Genome-wide Regulatory Networks Research Articles

Abstract 6545: Reversing lineage plasticity and drug resistance in lethal prostate cancer

Abstract Although major mechanisms of resistance to targeted therapies include mutation of the drug target, recent evidence suggests cell-adaptive mechanisms as alternative avenues for escaping treatment. In several cancers, tumor cells become plastic in response to target inhibition, changing their identity and molecular programs without acquiring further mutations. In the case of castration-resistant prostate cancer (CRPC) treated with standard-of-care, next-generation anti-androgens enzalutamide, tumor cells often switch lineage to survive drug pressure, acquiring neuroendocrine (NE) features and thereby transforming into neuroendocrine prostate cancer (CRPC-NE). Existing targeted therapies for CRPC and CRPC-NE provide limited benefit, representing an unmet clinical need. Hence, the elucidation of tumor plasticity and consequent heterogeneity that contribute to drug resistance is of paramount importance to identify novel therapeutic opportunities. We have recently developed a network-based precision cancer medicine framework to characterize molecular and drug sensitivity profiles of ~100 Genetically-Engineered Mouse Models (GEMM)-derived tumors of prostate cancer (Vasciaveo, et al. 2023). This study revealed that tumors derived from GEMMs with the loss of function of both Pten and Trp53 (NPp53) align with CRPC patients and exhibit high heterogeneity in regulatory programs and histopathologic features, including NE differentiation. We then adopted the NPp53 model to generate several 3D organoid lines in order to study tumor plasticity and heterogeneity. To elucidate their tumors’ regulatory programs, we reverse-engineered genome-wide regulatory networks using multimodal single-cell RNASeq and ATACSeq data to identify Master Regulator (MR) proteins implementing tumor cell identities and characterize their epigenetic landscape. Our network-based regulatory analysis identified several distinct cell populations, including cells activating epithelial-to-mesenchymal (EMT) programs and NE differentiation, all resistant to enzalutamide treatment. Cross-species analysis aligned these cells to human CRPC and CRPC-NE tumors. In particular, our analyses have shed light into the roles of NSD2, a known MR of lethal prostate cancer, whose ablation restored sensitivity to enzalutamide. In summary, we leveraged single-cell, multimodal data from CRPC and CRPC-NE 3D mouse organoids to elucidate regulatory programs of drug resistant, lethal prostate cancer and characterize its heterogeneity. Importantly, we also identified a mechanism that can be exploited for therapeutic intervention to restore tumor cells’ sensitivity Citation Format: Alessandro Vasciaveo, Jia J. Li, Dimitrios Karagiannis, Xiao Chen, Andrea Califano, Chao Lu, Michael M. Shen. Reversing lineage plasticity and drug resistance in lethal prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6545.

Integrated small RNA, mRNA and protein omics reveal a miRNA network orchestrating metabolic maturation of the developing human heart

BackgroundAs the fetal heart develops, cardiomyocyte proliferation potential decreases while fatty acid oxidative capacity increases in a highly regulated transition known as cardiac maturation. Small noncoding RNAs, such as microRNAs (miRNAs), contribute to the establishment and control of tissue-specific transcriptional programs. However, small RNA expression dynamics and genome-wide miRNA regulatory networks controlling maturation of the human fetal heart remain poorly understood.ResultsTranscriptome profiling of small RNAs revealed the temporal expression patterns of miRNA, piRNA, circRNA, snoRNA, snRNA and tRNA in the developing human heart between 8 and 19 weeks of gestation. Our analysis demonstrated that miRNAs were the most dynamically expressed small RNA species throughout mid-gestation. Cross-referencing differentially expressed miRNAs and mRNAs predicted 6200 mRNA targets, 2134 of which were upregulated and 4066 downregulated as gestation progressed. Moreover, we found that downregulated targets of upregulated miRNAs, including hsa-let-7b, miR-1-3p, miR-133a-3p, miR-143-3p, miR-499a-5p, and miR-30a-5p predominantly control cell cycle progression. In contrast, upregulated targets of downregulated miRNAs, including hsa-miR-1276, miR-183-5p, miR-1229-3p, miR-615-3p, miR-421, miR-200b-3p and miR-18a-3p, are linked to energy sensing and oxidative metabolism. Furthermore, integrating miRNA and mRNA profiles with proteomes and reporter metabolites revealed that proteins encoded in mRNA targets and their associated metabolites mediate fatty acid oxidation and are enriched as the heart develops.ConclusionsThis study presents the first comprehensive analysis of the small RNAome of the maturing human fetal heart. Our findings suggest that coordinated activation and repression of miRNA expression throughout mid-gestation is essential to establish a dynamic miRNA-mRNA-protein network that decreases cardiomyocyte proliferation potential while increasing the oxidative capacity of the maturing human fetal heart. Our results provide novel insights into the molecular control of metabolic maturation of the human fetal heart.

