Citations

SummaryBackgroundIrritable bowel syndrome (IBS) patients often experience meal‐associated symptoms. However, the underlying mechanisms are unclear.AimTo determine small intestinal mechanisms of lipid‐induced symptoms and rectal hypersensitivity in IBSMethodsWe recruited 26 IBS patients (12 IBS‐C, 14 IBS‐D) and 15 healthy volunteers (HV). In vivo permeability was assessed using saccharide excretion assay. Rectal sensitivity was assessed using a barostat before and after small bowel lipid infusion; symptoms were assessed throughout. Next, an extended upper endoscopy with probe‐based confocal laser endomicroscopy (pCLE) was performed with changes induced by lipids. Duodenal and jejunal mucosal biopsies were obtained for transcriptomics.ResultsFollowing lipid infusion, a higher proportion of HV than IBS patients reported no pain, no nausea, no fullness and no urgency (P < 0.05 for all). In a model adjusted for sex and anxiety, IBS‐C and IBS‐D patients had lower thresholds for first rectal sensation (P = 0.0007) and pain (P = 0.004) than HV. In vivo small intestinal permeability and mean pCLE scores were similar between IBS patients and HV. Post‐lipid, pCLE scores were higher than pre‐lipid but were not different between groups. Baseline duodenal transient receptor potential vanilloid (TRPV) 1 and 3 expression was increased in IBS‐D, and TRPV3 in IBS‐C. Duodenal TRPV1 expression correlated with abdominal pain (r = 0.51, FDR = 0.01), and inversely with first rectal sensation (r = −0.48, FDR = 0.01) and pain (r = −0.41, FDR = 0.02) thresholds.ConclusionLipid infusion elicits a greater symptom response in IBS patients than HV, which is associated with small intestinal expression of TRPV channels. TRPV‐mediated small intestinal chemosensitivity may mediate post‐meal symptoms in IBS.

Background: Thousands of gene fusions have been reported in prostate cancer, but their authenticity, incidence, and tumor specificity have not been thoroughly evaluated, nor have their genomic characteristics been carefully explored. Methods: We developed FusionVet to dedicatedly validate known fusion genes using RNA-seq alignments. Using FusionVet, we re-assessed 2727 gene fusions reported from 36 studies using the RNA-seq data generated by The Cancer Genome Atlas (TCGA). We also explored their genomic characteristics and interrogated the transcriptomic and DNA methylomic consequences of the E26 transformation-specific (ETS) fusions. Results: We found that nearly two-thirds of reported fusions are intra-chromosomal, and 80% of them were formed between 2 protein-coding genes. Although most (76%) genes were fused to only 1 partner, we observed many fusion hub genes that have multiple fusion partners, including ETS family genes, androgen receptor signaling pathway genes, tumor suppressor genes, and proto-oncogenes. More than 90% of the reported fusions cannot be validated by TCGA RNA-seq data. For those fusions that can be validated, 5% were detected from tumor and normal samples with similar frequencies, and only 4% (120 fusions) were tumor-specific. The occurrences of ERG, ETV1, and ETV4 fusions were mutually exclusive, and their fusion statuses were tightly associated with overexpressions. Besides, we found ERG fusions were significantly co-occurred with PTEN deletion but mutually exclusive with common genomic alterations such as SPOP mutation and FOXA1 mutation. Conclusions: Most of the reported fusion genes cannot be validated by TCGA samples. The ETS family and androgen response genes were significantly enriched in prostate cancer–specific fusion genes. Transcription activity was significantly repressed, and the DNA methylation was significantly increased in samples carrying ERG fusion.

Abstract Immune checkpoint blockade (ICB) has revolutionized cancer therapy. However, the response of patients to ICB is difficult to predict. Here, we examined 81 patients with lung cancer under ICB treatment and found that patients with MET amplification were resistant to ICB and had a poor progression-free survival. Tumors with MET amplifications had significantly decreased STING levels and antitumor T-cell infiltration. Furthermore, we performed deep single-cell RNA sequencing on more than 20,000 single immune cells and identified an immunosuppressive signature with increased subsets of XIST- and CD96-positive exhausted natural killer (NK) cells and decreased CD8+ T-cell and NK-cell populations in patients with MET amplification. Mechanistically, we found that oncogenic MET signaling induces phosphorylation of UPF1 and downregulates tumor cell STING expression via modulation of the 3′-UTR length of STING by UPF1. Decreased efficiency of ICB by MET amplification can be overcome by inhibiting MET. Significance: We suggest that the combination of MET inhibitor together with ICB will overcome ICB resistance induced by MET amplification. Our report reveals much-needed information that will benefit the treatment of patients with primary MET amplification or EGFR–tyrosine kinase inhibitor resistant-related MET amplification. This article is highlighted in the In This Issue feature, p. 2659

Significance The multiple-domain protein ZMYND8 is well known as a chromatin reader that recognizes methylated, acetylated, and/or mutated histones. Here we report that ZMYND8 is overexpressed in human ccRCC, and that it preferentially binds T487-phosphorylated EZH2. We show that in hypoxia-stimulated or von Hippel–Lindau–deficient cells, ZMYND8 binding of EZH2 is important for maintenance of EZH2 phosphorylation level, inhibition of PRC2 complex formation, and enhanced interaction between EZH2 and FOXM1. Our results reveal a reader function of ZMYND8 in recognizing a phosphorylation “code” on a nonhistone protein. Our findings also identify ZMYND8 binding as a facet of regulation and function of EZH2 phosphorylation in the Polycomb-dependent to -independent switch of EZH2/PRC2 activities.

