The project fully progressed as anticipated in the original proposal with early exiting scientific discoveries:
A postdoctoral fellow with computational expertise was hired after beginning of the project and setup analysis pipelines for detecting lncRNA detection in RNA-seq data. He quantified lncRNA in adipose tissue and liver from two mouse models of obesity and type 2 diabetes and from human obesity liver samples. Using this data, we identified first candidate lncRNAs to investigate . We devised a software pipeline that increases the numbers of detected lncRNAs in RNA-seq data which will be of great benefit to the whole project. We created computational approaches that allow unexperienced users to compare their RNA-Seq data in an automated fashion alongside thousands of publicly available NGS datasets (obtained from ENCODE project,
https://www.encodeproject.org/vv(opens in new window)) using novel Visual Analytic visualisation principles. This software suite (called 'sonar; s•nR) was published recently (Klemm et al. BMC Genomics, 2019).
Another wet-lab postdoc obtained crucial and existing biological insights into the physiology controlling lncRNA regulation in metabolic disease. By exposing mice to fasting/re-feeding paradigms and performing RNA-seq, we identified a novel signaling pathway in liver cells that represses many lncRNAs in humans and mice. Interestingly, we could not observe the same down-regulation of protein-coding genes in obesity. A role for this specific transcription factor TF) termed MAFG, and MAFG-dependent roles in metabolism were shown in primary hepatocytes and mice. We also identified a MAFG-dependent lncRNA that we call 'lincIRS2' which is repressed by this signaling axis in primary liver cells but also in liver and, when knocked out, triggered metabolic impairments already in knockout mice. The findings from this study were published recently (Pradas-Juni et al Nat Commun 2020). We also established techniques for generation of lncRNA KO mouse lines using CRISPR/Cas9. A manuscript describing the methodology was published recently (Hansmeier et al. Noncoding RNA 2019).
We also identified another LncRNA termed H19, which exerted instrumental roles in controlling thermogenic activation in (brown) adipocytes, a cell type with anti-obesity properties. We found that H19 drives brown adipose tissue (BAT) catabolism by epigenetic regulation of mono-allelically expressed ('imprinted') genes in BAT. We found that human H19 in human adipose tissue also inversely correlated with parameters of metabolic health (e.g. BMI). A manuscript describing these findings were recently published (Schmidt et al. Nat Commun 2018).
In the last part of our ERC projects, where we are interested in sperm noncoding RNA expression and the epigenetic effects of paternal (from the father) obesity on progeny metabolic health, we hired an expert in mouse physiology, DNA methylation and gametic (sperm) epigenetics. We established protocols for isolation and RNA-Seq of mature sperm from lean and obese mice and found that paternal obesity (F0 generation) is instrumental for controlling metabolism in offsprings (F1 generation) . Based on multi -omic studies (transcriptomics, metabolomics, proteomics, small noncoding transcriptomics) in liver and adipose tissue from F0 and F1 generation, we could delineate a novel signaling axis where obesity-associated induction of miRNA miR-Let7 in obese adipocytes and sperm is coupled to metabolic deterioration in F0 and F1. The findings are currently prepared for publication in a high-impact journal in the field.
