Early transcriptome changes associated with western diet induced NASH in Ldlr−/− mice points to activation of hepatic macrophages and an acute phase response

BackgroundNonalcoholic fatty liver disease (NAFLD) is a global health problem. Identifying early gene indicators contributing to the onset and progression of NAFLD has the potential to develop novel targets for early therapeutic intervention. We report on the early and late transcriptomic signatures of western diet (WD)-induced nonalcoholic steatohepatitis (NASH) in female and male Ldlr−/− mice, with time-points at 1 week and 40 weeks on the WD. Control Ldlr−/− mice were maintained on a low-fat diet (LFD) for 1 and 40 weeks.MethodsThe approach included quantitation of anthropometric and hepatic histology markers of disease as well as the hepatic transcriptome.ResultsOnly mice fed the WD for 40 weeks revealed evidence of NASH, i.e., hepatic steatosis and fibrosis. RNASeq transcriptome analysis, however, revealed multiple cell-specific changes in gene expression after 1 week that persisted to 40 weeks on the WD. These early markers of disease include induction of acute phase response (Saa1-2, Orm2), fibrosis (Col1A1, Col1A2, TGFβ) and NASH associated macrophage (NAM, i.e., Trem2 high, Mmp12 low). We also noted the induction of transcripts associated with metabolic syndrome, including Mmp12, Trem2, Gpnmb, Lgals3 and Lpl. Finally, 1 week of WD feeding was sufficient to significantly induce TNFα, a cytokine involved in both hepatic and systemic inflammation.ConclusionThis study revealed early onset changes in the hepatic transcriptome that develop well before any anthropometric or histological evidence of NALFD or NASH and pointed to cell-specific targeting for the prevention of disease progression

Predicting Cancer Immunotherapy Response From Gut Microbiomes Using Machine Learning Models

Cancer immunotherapy has significantly improved patient survival. Yet, half of patients do not respond to immunotherapy. Gut microbiomes have been linked to clinical responsiveness of melanoma patients on immunotherapies; however, different taxa have been associated with response status with implicated taxa inconsistent between studies. We used a tumor-agnostic approach to find common gut microbiome features of response among immunotherapy patients with different advanced stage cancers. A combined meta-analysis of 16S rRNA gene sequencing data from our mixed tumor cohort and three published immunotherapy gut microbiome datasets from different melanoma patient cohorts found certain gut bacterial taxa correlated with immunotherapy response status regardless of tumor type. Using multivariate selbal analysis, we identified two separate groups of bacterial genera associated with responders versus non-responders. Statistical models of gut microbiome community features showed robust prediction accuracy of immunotherapy response in amplicon sequencing datasets and in cross-sequencing platform validation with shotgun metagenomic datasets. Results suggest baseline gut microbiome features may be predictive of clinical outcomes in oncology patients on immunotherapies, and some of these features may be generalizable across different tumor types, patient cohorts, and sequencing platforms. Findings demonstrate how machine learning models can reveal microbiome-immunotherapy interactions that may ultimately improve cancer patient outcomes

Interaction between the microbiome and TP53 in human lung cancer.

BACKGROUND: Lung cancer is the leading cancer diagnosis worldwide and the number one cause of cancer deaths. Exposure to cigarette smoke, the primary risk factor in lung cancer, reduces epithelial barrier integrity and increases susceptibility to infections. Herein, we hypothesize that somatic mutations together with cigarette smoke generate a dysbiotic microbiota that is associated with lung carcinogenesis. Using lung tissue from 33 controls and 143 cancer cases, we conduct 16S ribosomal RNA (rRNA) bacterial gene sequencing, with RNA-sequencing data from lung cancer cases in The Cancer Genome Atlas serving as the validation cohort. RESULTS: Overall, we demonstrate a lower alpha diversity in normal lung as compared to non-tumor adjacent or tumor tissue. In squamous cell carcinoma specifically, a separate group of taxa are identified, in which Acidovorax is enriched in smokers. Acidovorax temporans is identified within tumor sections by fluorescent in situ hybridization and confirmed by two separate 16S rRNA strategies. Further, these taxa, including Acidovorax, exhibit higher abundance among the subset of squamous cell carcinoma cases with TP53 mutations, an association not seen in adenocarcinomas. CONCLUSIONS: The results of this comprehensive study show both microbiome-gene and microbiome-exposure interactions in squamous cell carcinoma lung cancer tissue. Specifically, tumors harboring TP53 mutations, which can impair epithelial function, have a unique bacterial consortium that is higher in relative abundance in smoking-associated tumors of this type. Given the significant need for clinical diagnostic tools in lung cancer, this study may provide novel biomarkers for early detection

Estimation of low frequency antigen-presenting cells with a novel RELISPOT assay

Adequate presentation of self and foreign antigens is a key factor for efficient T-cell immunosurveillance against pathogens and tumors. Cells presenting foreign antigens usually comprise a rare population and are difficult to detect even at the peak of infection. Here we demonstrate a CD8(+) T-cell-based approach that allows detection of specific antigen-presenting cells (APC) at a frequency of less than 0.0005%. When T cells are in excess, they form rosettes with rare APCs, which appear as single spots in an IFN-gamma ELISPOT assay. Using this RELISPOT (Rosette ELISPOT) method we demonstrate the dynamic interplay between CD8 T cells and professional and non-professional APCs following virus challenge

Intraluminal containment of commensal outgrowth in the gut during infection-induced dysbiosis

