Genome-Wide ChIP-seq and RNA-seq Analyses of STAT3 Target Genes in TLRs Activated Human Peripheral Blood B Cells

Abstract

Toll like receptors (TLRs) induced response plays a vital role in B-cell development and activation, in which TLR7-mediated and TLR9-mediated response interact together and play antagonistic or cooperative roles at different situations. Previous studies showed that the transcription factor signal transducer and activator of transcription (STAT) 3 was one of the key transcriptional factors (TFs) needed for both TLR7 and TLR9 signaling in B cell, and patients with autosomal dominant hyper IgE syndromes (AD-HIES) due to STAT3 mutations having defective TLRs response in B cells. However, how STAT3 affects its target genes and the downstream signaling pathways in B cell upon TLRs stimulation remains unclarified on a genome-wide level. ChIP-seq and RNA-seq was used in this study to identify the STAT3 targets in response to TLRs stimulation in human B cell. STAT3 ChIP-seq results showed a total of 611 and 2,289 differential STAT3-binding sites in human B cell after TLR7 and TLR9 agonists stimulation, respectively. RNA-seq results showed 1,186 and 1,775 differentially expressed genes after TLR7 and TLR9 activation, respectively. We identified 47 primary STAT3 target genes after TLR7 activation and 189 target genes after TLR9 activation in B cell by integration of STAT3 ChIP-seq and RNA-seq data. Among these STAT3 primary targets, we identified 7 TFs and 18 TFs for TLR7 and TLR9 response, respectively. Besides, we showed that STAT3 might regulate TLR9, but not TLR7 response in B cells through directly regulating integrin signaling pathway, which might further affect the antagonism between TLR7 and TLR9 signaling in B cell. Our study provides insights into the molecular mechanism of human TLRs response in B cell and how it can be regulated, which helps to better understand and modulate TLR-mediated pathogenic immune responses in B cell.

The adaptive evolution of cancer driver genes

“…Positive selection was detected in the main primate lineages under the branch-site model using PAML [ 26 , 27 ]. Eight genes covering 11 cancer types were determined to be under positive selection in the human lineage under the branch-site model in PAML (Table 1 ), including the neuronal stress related gene, DROSHA [ 28 ], the immune related gene, LY75-CD302 [ 29 ], the neurodegenerative diseases related gene, RBFOX1 [ 30 ], the neurocognitive related gene, NRG1 [ 31 ], the viral and bacterial response related gene, STAT3 [ 32 ], the brain development related gene, NIN [ 33 ], the zinc finger protein gene, ZNF814, and the immune response and antiviral response related gene, TRAF3 [ 34 ]. Among which, three genes, including NIN, NRG1 and RBFOX1 have been reported under positive selection [ 33 , 35 , 36 ].…”

Mechanisms and biotechnological applications of transcription factors

“…ChIP has proven to be an effective method for identifying potential TFBSs within the genome and has been widely utilized in various species. For instance, it has been used to discover TFBSs for a large population of TFs in yeast [ 66 , 67 ], identify direct targets TFBSs of the Caenorhabditis elegans TF, DAF-16 [ 68 ], and identify the STAT3's TFBSs in human B cell [ 69 ].…”

Supplementary Methods S1 from SPOP Mutations Target STING1 Signaling in Prostate Cancer and Create Therapeutic Vulnerabilities to PARP Inhibitor–Induced Growth Suppression

“…Published STAT3 ChIP-seq datasets were downloaded and analyzed utilizing Integrative Genomics Viewer (IGV) (28)(29)(30)(31)(32)(33)(34). A public database for aggregated analysis of STAT3 ChIP-seq data ENCODE Transcription Factor Targets dataset (https://maayanlab.cloud/Harmonizome/ gene_set/STAT3/ENCODE+Transcription+Factor+Targets) was provided to predict STAT3 targets (35).…”