Rationale: The heart undergoes dramatic developmental changes during the prenatal to postnatal transition, including maturation of cardiac myocyte energy metabolic and contractile machinery. Delineation of the mechanisms involved in cardiac postnatal development could provide new insight into the fetal shifts that occur in the diseased heart and unveil strategies for driving maturation of stem cell–derived cardiac myocytes. Objective: To delineate transcriptional drivers of cardiac maturation. Methods and Results: We hypothesized that ERR (estrogen-related receptor) α and γ, known transcriptional regulators of postnatal mitochondrial biogenesis and function, serve a role in the broader cardiac maturation program. We devised a strategy to knockdown the expression of ERRα and γ in heart after birth (pn-csERRα/γ [postnatal cardiac-specific ERRα/γ]) in mice. With high levels of knockdown, pn-csERRα/γ knockdown mice exhibited cardiomyopathy with an arrest in mitochondrial maturation. RNA sequence analysis of pn-csERRα/γ knockdown hearts at 5 weeks of age combined with chromatin immunoprecipitation with deep sequencing and functional characterization conducted in human induced pluripotent stem cell–derived cardiac myocytes (hiPSC-CM) demonstrated that ERRγ activates transcription of genes involved in virtually all aspects of postnatal developmental maturation, including mitochondrial energy transduction, contractile function, and ion transport. In addition, ERRγ was found to suppress genes involved in fibroblast activation in hearts of pn-csERRα/γ knockdown mice. Disruption of Esrra and Esrrg in mice during fetal development resulted in perinatal lethality associated with structural and genomic evidence of an arrest in cardiac maturation, including persistent expression of early developmental and noncardiac lineage gene markers including cardiac fibroblast signatures. Lastly, targeted deletion of ESRRA and ESRRG in hiPSC-CM derepressed expression of early (transcription factor 21 or TCF21) and mature (periostin, collagen type III) fibroblast gene signatures. Conclusions: ERRα and γ are critical regulators of cardiac myocyte maturation, serving as transcriptional activators of adult cardiac metabolic and structural genes, an.d suppressors of noncardiac lineages including fibroblast determination.

Background: Although many computational methods have been developed to predict protein subcellular localization, most of the methods are limited to the prediction of single-location proteins. Multi-location proteins are either not considered or assumed not existing. However, proteins with multiple locations are particularly interesting because they may have special biological functions, which are essential to both basic research and drug discovery. Results: This paper proposes an efficient multi-label predictor, namely mGOASVM, for predicting the subcellular localization of multi-location proteins. Given a protein, the accession numbers of its homologs are obtained via BLAST search. Then, the original accession number and the homologous accession numbers of the protein are used as keys to search against the Gene Ontology (GO) annotation database to obtain a set of GO terms. Given a set of training proteins, a set of T relevant GO terms is obtained by finding all of the GO terms in the GO annotation database that are relevant to the training proteins. These relevant GO terms then form the basis of a T-dimensional Euclidean space on which the GO vectors lie. A support vector machine (SVM) classifier with a new decision scheme is proposed to classify the multi-label GO vectors. The mGOASVM predictor has the following advantages: (1) it uses the frequency of occurrences of GO terms for feature representation; (2) it selects the relevant GO subspace which can substantially speed up the prediction without compromising performance; and (3) it adopts an efficient multi-label SVM classifier which significantly outperforms other predictors. Briefly, on two recently published virus and plant datasets, mGOASVM achieves an actual accuracy of 88.9% and 87.4%, respectively, which are significantly higher than those achieved by the state-of-the-art predictors such as iLoc-Virus (74.8%) and iLoc-Plant (68.1%). Conclusions: mGOASVM can efficiently predict the subcellular locations of multi-label proteins. The mGOASVM predictor is available online athttp://bioinfo.eie.polyu.edu.hk/mGoaSvmServer/mGOASVM.html.

To process large-scale single-cell RNA-sequencing (scRNA-seq) data effectively without excessive distortion during dimension reduction, we present SHARP, an ensemble random projection-based algorithm that is scalable to clustering 10 million cells. Comprehensive benchmarking tests on 17 public scRNA-seq data sets show that SHARP outperforms existing methods in terms of speed and accuracy. Particularly, for large-size data sets (more than 40,000 cells), SHARP runs faster than other competitors while maintaining high clustering accuracy and robustness. To the best of our knowledge, SHARP is the only R-based tool that is scalable to clustering scRNA-seq data with 10 million cells.

Degradation of RBM39 by indisulam leads to exceptional response in high-risk neuroblastoma.

Protein subcellular localization prediction, as an essential step to elucidate the functions in vivo of proteins and identify drugs targets, has been extensively studied in previous decades. Instead of only determining subcellular localization of single-label proteins, recent studies have focused on predicting both single- and multi-location proteins. Computational methods based on Gene Ontology (GO) have been demonstrated to be superior to methods based on other features. However, existing GO-based methods focus on the occurrences of GO terms and disregard their relationships. This paper proposes a multi-label subcellular-localization predictor, namely HybridGO-Loc, that leverages not only the GO term occurrences but also the inter-term relationships. This is achieved by hybridizing the GO frequencies of occurrences and the semantic similarity between GO terms. Given a protein, a set of GO terms are retrieved by searching against the gene ontology database, using the accession numbers of homologous proteins obtained via BLAST search as the keys. The frequency of GO occurrences and semantic similarity (SS) between GO terms are used to formulate frequency vectors and semantic similarity vectors, respectively, which are subsequently hybridized to construct fusion vectors. An adaptive-decision based multi-label support vector machine (SVM) classifier is proposed to classify the fusion vectors. Experimental results based on recent benchmark datasets and a new dataset containing novel proteins show that the proposed hybrid-feature predictor significantly outperforms predictors based on individual GO features as well as other state-of-the-art predictors. For readers' convenience, the HybridGO-Loc server, which is for predicting virus or plant proteins, is available online at http://bioinfo.eie.polyu.edu.hk/HybridGoServer/.