Genome-wide chromatin interaction profiling reveals a vital role of super-enhancers and rearrangements in host enhancer contacts during BmNPV infection.

As influential regulatory elements in the genome, enhancers control gene expression under specific cellular conditions, and such connections are dynamic under different conditions. However, because of the lack of a genome-wide enhancer-gene connection map, the roles and regulatory pattern of enhancers were poorly investigated in insects, and the dynamic changes of enhancer contacts and functions under different conditions remain elusive. Here, combining Hi-C, ATAC-seq, and H3K27ac ChIP-seq data, we generate the genome-wide enhancer-gene map of silkworm and identify super-enhancers with a role in regulating the expression of vital genes related to cell state maintenance through a sophisticated interaction network. Additionally, a radical attenuation of chromatin interactions is found after infection of Bombyx mori nucleopolyhedrovirus (BmNPV), the main pathogen of silkworm, which directly rearranges the enhancer contacts. Such a rearrangement disturbs the intrinsic enhancer-gene connections in several antiviral genes, resulting in reduced expression of these genes, which accelerates viral infection. Overall, our results reveal the regulatory role of super-enhancers and shed new light on the mechanisms and dynamic changes of the genome-wide enhancer regulatory network.

Genome-wide promoter responses to CRISPR perturbations of regulators reveal regulatory networks in Escherichia coli

Elucidating genome-scale regulatory networks requires a comprehensive collection of gene expression profiles, yet measuring gene expression responses for every transcription factor (TF)-gene pair in living prokaryotic cells remains challenging. Here, we develop pooled promoter responses to TF perturbation sequencing (PPTP-seq) via CRISPR interference to address this challenge. Using PPTP-seq, we systematically measure the activity of 1372 Escherichia coli promoters under single knockdown of 183 TF genes, illustrating more than 200,000 possible TF-gene responses in one experiment. We perform PPTP-seq for E. coli growing in three different media. The PPTP-seq data reveal robust steady-state promoter activities under most single TF knockdown conditions. PPTP-seq also enables identifications of, to the best of our knowledge, previously unknown TF autoregulatory responses and complex transcriptional control on one-carbon metabolism. We further find context-dependent promoter regulation by multiple TFs whose relative binding strengths determined promoter activities. Additionally, PPTP-seq reveals different promoter responses in different growth media, suggesting condition-specific gene regulation. Overall, PPTP-seq provides a powerful method to examine genome-wide transcriptional regulatory networks and can be potentially expanded to reveal gene expression responses to other genetic elements.

