Linking Proteomic and Transcriptional Data through the Interactome and Epigenome Reveals a Map of Oncogene-induced Signaling

Cellular signal transduction generally involves cascades of post-translational protein modifications that rapidly catalyze changes in protein-DNA interactions and gene expression. High-throughput measurements are improving our ability to study each of these stages individually, but do not capture the connections between them. Here we present an approach for building a network of physical links among these data that can be used to prioritize targets for pharmacological intervention. Our method recovers the critical missing links between proteomic and transcriptional data by relating changes in chromatin accessibility to changes in expression and then uses these links to connect proteomic and transcriptome data. We applied our approach to integrate epigenomic, phosphoproteomic and transcriptome changes induced by the variant III mutation of the epidermal growth factor receptor (EGFRvIII) in a cell line model of glioblastoma multiforme (GBM). To test the relevance of the network, we used small molecules to target highly connected nodes implicated by the network model that were not detected by the experimental data in isolation and we found that a large fraction of these agents alter cell viability. Among these are two compounds, ICG-001, targeting CREB binding protein (CREBBP), and PKF118–310, targeting β-catenin (CTNNB1), which have not been tested previously for effectiveness against GBM. At the level of transcriptional regulation, we used chromatin immunoprecipitation sequencing (ChIP-Seq) to experimentally determine the genome-wide binding locations of p300, a transcriptional co-regulator highly connected in the network. Analysis of p300 target genes suggested its role in tumorigenesis. We propose that this general method, in which experimental measurements are used as constraints for building regulatory networks from the interactome while taking into account noise and missing data, should be applicable to a wide range of high-throughput datasets.National Science Foundation (U.S.) (DB1-0821391)National Institutes of Health (U.S.) (Grant U54-CA112967)National Institutes of Health (U.S.) (Grant R01-GM089903)National Institutes of Health (U.S.) (P30-ES002109

Network-Based Interpretation of Diverse High-Throughput Datasets through the Omics Integrator Software Package

High-throughput, ‘omic’ methods provide sensitive measures of biological responses to perturbations. However, inherent biases in high-throughput assays make it difficult to interpret experiments in which more than one type of data is collected. In this work, we introduce Omics Integrator, a software package that takes a variety of ‘omic’ data as input and identifies putative underlying molecular pathways. The approach applies advanced network optimization algorithms to a network of thousands of molecular interactions to find high-confidence, interpretable subnetworks that best explain the data. These subnetworks connect changes observed in gene expression, protein abundance or other global assays to proteins that may not have been measured in the screens due to inherent bias or noise in measurement. This approach reveals unannotated molecular pathways that would not be detectable by searching pathway databases. Omics Integrator also provides an elegant framework to incorporate not only positive data, but also negative evidence. Incorporating negative evidence allows Omics Integrator to avoid unexpressed genes and avoid being biased toward highly-studied hub proteins, except when they are strongly implicated by the data. The software is comprised of two individual tools, Garnet and Forest, that can be run together or independently to allow a user to perform advanced integration of multiple types of high-throughput data as well as create condition-specific subnetworks of protein interactions that best connect the observed changes in various datasets. It is available at http://fraenkel.mit.edu/omicsintegrator and on GitHub at https://github.com/fraenkel-lab/OmicsIntegrator.National Institutes of Health (U.S.) (grant U54CA112967)National Institutes of Health (U.S.) (grant U01CA184898)National Institutes of Health (U.S.) (grant U54NS091046)National Institutes of Health (U.S.) (grant R01GM089903

A constraint optimization framework for discovery of cellular signaling and regulatory networks

Thesis (Ph. D.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2011.Cataloged from PDF version of thesis.Includes bibliographical references.Cellular signaling and regulatory networks underlie fundamental biological processes such as growth, differentiation, and response to the environment. Although there are now various high-throughput methods for studying these processes, knowledge of them remains fragmentary. Typically, the majority of hits identified by transcriptional, proteomic, and genetic assays lie outside of the expected pathways. In addition, not all components in the regulatory networks can be exposed in one experiment because of systematic biases in the assays. These unexpected and hidden components of the cellular response are often the most interesting, because they can provide new insights into biological processes and potentially reveal new therapeutic approaches. However, they are also the most difficult to interpret. We present a technique, based on the Steiner tree problem, that uses a probabilistic protein-protein interaction network and high confidence measurement and prediction of protein-DNA interactions, to determine how these hits are organized into functionally coherent pathways, revealing many components of the cellular response that are not readily apparent in the original data. We report the results of applying this method to (1) phosphoproteomic and transcriptional data from the pheromone response in yeast, and (2) phosphoproteomic, DNaseI hypersensitivity sequencing and mRNA profiling data from the U87MG glioblastoma cell lines over-expressing the variant III mutant of the epidermal growth factor receptor (EGFRvIII). In both cases the method identifies changes in diverse cellular processes that extend far beyond the expected pathways. Analysis of the EGFRVIII network connectivity property and transcriptional regulators that link observed changes in protein phosphorylation and differential expression suggest a few intriguing hypotheses that may lead to improved therapeutic strategy for glioblastoma.by Shao-shan Carol Huang.Ph.D

