Cohenbuch5961

Genetic alterations in these pathways result in expansion of FAS-controlled T cells, which can cause significant lymphoproliferative disease.Accurate prediction of gene regulatory rules is important towards understanding of cellular processes. Existing computational algorithms devised for bulk transcriptomics typically require a large number of time points to infer gene regulatory networks (GRNs), are applicable for a small number of genes and fail to detect potential causal relationships effectively. Here, we propose a novel approach 'TENET' to reconstruct GRNs from single cell RNA sequencing (scRNAseq) datasets. Employing transfer entropy (TE) to measure the amount of causal relationships between genes, TENET predicts large-scale gene regulatory cascades/relationships from scRNAseq data. TENET showed better performance than other GRN reconstructors, in identifying key regulators from public datasets. Specifically from scRNAseq, TENET identified key transcriptional factors in embryonic stem cells (ESCs) and during direct cardiomyocytes reprogramming, where other predictors failed. We further demonstrate that known target genes have significantly higher TE values, and TENET predicted higher TE genes were more influenced by the perturbation of their regulator. Using TENET, we identified and validated that Nme2 is a culture condition specific stem cell factor. These results indicate that TENET is uniquely capable of identifying key regulators from scRNAseq data.The Transporter Classification Database (TCDB; tcdb.org) is a freely accessible reference resource, which provides functional, structural, mechanistic, medical and biotechnological information about transporters from organisms of all types. TCDB is the only transport protein classification database adopted by the International Union of Biochemistry and Molecular Biology (IUBMB) and now (October 1, 2020) consists of 20 653 proteins classified in 15 528 non-redundant transport systems with 1567 tabulated 3D structures, 18 336 reference citations describing 1536 transporter families, of which 26% are members of 82 recognized superfamilies. Overall, this is an increase of over 50% since the last published update of the database in 2016. This comprehensive update of the database contents and features include (i) adoption of a chemical ontology for substrates of transporters, (ii) inclusion of new superfamilies, (iii) a domain-based characterization of transporter families for the identification of new members as well as functional and evolutionary relationships between families, (iv) development of novel software to facilitate curation and use of the database, (v) addition of new subclasses of transport systems including 11 novel types of channels and 3 types of group translocators and (vi) the inclusion of many man-made (artificial) transmembrane pores/channels and carriers.The heat shock response is a crucial system for survival of organisms under heat stress. During heat shock stress, gene expression is globally suppressed, but expression of some genes, such as chaperone genes, is selectively promoted. These selectively activated genes have critical roles in the heat shock response, so it is necessary to discover heat-inducible genes to reveal the overall heat shock response picture. The expression profiling of heat-inducible protein-coding genes has been well-studied, but that of noncoding genes remains unclear in mammalian systems. Here, we used RNA-seq analysis of heat shock-treated A549 cells to identify seven novel long noncoding RNAs that responded to heat shock. We focused on CTD-2377D24.6 RNA, which is most significantly induced by heat shock, and found that the promoter region of CTD-2377D24.6 contains the binding site for transcription factor HSF1 (heat shock factor 1), which plays a central role in the heat shock response. We confirmed that HSF1 knockdown canceled the induction of CTD-2377D24.6 RNA upon heat shock. These results suggest that CTD-2377D24.6 RNA is a novel heat shock-inducible transcript that is transcribed by HSF1.Centrosomes, composed of centrioles that recruit a pericentriolar material (PCM) matrix assembled from PCNT and CDK5RAP2, catalyze mitotic spindle assembly. learn more Here, we inhibit centriole formation and/or remove PCNT-CDK5RAP2 in RPE1 cells to address their relative contributions to spindle formation. While CDK5RAP2 and PCNT are normally dispensable for spindle formation, they become essential when centrioles are absent. Acentriolar spindle assembly is accompanied by the formation of foci containing PCNT and CDK5RAP2 via a microtubule and Polo-like kinase 1-dependent process. Foci formation and spindle assembly require PCNT-CDK5RAP2-dependent matrix assembly and the ability of CDK5RAP2 to recruit γ-tubulin complexes. Thus, the PCM matrix can self-organize independently of centrioles to generate microtubules for spindle assembly; conversely, an alternative centriole-anchored mechanism supports spindle assembly when the PCM matrix is absent. Extension to three cancer cell lines revealed similar results in HeLa cells, whereas DLD1 and U2OS cells could assemble spindles in the absence of centrioles and PCNT-CDK5RAP2, suggesting cell type variation in spindle assembly mechanisms.The Zebrafish Information Network (ZFIN) (https//zfin.org/) is the database for the model organism, zebrafish (Danio rerio). ZFIN expertly curates, organizes, and provides a wide array of zebrafish genetic and genomic data, including genes, alleles, transgenic lines, gene expression, gene function, mutant phenotypes, orthology, human disease models, gene and mutant nomenclature, and reagents. New features at ZFIN include major updates to the home page and the gene page, the two most used pages at ZFIN. Data including disease models, phenotypes, expression, mutants and gene function continue to be contributed to The Alliance of Genome Resources for integration with similar data from other model organisms.Why proteins are fuzzy? Constant adaptation to the cellular environment requires a wide range of changes in protein structure and interactions. Conformational ensembles of disordered proteins in particular exhibit large shifts to activate or inhibit alternative pathways. Fuzziness is critical for liquid-liquid phase separation and conversion of biomolecular condensates into fibrils. Interpretation of these phenomena presents a challenge for the classical structure-function paradigm. Here I discuss a multi-valued formalism, based on fuzzy logic, which can be applied to describe complex cellular behavior of proteins.