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Copy number variation (CNV) is a common type of mutation that often drives cancer progression. With advances in next-generation sequencing (NGS), CNVs can be detected in a detailed manner via newly developed computational tools but quality of such CNV calls has not been carefully evaluated. We analyzed CNV calls reported by 6 cutting-edge callers for 91 samples which were derived from the same cancer cell line, prepared and sequenced by varying the following factors: type of tissue sample (Fresh vs. Formalin Fixed Paraffin Embedded (FFPE)), library DNA amount, tumor purity, sequencing platform (Whole-Genome Sequencing (WGS) versus Whole-Exome Sequencing (WES)), and sequencing coverage. We found that callers greatly determined the pattern of CNV calls. Calling quality was drastically impaired by low purity (

SummaryThe metabolite acetyl-CoA serves as an essential element for a wide range of cellular functions including ATP production, lipid synthesis and protein acetylation. Intracellular acetyl-CoA concentrations are associated with nutrient availability, but the mechanisms by which a cell responds to fluctuations in acetyl-CoA levels remain elusive. Here, we generate a cell system to selectively manipulate the nucleo-cytoplasmic levels of acetyl-CoA using CRISPR-mediated gene editing and acetate supplementation of the culture media. Using this system and quantitative omics analyses, we demonstrate that acetyl-CoA depletion alters the integrity of the nucleolus, impairing ribosomal RNA synthesis and evoking the ribosomal protein-dependent activation of p53. This nucleolar remodeling appears to be mediated through the class IIa HDAC deacetylases regulating the phase state of the nucleolus. Our findings highlight acetylation-mediated control of the nucleolus as an important hub linking acetyl-CoA fluctuations to cellular stress responses.

A growing number of long non-coding RNAs (lncRNAs) have emerged as vital metabolic regulators in research animals suggesting that lncRNAs could also play an important role in human metabolism. However, most human lncRNAs are non-conserved, vastly limiting our ability to identify human lncRNA metabolic regulators (hLMRs). As the sequence-function relation of lncRNAs has yet to be established, the identification of lncRNA metabolic regulators in animals often relies on their regulations by experimental metabolic conditions. But it is very challenging to apply this strategy to human lncRNAs because well-controlled human data are much limited in scope and often confounded by genetic heterogeneity. In this study, we establish an efficient pipeline to identify putative hLMRs that are metabolically sensitive, disease-relevant, and population applicable. We first progressively processed human transcriptome data to select human liver lncRNAs that exhibit highly dynamic expression in the general population, show differential expression in a metabolic disease population, and response to dietary intervention in a small disease cohort. We then experimentally demonstrated the responsiveness of selected hepatic lncRNAs to defined metabolic milieus in a liver-specific humanized mouse model. Furthermore, by extracting a concise list of protein-coding genes that are persistently correlated with lncRNAs in general and metabolic disease populations, we predicted the specific function for each hLMR. Using gain- and loss-of-function approaches in humanized mice as well as ectopic expression in conventional mice, we were able to validate the regulatory role of one non-conserved hLMR in cholesterol metabolism. Mechanistically, this hLMR binds to an RNA-binding protein, PTBP1, to modulate the transcription of cholesterol synthesis genes. In summary, our study provides a pipeline to overcome the variabilities intrinsic to human data to enable the efficient identification and functional definition of hLMRs. The combination of this bioinformatic framework and humanized murine model will enable broader systematic investigation of the physiological role of disease-relevant human lncRNAs in metabolic homeostasis.

The Glucocorticoid Receptor (GR) co-ordinates metabolic and behavioural responses to stressors. We hypothesised that GR influences behaviour by modulating specific epigenetic and transcriptional processes in the brain. Using the zebrafish as a model organism, the brain methylomes of wild-type and grs357 mutant adults were analysed and GR-sensitive, differentially methylated regions (GR-DMRs) were identified. Two genes with GR-DMRs exhibited distinct methylation and transcriptional sensitivities to GR: the widely expressed direct GR target fkbp5 and neuron-specific aplp1. In larvae, neural activity is required for GR-mediated transcription of aplp1, but not for that of fkbp5. GR regulates metabotropic glutamate receptor gene expression, the activities of which also modulated aplp1 expression, implicating synaptic neurotransmission as an effector of GR function upstream of aplp1. Our results identify two distinct routes of GR-regulated transcription in the brain, including a pathway through which GR couples endocrine signalling to synaptic activity-regulated transcription by modulating metabotropic glutamate receptor expression.

We have assembled a comprehensivelongnon-codingRNAknowledgebase (lncRNAKB) of 77,199 annotated human lncRNAs (224,286 transcripts) by methodically integrating widely used lncRNAs resources. To facilitate functional characterization of lncRNAs, we employed Genotype-Tissue Expression (GTEx) project to provide tissue-specific gene expression profiles of lncRNAs in 31 solid organ tissues. Additional information includes network analysis to identify co-expressed gene modules to potentially delineate lncRNA function. Tissue-specificity, phylogenetic conservation scores and coding potential for lncRNAs are included. Finally, using whole genome sequencing data from GTEx, expression quantitative trait loci (cis-eQTL) regulated lncRNAs were calculated in all tissues. lncRNAKB is available athttp://www.lncrnakb.org.

In single-cell RNA-seq analysis, clustering cells into groups and differentiating cell groups by marker genes are two separate steps for investigating cell identity. However, results in clustering greatly affect the ability to differentiate between cell groups. We develop IKAP – an algorithm identifying major cell groups that improves differentiating by tuning parameters for clustering. Using multiple datasets, we demonstrate IKAP improves identification of major cell types and facilitates cell ontology curation.