Molecular MicrobiologyMicrobiologyPeer-reviewed journal article
Genome-wide analysis of in vivo CcpA binding with and without its key co-factor HPr in the major human pathogen group A Streptococcus.
S. DebRoy, V. A. Tobar, G. Galvez, S. Arora, X. Liang, N. M. Horstmann, V. Maracaja-Coutinho, M. Latorre, M. Hook, A. R. Flores, S. Shelburne
Abstract
Catabolite control protein A (CcpA) is a master regulator of carbon source utilization and contributes to the virulence of numerous medically important Gram-positive bacteria. Most functional assessments of CcpA, including interaction with its key co-factor HPr, have been performed in non-pathogenic bacteria. In this study we aimed to identify the in vivo DNA binding profile of CcpA and assess the extent to which HPr is required for CcpA-mediated regulation and DNA binding in the major human pathogen group A Streptococcus (GAS). Using a combination RNAseq/ChIPseq approach, we found that CcpA affects transcript levels of 514 of 1667 GAS genes (31%) whereas direct DNA binding was identified for 105 GAS genes. Three of the directly regulated genes encode the key GAS virulence factors Streptolysin S, PrtS (IL-8 degrading proteinase), and SpeB (cysteine protease). Mutating CcpA Val301 to Ala (strain 2221-CcpA-V301A) abolished interaction between CcpA and HPr and impacted the transcript levels of 205 genes (40%) in the total CcpA regulon. By ChIPseq analysis, CcpAV301A bound to DNA from 74% of genes bound by wild-type CcpA, but generally with lower affinity. These data delineate the direct CcpA regulon and clarify the HPr-dependent and independent activities of CcpA in a key pathogenic bacterium. Data sharing and data availabilityThe data that support the findings of this study are available from the corresponding author upon reasonable request.
- organism: bacteria (other)
- assay: ChIP-seq
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: variant calling
- approach: experimental assay
- approach: statistical model
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