A network medicine approach to build a comprehensive atlas for the prognosis of human cancer

The Cancer Genome Atlas project has generated multi-dimensional and highly integrated genomic data from a large number of patient samples with detailed clinical records across many cancer types, but it remains unclear how to best integrate the massive amount of genomic data into clinical practice. We report here our methodology to build a multi-dimensional subnetwork atlas for cancer prognosis to better investigate the potential impact of multiple genetic and epigenetic (gene expression, copy number variation, microRNA expression and DNA methylation) changes on the molecular states of networks that in turn affects complex cancer survivorship. We uncover an average of 38 novel subnetworks in the protein-protein interaction network that correlate with prognosis across four prominent cancer types. The clinical utility of these subnetwork biomarkers was further evaluated by prognostic impact evaluation, functional enrichment analysis, drug target annotation, tumor stratification and independent validation. Some pathways including the dynactin, cohesion and pyruvate dehydrogenase-related subnetworks are identified as promising new targets for therapy in specific cancer types. In conclusion, this integrative analysis of existing protein interactome and cancer genomics data allows us to systematically dissect the molecular mechanisms that underlie unexpected outcomes for cancer, which could be used to better understand and predict clinical outcomes, optimize treatment and to provide new opportunities for developing therapeutics related to the subnetworks identified

Distinct Roles of Brd2 and Brd4 in Potentiating the Transcriptional Program for Th17 Cell Differentiation

The BET proteins are major transcriptional regulators and have emerged as new drug targets, but their functional distinction has remained elusive. In this study, we report that the BET family members Brd2 and Brd4 exert distinct genomic functions at genes whose transcription they co-regulate during mouse T-helper 17 (Th17) cell differentiation. Brd2 is associated with the chromatin insulator CTCF and the cohesin complex to support cis-regulatory enhancer assembly for gene transcriptional activation. In this context, Brd2 binds the transcription factor Stat3 in an acetylation-sensitive manner and facilitates Stat3 recruitment to active enhancers occupied with transcription factors Irf4 and Batf. In parallel, Brd4 temporally controls RNA polymerase II (Pol II) processivity during transcription elongation through cyclinT1/Cdk9 recruitment and Pol II Ser2 phosphorylation. Collectively, our study uncovers both separate and interdependent Brd2 and Brd4 functions in potentiating the genetic program required for Th17 cell development and adaptive immunity., , Cheung et al. uncover both separate and interdependent Brd2 and Brd4 genomic functions in potentiating the genetic program required for Th17 cell development and adaptive immunity. Brd2 interacts with transcription factor Stat3 and chromatin insulator CTCF/cohesin complex to support enhancer assembly, whereas Brd4 temporally controls RNA PolII for transcription elongation

5-Fluorouracil targets thymidylate synthase in the selective suppression of TH17 cell differentiation

While it is well established that treatment of cancer patients with 5-Fluorouracil (5-FU) can result in immune suppression, the exact function of 5-FU in the modulation of immune cells has not been fully established. We found that low dose 5-FU selectively suppresses TH17 and TH1 cell differentiation without apparent effect on Treg, TH2, and significantly suppresses thymidylate synthase (TS) expression in TH17 and TH1 cells but has a lesser effect in tumor cells and macrophages. Interestingly, the basal expression of TS varies significantly between T helper phenotypes and knockdown of TS significantly impairs TH17 and TH1 cell differentiation without affecting the differentiation of either Treg or TH2 cells. Finally, low dose 5-FU is effective in ameliorating colitis development by suppressing TH17 and TH1 cell development in a T cell transfer colitis model. Taken together, the results highlight the importance of the anti-inflammatory functions of low dose 5-FU by selectively suppressing TH17 and TH1 immune responses

T cell–derived inducible nitric oxide synthase switches off TH17 cell differentiation

