Nicolas Gaiani

The myostatin protein is a potent negative regulator of skeletal muscle growth encoded by the MSTN gene. MSTN loss‐of‐function variants lead to a particular cattle phenotype characterized by an increase in skeletal muscle mass, known as “double muscling” or “double muscled”. However, most of the MSTN causal variants that have been linked to this phenotype lack experimental validation. This is the case, for example, for the five missense MSTN variants reported to be causal according to the Online Mendelian Inheritance in Animals. RNA splicing plays a major role in regulating gene expression; therefore, exploring the effects of variants on RNA splicing may provide relevant information on their functional impact. Here, we have set up a full‐length gene assay (FLGA) to functionally assess MSTN splicing variants, and we have used it to test the five missense variants plus a well‐described deep intronic splicing variant as a positive control. We also evaluated the performances of SpliceAI and Pangolin, two deep learning‐based splice predictors, to identify potential splicing effects of these six variants. Our FLGA system performed well and showed that none of the missense variants has an effect on splicing, unlike the positive control. For each variant, splicing program predictions were perfectly concordant with the effect observed in the FLGA. We have produced a relevant and powerful assay to analyze MSTN splicing variants in cattle. SpliceAI and Pangolin may be efficiently used to screen large datasets of MSTN variants and sort the best candidates prior to experimental validation using an FLGA.

GWAS conducted directly on imputed whole genome sequence have led to the identification of numerous genetic variants associated with agronomic traits in cattle. However, such variants are often simply markers in linkage disequilibrium with the actual causal variants, which is a limiting factor for the development of accurate genomic predictions. It is possible to identify causal variants by integrating information on how variants impact gene expression into GWAS output. RNA splicing plays a major role in regulating gene expression. Thus, assessing the effect of variants on RNA splicing may explain their function. Here, we use a high-throughput strategy to functionally analyse putative splice-disrupting variants in the bovine genome. Using GWAS, massively parallel reporter assay and deep learning algorithms designed to predict splice-disrupting variants, we identify 38 splice-disrupting variants associated with complex traits in cattle, three of which could be classified as causal. Our results indicate that splice-disrupting variants are widely found in the quantitative trait loci related to these phenotypes. Using our combined approach, we also assess the validity of splicing predictors originally developed to analyse human variants in the context of the bovine genome.

Mitotic bookmarking transcription factors (TFs) are thought to mediate rapid and accurate reactivation after mitotic gene silencing. However, the loss of individual bookmarking TFs often leads to the deregulation of only a small proportion of their mitotic targets, raising doubts on the biological significance and importance of their bookmarking function. Here we used targeted proteomics of the mitotic bookmarking TF ESRRB, an orphan nuclear receptor, to discover a large redundancy in mitotic binding among members of the protein super-family of nuclear receptors. Focusing on the nuclear receptor NR5A2, which together with ESRRB is essential in maintaining pluripotency in mouse embryonic stem cells, we demonstrate conjoint bookmarking activity of both factors on promoters and enhancers of a large fraction of active genes, particularly those most efficiently reactivated in G1. Upon fast and simultaneous degradation of both factors during mitotic exit, hundreds of mitotic targets of ESRRB/NR5A2, including key players of the pluripotency network, display attenuated transcriptional reactivation. We propose that redundancy in mitotic bookmarking TFs, especially nuclear receptors, confers robustness to the reestablishment of gene regulatory networks after mitosis.

DGAT1 is playing a major role in fat metabolism and triacylglyceride synthesis. Only two DGAT1 loss-of-function variants altering milk production traits in cattle have been reported to date, namely p.M435L and p.K232A. The p.M435L variant is a rare alteration and has been associated with skipping of exon 16 which results in a non-functional truncated protein, and the p.K232A-containing haplotype has been associated with modifications of the splicing rate of several DGAT1 introns. In particular, the direct causality of the p.K232A variant in decreasing the splicing rate of the intron 7 junction was validated using a minigene assay in MAC-T cells. As both these DGAT1 variants were shown to be spliceogenic, we developed a full-length gene assay (FLGA) to re-analyse p.M435L and p.K232A variants in HEK293T and MAC-T cells. Qualitative RT-PCR analysis of cells transfected with the full-length DGAT1 expression construct carrying the p.M435L variant highlighted complete skipping of exon 16. The same analysis performed using the construct carrying the p.K232A variant showed moderate differences compared to the wild-type construct, suggesting a possible effect of this variant on the splicing of intron 7. Finally, quantitative RT-PCR analyses of cells transfected with the p.K232A-carrying construct did not show any significant modification on the splicing rate of introns 1, 2 and 7. In conclusion, the DGAT1 FLGA confirmed the p.M435L impact previously observed in vivo, but invalidated the hypothesis whereby the p.K232A variant strongly decreased the splicing rate of intron 7.