NF-κB/p52 augments ETS1 binding genome-wide to promote glioma progression

Gliomas are highly invasive and chemoresistant cancers, making them challenging to treat. Chronic inflammation is a key driver of glioma progression as it promotes aberrant activation of inflammatory pathways such as NF-κB signalling, which drives cancer cell invasion and angiogenesis. NF-κB factors typically dimerise with its own family members, but emerging evidence of their promiscuous interactions with other oncogenic factors has been reported to promote transcription of new target genes and function. Here, we show that non-canonical NF-κB activation directly regulates p52 at the ETS1 promoter, activating its expression. This impacts the genomic and transcriptional landscape of ETS1 in a glioma-specific manner. We further show that enhanced non-canonical NF-κB signalling promotes the co-localisation of p52 and ETS1, resulting in transcriptional activation of non-κB and/or non-ETS glioma-promoting genes. We conclude that p52-induced ETS1 overexpression in glioma cells remodels the genome-wide regulatory network of p52 and ETS1 to transcriptionally drive cancer progression.

A Network-Based Approach for Improving Annotation of Transcription Factor Functions and Binding Sites in Arabidopsis thaliana.

Transcription factors are an integral component of the cellular machinery responsible for regulating many biological processes, and they recognize distinct DNA sequence patterns as well as internal/external signals to mediate target gene expression. The functional roles of an individual transcription factor can be traced back to the functions of its target genes. While such functional associations can be inferred through the use of binding evidence from high-throughput sequencing technologies available today, including chromatin immunoprecipitation sequencing, such experiments can be resource-consuming. On the other hand, exploratory analysis driven by computational techniques can alleviate this burden by narrowing the search scope, but the results are often deemed low-quality or non-specific by biologists. In this paper, we introduce a data-driven, statistics-based strategy to predict novel functional associations for transcription factors in the model plant Arabidopsis thaliana. To achieve this, we leverage one of the largest available gene expression compendia to build a genome-wide transcriptional regulatory network and infer regulatory relationships among transcription factors and their targets. We then use this network to build a pool of likely downstream targets for each transcription factor and query each target pool for functionally enriched gene ontology terms. The results exhibited sufficient statistical significance to annotate most of the transcription factors in Arabidopsis with highly specific biological processes. We also perform DNA binding motif discovery for transcription factors based on their target pool. We show that the predicted functions and motifs strongly agree with curated databases constructed from experimental evidence. In addition, statistical analysis of the network revealed interesting patterns and connections between network topology and system-level transcriptional regulation properties. We believe that the methods demonstrated in this work can be extended to other species to improve the annotation of transcription factors and understand transcriptional regulation on a system level.

MAPK Gene Family in Lactuca sativa: Genome-Wide Identification, Regulatory Network, and Expression Patterns in Stem Development and Stress Responses

Mitogen-activated protein kinases (MAPKs) play essential roles in the process of stress response and plant growth and development. MAPK family genes have been identified in many plant species. In this study, 18 LsMAPK genes were identified in lettuce (Lactuca sativa). The LsMAPK members were divided into Group A, B, C, and D by phylogenetic tree analysis among Arabidopsis, rice, and lettuce. Cis-elements, which relate to abiotic stress, phytohormone response, and transcription factor binding site, were identified to exist in the promoter region of LsMAPK genes. Chromosomal location analysis showed the LsMAPK genes were distributed on eight chromosomes except chromosome 6. Interaction network analysis showed that LsMAPKs could interact with MAPK kinase (MAPKK), protein-tyrosine-phosphatase (PTP), and transcription factors (WRKY, bZIP). Quantitative reverse transcription PCR (qRT-PCR) showed that LsMAPK genes were induced by different abiotic stresses, hormone response, and stem enlargement. The comprehensive identification and characterization of LsMAPK genes in stem lettuce will lay a theoretical foundation for the functional analysis of LsMAPK genes and advance our knowledge of the regulatory mechanism of MAPK genes in plants.

