scholarly journals Prostate Cancer Gene Regulatory Network Inferred from RNA-Seq Data

2019 ◽  
Vol 20 (1) ◽  
pp. 38-48 ◽  
Author(s):  
Daniel Moore ◽  
Ricardo de Matos Simoes ◽  
Matthias Dehmer ◽  
Frank Emmert-Streib
Keyword(s):  
Prostate Cancer ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
The Cancer Genome Atlas ◽  
Cancer Gene ◽  
Cancer Genes ◽  
Rna Seq ◽  
Data Set ◽  
Gene Regulatory ◽  
Functional Components

Background: Cancer is a complex disease with a lucid etiology and in understanding the causation, we need to appreciate this complexity. Objective: Here we are aiming to gain insights into the genetic associations of prostate cancer through a network-based systems approach using the BC3Net algorithm. Methods: Specifically, we infer a prostate cancer Gene Regulatory Network (GRN) from a large-scale gene expression data set of 333 patient RNA-seq profiles obtained from The Cancer Genome Atlas (TCGA) database. Results: We analyze the functional components of the inferred network by extracting subnetworks based on biological process information and interpret the role of known cancer genes within each process. Furthermore, we investigate the local landscape of prostate cancer genes and discuss pathological associations that may be relevant in the development of new targeted cancer therapies. Conclusion: Our network-based analysis provides a practical systems biology approach to reveal the collective gene-interactions of prostate cancer. This allows a close interpretation of biological activity in terms of the hallmarks of cancer.

scholarly journals A gene regulatory network for tiller development mediated by Tin8 in maize

2021 ◽  
Author(s):  
Zhelong Lin ◽  
Leina Zhou ◽  
Shuyang Zhong ◽  
Xiaojian Fang ◽  
Hangqin Liu ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Strong Interaction ◽  
Regulatory Network ◽  
Tiller Number ◽  
Chromosome 8 ◽  
Chromosome 1 ◽  
Rna Seq ◽  
Hormonal Responses ◽  
Gene Regulatory ◽  
Tiller Bud

Abstract The complex gene regulatory network underlying maize tiller development remains largely unknown. Here we identified two major quantitative trait loci (QTLs) for tiller number, Tin8 on chromosome 8 and the previously known Tb1 on chromosome 1, in a population derived from a teosinte–maize cross. Map-based cloning and association mapping revealed that Tin8 corresponding to Zcn8 encoding a PEBP-related kinase, is down-regulated in transcription and thus results in decreased tiller number. Strong interaction between Tin8 and the key gen Tb1 was detected for tiller number. Further RNA-seq analysis showed that the expressions of 13 genes related to tiller development were controlled by Tin8. Our results support the existence of a complex gene regulatory network for the outgrowth of maize tiller bud, in which Zcn8 controls 13 tiller-related genes including four genes for hormonal responses. Especially, Zcn8 represses Gt1, D14 and Tru1, through the interaction of Tb1.

scholarly journals BMP2-dependent gene regulatory network analysis reveals Klf4 as a novel transcription factor of osteoblast differentiation

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Shuaitong Yu ◽  
Jinqiang Guo ◽  
Zheyi Sun ◽  
Chujiao Lin ◽  
Huangheng Tao ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Transcription Factor ◽  
Gene Regulatory Network ◽  
Stromal Cells ◽  
Regulatory Network ◽  
Bone Marrow Stromal Cells ◽  
Osteoblast Differentiation ◽  
Marrow Stromal Cells ◽  
Rna Seq ◽  
Gene Regulatory

AbstractTranscription factors (TFs) regulate the expression of target genes, inducing changes in cell morphology or activities needed for cell fate determination and differentiation. The BMP signaling pathway is widely regarded as one of the most important pathways in vertebrate skeletal biology, of which BMP2 is a potent inducer, governing the osteoblast differentiation of bone marrow stromal cells (BMSCs). However, the mechanism by which BMP2 initiates its downstream transcription factor cascade and determines the direction of differentiation remains largely unknown. In this study, we used RNA-seq, ATAC-seq, and animal models to characterize the BMP2-dependent gene regulatory network governing osteoblast lineage commitment. Sp7-Cre; Bmp2fx/fx mice (BMP2-cKO) were generated and exhibited decreased bone density and lower osteoblast number (n > 6). In vitro experiments showed that BMP2-cKO mouse bone marrow stromal cells (mBMSCs) had an impact on osteoblast differentiation and deficient cell proliferation. Osteogenic medium induced mBMSCs from BMP2-cKO mice and control were subjected to RNA-seq and ATAC-seq analysis to reveal differentially expressed TFs, along with their target open chromatin regions. Combined with H3K27Ac CUT&Tag during osteoblast differentiation, we identified 2338 BMP2-dependent osteoblast-specific active enhancers. Motif enrichment assay revealed that over 80% of these elements were directly targeted by RUNX2, DLX5, MEF2C, OASIS, and KLF4. We deactivated Klf4 in the Sp7 + lineage to validate the role of KLF4 in osteoblast differentiation of mBMSCs. Compared to the wild-type, Sp7-Cre; Klf4fx/+ mice (KLF4-Het) were smaller in size and had abnormal incisors resembling BMP2-cKO mice. Additionally, KLF4-Het mice had fewer osteoblasts and decreased osteogenic ability. RNA-seq and ATAC-seq revealed that KLF4 mainly “co-bound” with RUNX2 to regulate downstream genes. Given the significant overlap between KLF4- and BMP2-dependent NFRs and enriched motifs, our findings outline a comprehensive BMP2-dependent gene regulatory network specifically governing osteoblast differentiation of the Sp7 + lineage, in which Klf4 is a novel transcription factor.

