scholarly journals Integrative multi-omics analyses identify cell-type disease genes and regulatory networks across schizophrenia and Alzheimer’s disease

2020 ◽  
Author(s):  
Mufang Ying ◽  
Peter Rehani ◽  
Panagiotis Roussos ◽  
Daifeng Wang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Regulatory Elements ◽  
Disease Genes ◽  
Omics Data ◽  
Cell Type ◽  
Cellular Resolution ◽  
Gene Regulatory

AbstractStrong phenotype-genotype associations have been reported across brain diseases. However, understanding underlying gene regulatory mechanisms remains challenging, especially at the cellular level. To address this, we integrated the multi-omics data at the cellular resolution of the human brain: cell-type chromatin interactions, epigenomics and single cell transcriptomics, and predicted cell-type gene regulatory networks linking transcription factors, distal regulatory elements and target genes (e.g., excitatory and inhibitory neurons, microglia, oligodendrocyte). Using these cell-type networks and disease risk variants, we further identified the cell-type disease genes and regulatory networks for schizophrenia and Alzheimer’s disease. The celltype regulatory elements (e.g., enhancers) in the networks were also found to be potential pleiotropic regulatory loci for a variety of diseases. Further enrichment analyses including gene ontology and KEGG pathways revealed potential novel cross-disease and disease-specific molecular functions, advancing knowledge on the interplays among genetic, transcriptional and epigenetic risks at the cellular resolution between neurodegenerative and neuropsychiatric diseases. Finally, we summarized our computational analyses as a general-purpose pipeline for predicting gene regulatory networks via multi-omics data.

scholarly journals Analytical Strategy to Prioritize Alzheimer’s Disease Candidate Genes in Gene Regulatory Networks Using Public Expression Data

2017 ◽  
Vol 59 (4) ◽  
pp. 1237-1254 ◽  
Author(s):  
Shweta Bagewadi Kawalia ◽  
Tamara Raschka ◽  
Mufassra Naz ◽  
Ricardo de Matos Simoes ◽  
Philipp Senger ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Candidate Genes ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Expression Data ◽  
Analytical Strategy ◽  
Public Expression ◽  
Gene Regulatory

scholarly journals Mutations in Transcriptional Regulators Allow Selective Engineering of Signal Integration Logic

mBio ◽  
2014 ◽  
Vol 5 (3) ◽  
Author(s):  
Szabolcs Semsey
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Regulatory Protein ◽  
Point Mutations ◽  
Building Blocks ◽  
Regulatory Elements ◽  
Regulatory Proteins ◽  
Single Amino Acid ◽  
Signal Integration ◽  
Gene Regulatory

ABSTRACT Bacterial cells monitor their environment by sensing a set of signals. Typically, these environmental signals affect promoter activities by altering the activity of transcription regulatory proteins. Promoters are often regulated by more than one regulatory protein, and in these cases the relevant signals are integrated by certain logic. In this work, we study how single amino acid substitutions in a regulatory protein (GalR) affect transcriptional regulation and signal integration logic at a set of engineered promoters. Our results suggest that point mutations in regulatory genes allow independent evolution of regulatory logic at different promoters. IMPORTANCE Gene regulatory networks are built from simple building blocks, such as promoters, transcription regulatory proteins, and their binding sites on DNA. Many promoters are regulated by more than one regulatory input. In these cases, the inputs are integrated and allow transcription only in certain combinations of input signals. Gene regulatory networks can be easily rewired, because the function of cis-regulatory elements and promoters can be altered by point mutations. In this work, we tested how point mutations in transcription regulatory proteins can affect signal integration logic. We found that such mutations allow context-dependent engineering of signal integration logic at promoters, further contributing to the plasticity of gene regulatory networks.

scholarly journals Synthetic gene-regulatory networks in the opportunistic human pathogenStreptococcus pneumoniae

2020 ◽  
Vol 117 (44) ◽  
pp. 27608-27619 ◽  
Author(s):  
Robin A. Sorg ◽  
Clement Gallay ◽  
Laurye Van Maele ◽  
Jean-Claude Sirard ◽  
Jan-Willem Veening
Keyword(s):  
Gene Expression ◽  
Virulence Factor ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Combinatorial Libraries ◽  
Regulatory Elements ◽  
Synthetic Gene ◽  
Expression Control ◽  
Gene Expression Control ◽  
Gene Regulatory