Reconstruction and analysis of transcriptome regulatory network of Methanobrevibacter ruminantium M1

Global regulatory network modeling is a great concern of addressing metabolic complexity especially of a non-model organism to prioritize novel drug and vaccine targets. In this study, a knowledge-based bottom-up approach was used to reconstruct a transcriptional regulatory network of Methanobrevibacter ruminantium M1, one of the predominant hydrogenotrophic methanogens, contributing enteric methane emission in ruminants. The network robustness of the resulted model was validated with topological parameters and indicated its stability, consistency, and biological reliability. Each regulon-associated gene was assessed with gene ontology terms and literature support for functional confidence to infer its transcriptional regulatory mechanisms. The inferred genome-wide regulatory network comprises 164 predicted regulons associated with 235 coding genes. The results of this study have identified six DNA-binding motifs in 204 promoter regions interacting with 194 genes. It also described the metabolic gene regulation could be controlled either directly or indirectly under the respective promoter site in the regulatory network. As shown by our analysis, we proposed regulatory mechanisms of 7 important regulons such as PhoB (mru_0135), PrrA (mru_0526), NhaR (mru_0780), SigB (32-mer) (mru_1470), CmbR (mru_1544), MetR (mru_1915) and PpsR (mru_2036) at different environmental niche. Our intensive knowledge about the transcriptional regulatory mechanisms of this organism provides an insight into the understanding of rumen lifestyle and growth physiology in the ruminants.

NHX Gene Family in Camellia sinensis: In-silico Genome-Wide Identification, Expression Profiles, and Regulatory Network Analysis.

Salt stress affects the plant growth and productivity worldwide and NHX is one of those genes that are well known to improve salt tolerance in transgenic plants. It is well characterized in several plants, such as Arabidopsis thaliana and cotton; however, not much is known about NHXs in tea plant. In the present study, NHX genes of tea were obtained through a genome-wide search using A. thaliana as reference genome. Out of the 9 NHX genes in tea, 7 genes were localized in vacuole while the remaining 2 genes were localized in the endoplasmic reticulum (ER; CsNHX8) and plasma membrane (PM; CsNHX9), respectively. Furthermore, phylogenetic relationships along with structural analysis which includes gene structure, location, and protein-conserved motifs and domains were systematically examined and further, predictions were validated by the expression analysis. The dN/dS values show that the majority of tea NHX genes is subjected to strong purifying selection under the course of evolution. Also, functional interaction was carried out in Camellia sinensis based on the orthologous genes in A. thaliana. The expression profiles linked to various stress treatments revealed wide involvement of NHX genes from tea in response to various abiotic factors. This study provides the targets for further comprehensive identification, functional study, and also contributed for a better understanding of the NHX regulatory network in C. sinensis.

Comparative phenotypic and transcriptomic analyses unravel conserved and distinct mechanisms underlying shade avoidance syndrome in Brassicaceae vegetables

BackgroundPlants grown under shade are exposed to low red/far-red ratio, thereby triggering an array of altered phenotypes called shade avoidance syndrome (SAS). Shade negatively influences plant growth, leading to a reduction in agricultural productivity. Understanding of SAS is crucial for sustainable agricultural practices, especially for high-density indoor farming. Brassicaceae vegetables are widely consumed around the world and are commonly cultivated in indoor farms. However, our understanding of SAS in Brassicaceae vegetables and their genome-wide transcriptional regulatory networks are still largely unexplored.ResultsShade induced common signs of SAS, including hypocotyl elongation and reduced carotenoids/anthocyanins biosynthesis, in two different Brassicaceae species: Brassica rapa (Choy Sum and Pak Choy) and Brassica oleracea (Kai Lan). Phenotype-assisted transcriptome analysis identified a set of genes induced by shade in these species, many of which were related to auxin biosynthesis and signaling [e.g. YUCCA8 (YUC8), YUC9, and INDOLE-3-ACETIC ACID INDUCIBLE (IAAs)] and other phytohormones signaling pathways including brassinosteroids and ethylene. The genes functioning in plant defense (e.g. MYB29 and JASMONATE-ZIM-DOMAIN PROTEIN 9) as well as in biosynthesis of anthocyanins and glucosinolates were repressed upon shade. Besides, each species also exhibited distinct SAS phenotypes. Shade strongly reduced primary roots and elongated petioles of B. oleracea, Kai Lan. However, these SAS phenotypes were not clearly recognized in B. rapa, Choy Sum and Pak Choy. Some auxin signaling genes (e.g. AUXIN RESPONSE FACTOR 19, IAA10, and IAA20) were specifically induced in B. oleracea, while homologs in B. rapa were not up-regulated under shade. Contrastingly, shade-exposed B. rapa vegetables triggered the ethylene signaling pathway earlier than B. oleracea, Kai Lan. Interestingly, shade induced the transcript levels of LONG HYPOCOTYL IN FAR-RED 1 (HFR1) homolog in only Pak Choy as B. rapa. As HFR1 is a key negative regulator of SAS in Arabidopsis, our finding suggests that Pak Choy HFR1 homolog may also function in conferring higher shade tolerance in this variety.ConclusionsOur study shows that two Brassicaceae species not only share a conserved SAS mechanism but also exhibit distinct responses to shade, which will provide comprehensive information to develop new shade-tolerant cultivars that are suitable for high-density indoor farms.