scholarly journals Intracellular and Intercellular Gene Regulatory Network Inference From Time-Course Individual RNA-Seq

2021 ◽  
Vol 1 ◽  
Author(s):  
Makoto Kashima ◽  
Yuki Shida ◽  
Takashi Yamashiro ◽  
Hiromi Hirata ◽  
Hiroshi Kurosaka
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Time Course ◽  
Network Inference ◽  
Molecular Mechanisms ◽  
Rna Seq ◽  
Gene Regulatory Network Inference ◽  
Gene Regulatory ◽  
Multicellular Organisms

Gene regulatory network (GRN) inference is an effective approach to understand the molecular mechanisms underlying biological events. Generally, GRN inference mainly targets intracellular regulatory relationships such as transcription factors and their associated targets. In multicellular organisms, there are both intracellular and intercellular regulatory mechanisms. Thus, we hypothesize that GRNs inferred from time-course individual (whole embryo) RNA-Seq during development can reveal intercellular regulatory relationships (signaling pathways) underlying the development. Here, we conducted time-course bulk RNA-Seq of individual mouse embryos during early development, followed by pseudo-time analysis and GRN inference. The results demonstrated that GRN inference from RNA-Seq with pseudo-time can be applied for individual bulk RNA-Seq similar to scRNA-Seq. Validation using an experimental-source-based database showed that our approach could significantly infer GRN for all transcription factors in the database. Furthermore, the inferred ligand-related and receptor-related downstream genes were significantly overlapped. Thus, the inferred GRN based on whole organism could include intercellular regulatory relationships, which cannot be inferred from scRNA-Seq based only on gene expression data. Overall, inferring GRN from time-course bulk RNA-Seq is an effective approach to understand the regulatory relationships underlying biological events in multicellular organisms.

UNCOVER CONTEXT-SPECIFIC GENE REGULATION BY TRANSCRIPTION FACTORS AND microRNAs USING BAYESIAN SPARSE NONNEGATIVE FACTOR REGRESSION

2012 ◽  
Vol 20 (04) ◽  
pp. 377-402
Author(s):  
JIA MENG ◽  
YIDONG CHEN ◽  
YUFEI HUANG
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Gibbs Sampling ◽  
Regulatory Network ◽  
Messenger Rna ◽  
The Cancer Genome Atlas ◽  
Specific Gene ◽  
Data Types ◽  
Gene Regulatory ◽  
Factor Regression

In multicellular organisms, transcription factors (TFs) and microRNAs (miRNA) embody two largest families of molecules that modulate messenger RNA (mRNA) expressions through transcriptional and post-transcriptional regulations. While mRNA and microRNA expressions can be measured by microarray technique, the activities of transcription factors manifested by their protein expression are still difficult to observe, making it usually a complex problem to reconstruct a collaborative gene regulatory network (GRN) by TFs and miRNAs from expression data. In this paper, a novel Bayesian sparse non-negative factor regression (BSNFR) model is proposed for modeling the joint regulations of mRNAs by TFs and miRNAs and integration of multiple data types including gene expressions, microRNA expressions, TF targeted genes, and microRNA targets. Powered by a Gibbs sampling solution, BSNFR can infer both the TF/microRNA-mediated mRNA regulations and the unknown TF activities. Additionally, since BSNFR directly models the non-negative activities of TFs, it avoids the common problem of sign ambiguity with factor models and is capable of accurate prediction of the types (up or down) of regulations as well. BSNFR also includes a nonparametric Bayesian model for the latent factor activities, which enables the discovery of the clustering effects among samples due to (disease) subtypes. The proposed BSNFR model and the developed Gibbs sampling solution were validated on simulated systems and applied to real data of glioblastoma multiforme (GBM) patients from The Cancer Genome Atlas (TCGA). A GBM specific gene regulatory network by TFs and miRNAs was reconstructed. This GBM network includes 107 regulations recorded in the existing databases and 16 new regulations. Functional analysis suggests that the regulated genes are enriched in cell cycle and P53 pathways. In addition, BSNFR also identified 3 clusters among GBM patient samples, two of which demonstrates significant survival differences (p=0.004). Finally, the estimated TF activities imply that EGR-1 is significantly correlated with patient survivals (p=0.004) and may be used as a prognostic biomarker. The data and matlab code are available at: http://compgenomics.cbi.utsa.edu/BSNFR .

