The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks.

Inference and analysis of gene regulatory networks (GRNs) require software that integrates multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is a collection of open-source methods to infer GRNs, conduct differential network analyses, estimate community structure, and explore the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages and between methods to allow better integration of these tools into analytical pipelines. We demonstrate the utility using multi-omic data from the Cancer Cell Line Encyclopedia. We will continue to expand the netZoo to incorporate additional methods.

GRAND: a database of gene regulatory network models across human conditions.

Gene regulation plays a fundamental role in shaping tissue identity, function, and response to perturbation. Regulatory processes are controlled by complex networks of interacting elements, including transcription factors, miRNAs and their target genes. The structure of these networks helps to determine phenotypes and can ultimately influence the development of disease or response to therapy. We developed GRAND (https://grand.networkmedicine.org) as a database for computationally-inferred, context-specific gene regulatory network models that can be compared between biological states, or used to predict which drugs produce changes in regulatory network structure. The database includes 12 468 genome-scale networks covering 36 human tissues, 28 cancers, 1378 unperturbed cell lines, as well as 173 013 TF and gene targeting scores for 2858 small molecule-induced cell line perturbation paired with phenotypic information. GRAND allows the networks to be queried using phenotypic information and visualized using a variety of interactive tools. In addition, it includes a web application that matches disease states to potentially therapeutic small molecule drugs using regulatory network properties.

Cigarette smoking-associated isoform switching and 3' UTR lengthening via alternative polyadenylation.

Cigarette smoking induces a profound transcriptomic and systemic inflammatory response. Previous studies have focused on gene level differential expression of smoking, but the genome-wide effects of smoking on alternative isoform regulation have not yet been described. We conducted RNA sequencing in whole-blood samples of 454 current and 767 former smokers in the COPDGene Study, and we analyzed the effects of smoking on differential usage of isoforms and exons. At 10% FDR, we detected 3167 differentially expressed genes, 945 differentially used isoforms and 160 differentially used exons. Isoform switch analysis revealed widespread 3' UTR lengthening associated with cigarette smoking. The lengthening of these 3' UTRs was consistent with alternative usage of distal polyadenylation sites, and these extended 3' UTR regions were significantly enriched with functional sequence elements including microRNA and RNA-protein binding sites. These findings warrant further studies on alternative polyadenylation events as potential biomarkers and novel therapeutic targets for smoking-related diseases.

Alternative poly-adenylation modulates α1-antitrypsin expression in chronic obstructive pulmonary disease.

α1-anti-trypsin (A1AT), encoded by SERPINA1, is a neutrophil elastase inhibitor that controls the inflammatory response in the lung. Severe A1AT deficiency increases risk for Chronic Obstructive Pulmonary Disease (COPD), however, the role of A1AT in COPD in non-deficient individuals is not well known. We identify a 2.1-fold increase (p = 2.5x10-6) in the use of a distal poly-adenylation site in primary lung tissue RNA-seq in 82 COPD cases when compared to 64 controls and replicate this in an independent study of 376 COPD and 267 controls. This alternative polyadenylation event involves two sites, a proximal and distal site, 61 and 1683 nucleotides downstream of the A1AT stop codon. To characterize this event, we measured the distal ratio in human primary tissue short read RNA-seq data and corroborated our results with long read RNA-seq data. Integrating these results with 3' end RNA-seq and nanoluciferase reporter assay experiments we show that use of the distal site yields mRNA transcripts with over 50-fold decreased translation efficiency and A1AT expression. We identified seven RNA binding proteins using enhanced CrossLinking and ImmunoPrecipitation precipitation (eCLIP) with one or more binding sites in the SERPINA1 3' UTR. We combined these data with measurements of the distal ratio in shRNA knockdown experiments, nuclear and cytoplasmic fractionation, and chemical RNA structure probing. We identify Quaking Homolog (QKI) as a modulator of SERPINA1 mRNA translation and confirm the role of QKI in SERPINA1 translation with luciferase reporter assays. Analysis of single-cell RNA-seq showed differences in the distribution of the SERPINA1 distal ratio among hepatocytes, macrophages, αß-Tcells and plasma cells in the liver. Alveolar Type 1,2, dendritic cells and macrophages also vary in their distal ratio in the lung. Our work reveals a complex post-transcriptional mechanism that regulates alternative polyadenylation and A1AT expression in COPD.

Gene regulatory network inference as relaxed graph matching.

