scholarly journals TopicNet: a framework for measuring transcriptional regulatory network change

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i474-i481
Author(s):  
Shaoke Lou ◽  
Tianxiao Li ◽  
Xiangmeng Kong ◽  
Jing Zhang ◽  
Jason Liu ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Regulatory Networks ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Transcriptional Regulatory Network ◽  
Network Connectivity ◽  
Supplementary Information ◽  
Differential Survival ◽  
Network Change ◽  
Low Dimensional

Abstract Motivation Recently, many chromatin immunoprecipitation sequencing experiments have been carried out for a diverse group of transcription factors (TFs) in many different types of human cells. These experiments manifest large-scale and dynamic changes in regulatory network connectivity (i.e. network ‘rewiring’), highlighting the different regulatory programs operating in disparate cellular states. However, due to the dense and noisy nature of current regulatory networks, directly comparing the gains and losses of targets of key TFs across cell states is often not informative. Thus, here, we seek an abstracted, low-dimensional representation to understand the main features of network change. Results We propose a method called TopicNet that applies latent Dirichlet allocation to extract functional topics for a collection of genes regulated by a given TF. We then define a rewiring score to quantify regulatory-network changes in terms of the topic changes for this TF. Using this framework, we can pinpoint particular TFs that change greatly in network connectivity between different cellular states (such as observed in oncogenesis). Also, incorporating gene expression data, we define a topic activity score that measures the degree to which a given topic is active in a particular cellular state. And we show how activity differences can indicate differential survival in various cancers. Availability and Implementation The TopicNet framework and related analysis were implemented using R and all codes are available at https://github.com/gersteinlab/topicnet. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals TopicNet: a framework for measuring transcriptional regulatory network change

2019 ◽  
Author(s):  
Shaoke Lou ◽  
Tianxiao Li ◽  
Xiangmeng Kong ◽  
Jing Zhang ◽  
Jason Liu ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Regulatory Networks ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Transcriptional Regulatory Network ◽  
Network Connectivity ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Differential Survival ◽  
Network Change

SummaryNext generation sequencing data highlights comprehensive and dynamic changes in the human gene regulatory network. Moreover, changes in regulatory network connectivity (network “rewiring”) manifest different regulatory programs in multiple cellular states. However, due to the dense and noisy nature of the connectivity in regulatory networks, directly comparing the gains and losses of targets of key TFs is not that informative. Thus, here, we seek a abstracted lower-dimensional representation to understand the main features of network change. In particular, we propose a method called TopicNet that applies latent Dirichlet allocation (LDA) to extract meaningful functional topics for a collection of genes regulated by a TF. We then define a rewiring score to quantify the large-scale changes in the regulatory network in terms of topic change for a TF. Using this framework, we can pinpoint particular TFs that change greatly in network connectivity between different cellular states. This is particularly relevant in oncogenesis. Also, incorporating gene-expression data, we define a topic activity score that gives the degree that a topic is active in a particular cellular state. Furthermore, we show how activity differences can highlight differential survival in certain cancers.

Transcriptional Regulatory Network Topology with Applications to Bio-inspired Networking: A Survey

2022 ◽  
Vol 54 (8) ◽  
pp. 1-36
Author(s):  
Satyaki Roy ◽  
Preetam Ghosh ◽  
Nirnay Ghosh ◽  
Sajal K. Das
Keyword(s):  
Communication Networks ◽  
Biological Networks ◽  
Regulatory Network ◽  
Large Scale ◽  
Data Transfer ◽  
Transcriptional Regulatory Network ◽  
Future Research ◽  
Transcriptional Regulatory ◽  
Iot Devices ◽  
Networking Protocols

The advent of the edge computing network paradigm places the computational and storage resources away from the data centers and closer to the edge of the network largely comprising the heterogeneous IoT devices collecting huge volumes of data. This paradigm has led to considerable improvement in network latency and bandwidth usage over the traditional cloud-centric paradigm. However, the next generation networks continue to be stymied by their inability to achieve adaptive, energy-efficient, timely data transfer in a dynamic and failure-prone environment—the very optimization challenges that are dealt with by biological networks as a consequence of millions of years of evolution. The transcriptional regulatory network (TRN) is a biological network whose innate topological robustness is a function of its underlying graph topology. In this article, we survey these properties of TRN and the metrics derived therefrom that lend themselves to the design of smart networking protocols and architectures. We then review a body of literature on bio-inspired networking solutions that leverage the stated properties of TRN. Finally, we present a vision for specific aspects of TRNs that may inspire future research directions in the fields of large-scale social and communication networks.

