Reconstructing SARS-CoV-2 response signaling and regulatory networks.
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ABSTRACT:
SUBMITTER: Ding J
PROVIDER: S-EPMC7574259 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The coronavirus disease 2019 (COVID-19) pandemic has greatly damaged human society, but the origins and early transmission patterns of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pathogen remain unclear. Here, we reconstructed the transmission networks of SARS-CoV-2 during the first three and six months since its first report based on ancestor-offspring relationships using BANAL-52-referenced mutations. We explored the position (i.e., root, middle, or tip) of early detected samples in the evolutionary tree of SARS-CoV-2. In total, 6 799 transmission chains and 1 766 transmission networks were reconstructed, with chain lengths ranging from 1-9 nodes. The root node samples of the 1 766 transmission networks were from 58 countries or regions and showed no common ancestor, indicating the occurrence of many independent or parallel transmissions of SARS-CoV-2 when first detected (i.e., all samples were located at the tip position of the evolutionary tree). No root node sample was found in any sample ( n=31, all from the Chinese mainland) collected in the first 15 days from 24 December 2019. Results using six-month data or RaTG13-referenced mutation data were similar. The reconstruction method was verified using a simulation approach. Our results suggest that SARS-CoV-2 may have already been spreading independently worldwide before the outbreak of COVID-19 in Wuhan, China. Thus, a comprehensive global survey of human and animal samples is essential to explore the origins of SARS-CoV-2 and its natural reservoirs and hosts.
| S-EPMC10236306 | biostudies-literature
Project description:The current pandemic of SARS-CoV-2 has caused extensive damage to society. The characterization of SARS-CoV-2 profiles has been addressed by researchers globally with the aim of resolving this disruptive crisis. This investigation process is indispensable to understand how SARS-CoV-2 behaves in human host cells. However, little is known about the systematic molecular mechanisms involved in the effects of SARS-CoV-2 infection on human host cells. Here, we present gene-to-gene regulatory networks in response to SARS-CoV-2 using a Bayesian network. We examined the dynamic changes in the SARS-CoV-2-purturbated networks established by our proposed framework for gene network analysis, thus revealing that interferon signaling gradually switched to the subsequent inflammatory cytokine signaling cascades. Furthermore, we succeeded in capturing a COVID-19 patient-specific network in which transduction of these signals was concurrently induced. This enabled us to explore the local regulatory systems influenced by SARS-CoV-2 in host cells more precisely at an individual level. Our panel of network analyses has provided new insights into SARS-CoV-2 research from the perspective of cellular systems.
| S-EPMC8160150 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2016-05-13 | E-GEOD-72533 | biostudies-arrayexpress
Project description:This SuperSeries is composed of the SubSeries listed below.
2016-05-13 | GSE72533 | GEO
Project description:Transcription regulation is a fundamental biological process, and extensive efforts have been made to dissect its mechanisms through direct biological experiments and regulation modeling based on physical-chemical principles and mathematical formulations. Despite these efforts, transcription regulation is yet not well understood because of its complexity and limitations in biological experiments. Recent advances in high throughput technologies have provided substantial amounts and diverse types of genomic data that reveal valuable information on transcription regulation, including DNA sequence data, protein-DNA binding data, microarray gene expression data, and others. In this article, we propose a Bayesian error analysis model to integrate protein-DNA binding data and gene expression data to reconstruct transcriptional regulatory networks. There are two unique aspects to this proposed model. First, transcription is modeled as a set of biochemical reactions, and a linear system model with clear biological interpretation is developed. Second, measurement errors in both protein-DNA binding data and gene expression data are explicitly considered in a Bayesian hierarchical model framework. Model parameters are inferred through Markov chain Monte Carlo. The usefulness of this approach is demonstrated through its application to infer transcriptional regulatory networks in the yeast cell cycle.
| S-EPMC1472417 | biostudies-literature
Project description:BackgroundGene Regulatory Networks (GRNs) have been previously studied by using Boolean/multi-state logics. While the gene expression values are usually scaled into the range [0, 1], these GRN inference methods apply a threshold to discretize the data, resulting in missing information. Most of studies apply fuzzy logics to infer the logical gene-gene interactions from continuous data. However, all these approaches require an a priori known network structure.ResultsHere, by introducing a new probabilistic logic for continuous data, we propose a novel logic-based approach (called the LogicNet) for the simultaneous reconstruction of the GRN structure and identification of the logics among the regulatory genes, from the continuous gene expression data. In contrast to the previous approaches, the LogicNet does not require an a priori known network structure to infer the logics. The proposed probabilistic logic is superior to the existing fuzzy logics and is more relevant to the biological contexts than the fuzzy logics. The performance of the LogicNet is superior to that of several Mutual Information-based and regression-based tools for reconstructing GRNs.ConclusionsThe LogicNet reconstructs GRNs and logic functions without requiring prior knowledge of the network structure. Moreover, in another application, the LogicNet can be applied for logic function detection from the known regulatory genes-target interactions. We also conclude that computational modeling of the logical interactions among the regulatory genes significantly improves the GRN reconstruction accuracy.
