Hypoxia-Inducible Factor 1α Stabilization Restores Epigenetic Control of Nitric Oxide Synthase 1 Expression and Reverses Gastroparesis in Female Diabetic Mice.

BACKGROUND & AIMS: Although depletion of neuronal nitric oxide synthase (NOS1)-expressing neurons contributes to gastroparesis, stimulating nitrergic signaling is not an effective therapy. We investigated whether hypoxia-inducible factor 1α (HIF1A), which is activated by high O2 consumption in central neurons, is a Nos1 transcription factor in enteric neurons and whether stabilizing HIF1A reverses gastroparesis. METHODS: Mice with streptozotocin-induced diabetes, human and mouse tissues, NOS1+ mouse neuroblastoma cells, and isolated nitrergic neurons were studied. Gastric emptying of solids and volumes were determined by breath test and single-photon emission computed tomography, respectively. Gene expression was analyzed by RNA-sequencing, microarrays, immunoblotting, and immunofluorescence. Epigenetic assays included chromatin immunoprecipitation sequencing (13 targets), chromosome conformation capture sequencing, and reporter assays. Mechanistic studies used Cre-mediated recombination, RNA interference, and clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9)-mediated epigenome editing. RESULTS: HIF1A signaling from physiological intracellular hypoxia was active in mouse and human NOS1+ myenteric neurons but reduced in diabetes. Deleting Hif1a in Nos1-expressing neurons reduced NOS1 protein by 50% to 92% and delayed gastric emptying of solids in female but not male mice. Stabilizing HIF1A with roxadustat (FG-4592), which is approved for human use, restored NOS1 and reversed gastroparesis in female diabetic mice. In nitrergic neurons, HIF1A up-regulated Nos1 transcription by binding and activating proximal and distal cis-regulatory elements, including newly discovered super-enhancers, facilitating RNA polymerase loading and pause-release, and by recruiting cohesin to loop anchors to alter chromosome topology. CONCLUSIONS: Pharmacologic HIF1A stabilization is a novel, translatable approach to restoring nitrergic signaling and treating diabetic gastroparesis. The newly recognized effects of HIF1A on chromosome topology may provide insights into physioxia- and ischemia-related organ function.

Interactive enhancer hubs (iHUBs) mediate transcriptional reprogramming and adaptive resistance in pancreatic cancer.

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.

Dysfunctional ERG signaling drives pulmonary vascular aging and persistent fibrosis.

Vascular dysfunction is a hallmark of chronic diseases in elderly. The contribution of the vasculature to lung repair and fibrosis is not fully understood. Here, we performed an epigenetic and transcriptional analysis of lung endothelial cells (ECs) from young and aged mice during the resolution or progression of bleomycin-induced lung fibrosis. We identified the transcription factor ETS-related gene (ERG) as putative orchestrator of lung capillary homeostasis and repair, and whose function is dysregulated in aging. ERG dysregulation is associated with reduced chromatin accessibility and maladaptive transcriptional responses to injury. Loss of endothelial ERG enhances paracrine fibroblast activation in vitro, and impairs lung fibrosis resolution in young mice in vivo. scRNA-seq of ERG deficient mouse lungs reveales transcriptional and fibrogenic abnormalities resembling those associated with aging and human lung fibrosis, including reduced number of general capillary (gCap) ECs. Our findings demonstrate that lung endothelial chromatin remodeling deteriorates with aging leading to abnormal transcription, vascular dysrepair, and persistent fibrosis following injury.

Human transcriptomic response to periprosthetic joint infection.