Bipartite network inference is a ubiquitous problem across disciplines. One important example in the field molecular biology is gene regulatory network inference. Gene regulatory networks are an instrumental tool aiding in the discovery of the molecular mechanisms driving diverse diseases, including cancer. However, only noisy observations of the projections of these regulatory networks are typically assayed. In an effort to better estimate regulatory networks from their noisy projections, we formulate a non-convex but analytically tractable optimization problem called OTTER. This problem can be interpreted as relaxed graph matching between the two projections of the bipartite network. OTTER's solutions can be derived explicitly and inspire a spectral algorithm, for which we provide network recovery guarantees. We also provide an alternative approach based on gradient descent that is more robust to noise compared to the spectral algorithm. Interestingly, this gradient descent approach resembles the message passing equations of an established gene regulatory network inference method, PANDA. Using three cancer-related data sets, we show that OTTER outperforms state-of-the-art inference methods in predicting transcription factor binding to gene regulatory regions. To encourage new graph matching applications to this problem, we have made all networks and validation data publicly available.

Gene Targeting in Disease Networks.

Profiling of whole transcriptomes has become a cornerstone of molecular biology and an invaluable tool for the characterization of clinical phenotypes and the identification of disease subtypes. Analyses of these data are becoming ever more sophisticated as we move beyond simple comparisons to consider networks of higher-order interactions and associations. Gene regulatory networks (GRNs) model the regulatory relationships of transcription factors and genes and have allowed the identification of differentially regulated processes in disease systems. In this perspective, we discuss gene targeting scores, which measure changes in inferred regulatory network interactions, and their use in identifying disease-relevant processes. In addition, we present an example analysis for pancreatic ductal adenocarcinoma (PDAC), demonstrating the power of gene targeting scores to identify differential processes between complex phenotypes, processes that would have been missed by only performing differential expression analysis. This example demonstrates that gene targeting scores are an invaluable addition to gene expression analysis in the characterization of diseases and other complex phenotypes.

Nongenic cancer-risk SNPs affect oncogenes, tumour-suppressor genes, and immune function.

BACKGROUND: Genome-wide association studies (GWASes) have identified many noncoding germline single-nucleotide polymorphisms (SNPs) that are associated with an increased risk of developing cancer. However, how these SNPs affect cancer risk is still largely unknown. METHODS: We used a systems biology approach to analyse the regulatory role of cancer-risk SNPs in thirteen tissues. By using data from the Genotype-Tissue Expression (GTEx) project, we performed an expression quantitative trait locus (eQTL) analysis. We represented both significant cis- and trans-eQTLs as edges in tissue-specific eQTL bipartite networks. RESULTS: Each tissue-specific eQTL network is organised into communities that group sets of SNPs and functionally related genes. When mapping cancer-risk SNPs to these networks, we find that in each tissue, these SNPs are significantly overrepresented in communities enriched for immune response processes, as well as tissue-specific functions. Moreover, cancer-risk SNPs are more likely to be 'cores' of their communities, influencing the expression of many genes within the same biological processes. Finally, cancer-risk SNPs preferentially target oncogenes and tumour-suppressor genes, suggesting that they may alter the expression of these key cancer genes. CONCLUSIONS: This approach provides a new way of understanding genetic effects on cancer risk and provides a biological context for interpreting the results of GWAS cancer studies.

Identification of an emphysema-associated genetic variant near TGFB2 with regulatory effects in lung fibroblasts.

Murine studies have linked TGF-ß signaling to emphysema, and human genome-wide association studies (GWAS) studies of lung function and COPD have identified associated regions near genes in the TGF-ß superfamily. However, the functional regulatory mechanisms at these loci have not been identified. We performed the largest GWAS of emphysema patterns to date, identifying 10 GWAS loci including an association peak spanning a 200 kb region downstream from TGFB2. Integrative analysis of publicly available eQTL, DNaseI, and chromatin conformation data identified a putative functional variant, rs1690789, that may regulate TGFB2 expression in human fibroblasts. Using chromatin conformation capture, we confirmed that the region containing rs1690789 contacts the TGFB2 promoter in fibroblasts, and CRISPR/Cas-9 targeted deletion of a ~ 100 bp region containing rs1690789 resulted in decreased TGFB2 expression in primary human lung fibroblasts. These data provide novel mechanistic evidence linking genetic variation affecting the TGF-ß pathway to emphysema in humans.