scholarly journals A multiple genomic data fused SF2 prediction model, signature identification, and gene regulatory network inference for personalized radiotherapy

2020 ◽  
Vol 19 ◽  
pp. 153303382090911
Author(s):  
Qi-en He ◽  
Yi-fan Tong ◽  
Zhou Ye ◽  
Li-xia Gao ◽  
Yi-zhi Zhang ◽  
...  
Keyword(s):  
Prediction Model ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Large Scale ◽  
Network Inference ◽  
Treatment Options ◽  
Genomic Data ◽  
Full Potential ◽  
Gene Regulatory Network Inference ◽  
Signature Genes

Radiotherapy is one of the most important cancer treatments, but its response varies greatly among individual patients. Therefore, the prediction of radiosensitivity, identification of potential signature genes, and inference of their regulatory networks are important for clinical and oncological reasons. Here, we proposed a novel multiple genomic fused partial least squares deep regression method to simultaneously analyze multi-genomic data. Using 60 National Cancer Institute cell lines as examples, we aimed to identify signature genes by optimizing the radiosensitivity prediction model and uncovering regulatory relationships. A total of 113 signature genes were selected from more than 20,000 genes. The root mean square error of the model was only 0.0025, which was much lower than previously published results, suggesting that our method can predict radiosensitivity with the highest accuracy. Additionally, our regulatory network analysis identified 24 highly important ‘hub’ genes. The data analysis workflow we propose provides a unified and computational framework to harness the full potential of large-scale integrated cancer genomic data for integrative signature discovery. Furthermore, the regression model, signature genes, and their regulatory network should provide a reliable quantitative reference for optimizing personalized treatment options, and may aid our understanding of cancer progress mechanisms.

scholarly journals Single-Cell Transcriptomics-Based Study of Transcriptional Regulatory Features in the Mouse Brain Vasculature

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Wei-Wei Lin ◽  
Lin-Tao Xu ◽  
Yi-Sheng Chen ◽  
Ken Go ◽  
Chenyu Sun ◽  
...  
Keyword(s):  
Single Cell ◽  
Mouse Brain ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Network ◽  
Rna Seq ◽  
Brain Vasculature ◽  
Transcriptional Regulatory ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Background. The critical role of vascular health on brain function has received much attention in recent years. At the single-cell level, studies on the developmental processes of cerebral vascular growth are still relatively few. Techniques for constructing gene regulatory networks (GRNs) based on single-cell transcriptome expression data have made significant progress in recent years. Herein, we constructed a single-cell transcriptional regulatory network of mouse cerebrovascular cells. Methods. The single-cell RNA-seq dataset of mouse brain vessels was downloaded from GEO (GSE98816). This cell clustering was annotated separately using singleR and CellMarker. We then used a modified version of the SCENIC method to construct GRNs. Next, we used a mouse version of SEEK to assess whether genes in the regulon were coexpressed. Finally, regulatory module analysis was performed to complete the cell type relationship quantification. Results. Single-cell RNA-seq data were used to analyze the heterogeneity of mouse cerebrovascular cells, whereby four cell types including endothelial cells, fibroblasts, microglia, and oligodendrocytes were defined. These subpopulations of cells and marker genes together characterize the molecular profile of mouse cerebrovascular cells. Through these signatures, key transcriptional regulators that maintain cell identity were identified. Our findings identified genes like Lmo2, which play an important role in endothelial cells. The same cell type, for instance, fibroblasts, was found to have different regulatory networks, which may influence the functional characteristics of local tissues. Conclusions. In this study, a transcriptional regulatory network based on single-cell analysis was constructed. Additionally, the study identified and profiled mouse cerebrovascular cells using single-cell transcriptome data as well as defined TFs that affect the regulatory network of the mouse brain vasculature.

scholarly journals DCI: learning causal differences between gene regulatory networks

2021 ◽  
Author(s):  
Anastasiya Belyaeva ◽  
Chandler Squires ◽  
Caroline Uhler
Keyword(s):  
Gene Expression ◽  
Causal Inference ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Large Scale ◽  
Developmental Time ◽  
Supplementary Information ◽  
Causal Graph ◽  
Causal Graphs ◽  
Gene Regulatory

