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An obesogenic diet adversely affects the endogenous mammalian circadian clock, altering daily activity and metabolism, and resulting in obesity. We investigated whether an obese pregnancy can alter the molecular clock in the offspring hypothalamus, resulting in changes to their activity and feeding rhythms. Female mice were fed a control (C, 7% kcal fat) or high fat diet (HF, 45% kcal fat) before mating and throughout pregnancy. Male offspring were fed the C or HF diet postweaning, resulting in four offspring groups: C/C, C/HF, HF/C, and HF/HF. Daily activity and food intake were monitored, and at 15 weeks of age were killed at six time-points over 24 h. The clock genes Clock, Bmal1, Per2, and Cry2 in the suprachiasmatic nucleus (SCN) and appetite genes Npy and Pomc in the arcuate nucleus (ARC) were measured. Daily activity and feeding cycles in the HF/C, C/HF, and HF/HF offspring were altered, with increased feeding bouts and activity during the day and increased food intake but reduced activity at night. Gene expression patterns and levels of Clock, Bmal1, Per2, and Cry2 in the SCN and Npy and Pomc in the ARC were altered in HF diet-exposed offspring. The altered expression of hypothalamic molecular clock components and appetite genes, together with changes in activity and feeding rhythms, could be contributing to offspring obesity.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.An amendment to this paper has been published and can be accessed via a link at the top of the paper.

PurposeDiagnosis of genetic disorders is hampered by large numbers of variants of uncertain significance (VUSs) identified through next-generation sequencing. Many such variants may disrupt normal RNA splicing. We examined effects on splicing of a large cohort of clinically identified variants and compared performance of bioinformatic splicing prediction tools commonly used in diagnostic laboratories.MethodsTwo hundred fifty-seven variants (coding and noncoding) were referred for analysis across three laboratories. Blood RNA samples underwent targeted reverse transcription polymerase chain reaction (RT-PCR) analysis with Sanger sequencing of PCR products and agarose gel electrophoresis. Seventeen samples also underwent transcriptome-wide RNA sequencing with targeted splicing analysis based on Sashimi plot visualization. Bioinformatic splicing predictions were obtained using Alamut, HSF 3.1, and SpliceAI software.ResultsEighty-five variants (33%) were associated with abnormal splicing. The most frequent abnormality was upstream exon skipping (39/85 variants), which was most often associated with splice donor region variants. SpliceAI had greatest accuracy in predicting splicing abnormalities (0.91) and outperformed other tools in sensitivity and specificity.ConclusionSplicing analysis of blood RNA identifies diagnostically important splicing abnormalities and clarifies functional effects of a significant proportion of VUSs. Bioinformatic predictions are improving but still make significant errors. RNA analysis should therefore be routinely considered in genetic disease diagnostics.