Periprosthetic joint infection (PJI), a devastating complication of total joint replacement, is of incompletely understood pathogenesis and may sometimes be challenging to clinically distinguish from other causes of arthroplasty failure. We characterized human gene expression in 93 specimens derived from surfaces of resected arthroplasties, comparing transcriptomes of subjects with infection- versus non-infection-associated arthroplasty failure. Differential gene expression analysis confirmed 28 previously reported potential biomarkers of PJI, including bactericidal/permeability increasing protein (BPI), cathelicidin antimicrobial peptide (CAMP), C-C-motif chemokine ligand 3 (CCL3), 4(CCL4) and C-X-C-motif chemokine ligand 2 (CXCL2), colony stimulating factor 2 receptor beta (CSF2RB), colony stimulating factor 3 (CSF3), alpha-defensin (DEFA4), Fc fragment of IgG receptor 1B (CD64B), intercellular adhesion molecule 1 (ICAM1), interferon gamma (IFNG), interleukin 13 receptor subunit alpha 2 (IL13RA2), interleukin 17D (IL17D), interleukin 1 (IL1A, IL1B, IL1RN), interleukin 2 receptors (IL2RA, IL2RG), interleukin 5 receptor (IL5RA), interleukin 6 (IL6), interleukin 8 (IL8), lipopolysaccharide binding protein (LBP), lipocalin (LCN2), lactate dehydrogenase C (LDHC), lactotransferrin (LTF), matrix metallopeptidase 3 (MMP3), peptidase inhibitor 3 (PI3), and vascular endothelial growth factor A (VEGFA), and identified three novel molecules of potential diagnostic use for detection of PJI, namely C-C-motif chemokine ligand CCL20, coagulation factor VII (F7), and B cell receptor FCRL4. Comparative analysis of infections caused by staphylococci versus bacteria other than staphylococci and Staphylococcus aureus versus Staphylococcus epidermidis showed elevated expression of interleukin 13 (IL13), IL17D, and MMP3 in staphylococcal infections, and of IL1B, IL8, and platelet factor PF4V1 in S. aureus compared to S. epidermidis infections. Pathway analysis of over-represented genes suggested activation of host immune response and cellular maintenance and repair functions in response to invasion of infectious agents. The data presented provides new potential targets for diagnosis of PJI and for differentiation of PJI caused by different infectious agents.

Concordance between predicted HLA type using next generation sequencing data generated for non-HLA purposes and clinical HLA type.

We explored the feasibility of obtaining accurate HLA type using pre-existing NGS data not generated for HLA purposes. 83 exomes and 500 targeted NGS pharmacogenomic panels were analyzed using Omixon HLA Explore, OptiType, and/or HLA-Genotyper software. Results were compared against clinical HLA genotyping. 765 (94.2%) Omixon and 769 (94.7%) HLA-Genotyper of 812 germline allele calls across class I/II loci and 402 (99.5%) of 404 OptiType class I calls were concordant to the second field (i.e. HLA-A*02:01). An additional 19 (2.3%) Omixon, 39 (4.8%) HLA-Genotyper, and 2 (0.5%) OptiType allele calls were first field concordant (i.e. HLA-A*02). Using Omixon, four alleles (0.4%) were discordant and 24 (3.0%) failed to call, while 4 alleles (0.4%) were discordant using HLA-Genotyper. Tumor exomes were also evaluated and were 85.4%, 91.6%, and 100% concordant (Omixon and HLA-Genotyper with 96 alleles tested, and Optitype with 48 class I alleles, respectively). The 15 exomes and 500 pharmacogenomic panels were 100% concordant for each pharmacogenomic allele tested. This work has broad implications spanning future clinical care (pharmacogenomics, tumor response to immunotherapy, autoimmunity, etc.) and research applications.

PatternCNV: a versatile tool for detecting copy number changes from exome sequencing data.

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/.

Impact of library preparation on downstream analysis and interpretation of RNA-Seq data: comparison between Illumina PolyA and NuGEN Ovation protocol.

OBJECTIVES: The sequencing by the PolyA selection is the most common approach for library preparation. With limited amount or degraded RNA, alternative protocols such as the NuGEN have been developed. However, it is not yet clear how the different library preparations affect the downstream analyses of the broad applications of RNA sequencing. METHODS AND MATERIALS: Eight human mammary epithelial cell (HMEC) lines with high quality RNA were sequenced by Illumina's mRNA-Seq PolyA selection and NuGEN ENCORE library preparation. The following analyses and comparisons were conducted: 1) the numbers of genes captured by each protocol; 2) the impact of protocols on differentially expressed gene detection between biological replicates; 3) expressed single nucleotide variant (SNV) detection; 4) non-coding RNAs, particularly lincRNA detection; and 5) intragenic gene expression. RESULTS: Sequences from the NuGEN protocol had lower (75%) alignment rate than the PolyA (over 90%). The NuGEN protocol detected fewer genes (12-20% less) with a significant portion of reads mapped to non-coding regions. A large number of genes were differentially detected between the two protocols. About 17-20% of the differentially expressed genes between biological replicates were commonly detected between the two protocols. Significantly higher numbers of SNVs (5-6 times) were detected in the NuGEN samples, which were largely from intragenic and intergenic regions. The NuGEN captured fewer exons (25% less) and had higher base level coverage variance. While 6.3% of reads were mapped to intragenic regions in the PolyA samples, the percentages were much higher (20-25%) for the NuGEN samples. The NuGEN protocol did not detect more known non-coding RNAs such as lincRNAs, but targeted small and "novel" lincRNAs. CONCLUSION: Different library preparations can have significant impacts on downstream analysis and interpretation of RNA-seq data. The NuGEN provides an alternative for limited or degraded RNA but it has limitations for some RNA-seq applications.