Reconstruction of the temporal signaling network in Salmonella-infected human cells

Salmonella enterica is a bacterial pathogen that usually infects its host through food sources. Translocation of the pathogen proteins into the host cells leads to changes in the signaling mechanism either by activating or inhibiting the host proteins. Using high-throughput ‘omic’ technologies, changes in the signaling components can be quantified at different levels; however, experimental hits are usually incomplete to represent the whole signaling system as some driver proteins stay hidden within the experimental data. Given that the bacterial infection modifies the response network of the host, more coherent view of the underlying biological processes and the signaling networks can be obtained by using a network modeling approach based on the reverse engineering principles in which a confident region from the protein interactome is found by inferring hits from the omic experiments. In this work, we have used a published temporal phosphoproteomic dataset of Salmonella-infected human cells and reconstructed the temporal signaling network of the human host by integrating the interactome and the phosphoproteomic datasets. We have combined two well-established network modeling frameworks, the Prize-collecting Steiner Forest (PCSF) approach and the Integer Linear Programming (ILP) based edge inference approach. The resulting network conserves the information on temporality, direction of interactions, while revealing hidden entities in the signaling, such as the SNARE binding, mTOR signaling, immune response, cytoskeleton organization, and apoptosis pathways. Targets of the Salmonella effectors in the host cells such as CDC42, RHOA, 14-3-3δ, Syntaxin family, Oxysterol-binding proteins were included in the reconstructed signaling network although they were not present in the initial phosphoproteomic data. We believe that integrated approaches have a high potential for the identification of clinical targets in infectious diseases, especially in the Salmonella infections

A Systems Biology Approach to Epigenetic Gene Regulation

The ability to control when, and how much of the genetic code is being expressed is the underlying principle behind gene regulation. Control of gene production is able to influence a cell's phenotype by determining which structural components of the cell's observable traits (shape, growth, and behavior) are made. In multicellular organism’s different cell types are able to arise from the same genetic code due to a difference in the patterns of genes being expressed. Essentially anywhere in the process of gene expression from transcription, RNA processing, translation, and post-translational modifications of the protein is subject to regulation. As transcription is the first step in the process of gene expression, it is the first level of regulation for influencing the cell phenotype. The actions of transcription factors, histone modifiers, and other proteins work together to influence RNA polymerase's ability to complete the process of transcription. The actions of transcription factors are able to influence transcription by controlling the ability of RNA polymerase to be recruited to the start of a protein coding region and histone modifiers can rearrange the histones of the chromatin causing entire regions of a chromosome to become exposed or sequestered. These transcriptional regulators are able to work in a combinatorial fashion with one another to either activate and/or repress wide repertoires of transcriptional targets. Mapping out a network of interactions between these transcriptional regulators in gene expression programs allows researchers to understand how each protein is able to influence the phenotype of the cell, and how mutations to any of these transcriptional regulators are able to drive the cell into a diseased state. In the case of cancer, changes in the mechanisms of gene regulation brought on by mutations to these transcriptional regulators may drive the cell's hyper proliferative state. With the creation of next generation sequencing researchers are now better able to define where regulation is taking place in the genome, and how much it is able to influence gene expression. This gives researchers the ability to build these gene regulatory networks and evaluate their impact on gene expression. The subsequent chapters of this dissertation are a reflection of my published work investigating the contribution of oncogenic processes to gene regulatory networks in cancer through the study of hyperactivating somatic mutation of a histone modifier, changes in transcription factor response element specificity, epigenetic regulation of transcription factor signaling, and a transcription factor coactivation network

Impacts of cannabinoid epigenetics on human development: Reflections on Murphy et. al. \u27cannabinoid exposure and altered DNA methylation in rat and human sperm\u27 epigenetics 2018; 13: 1208-1221