| S-EPMC7372900 | biostudies-literature
Project description:The mechanistic links between transcription factors and the epigenetic landscape, which coordinate the deregulation of gene networks during cell transformation are largely unknown. We used an isogenic model of stepwise tumorigenic transformation of human primary cells to monitor the progressive deregulation of gene networks upon immortalization and oncogene-induced transformation. By combining transcriptome and epigenome data for each step during transformation and by integrating transcription factor (TF) - target gene associations, we identified 142 TFs and 24 chromatin remodelers/modifiers (CRMs), which are preferentially associated with specific co-expression paths that originate from deregulated gene programming during tumorigenesis. These TFs are involved in the regulation of divers processes, including cell differentiation, immune response and establishment/modification of the epigenome. Unexpectedly, the analysis of chromatin state dynamics revealed patterns that distinguish groups of genes, which are not only co-regulated but also functionally related. Further decortication of TF targets enabled us to define potential key regulators of cell transformation, which are engaged in RNA metabolism and chromatin remodelling. Our study suggests a direct implication of CRMs in oncogene-induced tumorigenesis and identifies new CRMs involved in this process. This is the first comprehensive view of gene regulatory networks that are altered during the process of stepwise human cellular tumorigenesis in a virtually isogenic system. Affymetrix SNP arrays (250K) were performed according to the manufacturer's directions on DNA extracted from BJ, BJEL and BJELM cells
2016-05-13 | E-GEOD-72531 | biostudies-arrayexpress
Project description:The mechanistic links between transcription factors and the epigenetic landscape, which coordinate the deregulation of gene networks during cell transformation are largely unknown. We used an isogenic model of stepwise tumorigenic transformation of human primary cells to monitor the progressive deregulation of gene networks upon immortalization and oncogene-induced transformation. By combining transcriptome and epigenome data for each step during transformation and by integrating transcription factor (TF) - target gene associations, we identified 142 Tfs and 24 chromatin remodelers/modifiers (CRMs), which are preferentially associated with specific co-expression paths that originate from deregulated gene programming during tumorigenesis. These Tfs are involved in the regulation of divers processes, including cell differentiation, immune response and establishment/modification of the epigenome. Unexpectedly, the analysis of chromatin state dynamics revealed patterns that distinguish groups of genes, which are not only co-regulated but also functionally related. Further decortication of TF targets enabled us to define potential key regulators of cell transformation, which are engaged in RNA metabolism and chromatin remodelling. Our study suggests a direct implication of CRMs in oncogene-induced tumorigenesis and identifies new CRMs involved in this process. This is the first comprehensive view of gene regulatory networks that are altered during the process of stepwise human cellular tumorigenesis in a virtually isogenic system. Transcriptional activity in BJEL and BJELM cells has been evaluated and compared with that found in BJ cells; 2 biological replicates per cell line
2016-05-13 | E-GEOD-72530 | biostudies-arrayexpress
Project description:The mechanistic links between transcription factors and the epigenetic landscape, which coordinate the deregulation of gene networks during cell transformation are largely unknown. We used an isogenic model of stepwise tumorigenic transformation of human primary cells to monitor the progressive deregulation of gene networks upon immortalization and oncogene-induced transformation. By combining transcriptome and epigenome data for each step during transformation and by integrating transcription factor (TF) - target gene associations, we identified 142 Tfs and 24 chromatin remodelers/modifiers (CRMs), which are preferentially associated with specific co-expression paths that originate from deregulated gene programming during tumorigenesis. These Tfs are involved in the regulation of divers processes, including cell differentiation, immune response and establishment/modification of the epigenome. Unexpectedly, the analysis of chromatin state dynamics revealed patterns that distinguish groups of genes, which are not only co-regulated but also functionally related. Further decortication of TF targets enabled us to define potential key regulators of cell transformation, which are engaged in RNA metabolism and chromatin remodelling. Our study suggests a direct implication of CRMs in oncogene-induced tumorigenesis and identifies new CRMs involved in this process. This is the first comprehensive view of gene regulatory networks that are altered during the process of stepwise human cellular tumorigenesis in a virtually isogenic system.
2016-05-13 | GSE72530 | GEO
Project description:The mechanistic links between transcription factors and the epigenetic landscape, which coordinate the deregulation of gene networks during cell transformation are largely unknown. We used an isogenic model of stepwise tumorigenic transformation of human primary cells to monitor the progressive deregulation of gene networks upon immortalization and oncogene-induced transformation. By combining transcriptome and epigenome data for each step during transformation and by integrating transcription factor (TF) - target gene associations, we identified 142 TFs and 24 chromatin remodelers/modifiers (CRMs), which are preferentially associated with specific co-expression paths that originate from deregulated gene programming during tumorigenesis. These TFs are involved in the regulation of divers processes, including cell differentiation, immune response and establishment/modification of the epigenome. Unexpectedly, the analysis of chromatin state dynamics revealed patterns that distinguish groups of genes, which are not only co-regulated but also functionally related. Further decortication of TF targets enabled us to define potential key regulators of cell transformation, which are engaged in RNA metabolism and chromatin remodelling. Our study suggests a direct implication of CRMs in oncogene-induced tumorigenesis and identifies new CRMs involved in this process. This is the first comprehensive view of gene regulatory networks that are altered during the process of stepwise human cellular tumorigenesis in a virtually isogenic system.
2016-05-13 | GSE72531 | GEO