Identification of sleep and circadian alternative polyadenylation sites associated with APA-linked human brain disorders
Disruption of sleep and circadian rhythms are a comorbid feature of many pathologies, and can negatively influence many health conditions, including degenerative disease, metabolic illness, cancer, and various neurological disorders. Genetic association studies linking sleep and circadian disturbances with disease susceptibility have mainly focused on changes in gene expression due to mutations, such as single-nucleotide polymorphisms. The interaction between sleep and/or circadian rhythms with the use of Alternative Polyadenylation (APA) has been largely undescribed, particularly in the context of other disorders. APA is a process that generates various transcript isoforms of the same gene affecting its mRNA translation, stability, localization, and subsequent function. Here we identified unique polyadenylation sites (PASs) expressed in rat brain over time-of-day, immediately following sleep deprivation, and the subsequent recovery period. From these data, we performed a meta-analysis of sleep- or circadian-associated PASs with recently described APA-linked brain disorder susceptibility genes. Overall design: We investigated alternative poly(A) site (APA) usage in the central brain of Long Evans rats for variation (1) over time of day or (2) with increased sleep pressure after sleep deprivation. The animals were maintained on a 12h light: 12h dark cycle. For the time-of-day experiments, 5 replicate libraries from individual rats were prepared from each timepoint; ZT2, 6, 10, 14, 18, or 22. Cycling in specific APAs was evaluated using the meta2d suite of algorithms. To look for APAs affected by sleep pressure, 5 replicate libraries were prepared from individual rats immediately after 6h of sleep-deprivation ("recovery 0" (R0), as well as after 2, 4, 6 or 10h of recovery. APA counts from rats recovering from 6 h SD were contrasted with time-matched controls (R0 vs ZT6, R2 vs ZT8, R4 vs ZT10, R8 vs ZT14). We removed high variation from the first principal component systematically, resulting in improved variance estimates for low read counts. Prcomp (in R) was used to perform PCA and find eigenvectors by way of singular value decomposition. DESeq-2 with "Apeglm" Shrinkage and the Wald Test were used to generate test statistics in R software. The FDRtool was used to determine the Local FDR.