scholarly journals PI3K Signaling Specifies Proximal-Distal Fate by Driving a Developmental Gene Regulatory Network in Sox9+ Lung Progenitors

2021 ◽  
Author(s):  
Divya Khattar ◽  
Sharlene Fernandes ◽  
John Snowball ◽  
Minzhe Guo ◽  
Matthew C Gillen ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Embryonic Development ◽  
Progenitor Cells ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Chromatin Accessibility ◽  
Pi3k Signaling ◽  
Rna Seq ◽  
Gene Regulatory

The tips of the developing respiratory buds are home to important progenitor cells marked by the expression of SOX9 and ID2. Early in embryonic development (prior to E13.5), SOX9+ progenitors are multipotent, generating both airway and alveolar epithelium, but are selective progenitors of alveolar epithelial cells later in development. Transcription factors, including Sox9, Etv5, Irx, Mycn, and Foxp1/2 interact in complex gene regulatory networks to control proliferation and differentiation of SOX9+ progenitors. Molecular mechanisms by which these transcription factors and other signaling pathways control chromatin state to establish and maintain cell-type identity are not well-defined. Herein, we analyze paired gene expression (RNA-Seq) and chromatin accessibility (ATAC-Seq) data from SOX9+ epithelial progenitor cells (EPCs) during embryonic development. Widespread changes in chromatin accessibility were observed between E11.5 and E16.5, particularly at distal cis-regulatory elements (e.g. enhancers). Gene regulatory network (GRN) inference identified a common SOX9+ progenitor GRN, implicating phosphoinositide 3-kinase (PI3K) signaling in the developmental regulation of SOX9+ progenitor cells. Consistent with this model, conditional ablation of PI3K signaling in the developing lung epithelium in vivo and in lung explants in vitro resulted in an expansion of the SOX9+ EPC population and impaired epithelial cell differentiation. These data demonstrate that PI3K signaling is required for epithelial patterning during lung organogenesis, and emphasize the combinatorial power of paired RNA-Seq and ATAC-Seq in defining regulatory networks in development.

scholarly journals Approximate inference of gene regulatory network models from RNA-Seq time series data

2017 ◽  
Author(s):  
Thomas Thorne
Keyword(s):  
Time Series ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Time Series Data ◽  
Stem Cell Differentiation ◽  
Series Data ◽  
Model Parameters ◽  
Rna Seq ◽  
Sparse Regression ◽  
Gene Regulatory

AbstractInference of gene regulatory network structures from RNA-Seq data is challenging due to the nature of the data, as measurements take the form of counts of reads mapped to a given gene. Here we present a model for RNA-Seq time series data that applies a negative binomial distribution for the observations, and uses sparse regression with a horseshoe prior to learn a dynamic Bayesian network of interactions between genes. We use a variational inference scheme to learn approximate posterior distributions for the model parameters. The methodology is benchmarked on synthetic data designed to replicate the distribution of real world RNA-Seq data. We compare our method to other sparse regression approaches and information theoretic methods. We demonstrate an application of our method to a publicly available human neuronal stem cell differentiation RNA-Seq time series.

The primary transcriptome of hormogonia from a filamentous cyanobacterium defined by cappable-seq

Microbiology ◽  
2021 ◽  
Vol 167 (11) ◽  
Author(s):  
Thomas V. Harwood ◽  
Douglas D. Risser
Keyword(s):  
Gene Regulatory Network ◽  
Sigma Factor ◽  
Regulatory Network ◽  
Filamentous Cyanobacterium ◽  
Data Set ◽  
Protein Coding ◽  
Content Type ◽  
Biologically Relevant ◽  
Gene Regulatory ◽  
Transcriptional Start Sites

Hormogonia are motile filaments produced by many filamentous cyanobacteria that function in dispersal, phototaxis and the establishment of nitrogen-fixing symbioses. The gene regulatory network promoting hormogonium development is initiated by the hybrid histidine kinase HrmK, which in turn activates a sigma factor cascade consisting of SigJ, SigC and SigF. In this study, cappable-seq was employed to define the primary transcriptome of developing hormogonia in the model filamentous cyanobacterium Nostoc punctiforme ATCC 29133 in both the wild-type, and sigJ, sigC and sigF mutant strains 6 h post-hormogonium induction. A total of 1544 transcriptional start sites (TSSs) were identified that are associated with protein-coding genes and are expressed at levels likely to lead to biologically relevant transcripts in developing hormogonia. TSS expression among the sigma-factor deletion strains was highly consistent with previously reported gene expression levels from RNAseq experiments, and support the current working model for the role of these genes in hormogonium development. Analysis of SigJ-dependent TSSs corroborated the presence of the previously identified J-Box in the −10 region of SigJ-dependent promoters. Additionally, the data presented provides new insights on sequence conservation within the −10 regions of both SigC- and SigF-dependent promoters, and demonstrates that SigJ and SigC coordinate complex co-regulation not only of hormogonium-specific genes at different loci, but within an individual operon. As progress continues on defining the hormogonium gene regulatory network, this data set will serve as a valuable resource.

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