Streptococcus pneumoniaecan cause disease in various human tissues and organs, including the ear, the brain, the blood, and the lung, and thus in highly diverse and dynamic environments. It is challenging to study how pneumococci control virulence factor expression, because cues of natural environments and the presence of an immune system are difficult to simulate in vitro. Here, we apply synthetic biology methods to reverse-engineer gene expression control inS. pneumoniae. A selection platform is described that allows for straightforward identification of transcriptional regulatory elements out of combinatorial libraries. We present TetR- and LacI-regulated promoters that show expression ranges of four orders of magnitude. Based on these promoters, regulatory networks of higher complexity are assembled, such as logic AND gates and IMPLY gates. We demonstrate single-copy genome-integrated toggle switches that give rise to bimodal population distributions. The tools described here can be used to mimic complex expression patterns, such as the ones found for pneumococcal virulence factors. Indeed, we were able to rewire gene expression of the capsule operon, the main pneumococcal virulence factor, to be externally inducible (YES gate) or to act as an IMPLY gate (only expressed in absence of inducer). Importantly, we demonstrate that these synthetic gene-regulatory networks are functional in an influenza A virus superinfection murine model of pneumonia, paving the way for in vivo investigations of the importance of gene expression control on the pathogenicity ofS. pneumoniae.

scholarly journals Mapping gene regulatory networks from single-cell omics data

2018 ◽  
Vol 17 (4) ◽  
pp. 246-254 ◽  
Author(s):  
Mark W E J Fiers ◽  
Liesbeth Minnoye ◽  
Sara Aibar ◽  
Carmen Bravo González-Blas ◽  
Zeynep Kalender Atak ◽  
...  
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Omics Data ◽  
Mapping Gene ◽  
Gene Regulatory

scholarly journals Mapping gene regulatory networks of primary CD4+T cells using single-cell genomics and genome engineering

2019 ◽  
Author(s):  
Rachel E. Gate ◽  
Min Cheol Kim ◽  
Andrew Lu ◽  
David Lee ◽  
Eric Shifrut ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Genome Engineering ◽  
Regulatory Elements ◽  
Mapping Gene ◽  
Gene Regulatory ◽  
Novel Interactions

AbstractGene regulatory programs controlling the activation and polarization of CD4+T cells are incompletely mapped and the interindividual variability in these programs remain unknown. We sequenced the transcriptomes of ~160k CD4+T cells from 9 donors following pooled CRISPR perturbation targeting 140 regulators. We identified 134 regulators that affect T cell functionalization, includingIRF2as a positive regulator of Th2polarization. Leveraging correlation patterns between cells, we mapped 194 pairs of interacting regulators, including known (e.g.BATFandJUN) and novel interactions (e.g.ETS1andSTAT6). Finally, we identified 80 natural genetic variants with effects on gene expression, 48 of which are modified by a perturbation. In CD4+T cells, CRISPR perturbations can influencein vitropolarization and modify the effects oftransandcisregulatory elements on gene expression.

scholarly journals Predicting gene regulatory networks from cell atlases

2020 ◽  
Author(s):  
Andreas Fønss Møller ◽  
Kedar Nath Natarajan
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Cell Types ◽  
Mouse Cell ◽  
Cell Type ◽  
Integrated Network ◽  
Mouse Tissues ◽  
Cell Type Specific ◽  
Gene Regulatory

AbstractRecent single-cell RNA-sequencing atlases have surveyed and identified major cell-types across different mouse tissues. Here, we computationally reconstruct gene regulatory networks from 3 major mouse cell atlases to capture functional regulators critical for cell identity, while accounting for a variety of technical differences including sampled tissues, sequencing depth and author assigned cell-type labels. Extracting the regulatory crosstalk from mouse atlases, we identify and distinguish global regulons active in multiple cell-types from specialised cell-type specific regulons. We demonstrate that regulon activities accurately distinguish individual cell types, despite differences between individual atlases. We generate an integrated network that further uncovers regulon modules with coordinated activities critical for cell-types, and validate modules using available experimental data. Inferring regulatory networks during myeloid differentiation from wildtype and Irf8 KO cells, we uncover functional contribution of Irf8 regulon activity and composition towards monocyte lineage. Our analysis provides an avenue to further extract and integrate the regulatory crosstalk from single-cell expression data.SummaryIntegrated single-cell gene regulatory network from three mouse cell atlases captures global and cell-type specific regulatory modules and crosstalk, important for cellular identity.

scholarly journals ChIP-Array 2: integrating multiple omics data to construct gene regulatory networks

2015 ◽  
Vol 43 (W1) ◽  
pp. W264-W269 ◽  
Author(s):  
Panwen Wang ◽  
Jing Qin ◽  
Yiming Qin ◽  
Yun Zhu ◽  
Lily Yan Wang ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Omics Data ◽  
Gene Regulatory
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