Integration Analysis Of Human Transcription Factors And Their Regulations In CAR-T Therapy

Transcription factor(TF)is a type of protein that regulates gene transcription by binding specific DNA sequences.As one of the most important regulatory factors in the regulation of gene expression,TFs involved in all kinds of biological processes of normal and diseases.The identification and annotation of TF are the basis for studying transcriptional regulation.There are about 1,600 TF genes in human genome.Accurate identification of human TF and systematic analysis of their target genes are helpful to further study TF-mediated transcriptional regulation.TFs and miRNAs as the main regulatory factors can form a feedforward loop or a feedback loop to jointly regulate the expression of target genes.This co-regulatory network has been found in a variety of cancers and diseases.Chimeric antigen receptor T-cell(CAR-T)immunotherapy has achieved significant progress in hematological diseases,especially in B-cell acute lymphoblastic leukemia(B-ALL).However,its therapeutic mechanism and regulatory factors are still unclear.In this study,the genome-wide TF and TF cofactor genes among nearly one hundred animal species were systematically identified.After detailed classification and annotation,an animal transcription factors database(Animal TFDB)was constructed.After that,we systematically analyzed all human TFs in terms of expression,regulation,mutation,and survival.Combined with transcriptome data and TF-miRNA co-regulatory network,we analyzed the transcriptome profiles and potential regulatory modules and key regulatory factors of BALL patients before and after infusion of CAR-T.The main results are as follows:First,this study collected all human TF families through literature search and manual proofreading.Hidden Markov models were constructed based on the conserved DNA binding domains(DBDs)for each TF family.These models were used to predict TF for all other mammalian species with whole genome-wide sequences.At the same time,TF cofactors were collected and classified according to their functions,and two-way optimal matching BLAST was used to find TF cofactor homologous genes in other remaining species.Using these data,we constructed an animal transcription factor database Animal TFDB3.0(http://bioinfo.life.hust.edu.cn/Animal TFDB/).Animal TFDB 3.0 contains125,135 TF genes and 80,060 transcription cofactor genes from 97 animal genomes.The database covers families,expression,pathway,phenotype,protein interaction,transcription factor binding site and other annotated information of TFs and cofactors.In addition,it also provides multiple search and browse methods(by family or species search or custom search),two online prediction tools "Predict TF" and "Predict TFBS"(predict the transcription factors and the transcription factor binding site on DNA sequences in batches,respectively),BLAST tools,and data download functions.Animal TFDB3.0 provides comprehensive annotation and classification of TF and cofactors,and will be a useful resource for studying TF and transcriptional regulation.Secondly,this study conducted a systematic analysis of human TFs.We revealed the overall expression trends of all human TFs in normal tissues and cancers.Specifically expressed TFs in a single tissue or cancer may be potential marker genes.Combining with the TF families,we found the difference in the distribution of target genes and co-regulation among different TF families.Then,we found that some special TF families with a small number of TFs have a large number of protein interaction pairs,suggesting their central role in transcriptional regulation.The contributions of different TF families to diseases are quite different.Although there are not many TFs in the TF?b ZIP family,it is the top TF family that enriched to the most pathways.Survival analysis shows that one-third of the TFs with significant prognosis in a single cancer are specifically highly expressed TFs,speculating that these TFs may be potential therapeutic targets for cancer.Finally,we found that 43 TFs whose mutations are closely related to survival,suggesting that they may be cancer-driven TFs.The systematic analysis of TF provides useful clues for further study of TF regulation mechanism.Final,based on transcription factor data,we reported the transcriptome profiles of bone marrow cells in four B-ALL patients before and after CD19-specific CAR-T therapy.CD19-CAR-T therapy remarkably reduced the number of leukemia cells,and three patients achieved bone marrow remission(minimal residual disease negative).The efficacy of CD19-CAR-T therapy on B-ALL was positively correlated with the abundance of CAR and immune cell subpopulations,e.g.,CD8+ T cells and natural killer(NK)cells,in the bone marrow.Additionally,CD19-CAR-T therapy mainly influenced the expression of genes linked to cell cycle and immune response pathways,including the NK cell mediated cytotoxicity and NOD-like receptor signaling pathways.The regulatory network analyses revealed that miRNAs(e.g.,miR-148a-3p and miR-375),acting as oncogenes or tumor suppressors,could regulate the crosstalk between the genes encoding TFs(e.g.,JUN and FOS)and histones involved in CD19-CAR-T therapy.Furthermore,many long non-coding RNAs showed a high degree of co-expression with TFs or histones and were associated with immune processes.These transcriptome analyses provided important clues for further understanding the gene expression and related mechanisms underlying the efficacy of CAR-T immunotherapy.