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Background: MicroRNAs (miRNAs) have been identified as important contributors to the regulation of early fetal cardiopulmonary development. However, miRNA expression profiles during late gestation and the early neonatal period are not fully elaborated in large mammals such as sheep (ovis aries). The aim of this study was to sequence miRNA from cardiopulmonary tissues in late gestation and neonate sheep to identify changes in miRNA expression. Methods: Illumina HiSeq next-generation deep sequencing (NGS) was performed on ovine tissues from the left (LV) and right ventricles (RV), lungs and pulmonary artery (PA) of preterm fetuses (128 days), near-term fetuses (140 days) (term = 148 days) and neonatal lambs (5 days). NGS reads were mapped to the sheep genome (OviAri) and published miRNA sequences. Results: Of 1345 cardiopulmonary miRNAs that were sequenced, relatively few major shifts in miRNA expression were detected with increased age from near term to neonates, and were confirmed by quantitative real-time PCR: bta-miR-146a (lung), bta-miR-22-3p (lung, LV), hsa-miR-335* (lung, PA), and miR-210 (lung, PA, LV). Conclusions: Sequencing of miRNA led to identification of four predominant miRNA in ovine cardiopulmonary tissues which alter expression during late gestation and the early neonatal period, concurrent with important functional changes in heart and lungs. [ABSTRACT FROM AUTHOR]

As single-cell RNA-sequencing (scRNA-seq) datasets have become more widespread the number of tools designed to analyse these data has dramatically increased. Navigating the vast sea of tools now available is becoming increasingly challenging for researchers. In order to better facilitate selection of appropriate analysis tools we have created the scRNA-tools database () to catalogue and curate analysis tools as they become available. Our database collects a range of information on each scRNA-seq analysis tool and categorises them according to the analysis tasks they perform. Exploration of this database gives insights into the areas of rapid development of analysis methods for scRNA-seq data. We see that many tools perform tasks specific to scRNA-seq analysis, particularly clustering and ordering of cells. We also find that the scRNA-seq community embraces an open-source and open-science approach, with most tools available under open-source licenses and preprints being extensively used as a means to describe methods. The scRNA-tools database provides a valuable resource for researchers embarking on scRNA-seq analysis and records the growth of the field over time. [ABSTRACT FROM AUTHOR]

Background: Several recent studies have demonstrated the effectiveness of deep sequencing for transcriptome analysis (RNA-seq) in mammals. As RNA-seq becomes more affordable, whole genome transcriptional profiling is likely to become the platform of choice for species with good genomic sequences. As yet, a rigorous analysis methodology has not been developed and we are still in the stages of exploring the features of the data. Results: We investigated the effect of transcript length bias in RNA-seq data using three different published data sets. For standard analyses using aggregated tag counts for each gene, the ability to call differentially expressed genes between samples is strongly associated with the length of the transcript. Conclusion: Transcript length bias for calling differentially expressed genes is a general feature of current protocols for RNA-seq technology. This has implications for the ranking of differentially expressed genes, and in particular may introduce bias in gene set testing for pathway analysis and other multi-gene systems biology analyses. [ABSTRACT FROM AUTHOR]

Changes in genetic regulation contribute to adaptations in natural populations and influence susceptibility to human diseases. Despite their potential phenotypic importance, the selective pressures acting on regulatory processes in general and gene expression levels in particular are largely unknown. Studies in model organisms suggest that the expression levels of most genes evolve under stabilizing selection, although a few are consistent with adaptive evolution. However, it has been proposed that gene expression levels in primates evolve largely in the absence of selective constraints. In this article, we discuss the microarray-based observations that led to these disparate interpretations. We conclude that in both primates and model organisms, stabilizing selection is likely to be the dominant mode of gene expression evolution. An important implication is that mutations affecting gene expression will often be deleterious and might underlie many human diseases. [Copyright &y& Elsevier]

Despite the increasing diagnostic rate of genomic sequencing, the genetic basis of more than 50% of heritable kidney disease remains unresolved. Kidney organoids differentiated from induced pluripotent stem cells (iPSCs) of individuals affected by inherited renal disease represent a potential, but unvalidated, platform for the functional validation of novel gene variants and investigation of underlying pathogenetic mechanisms. In this study, trio whole-exome sequencing of a prospectively identified nephronophthisis (NPHP) proband and her parents identified compound-heterozygous variants in IFT140 , a gene previously associated with NPHP-related ciliopathies. IFT140 plays a key role in retrograde intraflagellar transport, but the precise downstream cellular mechanisms responsible for disease presentation remain unknown. A one-step reprogramming and gene-editing protocol was used to derive both uncorrected proband iPSCs and isogenic gene-corrected iPSCs, which were differentiated to kidney organoids. Proband organoid tubules demonstrated shortened, club-shaped primary cilia, whereas gene correction rescued this phenotype. Differential expression analysis of epithelial cells isolated from organoids suggested downregulation of genes associated with apicobasal polarity, cell-cell junctions, and dynein motor assembly in proband epithelial cells. Matrigel cyst cultures confirmed a polarization defect in proband versus gene-corrected renal epithelium. As such, this study represents a “proof of concept” for using proband-derived iPSCs to model renal disease and illustrates dysfunctional cellular pathways beyond the primary cilium in the setting of IFT140 mutations, which are established for other NPHP genotypes. [ABSTRACT FROM AUTHOR]

Huntington's disease is caused by polyglutamine (polyQ)-expansion mutations in the CAG tandem repeat of the Huntingtin gene. The central feature of Huntington's disease pathology is the aggregation of mutant Huntingtin (Htt) protein into micrometer-sized inclusion bodies. Soluble mutant Htt states are most proteotoxic and trigger an enhanced risk of death whereas inclusions confer different changes to cellular health, and may even provide adaptive responses to stress. Yet the molecular mechanisms underpinning these changes remain unclear. Using the flow cytometry method of pulse-shape analysis (PulSA) to sort neuroblastoma (Neuro2a) cells enriched with mutant or wild-type Htt into different aggregation states, we clarified which transcriptional signatures were specifically attributable to cells before versus after inclusion assembly. Dampened CREB signalling was the most striking change overall and invoked specifically by soluble mutant Httex1 states. Toxicity could be rescued by stimulation of CREB signalling. Other biological processes mapped to different changes before and after aggregation included NF-kB signalling, autophagy, SUMOylation, transcription regulation by histone deacetylases and BRD4, NAD + biosynthesis, ribosome biogenesis and altered HIF-1 signalling. These findings open the path for therapeutic strategies targeting key molecular changes invoked prior to, and subsequently to, Httex1 aggregation. [ABSTRACT FROM AUTHOR]