Microbiome Biomarkers: One Step Closer in NAFLD Cirrhosis

An integrated analysis of fecal microbiome and metabolomic features distinguish non-cirrhotic NASH from healthy control populations

BACKGROUND AND AIMS

There is great interest in identifying microbiome features as reliable noninvasive diagnostic and/or prognostic biomarkers for non-cirrhotic NASH fibrosis. Several cross-sectional studies have reported gut microbiome features associated with advanced NASH fibrosis and cirrhosis, where the most prominent features are associated with cirrhosis. However, no large, prospectively collected data exist establishing microbiome features that discern non-cirrhotic NASH fibrosis, integrate the fecal metabolome as disease biomarkers, and are unconfounded by BMI and age.

APPROACH AND RESULTS

Results from shotgun metagenomic sequencing performed on fecal samples prospectively collected from 279 US patients with biopsy-proven NASH (F1-F3 fibrosis) enrolled in the REGENERATE I303 study were compared to those from 3 healthy control cohorts and integrated with the absolute quantification of fecal bile acids. Microbiota beta-diversity was different, and BMI- and age-adjusted logistic regression identified 12 NASH-associated species. Random forest prediction models resulted in an AUC of 0.75-0.81 in a receiver operator characteristic analysis. In addition, specific fecal bile acids were significantly lower in NASH and correlated with plasma C4 levels. Microbial gene abundance analysis revealed 127 genes increased in controls, many involving protein synthesis, whereas 362 genes were increased in NASH many involving bacterial environmental responses (false discovery rate < 0.01). Finally, we provide evidence that fecal bile acid levels may be a better discriminator of non-cirrhotic NASH versus health than either plasma bile acids or gut microbiome features.

CONCLUSIONS

These results may have value as a set of baseline characteristics of non-cirrhotic NASH against which therapeutic interventions to prevent cirrhosis can be compared and microbiome-based diagnostic biomarkers identified.

Mitigation of liver fibrosis via hepatic stellate cells mitochondrial apoptosis induced by metformin

Liver fibrosis, a disease characterized by the excessive accumulation of extracellular matrix originating from activated hepatic stellate cells (HSCs), is a common pathological response to chronic liver injury resulting from a variety of insults. However, drugs that effectively block the activation of HSCs have still not been adequately investigated. This study demonstrates that metformin decreased the number of activated-HSCs through induction of apoptosis, but did not impact numbers of hepatocytes. Metformin upregulated BAX activation with facilitation of BIM, BAD and PUMA; downregulated Bcl-2 and Bcl-xl, but did not affect Mcl-1. Additionally, metformin induced cytochrome c release from mitochondria into the cytoplasm, directly triggering caspase-9-mediated mitochondrial apoptosis. The decline in mitochondrial membrane potential (ΔΨm) and deposition of superoxide in mitochondria accelerated the destruction of the integrity of mitochondrial membrane. Moreover, we verified the therapeutic effect of metformin in our mouse model of liver fibrosis associated with nonalcoholic steatohepatitis (NASH) in which hepatic function, NASH lesions and fibrosis were improved by metformin. In conclusion, this study indicated that metformin has significant therapeutic value in NASH-derived liver fibrosis by inducing apoptosis in HSCs, but does not affect the proliferation of hepatocytes.

Novel application of survival models for predicting microbial community transitions with variable selection for eDNA

Survival analysis is a prolific statistical tool in medicine for inferring risk and time to disease-related events. However, it is under-utilized in microbiome research to predict microbial community mediated events, partly due to the sparsity and high dimensional nature of the data. We advance the application of Cox proportional hazards (Cox PH) survival models to environmental DNA (eDNA) data with feature selection suitable for filtering irrelevant and redundant taxonomic variables. Selection methods are compared in terms of false positives, sensitivity, and survival estimation accuracy in simulation and in a real data setting to forecast harmful cyanobacterial blooms. A novel extension of a method for selecting microbial biomarkers with survival data (SuRFCox) reliably outperforms other methods. We determine Cox PH models with SuRFCox selected predictors are more robust to varied signal, noise, and data correlation structure. SuRFCox also yields the most accurate and consistent prediction of blooms according to cross-validated testing by year over eight different bloom seasons. Identification of common biomarkers among validated survival forecasts over changing conditions has clear biological significance. Survival models with such biomarkers inform risk assessment and provide insight into the causes of critical community transitions. Importance In this paper, we report on a novel approach of selecting microorganisms for model-based prediction of the time to critical microbially-modulated events (e.g., harmful algal blooms, clinical outcomes, community shifts, etc.). Our novel method for identifying biomarkers from large, dynamic communities of microbes has broad utility to environmental and ecological impact risk assessment and public health. Results will also promote theoretical and practical advancements relevant to the biology of specific organisms. To address the unique challenge posed by diverse environmental conditions and sparse microbes, we developed a novel method of selecting predictors for modelling time-to-event data. Competing methods for selecting predictors are rigorously compared to determine which is the most accurate and generalizable. Model forecasts are applied to show suitable predictors can precisely quantify the risk over time of biological events like harmful cyanobacterial blooms.