scholarly journals Early cis-regulatory events in the formation of retinal horizontal cells

2020 ◽  
Author(s):  
Estie Schick ◽  
Kevin C. Gonzalez ◽  
Pooja Dutta ◽  
Kazi Hossain ◽  
Miruna G. Ghinia Tegla ◽  
...  
Keyword(s):  
Nuclear Receptors ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Horizontal Cell ◽  
Regulatory Elements ◽  
Negative Regulation ◽  
Horizontal Cells ◽  
Gene Regulatory ◽  
Induced Signal ◽  
Direct Activator

AbstractDuring retinal development, multipotent and restricted progenitor cells generate all of the neuronal cells of the retina. Among these are horizontal cells, which are interneurons that modulate the light-induced signal from photoreceptors. This study utilizes the identification of novel cis-regulatory elements as a method to examine the gene regulatory networks that direct the development of horizontal cells. Here we describe a screen for cis-regulatory elements, or enhancers, for the horizontal cell-associated genes PTF1A, ONECUT1 (OC1), TFAP2A (AP2A), and LHX1. The OC1ECR22 and Tfap2aACR5 elements were shown to be potential enhancers for OC1 and TFAP2A, respectively, and to be specifically active in developing horizontal cells. The OC1ECR22 element is activated by PTF1A and RBPJ, which translates to regulation of OC1 expression and suggests that PTF1A is a direct activator of OC1 expression in developing horizontal cells. The region within the Tfap2aACR5 element that is responsible for its activation was determined to be a 100 bp sequence named Motif 4. Both OC1ECR22 and Tfap2aACR5 are negatively regulated by the nuclear receptors THRB and RXRG, as is the expression of OC1 and AP2A, suggesting that nuclear receptors may have a role in the negative regulation of horizontal cell development.

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 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 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 Integrative analysis of multi-omics reveals gene regulatory networks across brain regions from risk variants to phenotypes of Alzheimer’s disease and Covid-19

2021 ◽  
Author(s):  
Saniya Khullar ◽  
Daifeng Wang
Keyword(s):  
Disease Progression ◽  
Temporal Lobe ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Target Genes ◽  
Disease Risk ◽  
Regulatory Elements ◽  
Brain Regions ◽  
Risk Variants ◽  
Gene Regulatory

AbstractBackgroundGenome-wide association studies have found many genetic risk variants associated with Alzheimer’s disease (AD). However, how these risk variants affect deeper phenotypes such as disease progression and immune response remains elusive. Also, our understanding of cellular and molecular mechanisms from disease risk variants to various phenotypes is still limited. To address these problems, we performed integrative multi-omics analysis from genotype, transcriptomics, and epigenomics for revealing gene regulatory mechanisms from disease variants to AD phenotypes.MethodFirst, we cluster gene co-expression networks and identify gene modules for various AD phenotypes given population gene expression data. Next, we predict the transcription factors (TFs) that significantly regulate the genes in each module and the AD risk variants (e.g., SNPs) interrupting the TF binding sites on the regulatory elements. Finally, we construct a full gene regulatory network linking SNPs, interrupted TFs, and regulatory elements to target genes for each phenotype. This network thus provides mechanistic insights of gene regulation from disease risk variants to AD phenotypes.ResultsWe applied our analysis to predict the gene regulatory networks in three major AD-relevant regions: hippocampus, dorsolateral prefrontal cortex (DLPFC), and lateral temporal lobe (LTL). These region networks provide a comprehensive functional genomic map linking AD SNPs to TFs and regulatory elements to target genes for various AD phenotypes. Comparative analyses further revealed cross-region-conserved and region-specific regulatory networks. For instance, AD SNPs rs13404184 and rs61068452 disrupt the bindings of TF SPI1 that regulates AD gene INPP5D in the hippocampus and lateral temporal lobe. However, SNP rs117863556 interrupts the bindings of TF REST to regulate GAB2 in the DLPFC only. Furthermore, driven by recent discoveries between AD and Covid-19, we found that many genes from our networks regulating Covid-19 pathways are also significantly differentially expressed in severe Covid patients (ICU), suggesting potential regulatory connections between AD and Covid. Thus, we used the machine learning models to predict severe Covid and prioritized highly predictive genes as AD-Covid genes. We also used Decision Curve Analysis to show that our AD-Covid genes outperform known Covid-19 genes for predicting Covid severity and deciding to send patients to ICU or not. In short, our results provide a deeper understanding of the interplay among multi-omics, brain regions, and AD phenotypes, including disease progression and Covid response. Our analysis is open-source available at https://github.com/daifengwanglab/ADSNPheno.

scholarly journals Conservation of dynamic characteristics of transcriptional regulatory elements in periodic biological processes

2020 ◽  
Author(s):  
Francis C. Motta ◽  
Robert C. Moseley ◽  
Bree Cummins ◽  
Anastasia Deckard ◽  
Steven B. Haase
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Large Fraction ◽  
Regulatory Elements ◽  
Natural World ◽  
Model Systems ◽  
Model Organisms ◽  
Core Network ◽  
The Core ◽  
Gene Regulatory