Abstract The tumor-suppressor protein RB acts as a transcription repressor via interaction of its pocket domain with an LXCXE motif in histone deacetylase (HDAC) proteins such as HDAC1. Here, we demonstrate that HDAC5 deficient for the LXCXE motif interacts with both RB-N (via an FXXXV motif) and RB-C segments, and such interactions are diminished by phosphorylation of RB serine-249/threonine-252 and threonine-821. HDAC5 was frequently downregulated or deleted in human cancers such as prostate cancer. Loss of HDAC5 increased histone H3 lysine 27 acetylation (H3K27-ac) and circumvented RB-mediated repression of cell-cycle–related pro-oncogenic genes. HDAC5 loss also conferred resistance to CDK4/6 inhibitors such as palbociclib in prostate and breast cancer cells in vitro and prostate tumors in vivo, but this effect was overcome by the BET-CBP/p300 dual inhibitor NEO2734. Our findings reveal an unknown role of HDAC5 in RB-mediated histone deacetylation and gene repression and define a new mechanism modulating CDK4/6 inhibitor therapeutic sensitivity in cancer cells. Significance: This study defines a previously uncharacterized role of HDAC5 in tumor suppression and provides a viable strategy to overcome CDK4/6 inhibitor resistance in HDAC5-deficent cancer.

ObjectiveTo compare the diagnostic performance of an artificial intelligence deep learning system with that of expert neuro‐ophthalmologists in classifying optic disc appearance.MethodsThe deep learning system was previously trained and validated on 14,341 ocular fundus photographs from 19 international centers. The performance of the system was evaluated on 800 new fundus photographs (400 normal optic discs, 201 papilledema [disc edema from elevated intracranial pressure], 199 other optic disc abnormalities) and compared with that of 2 expert neuro‐ophthalmologists who independently reviewed the same randomly presented images without clinical information. Area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were calculated.ResultsThe system correctly classified 678 of 800 (84.7%) photographs, compared with 675 of 800 (84.4%) for Expert 1 and 641 of 800 (80.1%) for Expert 2. The system yielded areas under the receiver operating characteristic curve of 0.97 (95% confidence interval [CI] = 0.96–0.98), 0.96 (95% CI = 0.94–0.97), and 0.89 (95% CI = 0.87–0.92) for the detection of normal discs, papilledema, and other disc abnormalities, respectively. The accuracy, sensitivity, and specificity of the system's classification of optic discs were similar to or better than the 2 experts. Intergrader agreement at the eye level was 0.71 (95% CI = 0.67–0.76) between Expert 1 and Expert 2, 0.72 (95% CI = 0.68–0.76) between the system and Expert 1, and 0.65 (95% CI = 0.61–0.70) between the system and Expert 2.InterpretationThe performance of this deep learning system at classifying optic disc abnormalities was at least as good as 2 expert neuro‐ophthalmologists. Future prospective studies are needed to validate this system as a diagnostic aid in relevant clinical settings. ANN NEUROL 2020;88:785–795

Abstract Motivation DNA methylation can be measured at the single CpG level using sodium bisulfite conversion of genomic DNA followed by sequencing or array hybridization. Many analytic tools have been developed, yet there is still a high demand for a comprehensive and multifaceted tool suite to analyze, annotate, QC and visualize the DNA methylation data. Results We developed the CpGtools package to analyze DNA methylation data generated from bisulfite sequencing or Illumina methylation arrays. The CpGtools package consists of three types of modules: (i) ‘CpG position modules’ focus on analyzing the genomic positions of CpGs, including associating other genomic and epigenomic features to a given list of CpGs and generating the DNA motif logo enriched in the genomic contexts of a given list of CpGs; (ii) ‘CpG signal modules’ are designed to analyze DNA methylation values, such as performing the PCA or t-SNE analyses, using Bayesian Gaussian mixture modeling to classify CpG sites into fully methylated, partially methylated and unmethylated groups, profiling the average DNA methylation level over user-specified genomics regions and generating the bean/violin plots and (iii) ‘differential CpG analysis modules’ focus on identifying differentially methylated CpGs between groups using different statistical methods including Fisher’s Exact Test, Student’s t-test, ANOVA, non-parametric tests, linear regression, logistic regression, beta-binomial regression and Bayesian estimation. Availability and implementation CpGtools is written in Python under the open-source GPL license. The source code and documentation are freely available at https://github.com/liguowang/cpgtools. Supplementary information Supplementary data are available at Bioinformatics online.