In conclusion, the OncoOmics strategy reveals essential genes capable of accelerating the development of targeted therapies for precision oncology.

Integrative proteomics and phosphoproteomics in pulmonary arterial hypertension

Pulmonary arterial endothelial cells (PAEC) are mechanistically linked to origins of pulmonary arterial hypertension (PAH). Here, global proteomics and phosphoproteomics of PAEC from PAH (n = 4) and healthy lungs (n = 5) were performed using LC-MS/MS to confirm known pathways and identify new areas of investigation in PAH. Among PAH and control cells, 170 proteins and 240 phosphopeptides were differentially expressed; of these, 45 proteins and 18 phosphopeptides were located in the mitochondria. Pathologic pathways were identified with integrative bioinformatics and human protein-protein interactome network analyses, then confirmed with targeted proteomics in PAH PAEC and non-targeted metabolomics and targeted high-performance liquid chromatography of metabolites in plasma from PAH patients (n = 30) and healthy controls (n = 12). Dysregulated pathways in PAH include accelerated one carbon metabolism, abnormal tricarboxylic acid (TCA) cycle flux and glutamate metabolism, dysfunctional arginine and nitric oxide pathways, and increased oxidative stress. Functional studies in cells confirmed abnormalities in glucose metabolism, mitochondrial oxygen consumption, and production of reactive oxygen species in PAH. Altogether, the findings indicate that PAH is typified by changes in metabolic pathways that are primarily found in mitochondria.

Protein interactome network analysis predicts novel breast cancer candidate genes with molecular signatures

Abstract Background Advancement in molecular biology research has allowed the measurement of multiomics data points from a single tumor biopsy sample in a reasonable time frame for making significant clinical decisions. There have been tremendous advances from the bioinformatics and systems biology perspective to analyse integrated multi‐scale analysis of the data will prove invaluable for a combined systems‐level understanding of the important biological network processes contributing to the initiation, progression and management of cancer. Cancer networks have greater challenge due to the aberrant functions of hundreds of genes that are translated to altered protein function within each cancer cell, to understand this complexity. Network-based methods have been used to analyse the complexity molecular interactions in the cell and can contribute to prediction of candidate genes responsible for cancer. Methods HI-II- 14 is the largest experimentally determined binary protein – protein interaction map is used for the study. These interactions were mapped to NCBI Entrez gene ID and excluded self-loop and redundant interactions. The known human breast cancer genes were obtained from the Sanger Cancer Gene Census which is a comprehensive catalogue of genes implicated in breast cancer and from Uniprot database. In total, we obtained 913 genes from both for analyses. The genes degree of connections and the centrality in the protein -protein networks were considered. The genome-wide mutation datasets of breast cancer were downloaded from TCGA as maf files. For each gene, we obtained the mutation frequency for each gene is calculated which is defined as the number of mutated samples divided by the total number of samples. The frequency of the top 100 ranked genes and the bottom 100 ranked genes were compared by Wilcox rank sum test. Results In this study, HI-II-14 human protein-protein interactome network is applied to prediction of novel breast cancer genes. Conclusions Our study suggests integrating interactome networks with multiomics datasets could provide novel insights into breast cancer-associated genes and underlying molecular mechanisms. Legal entity responsible for the study M. Peer Mohammed. Funding Has not received any funding. Disclosure The author has declared no conflicts of interest.

A genome-wide positioning systems network algorithm for in silico drug repurposing

Recent advances in DNA/RNA sequencing have made it possible to identify new targets rapidly and to repurpose approved drugs for treating heterogeneous diseases by the ‘precise’ targeting of individualized disease modules. In this study, we develop a Genome-wide Positioning Systems network (GPSnet) algorithm for drug repurposing by specifically targeting disease modules derived from individual patient’s DNA and RNA sequencing profiles mapped to the human protein-protein interactome network. We investigate whole-exome sequencing and transcriptome profiles from ~5,000 patients across 15 cancer types from The Cancer Genome Atlas. We show that GPSnet-predicted disease modules can predict drug responses and prioritize new indications for 140 approved drugs. Importantly, we experimentally validate that an approved cardiac arrhythmia and heart failure drug, ouabain, shows potential antitumor activities in lung adenocarcinoma by uniquely targeting a HIF1α/LEO1-mediated cell metabolism pathway. In summary, GPSnet offers a network-based, in silico drug repurposing framework for more efficacious therapeutic selections.

Global Transcriptional Insights of Pollen-Pistil Interactions Commencing Self-Incompatibility and Fertilization in Tea [Camellia sinensis (L.) O. Kuntze

This study explicates molecular insights commencing Self-Incompatibility (SI) and CC (cross-compatibility/fertilization) in self (SP) and cross (CP) pollinated pistils of tea. The fluorescence microscopy analysis revealed ceased/deviated pollen tubes in SP, while successful fertilization occurred in CP at 48 HAP. Global transcriptome sequencing of SP and CP pistils generated 109.7 million reads with overall 77.9% mapping rate to draft tea genome. Furthermore, concatenated de novo assembly resulted into 48,163 transcripts. Functional annotations and enrichment analysis (KEGG & GO) resulted into 3793 differentially expressed genes (DEGs). Among these, de novo and reference-based expression analysis identified 195 DEGs involved in pollen-pistil interaction. Interestingly, the presence of 182 genes [PT germination & elongation (67), S-locus (11), fertilization (43), disease resistance protein (30) and abscission (31)] in a major hub of the protein-protein interactome network suggests a complex signaling cascade commencing SI/CC. Furthermore, tissue-specific qRT-PCR analysis affirmed the localized expression of 42 DE putative key candidates in stigma-style and ovary, and suggested that LSI initiated in style and was sustained up to ovary with the active involvement of csRNS, SRKs & SKIPs during SP. Nonetheless, COBL10, RALF, FERONIA-rlk, LLG and MAPKs were possibly facilitating fertilization. The current study comprehensively unravels molecular insights of phase-specific pollen-pistil interaction during SI and fertilization, which can be utilized to enhance breeding efficiency and genetic improvement in tea.