Multisystem Analysis of Mycobacterium tuberculosis Reveals Kinase-Dependent Remodeling of the Pathogen-Environment Interface.

Tuberculosis is the leading killer among infectious diseases worldwide. Increasing multidrug resistance has prompted new approaches for tuberculosis drug development, including targeted inhibition of virulence determinants and of signaling cascades that control many downstream pathways. We used a multisystem approach to determine the effects of a potent small-molecule inhibitor of the essential Mycobacterium tuberculosis Ser/Thr protein kinases PknA and PknB. We observed differential levels of phosphorylation of many proteins and extensive changes in levels of gene expression, protein abundance, cell wall lipids, and intracellular metabolites. The patterns of these changes indicate regulation by PknA and PknB of several pathways required for cell growth, including ATP synthesis, DNA synthesis, and translation. These data also highlight effects on pathways for remodeling of the mycobacterial cell envelope via control of peptidoglycan turnover, lipid content, a SigE-mediated envelope stress response, transmembrane transport systems, and protein secretion systems. Integrated analysis of phosphoproteins, transcripts, proteins, and lipids identified an unexpected pathway whereby threonine phosphorylation of the essential response regulator MtrA decreases its DNA binding activity. Inhibition of this phosphorylation is linked to decreased expression of genes for peptidoglycan turnover, and of genes for mycolyl transferases, with concomitant changes in mycolates and glycolipids in the cell envelope. These findings reveal novel roles for PknA and PknB in regulating multiple essential cell functions and confirm that these kinases are potentially valuable targets for new antituberculosis drugs. In addition, the data from these linked multisystems provide a valuable resource for future targeted investigations into the pathways regulated by these kinases in the M. tuberculosis cell.IMPORTANCE Tuberculosis is the leading killer among infectious diseases worldwide. Increasing drug resistance threatens efforts to control this epidemic; thus, new antitubercular drugs are urgently needed. We performed an integrated, multisystem analysis of Mycobacterium tuberculosis responses to inhibition of its two essential serine/threonine protein kinases. These kinases allow the bacterium to adapt to its environment by phosphorylating cellular proteins in response to extracellular signals. We identified differentially phosphorylated proteins, downstream changes in levels of specific mRNA and protein abundance, and alterations in the metabolite and lipid content of the cell. These results include changes previously linked to growth arrest and also reveal new roles for these kinases in regulating essential processes, including growth, stress responses, transport of proteins and other molecules, and the structure of the mycobacterial cell envelope. Our multisystem data identify PknA and PknB as promising targets for drug development and provide a valuable resource for future investigation of their functions.

Thermodynamic measures of cancer: Gibbs free energy and entropy of protein-protein interactions.

Thermodynamics is an important driving factor for chemical processes and for life. Earlier work has shown that each cancer has its own molecular signaling network that supports its life cycle and that different cancers have different thermodynamic entropies characterizing their signaling networks. The respective thermodynamic entropies correlate with 5-year survival for each cancer. We now show that by overlaying mRNA transcription data from a specific tumor type onto a human protein-protein interaction network, we can derive the Gibbs free energy for the specific cancer. The Gibbs free energy correlates with 5-year survival (Pearson correlation of -0.7181, p value of 0.0294). Using an expression relating entropy and Gibbs free energy to enthalpy, we derive an empirical relation for cancer network enthalpy. Combining this with previously published results, we now show a complete set of extensive thermodynamic properties and cancer type with 5-year survival.

Exploiting synthetic lethality for the therapy of ABC diffuse large B cell lymphoma.

Knowledge of oncogenic mutations can inspire therapeutic strategies that are synthetically lethal, affecting cancer cells while sparing normal cells. Lenalidomide is an active agent in the activated B cell-like (ABC) subtype of diffuse large B cell lymphoma (DLBCL), but its mechanism of action is unknown. Lenalidomide kills ABC DLBCL cells by augmenting interferon ß (IFNß) production, owing to the oncogenic MYD88 mutations in these lymphomas. In a cereblon-dependent fashion, lenalidomide downregulates IRF4 and SPIB, transcription factors that together prevent IFNß production by repressing IRF7 and amplify prosurvival NF-κB signaling by transactivating CARD11. Blockade of B cell receptor signaling using the BTK inhibitor ibrutinib also downregulates IRF4 and consequently synergizes with lenalidomide in killing ABC DLBCLs, suggesting attractive therapeutic strategies.