Bhagwate, Aditya V.

The intestinal epithelium is exposed to diverse combinations of microbiota-derived compounds; however, the mechanisms by which the host integrates these signals remain poorly defined. Studying two highly abundant microbial metabolites, we identified the purine metabolite hypoxanthine as an effector metabolite that directly drives signaling and the short-chain fatty acid butyrate as a regulatory metabolite that conditions host responsiveness. Specifically, hypoxanthine activates the adenosine A1 receptor–TRPC4 axis in enterochromaffin (EC) cells, triggering calcium influx and serotonin release resulting in accelerated gastrointestinal transit locally and increased platelet activation systemically. In contrast, butyrate epigenetically upregulates specific G protein–coupled receptors and ion channels to enhance response to hypoxanthine and the neurotransmitters norepinephrine and dopamine. These findings define a cooperative signaling framework and highlight the role of EC cells as a distinct epithelial signaling hub that senses and integrates microbial metabolite signals to drive physiological responses. Our findings provide a mechanistic foundation for therapeutic strategies that leverage combinatorial microbial signaling.

Objective Pancreatic ductal adenocarcinoma (PDAC) displays a remarkable propensity towards therapy resistance. However, molecular epigenetic and transcriptional mechanisms enabling this are poorly understood. In this study, we aimed to identify novel mechanistic approaches to overcome or prevent resistance in PDAC. Design We used in vitro and in vivo models of resistant PDAC and integrated epigenomic, transcriptomic, nascent RNA and chromatin topology data. We identified a JunD-driven subgroup of enhancers, called interactive hubs (iHUBs), which mediate transcriptional reprogramming and chemoresistance in PDAC. Results iHUBs display characteristics typical for active enhancers (H3K27ac enrichment) in both therapy sensitive and resistant states but exhibit increased interactions and production of enhancer RNA (eRNA) in the resistant state. Notably, deletion of individual iHUBs was sufficient to decrease transcription of target genes and sensitise resistant cells to chemotherapy. Overlapping motif analysis and transcriptional profiling identified the activator protein 1 (AP1) transcription factor JunD as a master transcription factor of these enhancers. JunD depletion decreased iHUB interaction frequency and transcription of target genes. Moreover, targeting either eRNA production or signaling pathways upstream of iHUB activation using clinically tested small molecule inhibitors decreased eRNA production and interaction frequency, and restored chemotherapy responsiveness in vitro and in vivo. Representative iHUB target genes were found to be more expressed in patients with poor response to chemotherapy compared with responsive patients. Conclusion Our findings identify an important role for a subgroup of highly connected enhancers (iHUBs) in regulating chemotherapy response and demonstrate targetability in sensitisation to chemotherapy.

Abstract Motivation: RNA-seq has become the method of choice to quantify genes and exons, discover novel transcripts and detect fusion genes. However, reliable variant identification from RNA-seq data remains challenging because of the complexities of the transcriptome, the challenges of accurately mapping exon boundary spanning reads and the bias introduced during the sequencing library preparation. Method: We developed RVboost, a novel method specific for RNA variant prioritization. RVboost uses several attributes unique in the process of RNA library preparation, sequencing and RNA-seq data analyses. It uses a boosting method to train a model of ‘good quality’ variants using common variants from HapMap, and prioritizes and calls the RNA variants based on the trained model. We packaged RVboost in a comprehensive workflow, which integrates tools of variant calling, annotation and filtering. Results: RVboost consistently outperforms the variant quality score recalibration from the Genome Analysis Tool Kit and the RNA-seq variant-calling pipeline SNPiR in 12 RNA-seq samples using ground-truth variants from paired exome sequencing data. Several RNA-seq–specific attributes were identified as critical to differentiate true and false variants, including the distance of the variant positions to exon boundaries, and the percent of the reads supporting the variant in the first six base pairs. The latter identifies false variants introduced by the random hexamer priming during the library construction. Availability and implementation: The RVboost package is implemented to readily run in Mac or Linux environments. The software and user manual are available at http://bioinformaticstools.mayo.edu/research/rvboost/. Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract Motivation: Exome sequencing (exome-seq) data, which are typically used for calling exonic mutations, have also been utilized in detecting DNA copy number variations (CNVs). Despite the existence of several CNV detection tools, there is still a great need for a sensitive and an accurate CNV-calling algorithm with built-in QC steps, and does not require a paired reference for each sample. Results: We developed a novel method named PatternCNV, which (i) accounts for the read coverage variations between exons while leveraging the consistencies of this variability across different samples; (ii) reduces alignment BAM files to WIG format and therefore greatly accelerates computation; (iii) incorporates multiple QC measures designed to identify outlier samples and batch effects; and (iv) provides a variety of visualization options including chromosome, gene and exon-level views of CNVs, along with a tabular summarization of the exon-level CNVs. Compared with other CNV-calling algorithms using data from a lymphoma exome-seq study, PatternCNV has higher sensitivity and specificity. Availability and implementation: The software for PatternCNV is implemented using Perl and R, and can be used in Mac or Linux environments. Software and user manual are available at http://bioinformaticstools.mayo.edu/research/patterncnv/ , and R package at https://github.com/topsoil/patternCNV/ . Contact: Asmann.Yan@mayo.edu Supplementary information: Supplementary data are available at Bioinformatics online.