Big data mining powers fungal research: recent advances in fission yeast systems biology approaches

Biology research has entered into big data era. Systems biology approaches therefore become the powerful tools to obtain the whole landscape of how cell separate, grow, and resist the stresses. Fission yeast Schizosaccharomyces pombe is wonderful unicellular eukaryote model, especially studying its division and metabolism can facilitate to understanding the molecular mechanism of cancer and discovering anticancer agents. In this perspective, we discuss the recent advanced fission yeast systems biology tools, mainly focus on metabolomics profiling and metabolic modeling, protein-protein interactome and genetic interaction network, DNA sequencing and applications, and high-throughput phenotypic screening. We therefore hope this review can be useful for interested fungal researchers as well as bioformaticians.

Abstract B1-26: NetFill: A network-based method to identify a parsimonious set of mediators to link mRNA and protein signatures using existing knowledge

Abstract Introduction: Despite our general understanding of the genetic information flow from DNA to RNA to protein, the integrated analysis of multi-level omics data in a biologically meaningful way remains a challenging task due to the complex and dynamic nature of disease associated changes in molecular regulatory mechanisms. A case in point is the often observed phenomenon that key signatures associated with the same phenotypic difference do not share much in common between the mRNA and proteomic data even when they are measured on a common set of samples. Methods: We developed a computational method, NetFill, that integrates prior knowledge into the analysis of expression data to identify a parsimonious set of molecular mediators to connect the mRNA and protein signatures with plausible pathways/networks. NetFill formulates the problem mathematically as a flow-based linear programming under constraints to identify mediator/proteins in the protein-protein interactome network with the minimal cost cumulated from selected nodes and selected edges to link the mRNA and protein signatures. Data dependent node score and edge score were computed from the mRNA and proteomic expression data using Bayes' rule. By assuming conditional independence between different types of evidence, the node score was derived from the p values of t-tests between two conditions. Edge score was derived from the p-value of correlation between two proteins and edge confidence score from static PPI network. In the current implementation of NetFill, nine protein-protein interaction databases (BIND, BioGRID, DIP, HPRD, IntAct, MINT, MIPS, PDZBase, and Ractome) and three pathway databases (KEGG, Biocarta, and NCI) were combined to generate a large protein-protein interactome network with 101,398 interactions. To assess the confidence score of selected interactions, the network search algorithm was applied on 100 bootstrap with replacement samples of the mRNA and protein data. The final output of NetFill was a high-confidence network in which all edges had occurred in &amp;gt; 50% of the bootstrap sampling generated networks. Results: Resulted from separate consensus clustering analyses, an mRNA signature of 213 genes and a proteomic signature of 73 proteins for a common subtype/cluster of ovarian high-grade serous carcinoma (HGSC) samples had only 40 overlapping gene/proteins. NetFill identified a parsimonious network with 142 additional mediators to link the mRNA and proteomic signatures. Functional annotation revealed that Wnt signaling pathway (p = 0.01) and MAPK signaling pathway (p = 0.03) were significantly enriched in the identified network, indicating that these pathways could be potentially involved in this subtype of ovarian HGSC. Conclusion: The NetFill method and reported example demonstrated that existing knowledge of plausible interactions among genes and proteins could be used to help to functionally integrate mRNA and protein data in multi-level omics characterization of samples. Citation Format: Li Chen, Bai Zhang, Hui Zhang, Yue Wang, Daniel W. Chan, Zhen Zhang. NetFill: A network-based method to identify a parsimonious set of mediators to link mRNA and protein signatures using existing knowledge. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-26.

Next-generation sequencing to generate interactome datasets.

Next-generation sequencing (NGS) has not been applied to protein-protein interactome network mapping so far because the association between the members of each interacting pair would not be maintained in en masse sequencing. We describe a massively parallel interactome-mapping pipeline, “Stitch-Seq”, that combines PCR stitching with NGS. We use Stitch-Seq to generate a new human interactome dataset. Stitch-Seq is applicable to various interaction assays and should help expand interactome network mapping.

Empirically controlled mapping of the Caenorhabditis elegans protein-protein interactome network

To provide accurate biological hypotheses and elucidate global properties of cellular networks, systematic identification of protein-protein interactions must meet high quality standards.We present an expanded C. elegans protein-protein interaction network, or 'interactome' map, derived from testing a matrix of approximately 10,000 x approximately 10,000 proteins using a highly specific, high-throughput yeast two-hybrid system. Through a new empirical quality control framework, we show that the resulting data set (Worm Interactome 2007, or WI-2007) was similar in quality to low-throughput data curated from the literature. We filtered previous interaction data sets and integrated them with WI-2007 to generate a high-confidence consolidated map (Worm Interactome version 8, or WI8). This work allowed us to estimate the size of the worm interactome at approximately 116,000 interactions. Comparison with other types of functional genomic data shows the complementarity of distinct experimental approaches in predicting different functional relationships between genes or proteins