PmiREN2.0: from data annotation to functional exploration of plant microRNAs.

Nearly 200 plant genomes have been sequenced over the last two years, and new functions of plant microRNAs (miRNAs) have been revealed. Therefore, timely update of the plant miRNA databases by incorporating miRNAs from the newly sequenced species and functional information is required to provide useful resources for advancing plant miRNA research. Here we report the update of PmiREN2.0 (https://pmiren.com/) with an addition of 19 363 miRNA entries from 91 plants, doubling the amount of data in the original version. Meanwhile, abundant regulatory information centred on miRNAs was added, including predicted upstream transcription factors through binding motifs scanning and elaborate annotation of miRNA targets. As an example, a genome-wide regulatory network centred on miRNAs was constructed for Arabidopsis. Furthermore, phylogenetic trees of conserved miRNA families were built to expand the understanding of miRNA evolution across the plant lineages. These data are helpful to deduce the regulatory relationships concerning miRNA functions in diverse plants. Beside the new data, a suite of design tools was incorporated to facilitate experimental practice. Finally, a forum named ‘PmiREN Community’ was added for discussion and resource and new discovery sharing. With these upgrades, PmiREN2.0 should serve the community better and accelerate miRNA research in plants.

HReg-CNCC reconstructs a regulatory network in human cranial neural crest cells and annotates variants in a developmental context

Cranial Neural Crest Cells (CNCC) originate at the cephalic region from forebrain, midbrain and hindbrain, migrate into the developing craniofacial region, and subsequently differentiate into multiple cell types. The entire specification, delamination, migration, and differentiation process is highly regulated and abnormalities during this craniofacial development cause birth defects. To better understand the molecular networks underlying CNCC, we integrate paired gene expression & chromatin accessibility data and reconstruct the genome-wide human Regulatory network of CNCC (hReg-CNCC). Consensus optimization predicts high-quality regulations and reveals the architecture of upstream, core, and downstream transcription factors that are associated with functions of neural plate border, specification, and migration. hReg-CNCC allows us to annotate genetic variants of human facial GWAS and disease traits with associated cis-regulatory modules, transcription factors, and target genes. For example, we reveal the distal and combinatorial regulation of multiple SNPs to core TF ALX1 and associations to facial distances and cranial rare disease. In addition, hReg-CNCC connects the DNA sequence differences in evolution, such as ultra-conserved elements and human accelerated regions, with gene expression and phenotype. hReg-CNCC provides a valuable resource to interpret genetic variants as early as gastrulation during embryonic development. The network resources are available at https://github.com/AMSSwanglab/hReg-CNCC.

Sexual dimorphism in cancer: insights from transcriptional signatures in kidney tissue and renal cell carcinoma.

