Highlights d Development of a mouse model of pancreatic adenocarcinoma (PDA)-induced cachexia d Model develops progressive wasting associated with advancing pancreas pathology d Induction of cachexia in adult KPP mice models tissue loss in PDA cancer patients d Gene ontology of cachectic muscles from KPP mice resembles that of PDA patients

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Background The 5-hydroxytryptamine 2A receptor, encoded by HTR2A, is a major post-synaptic target for serotonin in the human brain and a therapeutic drug target. Despite hundreds of genetic associations investigating HTR2A polymorphisms in neuropsychiatric disorders and therapies, the role of genetic HTR2A variability in health and disease remains uncertain. Methods To discover and characterize regulatory HTR2A variants, we sequenced whole transcriptomes from ten human brain regions with massively-parallel RNA sequencing and measured allelic expression of multiple HTR2A mRNA transcript variants. Following discovery of functional variants, we further characterized their impact on genetic expression in vitro. Results Three polymorphisms modulate the use of novel alternative exons and untranslated regions (UTRs), changing expression of RNA and protein. The frequent promoter variant rs6311, widely implicated in human neuropsychiatric disorders, decreases usage of an upstream transcription start site encoding a longer 5′UTR with greater translation efficiency. rs76665058, located in an extended 3′UTR and unique to individuals of African descent, modulates allelic HTR2A mRNA expression. The third SNP, unannotated and present in only a single subject, directs alternative splicing of exon 2. Targeted analysis of HTR2A in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study reveals associations between functional variants and depression severity or citalopram response. Conclusions Regulatory polymorphisms modulate HTR2A mRNA expression in an isoform-specific manner, directing the usage of novel untranslated regions and alternative exons. These results provide a foundation for delineating the role of HTR2A and serotonin signaling in CNS disorders.

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BackgroundMeasuring allelic RNA expression ratios is a powerful approach for detecting cis-acting regulatory variants, RNA editing, loss of heterozygosity in cancer, copy number variation, and allele-specific epigenetic gene silencing. Whole transcriptome RNA sequencing (RNA-Seq) has emerged as a genome-wide tool for identifying allelic expression imbalance (AEI), but numerous factors bias allelic RNA ratio measurements. Here, we compare RNA-Seq allelic ratios measured in nine different human brain regions with a highly sensitive and accurate SNaPshot measure of allelic RNA ratios, identifying factors affecting reliable allelic ratio measurement. Accounting for these factors, we subsequently surveyed the variability of RNA editing across brain regions and across individuals.ResultsWe find that RNA-Seq allelic ratios from standard alignment methods correlate poorly with SNaPshot, but applying alternative alignment strategies and correcting for observed biases significantly improves correlations. Deploying these methods on a transcriptome-wide basis in nine brain regions from a single individual, we identified genes with AEI across all regions (SLC1A3, NHP2L1) and many others with region-specific AEI. In dorsolateral prefrontal cortex (DLPFC) tissues from 14 individuals, we found evidence for frequent regulatory variants affecting RNA expression in tens to hundreds of genes, depending on stringency for assigning AEI. Further, we find that the extent and variability of RNA editing is similar across brain regions and across individuals.ConclusionsThese results identify critical factors affecting allelic ratios measured by RNA-Seq and provide a foundation for using this technology to screen allelic RNA expression on a transcriptome-wide basis. Using this technology as a screening tool reveals tens to hundreds of genes harboring frequent functional variants affecting RNA expression in the human brain. With respect to RNA editing, the similarities within and between individuals leads us to conclude that this post-transcriptional process is under heavy regulatory influence to maintain an optimal degree of editing for normal biological function.

• Candidate SNP associations with survival outcomes after URD transplant are most likely false-positive findings.• Over 85% of candidate SNPs are not linked to a biochemical function; of those that are, about half are not linked to the candidate gene.Multiple candidate gene-association studies of non-HLA single-nucleotide polymorphisms (SNPs) and outcomes after blood or marrow transplant (BMT) have been conducted. We identified 70 publications reporting 45 SNPs in 36 genes significantly associated with disease-related mortality, progression-free survival, transplant-related mortality, and/or overall survival after BMT. Replication and validation of these SNP associations were performed using DISCOVeRY-BMT (Determining the Influence of Susceptibility COnveying Variants Related to one-Year mortality after BMT), a well-powered genome-wide association study consisting of 2 cohorts, totaling 2888 BMT recipients with acute myeloid leukemia, acute lymphoblastic leukemia, or myelodysplastic syndrome, and their HLA-matched unrelated donors, reported to the Center for International Blood and Marrow Transplant Research. Gene-based tests were used to assess the aggregate effect of SNPs on outcome. None of the previously reported significant SNPs replicated at P < .05 in DISCOVeRY-BMT. Validation analyses showed association with one previously reported donor SNP at P < .05 and survival; more associations would be anticipated by chance alone. No gene-based tests were significant at P < .05. Functional annotation with publicly available data shows these candidate SNPs most likely do not have biochemical function; only 13% of candidate SNPs correlate with gene expression or are predicted to impact transcription factor binding. Of these, half do not impact the candidate gene of interest; the other half correlate with expression of multiple genes. These findings emphasize the peril of pursing candidate approaches and the importance of adequately powered tests of unbiased genome-wide associations with BMT clinical outcomes given the ultimate goal of improving patient outcomes. (Blood. 2017;130(13):1585-1596

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Graphical Abstract Highlights d E2F expression during cell division, differentiation, and quiescence is measured in vivo d E2F3A, E2F8, and E2F4 accumulate sequentially in the nucleus of cycling cells d E2F3A-4 nuclear accumulation controls gene expression during cell-cycle exit d Deep learning tools are applied to nuclear segmentation of complex mammalian tissues In Brief The study of E2Fs in vivo has been challenging. Cuitiñ o et al. reconstruct the spatiotemporal expression of E2F activators (E2F3A) and canonical (E2F4) and atypical (E2F8) repressors during the mammalian cell cycle and propose that orchestrated accumulation of different E2F combinations control gene expression in proliferating (E2F3A-8-4) and differentiating (E2F3A-4) cells. SUMMARYOrchestrating cell-cycle-dependent mRNA oscillations is critical to cell proliferation in multicellular organisms. Even though our understanding of cellcycle-regulated transcription has improved significantly over the last three decades, the mechanisms remain untested in vivo. Unbiased transcriptomic profiling of G 0 , G 1 -S, and S-G 2 -M sorted cells from FUCCI mouse embryos suggested a central role for E2Fs in the control of cell-cycle-dependent gene expression. The analysis of gene expression and E2F-tagged knockin mice with tissue imaging and deep-learning tools suggested that post-transcriptional mechanisms universally coordinate the nuclear accumulation of E2F activators (E2F3A) and canonical (E2F4) and atypical (E2F8) repressors during the cell cycle in vivo. In summary, we mapped the spatiotemporal expression of sentinel E2F activators and canonical and atypical repressors at the singlecell level in vivo and propose that two distinct E2F modules relay the control of gene expression in cells actively cycling (E2F3A-8-4) and exiting the cycle (E2F3A-4) during mammalian development.