Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.

Network Medicine: A Network-Based Approach to Human Disease

Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole-genome sequencing, epigenetic surveys, expression profiling of coding and noncoding RNAs, single nucleotide polymorphism (SNP) and copy number profiling, and functional assays. Analysis of these large, diverse data sets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data pose a significant challenge to the development of such tools.

Integrative Genomics Viewer

Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here, we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease.

Interactome networks and human disease

A proteome-scale map of the human interactome network

The Biological General Repository for Interaction Datasets (BioGRID: http//thebiogrid.org) is an open access archive of genetic and protein interactions that are curated from the primary biomedical literature for all major model organism species. As of September 2012, BioGRID houses more than 500 000 manually annotated interactions from more than 30 model organisms. BioGRID maintains complete curation coverage of the literature for the budding yeast Saccharomyces cerevisiae, the fission yeast Schizosaccharomyces pombe and the model plant Arabidopsis thaliana. A number of themed curation projects in areas of biomedical importance are also supported. BioGRID has established collaborations and/or shares data records for the annotation of interactions and phenotypes with most major model organism databases, including Saccharomyces Genome Database, PomBase, WormBase, FlyBase and The Arabidopsis Information Resource. BioGRID also actively engages with the text-mining community to benchmark and deploy automated tools to expedite curation workflows. BioGRID data are freely accessible through both a user-defined interactive interface and in batch downloads in a wide variety of formats, including PSI-MI2.5 and tab-delimited files. BioGRID records can also be interrogated and analyzed with a series of new bioinformatics tools, which include a post-translational modification viewer, a graphical viewer, a REST service and a Cytoscape plugin.

https://www.pure.ed.ac.uk/ws/files/8144915/NAR_2013_Tyres_M.pdf

The BioGRID interaction database: 2013 update


 Background: Merkel Cell Carcinoma (MCC) is a highly aggressive neuroendocrine cutaneous malignancy arising from either Ultraviolet-induced mutagenesis or Merkel cell polyomavirus (MCPyV) integration. Pyrvinium Pamoate (PP), an FDA-approved anthelminthic drug, has been reported to exhibit anti-tumor abilities in multiple cancers. By integrating transcriptomic, network, and molecular analyses, we discovered that PP is broadly effective against Merkel cell carcinoma cell lines, and that PP targets multiple proposed MCC tumorigenesis pathways.
 Methods: We performed time-series RNA-seq profiling on a MCPyV inducible cell model and on PP-treated MCC cell lines, and inferred gene regulatory networks associated with PP response. We then integrated the network models with publicly available MCC tumor profiles and the LINCS L1000 database to explore the targets of PP in MCC. In addition, we performed protein, RNA, growth rate, metabolic, and apoptosis assays on multiple MCC cell lines exposed to PP.
 Results: Analyzing the effect of PP in the L1000 cancer cell line panel suggested that PP could inhibit key drivers of MCC tumorigenesis. We therefore tested the anti-proliferation and pro-apoptosis effects of PP in MCC cell lines. PP effectively inhibits proliferation of MCC at concentrations as low as 100 nM and in a dose and time-dependent manner. RNA-seq analysis of PP treated MCC cells suggested that PP triggers the p53-mediated intrinsic apoptotic signaling pathway, impairs the mitochondria’s oxidative phosphorylation function and induces endoplasmic reticulum (ER) stress. Immunoblotting intrinsic apoptotic pathway protein markers and measuring the IC50 of PP and the MDM2 inhibitor Nutlin-3a in p53WT and p53mut or p53−/− MCC cell lines, we found that PP does not induce cell apoptosis only through p53 activation, but also through other mechanisms such as increasing Unfolded Protein Response (UPR) stress and decreasing expression of the anti-apoptotic protein survivin (BIRC5). Extracellular flux analysis revealed decreased oxidative phosphorylation rate in PP treated MCC cells. Western blot analysis suggests that PP directly inhibits mitochondria complexes genes ATP5A, MTCO1 and NDUF58 and induces UPR-ER stress. PP has also been reported to inhibit the canonical Wnt pathway. We found that WNT5B, the ligand for the non-canonical Wnt pathway, was significantly decreased in MCC tumors and in our MCPyV inducible cell model, and that PP can upregulate WNT5B significantly. MCC cells treated with WNT5B recombinant protein showed a retarded MCC phenotype.
 Conclusion: Our results suggest that PP exerts anti-tumor activity through both non-specific growth inhibition - via the modulation of mitochondrial function, ER stress, and apoptotic signaling - and more specifically by perturbing the non-canonical Wnt pathway.
 Citation Format: Jiawen Yang, James T. Lim, Paul Victor, Rick G. Schnellmann, James A. DeCaprio, Megha Padi. Multi-omics analysis of mechanisms underlying sensitivity of Merkel cell carcinoma to pyrvinium pamoate. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4882.

