The Animal Transcription Factor DataBase (AnimalTFDB) is a resource aimed to provide the most comprehensive and accurate information for animal transcription factors (TFs) and cofactors. The AnimalTFDB has been maintained and updated for seven years and we will continue to improve it. Recently, we updated the AnimalTFDB to version 3.0 (http://bioinfo.life.hust.edu.cn/AnimalTFDB/) with more data and functions to improve it. AnimalTFDB contains 125,135 TF genes and 80,060 transcription cofactor genes from 97 animal genomes. Besides the expansion in data quantity, some new features and functions have been added. These new features are: (i) more accurate TF family assignment rules; (ii) classification of transcription cofactors; (iii) TF binding sites information; (iv) the GWAS phenotype related information of human TFs; (v) TF expressions in 22 animal species; (vi) a TF binding site prediction tool to identify potential binding TFs for nucleotide sequences; (vii) a separate human TF database web interface (HumanTFDB) was designed for better utilizing the human TFs. The new version of AnimalTFDB provides a comprehensive annotation and classification of TFs and cofactors, and will be a useful resource for studies of TF and transcription regulation.

Transcription factors (TFs) are proteins that interact with specific DNA sequences to regulate gene expression and play crucial roles in all kinds of biological processes. To keep up with new data and provide a more comprehensive resource for TF research, we updated the Animal Transcription Factor Database (AnimalTFDB) to version 4.0 (http://bioinfo.life.hust.edu.cn/AnimalTFDB4/) with up-to-date data and functions. We refined the TF family rules and prediction pipeline to predict TFs in genome-wide protein sequences from Ensembl. As a result, we predicted 274 633 TF genes and 150 726 transcription cofactor genes in AnimalTFDB 4.0 in 183 animal genomes, which are 86 more species than AnimalTFDB 3.0. Besides double data volume, we also added the following new annotations and functions to the database: (i) variations (including mutations) on TF genes in various human cancers and other diseases; (ii) predicted post-translational modification sites (including phosphorylation, acetylation, methylation and ubiquitination sites) on TFs in 8 species; (iii) TF regulation in autophagy; (iv) comprehensive TF expression annotation for 38 species; (v) exact and batch search functions allow users to search AnimalTFDB flexibly. AnimalTFDB 4.0 is a useful resource for studying TF and transcription regulation, which contains comprehensive annotation and classification of TFs and transcription cofactors.

Immune checkpoint blockade (ICB) therapy provides remarkable clinical benefits for multiple cancer types. Much work is currently being conducted to investigate the mechanisms of ICB therapy at the transcriptional level. Integrating the data produced by these studies will help us give more insight into the transcriptomic features of ICB therapy. We collected the transcriptome and clinical data of ICB-treated patient samples from the Gene Expression Omnibus, ArrayExpress, The Cancer Genome Atlas, and dbGaP databases. On the basis of the clinical information, all samples are initially classified into response/nonresponse or pretreatment/on-treatment groups. Differential expression, pathway enrichment, and immune cell infiltration analyses are performed between the samples from different groups. We also introduce the Response Score (RS) calculated by integrating the variability degree and the frequency of the dysregulated genes in the responders to evaluate the impact of gene expression on the response. Finally, all the abovementioned contents are integrated into the ICBatlas database. ICBatlas provides the transcriptome features of ICB therapy through the analysis of 1,515 ICB-treated samples from 25 studies across nine cancer types. The data in ICBatlas include clinical outcomes, treatment-related genes, biological pathways, and immune cell infiltration. Users can investigate the abovementioned transcriptome features in the response (R vs. NR) or treatment (Pre vs. On) modules at the data set, cancer type, or immune checkpoint level and compare the degree of gene impact on the response in the RS module. ICBatlas is the first database to show the transcriptome features on ICB therapy in human cancers and freely available at http://bioinfo.life.hust.edu.cn/ICBatlas/.

Chimeric antigen receptor (CAR) T cell therapy has exhibited dramatic anti-tumor efficacy in clinical trials. In this study, we reported the transcriptome profiles of bone marrow cells in four B cell acute lymphoblastic leukemia (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 microRNAs (e.g., miR-148a-3p and miR-375), acting as oncogenes or tumor suppressors, could regulate the crosstalk between the genes encoding transcription factors (TFs; e.g., JUN and FOS) and histones (e.g., HIST1H4A and HIST2H4A) involved in CD19-CAR-T therapy. Furthermore, many long non-coding RNAs showed a high degree of co-expression with TFs or histones (e.g., FOS and HIST1H4B) 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.

Myeloid differentiation primary response protein 88 (MYD88) is a critical universal adapter that transduces signaling from Toll-like and interleukin receptors to downstream nuclear factor-κB (NF-κB). MYD88L265P (leucine changed to proline at position 265) is a gain-of-function mutation that occurs frequently in B-cell malignancies such as Waldenstrom macroglobulinemia. In this study, E3 ligase RING finger protein family 138 (RNF138) catalyzed K63-linked nonproteolytic polyubiquitination of MYD88L265P, resulting in enhanced recruitment of interleukin-1 receptor–associated kinases and elevated NF-κB activation. However, RNF138 had little effect on wild-type MYD88 (MYD88WT). With either RNF138 knockdown or mutation on MYD88 ubiquitination sites, MYD88L265P did not constitutively activate NF-κB. A20, a negative regulator of NF-κB signaling, mediated K48-linked polyubiquitination of RNF138 for proteasomal degradation. Depletion of A20 further augmented MYD88L265P-mediated NF-κB activation and lymphoma growth. Furthermore, A20 expression correlated negatively with RNF138 expression and NF-κB activation in lymphomas with MYD88L265P and in those without. Strikingly, RNF138 expression correlated positively with NF-κB activation in lymphomas with MYD88L265P, but not in those without it. Our study revealed a novel mutation-specific biochemical reaction that drives B-cell oncogenesis, providing a therapeutic opportunity for targeting oncogenic MYD88L265P, while sparing MYD88WT, which is critical to innate immunity.

Tumor-repopulating cells (TRCs) are cancer stem cell (CSC)-like cells with highly tumorigenic and self-renewing abilities, which were selected from tumor cells in soft three-dimensional (3D) fibrin gels with unidentified mechanisms. Here we evaluated the transcriptome alteration during TRCs generation in 3D culture and revealed that a variety of molecules related with integrin/membrane and stemness were continuously altered by mechanical environment. Some key regulators such as MYC/STAT3/hsa-miR-199a-5p, were changed in the TRCs generation. They regulated membrane genes and the downstream mechanotransduction pathways such as Hippo/WNT/TGF-β/PI3K-AKT pathways, thus further affecting the expression of downstream cancer-related genes. By integrating networks for membrane proteins, the WNT pathway and cancer-related genes, we identified key molecules in the selection of TRCs, such as ATF4, SLC3A2, CCT3, and hsa-miR-199a-5p. Silencing ATF4 or CCT3 inhibited the selection and growth of TRCs whereas reduction of SLC3A2 or hsa-miR-199a-5p promoted TRCs growth. Further studies showed that CCT3 promoted cell proliferation and stemness in vitro, while its suppression inhibited TRCs-induced tumor formation. We also contemplated CCT3 as a stemness-related gene. Our findings provide insights in the mechanism of TRCs selection through transcriptome analysis.