RORγt is necessary for the generation of TH17 cells but the molecular mechanisms for the regulation of TH17 cells are still not fully understood. We show that activation of CD4+ T cells results in the expression of inducible nitric oxide synthase (iNOS). iNOS-deficient mice displayed enhanced TH17 cell differentiation but without major effects on either TH1 or TH2 cell lineages, whereas endothelial NOS (eNOS) or neuronal NOS (nNOS) mutant mice showed comparable TH17 cell differentiation compared with wild-type control mice. The addition of N6-(1-iminoethyl)-l-lysine dihydrochloride (L-NIL), the iNOS inhibitor, significantly enhanced TH17 cell differentiation, and S-nitroso-N-acetylpenicillamine (SNAP), the NO donor, dose-dependently reduced the percentage of IL-17–producing CD4+ T cells. NO mediates nitration of tyrosine residues in RORγt, leading to the suppression of RORγt-induced IL-17 promoter activation, indicating that NO regulates IL-17 expression at the transcriptional level. Finally, studies of an experimental model of colitis showed that iNOS deficiency results in more severe inflammation with an enhanced TH17 phenotype. These results suggest that NO derived from iNOS in activated T cells plays a negative role in the regulation of TH17 cell differentiation and highlight the importance of intrinsic programs for the control of TH17 immune responses

GM-CSF Controls Nonlymphoid Tissue Dendritic Cell Homeostasis but Is Dispensable for the Differentiation of Inflammatory Dendritic Cells

SummaryGM-CSF (Csf-2) is a critical cytokine for the in vitro generation of dendritic cells (DCs) and is thought to control the development of inflammatory DCs and resident CD103+ DCs in some tissues. Here we showed that in contrast to the current understanding, Csf-2 receptor acts in the steady state to promote the survival and homeostasis of nonlymphoid tissue-resident CD103+ and CD11b+ DCs. Absence of Csf-2 receptor on lung DCs abrogated the induction of CD8+ T cell immunity after immunization with particulate antigens. In contrast, Csf-2 receptor was dispensable for the differentiation and innate function of inflammatory DCs during acute injuries. Instead, inflammatory DCs required Csf-1 receptor for their development. Thus, Csf-2 is important in vaccine-induced CD8+ T cell immunity through the regulation of nonlymphoid tissue DC homeostasis rather than control of inflammatory DCs in vivo

Single-nucleus RNA sequencing of human pancreatic islets identifies novel gene sets and distinguishes β-cell subpopulations with dynamic transcriptome profiles

Abstract Background Single-cell RNA sequencing (scRNA-seq) provides valuable insights into human islet cell types and their corresponding stable gene expression profiles. However, this approach requires cell dissociation that complicates its utility in vivo. On the other hand, single-nucleus RNA sequencing (snRNA-seq) has compatibility with frozen samples, elimination of dissociation-induced transcriptional stress responses, and affords enhanced information from intronic sequences that can be leveraged to identify pre-mRNA transcripts. Methods We obtained nuclear preparations from fresh human islet cells and generated snRNA-seq datasets. We compared these datasets to scRNA-seq output obtained from human islet cells from the same donor. We employed snRNA-seq to obtain the transcriptomic profile of human islets engrafted in immunodeficient mice. In both analyses, we included the intronic reads in the snRNA-seq data with the GRCh38-2020-A library. Results First, snRNA-seq analysis shows that the top four differentially and selectively expressed genes in human islet endocrine cells in vitro and in vivo are not the canonical genes but a new set of non-canonical gene markers including ZNF385D, TRPM3, LRFN2, PLUT (β-cells); PTPRT, FAP, PDK4, LOXL4 (α-cells); LRFN5, ADARB2, ERBB4, KCNT2 (δ-cells); and CACNA2D3, THSD7A, CNTNAP5, RBFOX3 (γ-cells). Second, by integrating information from scRNA-seq and snRNA-seq of human islet cells, we distinguish three β-cell sub-clusters: an INS pre-mRNA cluster (β3), an intermediate INS mRNA cluster (β2), and an INS mRNA-rich cluster (β1). These display distinct gene expression patterns representing different biological dynamic states both in vitro and in vivo. Interestingly, the INS mRNA-rich cluster (β1) becomes the predominant sub-cluster in vivo. Conclusions In summary, snRNA-seq and pre-mRNA analysis of human islet cells can accurately identify human islet cell populations, subpopulations, and their dynamic transcriptome profile in vivo