Regulated transcription controls the diversity, developmental pathways and spatial organization of the hundreds of cell types that make up a mammal. Using single-molecule cDNA sequencing, we mapped transcription start sites (TSSs) and their usage in human and mouse primary cells, cell lines and tissues to produce a comprehensive overview of mammalian gene expression across the human body. We find that few genes are truly ‘housekeeping’, whereas many mammalian promoters are composite entities composed of several closely separated TSSs, with independent cell-type-specific expression profiles. TSSs specific to different cell types evolve at different rates, whereas promoters of broadly expressed genes are the most conserved. Promoter-based expression analysis reveals key transcription factors defining cell states and links them to binding-site motifs. The functions of identified novel transcripts can be predicted by coexpression and sample ontology enrichment analyses. The functional annotation of the mammalian genome 5 (FANTOM5) project provides comprehensive expression profiles and functional annotation of mammalian cell-type-specific transcriptomes with wide applications in biomedical research.

SignificanceDespite concerted efforts to identify causal genes that drive breast cancer (BC) initiation and progression, we have yet to establish robust signatures to stratify patient risk. Here we used in vivo transposon-based forward genetic screening to identify potentially relevant BC driver genes. Integrating this approach with survival prediction analysis, we identified six gene pairs that could prognose human BC subtypes into high-, intermediate-, and low-risk groups with high confidence and reproducibility. Furthermore, we identified susceptibility gene sets for basal and claudin-low subtypes (21 and 16 genes, respectively) that stratify patients into three relative risk subgroups. These signatures offer valuable prognostic insight into the genetic basis of BC and allow further exploration of the interconnectedness of BC driver genes during disease progression.

Background: Heart failure (HF) is the most common long-term complication of acute myocardial infarction (MI). Understanding plasma proteins associated with post-MI HF and their gene expression may identify new candidates for biomarker and drug target discovery. Methods: We employed aptamer-based affinity-capture plasma proteomics to measure 1305 plasma proteins at one month post-MI in a New Zealand cohort (CDCS) including 181 post-MI patients who were subsequently hospitalized for HF compared with 250 post-MI patients who remained event-free over a median follow-up of 4.9 years. We then correlated plasma proteins with left ventricular ejection fraction measured at 4 months post-MI and identified proteins potentially co-regulated in post-MI HF using Weighted Gene Co-expression Network Analysis (WCGNA). A Singapore cohort (IMMACULATE) of 223 post-MI patients, of which 33 patients were hospitalized for HF (median follow-up 2.0 years), was used for further candidate enrichment of plasma proteins using Fisher meta-analysis, resampling-based statistical testing and machine learning. We then cross-referenced differentially-expressed proteins with their differentially-expressed genes from single-cell transcriptomes of non-myocyte cardiac cells isolated from a murine MI model, and single-cell and single-nuclei transcriptomes of cardiac myocytes from murine HF models and human HF patients. Results: In the CDCS cohort, 212 differentially-expressed plasma proteins were significantly associated with subsequent HF events. Of these, 96 correlated with left ventricular ejection fraction measured at 4 months post-MI. WCGNA prioritised 63 of the 212 proteins that demonstrated significantly higher correlations among patients who developed post-MI HF compared with event-free controls (dataset 1). Cross-cohort meta-analysis of the IMMACULATE cohort identified 36 plasma proteins associated with post-MI HF (dataset 2) while single-cell transcriptomes identified 15 gene-protein candidates (dataset 3). The majority of prioritized proteins were of matricellular origin. The 6 most highly-enriched proteins that were common to all 3 datasets included well-established biomarkers of post-MI HF - N-terminal B-type natriuretic peptide and troponin T - as well as newly-emergent biomarkers - angiopoietin-2, thrombospondin-2, latent transforming growth factor-β binding protein-4 and follistatin-related protein-3. Conclusions: Large-scale human plasma proteomics, cross-referenced to unbiased cardiac cell transcriptomics at single-cell resolution, prioritized protein candidates associated with post-MI HF for further mechanistic and clinical validation.

Background: The human genome folds in 3 dimensions to form thousands of chromatin loops inside the nucleus, encasing genes and cis -regulatory elements for accurate gene expression control. Physical tethers of loops are anchored by the DNA-binding protein CTCF and the cohesin ring complex. Because heart failure is characterized by hallmark gene expression changes, it was recently reported that substantial CTCF-related chromatin reorganization underpins the myocardial stress–gene response, paralleled by chromatin domain boundary changes observed in CTCF knockout. Methods: We undertook an independent and orthogonal analysis of chromatin organization with mouse pressure-overload model of myocardial stress (transverse aortic constriction) and cardiomyocyte-specific knockout of Ctcf . We also downloaded published data sets of similar cardiac mouse models and subjected them to independent reanalysis. Results: We found that the cardiomyocyte chromatin architecture remains broadly stable in transverse aortic constriction hearts, whereas Ctcf knockout resulted in ≈99% abolition of global chromatin loops. Disease gene expression changes correlated instead with differential histone H3K27-acetylation enrichment at their respective proximal and distal interacting genomic enhancers confined within these static chromatin structures. Moreover, coregulated genes were mapped out as interconnected gene sets on the basis of their multigene 3D interactions. Conclusions: This work reveals a more stable genome-wide chromatin framework than previously described. Myocardial stress–gene transcription responds instead through H3K27-acetylation enhancer enrichment dynamics and gene networks of coregulation. Robust and intact CTCF looping is required for the induction of a rapid and accurate stress response.