Abstract 4882: Multi-omics analysis of mechanisms underlying sensitivity of Merkel cell carcinoma to pyrvinium pamoate

Transcriptional regulation is a critical process that determines cell fate and disease. One of the challenges in understanding transcriptional regulation is that there is no easy way to infer the main regulators from gene expression data. Many existing methods focus on estimating the activity of individual transcription factors (TFs) using static TF-gene interaction databases, but regulomes are often altered in different cell types and disease conditions. To address this problem, we developed a new algorithm – Transcriptional Inference using Gene Expression and Regulatory data (TIGER) – that leverages Bayesian matrix factorization to simultaneously infer TF regulomes and transcription factor (TF) activities from RNA-seq data. We show that, when applied to yeast, A375, and MCF7 TF knock-out datasets, TIGER can provide more accurate predictions than comparable methods. The application to single-cell RNA-seq data reveals TIGER’s potential for uncovering cell differentiation mechanisms. Our results reinforce the importance of incorporating context-specific regulation when studying the mechanisms driving disease in different cell types.

Joint inference of transcription factor activity and context-specific regulatory networks

Background: The tumor suppressor gene adenomatous polyposis coli (APC) is the initiating mutation in approximately 80% of all colorectal cancers (CRC), underscoring the importance of aberrant regulation of intracellular WNT signaling in CRC development. Recent studies have found that early-onset CRC exhibits an increased proportion of tumors lacking an APC mutation. We set out to identify mechanisms underlying APC mutation-negative (APCmut-) CRCs. Methods: We analyzed data from The Cancer Genome Atlas to compare clinical phenotypes, somatic mutations, copy number variations, gene fusions, RNA expression, and DNA methylation profiles between APCmut- and APC mutation-positive (APCmut+) microsatellite stable CRCs. Results: Transcriptionally, APCmut- CRCs clustered into two approximately equal groups. Cluster One was associated with enhanced mitochondrial activation. Cluster Two was strikingly associated with genetic inactivation or decreased RNA expression of the WNT antagonist RNF43, increased expression of the WNT agonist RSPO3, activating mutation of BRAF, or increased methylation and decreased expression of AXIN2. APCmut- CRCs exhibited evidence of increased immune cell infiltration, with significant correlation between M2 macrophages and RSPO3. Conclusions: APCmut- CRCs comprise two groups of tumors characterized by enhanced mitochondrial activation or increased sensitivity to extracellular WNT, suggesting that they could be respectively susceptible to inhibition of these pathways.

https://biorxiv.org/content/10.1101/2021.09.14.460319v1.full.pdf

Molecular Drivers of Tumor Progression in Microsatellite Stable APC Mutation-Negative Colorectal Cancers

Inference and analysis of cellular biological networks requires software tools that integrate multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is an open-source software suite to model biological networks, including context-specific gene regulatory networks and multi-omics partial correlation networks, to conduct differential analyses, estimate community structure, and model the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages (R, Python, MATLAB, and C) and between methods to allow a better integration of these tools into analytical pipelines. To demonstrate the value of this integrated toolkit, we analyzed the multi-omic data from the Cancer Cell Line Encyclopedia (CCLE) by inferring gene regulatory networks for each cancer cell line and associating network features with other phenotypic attributes such as drug sensitivity. This allowed us to identify transcription factors that play a critical role in both drug resistance and cancer development in melanoma. We also used netZoo to build a pan-cancer, multi-tiered CCLE map and used it to identify known metabolic hallmarks of cancer and to estimate novel context-specific elements that mediate post-transcriptional regulation. Because the netZoo tools are open-source and there is a growing community of both users and developers, we built an ecosystem to support community contributions, share use cases, and visualize networks online. As additional data types become available and our suite of methods grows, we will expand “the zoo” to incorporate an increasingly sophisticated collection of tools for network inference and analysis.

/pdf/the-network-zoo-a-multilingual-package-for-the-inference-and-24wnsmob.pdf

Megha Padi

Papers

Abstract 4882: Multi-omics analysis of mechanisms underlying sensitivity of Merkel cell carcinoma to pyrvinium pamoate

Joint inference of transcription factor activity and context-specific regulatory networks

Molecular Drivers of Tumor Progression in Microsatellite Stable APC Mutation-Negative Colorectal Cancers

The Network Zoo: a multilingual package for the inference and analysis of biological networks