Sexual dimorphism in cancer incidence and outcome is widespread. Understanding the underlying mechanisms is fundamental to improve cancer prevention and clinical management. Sex disparities are particularly striking in kidney cancer: across diverse populations, men consistently show unexplained 2-fold increased incidence and worse prognosis.We have characterized genome-wide expression and regulatory networks of 609 renal tumors and 256 non-tumor renal tissues. Normal kidney displayed sex-specific transcriptional signatures, including higher expression of X-linked tumor suppressor genes in women. Sex-dependent genotype–phenotype associations unraveled women-specific immune regulation. Sex differences were markedly expanded in tumors, with male-biased expression of key genes implicated in metabolism, non-malignant diseases with male predominance and carcinogenesis, including markers of tumor infiltrating leukocytes. Analysis of sex-dependent RCC progression and survival uncovered prognostic markers involved in immune response and oxygen homeostasis.In summary, human kidney tissues display remarkable sexual dimorphism at the molecular level. Sex-specific transcriptional signatures further shape renal cancer, with relevance for clinical management.

Genome-Wide Phylogenetic Analysis, Expression Pattern, and Transcriptional Regulatory Network of the Pig C/EBP Gene Family.

The CCAAT/enhancer binding protein (C/EBP) transcription factors (TFs) regulate many important biological processes, such as energy metabolism, inflammation, cell proliferation etc. A genome-wide gene identification revealed the presence of a total of 99 C/EBP genes in pig and 19 eukaryote genomes. Phylogenetic analysis showed that all C/EBP TFs were classified into 6 subgroups named C/EBPα, C/EBPβ, C/EBPδ, C/EBPε, C/EBPγ, and C/EBPζ. Gene expression analysis showed that the C/EBPα, C/EBPβ, C/EBPδ, C/EBPγ, and C/EBPζ genes were expressed ubiquitously with inconsistent expression patterns in various pig tissues. Moreover, a pig C/EBP regulatory network was constructed, including C/EBP genes, TFs and miRNAs. A total of 27 feed-forward loop (FFL) motifs were detected in the pig C/EBP regulatory network. Based on the RNA-seq data, gene expression patterns related to FFL sub-network were analyzed in 27 adult pig tissues. Certain FFL motifs may be tissue specific. Functional enrichment analysis indicated that C/EBP and its target genes are involved in many important biological pathways. These results provide valuable information that clarifies the evolutionary relationships of the C/EBP family and contributes to the understanding of the biological function of C/EBP genes.

Landscape and Dynamics of the Transcriptional Regulatory Network During Natural Killer Cell Differentiation

Natural killer (NK) cells are essential in controlling cancer and infection. However, little is known about the dynamics of the transcriptional regulatory machinery during NK cell differentiation. In this study, we applied the assay of transposase accessible chromatin with sequencing (ATAC-seq) technique in a home-developed in vitro NK cell differentiation system. Analysis of ATAC-seq data illustrated two distinct transcription factor (TF) clusters that dynamically regulate NK cell differentiation. Moreover, two TFs from the second cluster, FOS-like 2 (FOSL2) and early growth response 2 (EGR2), were identified as novel essential TFs that control NK cell maturation and function. Knocking down either of these two TFs significantly impacted NK cell differentiation. Finally, we constructed a genome-wide transcriptional regulatory network that provides a better understanding of the regulatory dynamics during NK cell differentiation.

Abstract IA12: From mouse to man: Dissecting cancer mechanisms using mouse models

