RNA out of the mist

Sex- and region-specific cortical and hippocampal whole genome transcriptome profiles from control and APP/PS1 Alzheimer's disease mice

A variety of Alzheimer's disease (AD) mouse models has been established and characterized within the last decades. To get an integrative view of the sophisticated etiopathogenesis of AD, whole genome transcriptome studies turned out to be indispensable. Here we carried out microarray data collection based on RNA extracted from the retrosplenial cortex and hippocampus of age-matched, eight months old male and female APP/PS1 AD mice and control animals to perform sex- and brain region specific analysis of transcriptome profiles. The results of our studies reveal novel, detailed insight into differentially expressed signature genes and related fold changes in the individual APP/PS1 subgroups. Gene ontology and Venn analysis unmasked that intersectional, upregulated genes were predominantly involved in, e.g., activation of microglial, astrocytic and neutrophilic cells, innate immune response/immune effector response, neuroinflammation, phagosome/proteasome activation, and synaptic transmission. The number of (intersectional) downregulated genes was substantially less in the different subgroups and related GO categories included, e.g., the synaptic vesicle docking/fusion machinery, synaptic transmission, rRNA processing, ubiquitination, proteasome degradation, histone modification and cellular senescence. Importantly, this is the first study to systematically unravel sex- and brain region-specific transcriptome fingerprints/signature genes in APP/PS1 mice. The latter will be of central relevance in future preclinical and clinical AD related studies, biomarker characterization and personalized medicinal approaches.

Mechanism and Effect of HNF4α Decrease in a Rat Model of Cirrhosis and Liver Failure

BACKGROUND & AIMS

HNF4α, a master regulator of liver development and the mature hepatocyte phenotype, is down-regulated in chronic and inflammatory liver disease. We used contemporary transcriptomics and epigenomics to study the cause and effects of this down-regulation and characterized a multicellular etiology.

METHODS

Progressive changes in the rat carbon tetrachloride model were studied by deep RNA sequencing and genome-wide chromatin immunoprecipitation sequencing analysis of transcription factor (TF) binding and chromatin modification. Studies compared decompensated cirrhosis with liver failure after 26 weeks of treatment with earlier compensated cirrhosis and with additional rat models of chronic fibrosis. Finally, to resolve cell-specific responses and intercellular signaling, we compared transcriptomes of liver, nonparenchymal, and inflammatory cells.

RESULTS

HNF4α was significantly lower in 26-week cirrhosis, part of a general reduction of TFs that regulate metabolism. Nevertheless, increased binding of HNF4α contributed to strong activation of major phenotypic genes, whereas reduced binding to other genes had a moderate phenotypic effect. Decreased Hnf4a expression was the combined effect of STAT3 and nuclear factor kappa B (NFκB) activation, which similarly reduced expression of other metabolic TFs. STAT/NFκB also induced de novo expression of Osmr by hepatocytes to complement induced expression of Osm by nonparenchymal cells.

CONCLUSIONS

Liver decompensation by inflammatory STAT3 and NFκB signaling was not a direct consequence of progressive cirrhosis. Despite significant reduction of Hnf4a expression, residual levels of this abundant TF still stimulated strong new gene expression. Reduction of HNF4α was part of a broad hepatocyte transcriptional response to inflammation.

Exploring the landscape of tools and resources for the analysis of long non-coding RNAs

In recent years, research on long non-coding RNAs (lncRNAs) has gained considerable attention due to the increasing number of newly identified transcripts. Several characteristics make their functional evaluation challenging, which called for the urgent need to combine molecular biology with other disciplines, including bioinformatics. Indeed, the recent development of computational pipelines and resources has greatly facilitated both the discovery and the mechanisms of action of lncRNAs. In this review, we present a curated collection of the most recent computational resources, which have been categorized into distinct groups: databases and annotation, identification and classification, interaction prediction, and structure prediction. As the repertoire of lncRNAs and their analysis tools continues to expand over the years, standardizing the computational pipelines and improving the existing annotation of lncRNAs will be crucial to facilitate functional genomics studies.