AbstractCell and circadian cycles control a large fraction of cell and organismal physiology by regulating large periodic transcriptional programs that encompass anywhere from 15-80% of the genome. The gene-regulatory networks (GRNs) controlling these programs were largely identified by genetics and chromosome mapping approaches in model systems, yet it is unlikely that we have identified all of the core GRN components. Moreover, large periodic transcriptional programs controlling a variety of processes certainly exist in important non-model organisms where genetic approaches to identifying networks are expensive, time-consuming or intractable. Ideally, the core network components could be identified using data-driven approaches on the transcriptome dynamics data already available. Previous work used dynamic gene expression features to identify sets of genes with periodic behavior; our work goes further to distinguish genes by role: core versus their non-regulatory outputs. Here we present a quantitative approach that can identify nodes of GRNs controlling cell or circadian cycles across taxa. There are practical applications of the approach for network biologists, but our findings reveal something unexpected—that there are quantifiable and fundamental shared features of these unrelated GRNs controlling disparate periodic phenotypes.Author summaryCircadian rhythms, cellular division, and the developmental cycles of a multitude of living creatures, including those responsible for infectious diseases, are among the many dynamic phenomena in the natural world that are known to be the eventual output of gene regulatory networks. Identifying the small number of specialized genes that control these dynamic behaviors is of fundamental importance to our understanding of life, and our treatment of disease, but is difficult because of the sheer size of the genomes. We show that the core genes in organisms separated by millions of years of evolution have remarkable similarities that can be used to identify them.

scholarly journals Waiting in the wings: what can we learn about gene co-option from the diversification of butterfly wing patterns?

2017 ◽  
Vol 372 (1713) ◽  
pp. 20150485 ◽  
Author(s):  
Chris D. Jiggins ◽  
Richard W. R. Wallbank ◽  
Joseph J. Hanly
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Spatial Information ◽  
Morphological Diversity ◽  
Regulatory Elements ◽  
Evolutionary Change ◽  
Divergent Evolution ◽  
Butterfly Wing ◽  
Wing Patterns ◽  
Gene Regulatory

A major challenge is to understand how conserved gene regulatory networks control the wonderful diversity of form that we see among animals and plants. Butterfly wing patterns are an excellent example of this diversity. Butterfly wings form as imaginal discs in the caterpillar and are constructed by a gene regulatory network, much of which is conserved across the holometabolous insects. Recent work in Heliconius butterflies takes advantage of genomic approaches and offers insights into how the diversification of wing patterns is overlaid onto this conserved network. WntA is a patterning morphogen that alters spatial information in the wing. Optix is a transcription factor that acts later in development to paint specific wing regions red. Both of these loci fit the paradigm of conserved protein-coding loci with diverse regulatory elements and developmental roles that have taken on novel derived functions in patterning wings. These discoveries offer insights into the ‘Nymphalid Ground Plan’, which offers a unifying hypothesis for pattern formation across nymphalid butterflies. These loci also represent ‘hotspots’ for morphological change that have been targeted repeatedly during evolution. Both convergent and divergent evolution of a great diversity of patterns is controlled by complex alleles at just a few genes. We suggest that evolutionary change has become focused on one or a few genetic loci for two reasons. First, pre-existing complex cis -regulatory loci that already interact with potentially relevant transcription factors are more likely to acquire novel functions in wing patterning. Second, the shape of wing regulatory networks may constrain evolutionary change to one or a few loci. Overall, genomic approaches that have identified wing patterning loci in these butterflies offer broad insight into how gene regulatory networks evolve to produce diversity. This article is part of the themed issue ‘Evo-devo in the genomics era, and the origins of morphological diversity’.

scholarly journals Deep conservation of cis -regulatory elements in metazoans

2013 ◽  
Vol 368 (1632) ◽  
pp. 20130020 ◽  
Author(s):  
Ignacio Maeso ◽  
Manuel Irimia ◽  
Juan J. Tena ◽  
Fernando Casares ◽  
José Luis Gómez-Skarmeta
Keyword(s):  
Gene Regulatory Networks ◽  
Morphological Variation ◽  
Regulatory Networks ◽  
Regulatory Elements ◽  
Developmental Processes ◽  
Ancestral Gene ◽  
New Approaches ◽  
Gene Regulatory

Despite the vast morphological variation observed across phyla, animals share multiple basic developmental processes orchestrated by a common ancestral gene toolkit. These genes interact with each other building complex gene regulatory networks (GRNs), which are encoded in the genome by cis -regulatory elements (CREs) that serve as computational units of the network. Although GRN subcircuits involved in ancient developmental processes are expected to be at least partially conserved, identification of CREs that are conserved across phyla has remained elusive. Here, we review recent studies that revealed such deeply conserved CREs do exist, discuss the difficulties associated with their identification and describe new approaches that will facilitate this search.

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