Abstract I have a longstanding interest in understanding how basic cellular mechanisms become co-opted in cancer. I have approached this problem by studying the transcriptional regulatory mechanisms of homeobox genes with the goal of understanding their roles in development, differentiation, and cancer. Over the years, my research program has evolved towards its current focus in which we have undertaken a multidisciplinary approach to investigate genitourinary malignancies, which includes using mechanism-based studies, analyses of genetically engineered mouse models (GEMMs), and state-of-the-art systems biology approaches. My research focus on prostate cancer has its origins in our discovery of Nkx3.1, a prostate-specific homeobox gene that is essential for prostate differentiation and whose loss of function predisposes to prostate cancer. The culmination of our studies over many years has led to a model in which NKX3.1 functions as a gatekeeper that promotes prostate differentiation and protects against prostate cancer initiation. Our recent work has shown that Nkx3.1 is sufficient to fully reprogram non-prostate epithelium to form prostate in vivo. Our work showing that Nkx3.1 mutant mice accurately model indolent prostate cancer led to the discovery of a 3-gene panel that distinguishes indolent and aggressive human prostate cancers, which we are now advancing to the clinic. Moreover, our coclinical prevention study of Nkx3.1 mutant mice led to the identification of a gene signature that predicts response to antiandrogens for men on active surveillance. Furthermore, our analysis of Nkx3.1 mutant mice provided the foundation for our extensive series of GEMMs that are based on genomic alterations that are prevalent in human prostate cancer and that represent the full spectrum of prostate cancer phenotypes, ranging from preinvasive to lethal metastatic disease. To capitalize on these GEMMs, we have generated genome-wide regulatory networks for mouse and human prostate cancer, which have enabled cross-species analyses that have identified key drivers of the specific stages of prostate cancer. We have expanded these computational approaches to integrate preclinical data from GEMMs with human clinical data to predict drug response and optimize drug combinations for human prostate cancer. These studies have identified cellular plasticity as a mechanism of drug resistance in prostate cancer. Citation Format: Cory Abate-Shen. From mouse to man: Dissecting cancer mechanisms using mouse models [abstract]. In: Proceedings of the AACR Special Conference on the Evolving Landscape of Cancer Modeling; 2020 Mar 2-5; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2020;80(11 Suppl):Abstract nr IA12.

A Bayesian data fusion based approach for learning genome-wide transcriptional regulatory networks

BackgroundReverse engineering of transcriptional regulatory networks (TRN) from genomics data has always represented a computational challenge in System Biology. The major issue is modeling the complex crosstalk among transcription factors (TFs) and their target genes, with a method able to handle both the high number of interacting variables and the noise in the available heterogeneous experimental sources of information.ResultsIn this work, we propose a data fusion approach that exploits the integration of complementary omics-data as prior knowledge within a Bayesian framework, in order to learn and model large-scale transcriptional networks. We develop a hybrid structure-learning algorithm able to jointly combine TFs ChIP-Sequencing data and gene expression compendia to reconstruct TRNs in a genome-wide perspective. Applying our method to high-throughput data, we verified its ability to deal with the complexity of a genomic TRN, providing a snapshot of the synergistic TFs regulatory activity.Given the noisy nature of data-driven prior knowledge, which potentially contains incorrect information, we also tested the method’s robustness to false priors on a benchmark dataset, comparing the proposed approach to other regulatory network reconstruction algorithms. We demonstrated the effectiveness of our framework by evaluating structural commonalities of our learned genomic network with other existing networks inferred by different DNA binding information-based methods.ConclusionsThis Bayesian omics-data fusion based methodology allows to gain a genome-wide picture of the transcriptional interplay, helping to unravel key hierarchical transcriptional interactions, which could be subsequently investigated, and it represents a promising learning approach suitable for multi-layered genomic data integration, given its robustness to noisy sources and its tailored framework for handling high dimensional data.

Integration of multi-omics data to mine cancer-related gene modules.