Recent data from the Kollins lab (\u27Cannabinoid exposure and altered DNA methylation in rat and human sperm\u27 Epigenetics 2018; 13: 1208-1221) indicated epigenetic effects of cannabis use on sperm in man parallel those in rats and showed substantial shifts in both hypo- and hyper-DNA methylation with the latter predominating. This provides one likely mechanism for the transgenerational transmission of epigenomic instability with sperm as the vector. It therefore contributes important pathophysiological insights into the probable mechanisms underlying the epidemiology of prenatal cannabis exposure potentially explaining diverse features of cannabis-related teratology including effects on the neuraxis, cardiovasculature, immune stimulation, secondary genomic instability and carcinogenesis related to both adult and pediatric cancers. The potentially inheritable and therefore multigenerational nature of these defects needs to be carefully considered in the light of recent teratological and neurobehavioural trends in diverse jurisdictions such as the USA nationally, Hawaii, Colorado, Canada, France and Australia, particularly relating to mental retardation, age-related morbidity and oncogenesis including inheritable cancerogenesis. Increasing demonstrations that the epigenome can respond directly and in real time and retain memories of environmental exposures of many kinds implies that the genome-epigenome is much more sensitive to environmental toxicants than has been generally realized. Issues of long-term multigenerational inheritance amplify these concerns. Further research particularly on the epigenomic toxicology of many cannabinoids is also required

Breaking the Immune Complexity of the Tumor Microenvironment Using Single-Cell Technologies

: Tumors are not a simple aggregate of transformed cells but rather a complicated ecosystem containing various components, including infiltrating immune cells, tumor-related stromal cells, endothelial cells, soluble factors, and extracellular matrix proteins. Profiling the immune contexture of this intricate framework is now mandatory to develop more effective cancer therapies and precise immunotherapeutic approaches by identifying exact targets or predictive biomarkers, respectively. Conventional technologies are limited in reaching this goal because they lack high resolution. Recent developments in single-cell technologies, such as single-cell RNA transcriptomics, mass cytometry, and multiparameter immunofluorescence, have revolutionized the cancer immunology field, capturing the heterogeneity of tumor-infiltrating immune cells and the dynamic complexity of tenets that regulate cell networks in the tumor microenvironment. In this review, we describe some of the current single-cell technologies and computational techniques applied for immune-profiling the cancer landscape and discuss future directions of how integrating multi-omics data can guide a new "precision oncology" advancement

A chromatin activity-based chemoproteomic approach reveals a transcriptional repressome for gene-specific silencing

Immune cells develop endotoxin tolerance (ET) after prolonged stimulation. ET increases the level of a repression mark H3K9me2 in the transcriptional-silent chromatin specifically associated with pro-inflammatory genes. However, it is not clear what proteins are functionally involved in this process. Here we show that a novel chromatin activity based chemoproteomic (ChaC) approach can dissect the functional chromatin protein complexes that regulate ET-associated inflammation. Using UNC0638 that binds the enzymatically active H3K9-specific methyltransferase G9a/GLP, ChaC reveals that G9a is constitutively active at a G9a-dependent mega-dalton repressome in primary endotoxin-tolerant macrophages. G9a/GLP broadly impacts the ET-specific reprogramming of the histone code landscape, chromatin remodeling, and the activities of select transcription factors. We discover that the G9a-dependent epigenetic environment promotes the transcriptional repression activity of c-Myc for gene-specific co-regulation of chronic inflammation. ChaC may be also applicable to dissect other functional protein complexes in the context of phenotypic chromatin architectures

Genome wide profiling of 5-formylcytosine and 5-carboxylcytosine in melanoma

Malignant melanoma, which comprises only 2% of skin cancers cases, but is the most lethal form of skin cancer. With the prevalence of melanoma continuing to rise, there is a greater need to elucidate the mechanisms underlying disease initiation and progression. Because mutations in melanoma-associated genes account for only 10% of cases, epigenome-altering environmental factors must have a role in pathogenesis. DNA methylation and demethylation are key epigenetics processes which govern cell differentiation and development. 5-methylcytosine (5mC) is a key epigenetic mark, which undergoes oxidation to 5-hydroxymethylcytosine (5hmC), 5formylcytosine (5fC) and 5carboxycytosine (5caC) during demethylation. In melanoma, it has been established that the loss of 5hmC is a cancer hallmark and is associated with poor prognostic outcome. The roles of 5fC/5caC, however, are not known. Here, I aimed to investigate the role of 5fC/5caC in melanoma and its contribution to disease development. Using methylase-assisted bisulfite sequencing, I have mapped the genome-wide distribution of 5fC/5caC at base pair resolution in two melanoma cell lines, A2058 and Mel Juso. In both cell lines, this modification is enriched at distal regulatory elements. Comparisons of differentially methylated sites and regions between the cell lines revealed that the products of 5fC/5caC enriched genes participate in cell adhesion and cell signaling, both of which are altered during melanoma initiation and progression. Increased levels of 5fC/5caC in these genes may be a contributing factor to this deregulation. Through these studies, we aim to identify distinct regions undergoing alterations in melanoma, which can serve as diagnostic and prognostic biomarkers.2018-06-16T00:00:00