SummaryShifts in commensal microbiota composition are emerging as a hallmark of gastrointestinal inflammation. In particular, outgrowth of γ-proteobacteria has been linked to the etiology of inflammatory bowel disease and the pathologic consequences of infections. Here we show that following acute Toxoplasma gondii gastrointestinal infection of mice, control of commensal outgrowth is a highly coordinated process involving both the host response and microbial signals. Notably, neutrophil emigration to the intestinal lumen results in the generation of organized intraluminal structures that encapsulate commensals and limit their contact with the epithelium. Formation of these luminal casts depends on the high-affinity N-formyl peptide receptor, Fpr1. Consequently, after infection, mice deficient in Fpr1 display increased microbial translocation, poor commensal containment, and increased mortality. Altogether, our study describes a mechanism by which the host rapidly contains commensal pathobiont outgrowth during infection. Further, these results reveal Fpr1 as a major mediator of host commensal interaction during dysbiosis

Multi‐omic network analysis identified betacellulin as a novel target of omega‐3 fatty acid attenuation of western diet‐induced nonalcoholic steatohepatitis

Abstract Clinical and preclinical studies established that supplementing diets with ω3 polyunsaturated fatty acids (PUFA) can reduce hepatic dysfunction in nonalcoholic steatohepatitis (NASH) but molecular underpinnings of this action were elusive. Herein, we used multi‐omic network analysis that unveiled critical molecular pathways involved in ω3 PUFA effects in a preclinical mouse model of western diet induced NASH. Since NASH is a precursor of liver cancer, we also performed meta‐analysis of human liver cancer transcriptomes that uncovered betacellulin as a key EGFR‐binding protein upregulated in liver cancer and downregulated by ω3 PUFAs in animals and humans with NASH. We then confirmed that betacellulin acts by promoting proliferation of quiescent hepatic stellate cells, inducing transforming growth factor–β2 and increasing collagen production. When used in combination with TLR2/4 agonists, betacellulin upregulated integrins in macrophages thereby potentiating inflammation and fibrosis. Taken together, our results suggest that suppression of betacellulin is one of the key mechanisms associated with anti‐inflammatory and anti‐fibrotic effects of ω3 PUFA on NASH

The antimicrobial peptide CRAMP is essential for colon homeostasis by maintaining microbiota balance

Abstract Commensal bacteria are critical for physiological functions in the gut, and dysbiosis in the gut may cause diseases. In this article, we report that mice deficient in cathelin-related antimicrobial peptide (CRAMP) were defective in the development of colon mucosa and highly sensitive to dextran sulfate sodium (DSS)-elicited colitis, as well as azoxymethane-mediated carcinogenesis. Pretreatment of CRAMP−/− mice with antibiotics markedly reduced the severity of DSS-induced colitis, suggesting CRAMP as a limiting factor on dysbiosis in the colon. This was supported by observations that wild-type (WT) mice cohoused with CRAMP−/− mice became highly sensitive to DSS-induced colitis, and the composition of fecal microbiota was skewed by CRAMP deficiency. In particular, several bacterial species that are typically found in oral microbiota, such as Mogibacterium neglectum, Desulfovibrio piger, and Desulfomicrobium orale, were increased in feces of CRAMP−/− mice and were transferred to WT mice during cohousing. When littermates of CRAMP+/− parents were examined, the composition of the fecal microbiota of WT pups and heterozygous parents was similar. In contrast, although the difference in fecal microbiota between CRAMP−/− and WT pups was small early on after weaning and single mouse housing, there was an increasing divergence with prolonged single housing. These results indicate that CRAMP is critical in maintaining colon microbiota balance and supports mucosal homeostasis, anti-inflammatory responses, and protection from carcinogenesis.</jats:p

Non-classical Immunity Controls Microbiota Impact on Skin Immunity and Tissue Repair

Mammalian barrier surfaces are constitutively colonized by numerous microorganisms. We explored how the microbiota was sensed by the immune system and the defining properties of such responses. Here, we show that a skin commensal can induce T cell responses in a manner that is restricted to non-classical MHC class I molecules. These responses are uncoupled from inflammation and highly distinct from pathogen-induced cells. Commensal-specific T cells express a defined gene signature that is characterized by expression of effector genes together with immunoregulatory and tissue-repair signatures. As such, non-classical MHCI-restricted commensal-specific immune responses not only promoted protection to pathogens, but also accelerated skin wound closure. Thus, the microbiota can induce a highly physiological and pleiotropic form of adaptive immunity that couples antimicrobial function with tissue repair. Our work also reveals that non-classical MHC class I molecules, an evolutionarily ancient arm of the immune system, can promote homeostatic immunity to the microbiota. [Display omitted] •Non-classical MHC class I molecules promote homeostatic immunity to the microbiota•Commensal-specific T cells express immunoregulatory and tissue repair signatures•Commensal-specific T cells accelerate wound closure Microbiota induce a form of adaptive immunity that couples antimicrobial function with tissue repair

Expression profiles of immunoregulatory genes in DCs activated by β-glucan or LPS.

<p>Human monocyte-derived DCs were cultured in the presence or absence of particulate β-glucan or LPS for the times shown. mRNA levels in cell lysates for 64 selected genes were quantitated by NanoString's nCounter technology. The gene symbols are listed in the leftmost column of the heat map. Technical replicates were collapsed and genes hierarchically clustered (rows, genes; columns, donors). Data from individual donors are expressed as fold change between the mRNA counts of each condition and the corresponding average levels of mRNA (baseline) across donors, times, and treatments.</p