Congenital heart disease (CHD) affects up to 1 % of live births1. Although a genetic etiology is indicated by an increased recurrence risk2,3, sporadic occurrence suggests that CHD genetics is complex4. Here, we show that hypoplastic left heart syndrome (HLHS), a severe CHD, is multigenic and genetically heterogeneous. Using mouse forward genetics, we report what is, to our knowledge, the first isolation of HLHS mutant mice and identification of genes causing HLHS. Mutations from seven HLHS mouse lines showed multigenic enrichment in ten human chromosome regions linked to HLHS5–7. Mutations in Sap130 and Pcdha9, genes not previously associated with CHD, were validated by CRISPR–Cas9 genome editing in mice as being digenic causes of HLHS. We also identified one subject with HLHS with SAP130 and PCDHA13 mutations. Mouse and zebrafish modeling showed that Sap130 mediates left ventricular hypoplasia, whereas Pcdha9 increases penetrance of aortic valve abnormalities, both signature HLHS defects. These findings show that HLHS can arise genetically in a combinatorial fashion, thus providing a new paradigm for the complex genetics of CHD.

Motivation Single-cell RNA sequencing (scRNA-seq) technologies enable the study of transcriptional heterogeneity at the resolution of individual cells and have an increasing impact on biomedical research. However, it is known that these methods sometimes wrongly consider two or more cells as single cells, and that a number of so-called doublets is present in the output of such experiments. Treating doublets as single cells in downstream analyses can severely bias a study’s conclusions, and therefore computational strategies for the identification of doublets are needed. Results With scds, we propose two new approaches for in silico doublet identification: Co-expression based doublet scoring (cxds) and binary classification based doublet scoring (bcds). The co-expression based approach, cxds, utilizes binarized (absence/presence) gene expression data and, employing a binomial model for the co-expression of pairs of genes, yields interpretable doublet annotations. bcds, on the other hand, uses a binary classification approach to discriminate artificial doublets from original data. We apply our methods and existing computational doublet identification approaches to four datasets with experimental doublet annotations and find that our methods perform at least as well as the state of the art, at comparably little computational cost. We observe appreciable differences between methods and across datasets and that no approach dominates all others. In summary, scds presents a scalable, competitive approach that allows for doublet annotation of datasets with thousands of cells in a matter of seconds. Availability and implementation scds is implemented as a Bioconductor R package (doi: 10.18129/B9.bioc.scds). Supplementary information Supplementary data are available at Bioinformatics online.

One million patients with congenital heart disease (CHD) live in the United States. They have a lifelong risk of developing heart failure. Current concepts do not sufficiently address mechanisms of heart failure development specifically for these patients. Here, analysis of heart tissue from an infant with tetralogy of Fallot with pulmonary stenosis (ToF/PS) labeled with isotope-tagged thymidine demonstrated that cardiomyocyte cytokinesis failure is increased in this common form of CHD. We used single-cell transcriptional profiling to discover that the underlying mechanism of cytokinesis failure is repression of the cytokinesis gene ECT2, downstream of β-adrenergic receptors (β-ARs). Inactivation of the β-AR genes and administration of the β-blocker propranolol increased cardiomyocyte division in neonatal mice, which increased the number of cardiomyocytes (endowment) and conferred benefit after myocardial infarction in adults. Propranolol enabled the division of ToF/PS cardiomyocytes in vitro. These results suggest that β-blockers could be evaluated for increasing cardiomyocyte division in patients with ToF/PS and other types of CHD.

BackgroundTransforming growth factor beta 1 (TGFβ1) plays a major role in many lung diseases including lung cancer, pulmonary hypertension, and pulmonary fibrosis. TGFβ1 activates a signal transduction cascade that results in the transcriptional regulation of genes in the nucleus, primarily through the DNA-binding transcription factor SMAD3. The objective of this study is to identify genome-wide scale map of SMAD3 binding targets and the molecular pathways and networks affected by the TGFβ1/SMAD3 signaling in lung epithelial cells.MethodologyWe combined chromatin immunoprecipitation with human promoter region microarrays (ChIP-on-chip) along with gene expression microarrays to study global transcriptional regulation of the TGFβ1/SMAD3 pathway in human A549 alveolar epithelial cells. The molecular pathways and networks associated with TGFβ1/SMAD3 signaling were identified using computational approaches. Validation of selected target gene expression and direct binding of SMAD3 to promoters were performed by quantitative real time RT-PCR and electrophoretic mobility shift assay on A549 and human primary lung epithelial cells.Results and ConclusionsKnown TGFβ1 target genes such as SERPINE1, SMAD6, SMAD7, TGFB1 and LTBP3, were found in both ChIP-on-chip and gene expression analyses as well as some previously unrecognized targets such as FOXA2. SMAD3 binding of FOXA2 promoter and changed expression were confirmed. Computational approaches combining ChIP-on-chip and gene expression microarray revealed multiple target molecular pathways affected by the TGFβ1/SMAD3 signaling. Identification of global targets and molecular pathways and networks associated with TGFβ1/SMAD3 signaling allow for a better understanding of the mechanisms that determine epithelial cell phenotypes in fibrogenesis and carcinogenesis as does the discovery of the direct effect of TGFβ1 on FOXA2.

Motivation: Single cell RNA sequencing (scRNA-seq) technologies enable the study of transcriptional heterogeneity at the resolution of individual cells and have an increasing impact on biomedical research. Specifically, high-throughput approaches that employ micro-fluidics in combination with unique molecular identifiers (UMIs) are capable of assaying many thousands of cells per experiment and are rapidly becoming commonplace. However, it is known that these methods sometimes wrongly consider two or more cells as single cells, and that a number of so-called doublets is present in the output of such experiments. Treating doublets as single cells in downstream analyses can severely bias a study's conclusions, and therefore computational strategies for the identification of doublets are needed. Here we present single cell doublet scoring (scds), a software tool for the in silico identification of doublets in scRNA-seq data.Results: With scds, we propose two new and complementary approaches for doublet identification: Co-expression based doublet scoring (cxds) and binary classification based doublet scoring (bcds). The co-expression based approach, cxds, utilizes binarized (absence/presence) gene expression data and employs a binomial model for the co-expression of pairs of genes and yields interpretable doublet annotations. bcds, on the other hand, uses a binary classification approach to discriminate artificial doublets from the original data. We apply our methods and existing doublet identification approaches to four data sets with experimental doublet annotations and find that our methods perform at least as well as the state of the art, but at comparably little computational cost. We also find appreciable differences between methods and across data sets, that no approach dominates all others, and we believe there is room for improvement in computational doublet identification as more data with experimental annotations becomes available. In the meanwhile, scds presents a scalable, competitive approach that allows for doublet annotations in thousands of cells in a matter of seconds.Availability and Implementation: scds is implemented as an R package and freely available at https://github.com/ kostkalab/scds.