Approximately 2% of colorectal cancer is linked to pre-existing inflammation known as colitis-associated cancer, but most develops in patients without underlying inflammatory bowel disease. Colorectal cancer often follows a genetic pathway whereby loss of the adenomatous polyposis coli (APC) tumour suppressor and activation of β-catenin are followed by mutations in K-Ras, PIK3CA and TP53, as the tumour emerges and progresses1,2. Curiously, however, ‘inflammatory signature’ genes characteristic of colitis-associated cancer are also upregulated in colorectal cancer3,4. Further, like most solid tumours, colorectal cancer exhibits immune/inflammatory infiltrates5, referred to as ‘tumour elicited inflammation’6. Although infiltrating CD4+ TH1 cells and CD8+ cytotoxic T cells constitute a positive prognostic sign in colorectal cancer7,8, myeloid cells and T-helper interleukin (IL)-17-producing (TH17) cells promote tumorigenesis5,6, and a ‘TH17 expression signature’ in stage I/II colorectal cancer is associated with a drastic decrease in disease-free survival9. Despite its pathogenic importance, the mechanisms responsible for the appearance of tumour-elicited inflammation are poorly understood. Many epithelial cancers develop proximally to microbial communities, which are physically separated from immune cells by an epithelial barrier10. We investigated mechanisms responsible for tumour-elicited inflammation in a mouse model of colorectal tumorigenesis, which, like human colorectal cancer, exhibits upregulation of IL-23 and IL-17. Here we show that IL-23 signalling promotes tumour growth and progression, and development of a tumoural IL-17 response. IL-23 is mainly produced by tumour-associated myeloid cells that are likely to be activated by microbial products, which penetrate the tumours but not adjacent tissue. Both early and late colorectal neoplasms exhibit defective expression of several barrier proteins. We propose that barrier deterioration induced by colorectal-cancer-initiating genetic lesions results in adenoma invasion by microbial products that trigger tumour-elicited inflammation, which in turn drives tumour growth.

Bacterial infections are very common and represent one of the most important reasons of progression of liver failure, development of liver-related complications, and mortality in patients with cirrhosis. In fact, bacterial infections may be a triggering factor for the occurrence of gastrointestinal bleeding, hypervolemic hyponatremia, hepatic encephalopathy, kidney failure, and development of acute-on-chronic liver failure. Moreover, infections are a very common cause of repeated hospitalizations, impaired health-related quality of life, and increased healthcare costs in cirrhosis. Bacterial infections develop as a consequence of immune dysfunction that occurs progressively during the course of cirrhosis. In a significant proportion of patients, infections are caused by gram-negative bacteria from intestinal origin, yet gram-positive bacteria are a frequent cause of infection, particularly in hospitalized patients. In recent years, infections caused by multidrug-resistant bacteria are becoming an important clinical problem in many countries. The reduction of the negative clinical impact of infections in patients with cirrhosis may be achieved by a combination of prophylactic measures, such as administration of antibiotics, to reduce the occurrence of infections in high-risk groups together with early identification and management of infection once it has developed. Investigation on the mechanisms of altered gut microflora, translocation of bacteria, and immune dysfunction may help develop more effective and safe methods of prevention compared to those that are currently available. Moreover, research on biomarkers of early infection may be useful in early diagnosis and treatment of infections. The current manuscript reports an in-depth review and a position statement on bacterial infections in cirrhosis.

In the past decade, an exciting realization has been that diverse liver diseases - ranging from nonalcoholic steatohepatitis, alcoholic steatohepatitis and cirrhosis to hepatocellular carcinoma - fall along a spectrum. Work on the biology of the gut-liver axis has assisted in understanding the basic biology of both alcoholic fatty liver disease and nonalcoholic fatty liver disease (NAFLD). Of immense importance is the advancement in understanding the role of the microbiome, driven by high-throughput DNA sequencing and improved computational techniques that enable the complexity of the microbiome to be interrogated, together with improved experimental designs. Here, we review gut-liver communications in liver disease, exploring the molecular, genetic and microbiome relationships and discussing prospects for exploiting the microbiome to determine liver disease stage and to predict the effects of pharmaceutical, dietary and other interventions at a population and individual level. Although much work remains to be done in understanding the relationship between the microbiome and liver disease, rapid progress towards clinical applications is being made, especially in study designs that complement human intervention studies with mechanistic work in mice that have been humanized in multiple respects, including the genetic, immunological and microbiome characteristics of individual patients. These 'avatar mice' could be especially useful for guiding new microbiome-based or microbiome-informed therapies.

SUMMARY The presence of advanced fibrosis in nonalcoholic fatty liver disease (NAFLD) is the most important predictor of liver mortality. There are limited data on the diagnostic accuracy of gut microbiota derived signature for predicting the presence of advanced fibrosis. In this prospective study, we characterized the gut microbiome compositions using whole-genome shotgun sequencing of DNA extracted from stool samples. This study included 86 uniquely well-characterized patients with biopsy-proven NAFLD, 72 of which had mild/moderate (stage 0–2 fibrosis) NAFLD, and 14 had advanced fibrosis (stage 3 or 4 fibrosis). We identified a set of forty features (p-value <0.006), which included 37 bacterial species that were used to construct a Random Forest classifier model to distinguish mild/moderate NAFLD from advanced fibrosis. The model had a robust diagnostic accuracy (AUC 0.936) for detecting advanced fibrosis. This study provides preliminary evidence for a novel fecal-microbiome derived metagenomic signature to detect advanced fibrosis in NAFLD.