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1.
Nat Biotechnol ; 39(10): 1270-1277, 2021 10.
Article in English | MEDLINE | ID: mdl-33927415

ABSTRACT

CRISPR screens have been used to connect genetic perturbations with changes in gene expression and phenotypes. Here we describe a CRISPR-based, single-cell combinatorial indexing assay for transposase-accessible chromatin (CRISPR-sciATAC) to link genetic perturbations to genome-wide chromatin accessibility in a large number of cells. In human myelogenous leukemia cells, we apply CRISPR-sciATAC to target 105 chromatin-related genes, generating chromatin accessibility data for ~30,000 single cells. We correlate the loss of specific chromatin remodelers with changes in accessibility globally and at the binding sites of individual transcription factors (TFs). For example, we show that loss of the H3K27 methyltransferase EZH2 increases accessibility at heterochromatic regions involved in embryonic development and triggers expression of genes in the HOXA and HOXD clusters. At a subset of regulatory sites, we also analyze changes in nucleosome spacing following the loss of chromatin remodelers. CRISPR-sciATAC is a high-throughput, single-cell method for studying the effect of genetic perturbations on chromatin in normal and disease states.


Subject(s)
Chromatin Assembly and Disassembly/genetics , Clustered Regularly Interspaced Short Palindromic Repeats/genetics , Gene Expression Profiling/methods , RNA-Seq/methods , Single-Cell Analysis/methods , Binding Sites , Chromatin/genetics , Chromatin/metabolism , Epigenomics , Humans , Leukemia, Myeloid/genetics , Nucleosomes/metabolism , Regulatory Elements, Transcriptional , Transcription Factors/metabolism , Transposases/metabolism
2.
BMC Syst Biol ; 11(1): 1, 2017 01 06.
Article in English | MEDLINE | ID: mdl-28061857

ABSTRACT

BACKGROUND: Enteric Escherichia coli survives the highly acidic environment of the stomach through multiple acid resistance (AR) mechanisms. The most effective system, AR2, decarboxylates externally-derived glutamate to remove cytoplasmic protons and excrete GABA. The first described system, AR1, does not require an external amino acid. Its mechanism has not been determined. The regulation of the multiple AR systems and their coordination with broader cellular metabolism has not been fully explored. RESULTS: We utilized a combination of ChIP-Seq and gene expression analysis to experimentally map the regulatory interactions of four TFs: nac, ntrC, ompR, and csiR. Our data identified all previously in vivo confirmed direct interactions and revealed several others previously inferred from gene expression data. Our data demonstrate that nac and csiR directly modulate AR, and leads to a regulatory network model in which all four TFs participate in coordinating acid resistance, glutamate metabolism, and nitrogen metabolism. This model predicts a novel mechanism for AR1 by which the decarboxylation enzymes of AR2 are used with internally derived glutamate. This hypothesis makes several testable predictions that we confirmed experimentally. CONCLUSIONS: Our data suggest that the regulatory network underlying AR is complex and deeply interconnected with the regulation of GABA and glutamate metabolism, nitrogen metabolism. These connections underlie and experimentally validated model of AR1 in which the decarboxylation enzymes of AR2 are used with internally derived glutamate.


Subject(s)
Escherichia coli/physiology , Protein Interaction Mapping , Computational Biology , Escherichia coli/drug effects , Escherichia coli/genetics , Escherichia coli/metabolism , Escherichia coli Proteins/genetics , Escherichia coli Proteins/metabolism , Gene Expression Profiling , Hydrogen-Ion Concentration , Phenotype
3.
Microbiol Spectr ; 2(2)2014 Apr.
Article in English | MEDLINE | ID: mdl-26105820

ABSTRACT

Transcription factors (TFs) play a central role in regulating gene expression in all bacteria. Yet until recently, studies of TF binding were limited to a small number of factors at a few genomic locations. Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) provides the ability to map binding sites globally for TFs, and the scalability of the technology enables the ability to map binding sites for every DNA binding protein in a prokaryotic organism. We have developed a protocol for ChIP-Seq tailored for use with mycobacteria and an analysis pipeline for processing the resulting data. The protocol and pipeline have been used to map over 100 TFs from Mycobacterium tuberculosis, as well as numerous TFs from related mycobacteria and other bacteria. The resulting data provide evidence that the long-accepted spatial relationship between TF binding site, promoter motif, and the corresponding regulated gene may be too simple a paradigm, failing to adequately capture the variety of TF binding sites found in prokaryotes. In this article we describe the protocol and analysis pipeline, the validation of these methods, and the results of applying these methods to M. tuberculosis.


Subject(s)
Chromatin Immunoprecipitation/methods , DNA/metabolism , Molecular Biology/methods , Mycobacterium tuberculosis/genetics , Sequence Analysis, DNA/methods , Transcription Factors/metabolism , Binding Sites , Computational Biology/methods , Genetics, Microbial/methods , Protein Binding
4.
Nature ; 499(7457): 178-83, 2013 Jul 11.
Article in English | MEDLINE | ID: mdl-23823726

ABSTRACT

We have taken the first steps towards a complete reconstruction of the Mycobacterium tuberculosis regulatory network based on ChIP-Seq and combined this reconstruction with system-wide profiling of messenger RNAs, proteins, metabolites and lipids during hypoxia and re-aeration. Adaptations to hypoxia are thought to have a prominent role in M. tuberculosis pathogenesis. Using ChIP-Seq combined with expression data from the induction of the same factors, we have reconstructed a draft regulatory network based on 50 transcription factors. This network model revealed a direct interconnection between the hypoxic response, lipid catabolism, lipid anabolism and the production of cell wall lipids. As a validation of this model, in response to oxygen availability we observe substantial alterations in lipid content and changes in gene expression and metabolites in corresponding metabolic pathways. The regulatory network reveals transcription factors underlying these changes, allows us to computationally predict expression changes, and indicates that Rv0081 is a regulatory hub.


Subject(s)
Gene Regulatory Networks , Hypoxia/genetics , Metabolic Networks and Pathways/genetics , Mycobacterium tuberculosis/genetics , Mycobacterium tuberculosis/metabolism , Adaptation, Physiological , Bacterial Proteins/genetics , Bacterial Proteins/metabolism , Binding Sites , Chromatin Immunoprecipitation , Gene Expression Profiling , Gene Regulatory Networks/genetics , Genomics , Hypoxia/metabolism , Lipid Metabolism/genetics , Models, Biological , Mycobacterium tuberculosis/drug effects , Mycobacterium tuberculosis/physiology , Oxygen/pharmacology , Proteolysis , RNA, Messenger/genetics , RNA, Messenger/metabolism , Reproducibility of Results , Sequence Analysis, DNA , Transcription Factors/genetics , Transcription Factors/metabolism , Tuberculosis/metabolism , Tuberculosis/microbiology
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