Abstract Summary Designing interventions to control gene regulation necessitates modeling a gene regulatory network by a causal graph. Currently, large-scale gene expression datasets from different conditions, cell types, disease states, and developmental time points are being collected. However, application of classical causal inference algorithms to infer gene regulatory networks based on such data is still challenging, requiring high sample sizes and computational resources. Here, we describe an algorithm that efficiently learns the differences in gene regulatory mechanisms between different conditions. Our difference causal inference (DCI) algorithm infers changes (i.e. edges that appeared, disappeared, or changed weight) between two causal graphs given gene expression data from the two conditions. This algorithm is efficient in its use of samples and computation since it infers the differences between causal graphs directly without estimating each possibly large causal graph separately. We provide a user-friendly Python implementation of DCI and also enable the user to learn the most robust difference causal graph across different tuning parameters via stability selection. Finally, we show how to apply DCI to single-cell RNA-seq data from different conditions and cell states, and we also validate our algorithm by predicting the effects of interventions. Availability and implementation Python package freely available at http://uhlerlab.github.io/causaldag/dci. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A novel probabilistic generator for large-scale gene association networks

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259193
Author(s):  
Tyler Grimes ◽  
Somnath Datta
Keyword(s):  
Gold Standard ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Large Scale ◽  
Network Inference ◽  
Real Data ◽  
R Package ◽  
Reference Network ◽  
Gene Association ◽  
Inference Methods

Motivation Gene expression data provide an opportunity for reverse-engineering gene-gene associations using network inference methods. However, it is difficult to assess the performance of these methods because the true underlying network is unknown in real data. Current benchmarks address this problem by subsampling a known regulatory network to conduct simulations. But the topology of regulatory networks can vary greatly across organisms or tissues, and reference-based generators—such as GeneNetWeaver—are not designed to capture this heterogeneity. This means, for example, benchmark results from the E. coli regulatory network will not carry over to other organisms or tissues. In contrast, probabilistic generators do not require a reference network, and they have the potential to capture a rich distribution of topologies. This makes probabilistic generators an ideal approach for obtaining a robust benchmarking of network inference methods. Results We propose a novel probabilistic network generator that (1) provides an alternative to address the inherent limitation of reference-based generators and (2) is able to create realistic gene association networks, and (3) captures the heterogeneity found across gold-standard networks better than existing generators used in practice. Eight organism-specific and 12 human tissue-specific gold-standard association networks are considered. Several measures of global topology are used to determine the similarity of generated networks to the gold-standards. Along with demonstrating the variability of network structure across organisms and tissues, we show that the commonly used “scale-free” model is insufficient for replicating these structures. Availability This generator is implemented in the R package “SeqNet” and is available on CRAN (https://cran.r-project.org/web/packages/SeqNet/index.html).

scholarly journals Trans-omic analysis reveals fed and fasting insulin signal across phosphoproteome, transcriptome, and metabolome

2017 ◽  
Author(s):  
Kentaro Kawata ◽  
Katsuyuki Yugi ◽  
Atsushi Hatano ◽  
Masashi Fujii ◽  
Yoko Tomizawa ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Insulin Action ◽  
Regulatory Networks ◽  
Dynamic Range ◽  
Transcriptional Regulatory Network ◽  
Integrated Analysis ◽  
List Type ◽  
Fasting Insulin ◽  
Cellular Functions ◽  
Insulin Signal

SUMMARYThe concentration and temporal pattern of insulin selectively regulate multiple cellular functions. To understand how insulin dynamics are interpreted by cells, we constructed a trans-omic network of insulin action in FAO hepatoma cells from three networks—a phosphorylation-dependent cellular functions regulatory network using phosphoproteomic data, a transcriptional regulatory network using phosphoproteomic and transcriptomic data, and a metabolism regulatory network using phosphoproteomic and metabolomic data. With the trans-omic regulatory network, we identified selective regulatory networks that mediate differential responses to insulin. Akt and Erk, hub molecules of insulin signaling, encode information of a wide dynamic range of dose and time of insulin. Down-regulated genes and metabolites in glycolysis had high sensitivity to insulin (fasting insulin signal); up-regulated genes and dicarboxylic acids in the TCA cycle had low sensitivity (fed insulin signal). This integrated analysis enables molecular insight into how cells interpret physiologically fed and fasting insulin signals.HighlightsWe constructed a trans-omic network of insulin action using multi-omic data.The trans-omic network integrates phosphorylation, transcription, and metabolism.We classified signaling, transcriptome, and metabolome by sensitivity to insulin.We identified fed and fasting insulin signal flow across the trans-omic network.

scholarly journals Revealing 29 sets of independently modulated genes inStaphylococcus aureus, their regulators, and role in key physiological response

2020 ◽  
Vol 117 (29) ◽  
pp. 17228-17239 ◽  
Author(s):  
Saugat Poudel ◽  
Hannah Tsunemoto ◽  
Yara Seif ◽  
Anand V. Sastry ◽  
Richard Szubin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Network ◽  
Expression Profiles ◽  
Mueller Hinton ◽  
Transcriptional Regulatory ◽  
Its Gene ◽  
Low Dimensional ◽  
Regulatory Organization