The identification of cancer-related genes is a major research goal, with implications for determining the pathogenesis of cancer and identifying biomarkers for early diagnosis and treatment. In this study, by integrating multi-omics data, including gene expression, DNA copy number variation, DNA methylation, transcription factors, miRNA, and lncRNA data, we propose a method for mining cancer-related genes based on network models. First, using random forest-based feature selection method multi-omics data are integrated to identify key regulatory factors that affect gene expression, and then genome-wide regulatory networks are constructed. Next, by comparing the regulatory networks of key candidate genes in variant samples and non-variant samples, a differential expression regulatory network is generated. The differential network contains a collection of abnormal regulatory genes of key candidate genes. Then, by introducing the functional similarity as a distance metric for gene sets, a density-based clustering method is used to mine gene modules related to cancer. We applied this method to LUSC (lung squamous cell carcinoma) and mined cancer-related gene modules composed of 20 genes. GO function and KEGG pathway analyses indicated that the modules were closely related to cancer. A survival analysis was used to verify that the excavated gene modules can effectively distinguish between high- and low-risk groups. Overall, these results suggest that the proposed method can be used to identify cancer-related gene modules, providing a basis for the development of biomarkers for diagnosis and treatment.

Genome-wide identification, expression profiles and regulatory network of MAPK cascade gene family in barley

BackgroundMitogen-activated protein kinase (MAPK) cascade is a conserved and universal signal transduction module in organisms. Although it has been well characterized in many plants, no systematic analysis has been conducted in barley.ResultsHere, we identified 20 MAPKs, 6 MAPKKs and 156 MAPKKKs in barley through a genome-wide search against the updated reference genome. Then, phylogenetic relationship, gene structure and conserved protein motifs organization of them were systematically analyzed and results supported the predictions. Gene duplication analysis revealed that segmental and tandem duplication events contributed to the expansion of barley MAPK cascade genes and the duplicated gene pairs were found to undergone strong purifying selection. Expression profiles of them were further investigated in different organs and under diverse abiotic stresses using the available 173 RNA-seq datasets, and then the tissue-specific and stress-responsive candidates were found. Finally, co-expression regulatory network of MAPK cascade genes was constructed by WGCNA tool, resulting in a complicated network composed of a total of 72 branches containing 46 HvMAPK cascade genes and 46 miRNAs.ConclusionThis study provides the targets for further functional study and also contribute to better understand the MAPK cascade regulatory network in barley and beyond.

Abstract 3390: RNA interactome topology perturbation analysis reveals candidate driver mutations in cancer

Abstract Identification of the driver genomic variants is important for precision therapy of human cancer. Interaction between RNA-binding proteins (RBPs) and RNA plays important roles in regulating cellular function. Decoding genome-wide protein-RNA regulatory networks as well as how cancer-related mutations impair RNA regulatory activities in cancer will help prioritizing candidate driver mutations. Here, we first explored the genetic alteration patterns of RBPs and found that candidate driver mutations are likely to occur on the surface of RBPs. This observations indicate that the driver mutations may perturb the RBP regulatory network in cancer. We then constructed protein-RNA interactome networks by integration of target binding screens and expression profiles. Regulatory network analysis highlights regulatory principles among interacting RBPs. In addition, somatic mutations selectively target functionally important genes (cancer genes, core fitness genes or conserved genes) in cancer. These regulatory patterns were further validated using independent data. A computational method (MERIT) and a web-based user friendly resource were further proposed to analyze the RBP-gene regulatory network perturbation induced by mutations across cancer types. Pan-cancer analysis also suggests that cancer cells selectively target "vulnerability" genes to perturb protein-RNA interactome that is involved in cancer hallmark-related functions. Based on the expression of perturbed RBP and target genes, we identified three subtypes of liver cancer with different survival rates. Specifically, we experimentally validated four pairs of RBP-gene interactions perturbed by mutations, which play critical roles in cell proliferation. Taken together, our results provide a valuable resource for characterizing somatic mutation-perturbed protein-RNA regulatory networks in cancer, yielding valuable insights into the genotype-phenotype relationships underlying human cancer, and potential biomarkers for precision medicine. Note: This abstract was not presented at the meeting. Citation Format: Yongsheng Li, Daniel J. McGrail, Juan Xu, Junyi Li, Song Yi, Nidhi Sahni. RNA interactome topology perturbation analysis reveals candidate driver mutations in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3390.