The ability ofStaphylococcus aureusto infect many different tissue sites is enabled, in part, by its transcriptional regulatory network (TRN) that coordinates its gene expression to respond to different environments. We elucidated the organization and activity of this TRN by applying independent component analysis to a compendium of 108 RNA-sequencing expression profiles from twoS. aureusclinical strains (TCH1516 and LAC). ICA decomposed theS. aureustranscriptome into 29 independently modulated sets of genes (i-modulons) that revealed: 1) High confidence associations between 21 i-modulons and known regulators; 2) an association between an i-modulon and σS, whose regulatory role was previously undefined; 3) the regulatory organization of 65 virulence factors in the form of three i-modulons associated with AgrR, SaeR, and Vim-3; 4) the roles of three key transcription factors (CodY, Fur, and CcpA) in coordinating the metabolic and regulatory networks; and 5) a low-dimensional representation, involving the function of few transcription factors of changes in gene expression between two laboratory media (RPMI, cation adjust Mueller Hinton broth) and two physiological media (blood and serum). This representation of the TRN covers 842 genes representing 76% of the variance in gene expression that provides a quantitative reconstruction of transcriptional modules inS. aureus, and a platform enabling its full elucidation.

Topology Analysis of a Metabolic Functional Gene Transcriptional Regulatory Network of Escherichia Coli

2013 ◽  
Vol 461 ◽  
pp. 648-653
Author(s):  
Qing Yu Zou ◽  
Fu Liu ◽  
Hou Tao
Keyword(s):  
Regulatory Network ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Network ◽  
Topological Analysis ◽  
Model Organisms ◽  
Transcriptional Regulatory Networks ◽  
Statistical Characterization ◽  
Functional Annotations ◽  
Physiological Processes ◽  
Transcriptional Regulatory

Under the perspectives of network science and systems biology, the characterizations of transcriptional regulatory networks (TRNs) beyond the context of model organisms have been studied extensively. However, little is still known about the structure and functionality of TRNs that control metabolic physiological processes. In this study, we present a newly version of the TRN of E.coli controlling metabolism based on functional annotations from GeneProtEC and Gene Ontology (GO). We also present an exhaustive topological analysis of the metabolic transcriptional regulatory network (MTRN), focusing on the main statistical characterization describing the topological structure and the comparison with TRN. From the results in this paper we infer that TRN and MTRN have very similar characteristic distribution.

scholarly journals Reconstruction of a Global Transcriptional Regulatory Network for Control of Lipid Metabolism in Yeast by Using Chromatin Immunoprecipitation with Lambda Exonuclease Digestion

mSystems ◽  
2018 ◽  
Vol 3 (4) ◽  
Author(s):  
David Bergenholm ◽  
Guodong Liu ◽  
Petter Holland ◽  
Jens Nielsen
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Lipid Metabolism ◽  
Chromatin Immunoprecipitation ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Network ◽  
Lambda Exonuclease ◽  
Transcriptional Regulatory ◽  
Exonuclease Digestion

ABSTRACT To build transcription regulatory networks, transcription factor binding must be analyzed in cells grown under different conditions because their responses and targets differ depending on environmental conditions. We performed whole-genome analysis of the DNA binding of five Saccharomyces cerevisiae transcription factors involved in lipid metabolism, Ino2, Ino4, Hap1, Oaf1, and Pip2, in response to four different environmental conditions in chemostat cultures, which allowed us to keep the specific growth rate constant. Chromatin immunoprecipitation with lambda exonuclease digestion (ChIP-exo) enabled the detection of binding events at a high resolution. We discovered a large number of unidentified targets and thus expanded functions for each transcription factor (e.g., glutamate biosynthesis as a target of Oaf1 and Pip2). Moreover, condition-dependent binding of transcription factors in response to cell metabolic state (e.g., differential binding of Ino2 between fermentative and respiratory metabolic conditions) was clearly suggested. Combining the new binding data with previously published data from transcription factor deletion studies revealed the high complexity of the transcriptional regulatory network for lipid metabolism in yeast, which involves the combinatorial and complementary regulation by multiple transcription factors. We anticipate that our work will provide insights into transcription factor binding dynamics that will prove useful for the understanding of transcription regulatory networks. IMPORTANCE Transcription factors play a crucial role in the regulation of gene expression and adaptation to different environments. To better understand the underlying roles of these adaptations, we performed experiments that give us high-resolution binding of transcription factors to their targets. We investigated five transcription factors involved in lipid metabolism in yeast, and we discovered multiple novel targets and condition-specific responses that allow us to draw a better regulatory map of the lipid metabolism.

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