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The gut microbiome has been implicated in cancer in several ways, as specific microbial signatures are known to promote cancer development and influence safety, tolerability and efficacy of therapies. The ‘omics’ technologies used for microbiome analysis continuously evolve and, although much of the research is still at an early stage, large-scale datasets of ever increasing size and complexity are being produced. However, there are varying levels of difficulty in realizing the full potential of these new tools, which limit our ability to critically analyse much of the available data. In this Perspective, we provide a brief overview on the role of gut microbiome in cancer and focus on the need, role and limitations of a machine learning-driven approach to analyse large amounts of complex health-care information in the era of big data. We also discuss the potential application of microbiome-based big data aimed at promoting precision medicine in cancer.Large-scale datasets of increasing size and complexity are being produced in the microbiome and oncology field. This Perspective discusses the potential to harness gut microbiome analysis, big data and machine learning in cancer, and the potential and limitations with this approach.

Diarrhoea is one of the most burdensome and common adverse events of chemotherapeutics, and has no standardised therapy to date. Increasing evidence suggests that the gut microbiome can influence the development of chemotherapy-induced diarrhoea. Here we report findings from a randomised clinical trial of faecal microbiota transplantation (FMT) to treat diarrhoea induced by tyrosine kinase inhibitors (TKI) in patients with metastatic renal cell carcinoma (ClinicalTrials.gov number: NCT04040712). The primary outcome is the resolution of diarrhoea four weeks after the end of treatments. Twenty patients are randomised to receive FMT from healthy donors or placebo FMT (vehicle only). Donor FMT is more effective than placebo FMT in treating TKI-induced diarrhoea, and a successful engraftment is observed in subjects receiving donor faeces. No serious adverse events are observed in both treatment arms. The trial meets pre-specified endpoints. Our findings suggest that the therapeutic manipulation of gut microbiota may become a promising treatment option to manage TKI-dependent diarrhoea. Tyrosine kinase inhibitors (TKIs) have improved the clinical outcomes of patients with metastatic renal cell carcinoma (mRCC), however TKI-related diarrhoea is a common and serious adverse effect. Here the authors show in a randomized clinical trial that faecal microbiota transplantation from healthy donors can improve TKI-induced diarrhoea in patients with mRCC.

Short-chain fatty acids (SCFAs) play a key role in health and disease, as they regulate gut homeostasis and their deficiency is involved in the pathogenesis of several disorders, including inflammatory bowel diseases, colorectal cancer, and cardiometabolic disorders. SCFAs are metabolites of specific bacterial taxa of the human gut microbiota, and their production is influenced by specific foods or food supplements, mainly prebiotics, by the direct fostering of these taxa. This Review provides an overview of SCFAs’ roles and functions, and of SCFA-producing bacteria, from their microbiological characteristics and taxonomy to the biochemical process that lead to the release of SCFAs. Moreover, we will describe the potential therapeutic approaches to boost the levels of SCFAs in the human gut and treat different related diseases.

Fecal microbiota transplantation (FMT) is highly effective against recurrent Clostridioides difficile infection and is considered a promising treatment for other microbiome-related disorders, but a comprehensive understanding of microbial engraftment dynamics is lacking, which prevents informed applications of this therapeutic approach. Here, we performed an integrated shotgun metagenomic systematic meta-analysis of new and publicly available stool microbiomes collected from 226 triads of donors, pre-FMT recipients and post-FMT recipients across eight different disease types. By leveraging improved metagenomic strain-profiling to infer strain sharing, we found that recipients with higher donor strain engraftment were more likely to experience clinical success after FMT ( P = 0.017) when evaluated across studies. Considering all cohorts, increased engraftment was noted in individuals receiving FMT from multiple routes (for example, both via capsules and colonoscopy during the same treatment) as well as in antibiotic-treated recipients with infectious diseases compared with antibiotic-naïve patients with noncommunicable diseases. Bacteroidetes and Actinobacteria species (including Bifidobacteria ) displayed higher engraftment than Firmicutes except for six under-characterized Firmicutes species. Cross-dataset machine learning predicted the presence or absence of species in the post-FMT recipient at 0.77 average AUROC in leave-one-dataset-out evaluation, and highlighted the relevance of microbial abundance, prevalence and taxonomy to infer post-FMT species presence. By exploring the dynamics of microbiome engraftment after FMT and their association with clinical variables, our study uncovered species-specific engraftment patterns and presented machine learning models able to predict donors that might optimize post-FMT specific microbiome characteristics for disease-targeted FMT protocols. Coupling microbial metagenomics with machine learning enables prediction of donor strain engraftment after fecal microbiota transplantation (FMT) for a range of diseases, and may help tailor design of FMT to optimize microbial engraftment and achieve clinical outcomes.

Atherosclerosis reflects a chronic inflammatory process of arteries. The origin of chronic vascular inflammation has been associated over a long time primarily with lipid disorders, but evidence from the past years has suggested that lipid-independent pathways are also involved. Recent research has demonstrated that the gastrointestinal microbiota has an impact on the development of atherosclerosis. Many clinical studies have revealed that there exist altered gut microbiota and increased intestinal abundance of bacteria from the oral cavity in atherosclerosis-related disorders such as cardiovascular disease or stroke, while several studies have demonstrated insights into underlying mechanisms. Various microbial-derived metabolites, such as the pathogen-associated molecular pattern endotoxin, trimethylamine N-oxide or imidazole propionate, contribute to atherosclerosis, while other bacterial metabolites, such as some tryptophan derivatives, might be protective. Furthermore, gut microbiota and lipid pathways are highly interactive, and the gut microbiota affects lipid absorption and storage, and the gut microbiota also contributes to vascular ageing. Interference with the gut microbiota by prebiotics, probiotics and antibiotics has demonstrated beneficial effects on atherosclerosis mainly in preclinical models. Overall, the gut microbiota has appeared as an important rheostat for vascular inflammation in atherosclerosis, which is controlled by host-microbe interactions that may be therapeutically exploited in the future.

A new study shows that a sustainable faecal microbiota transplantation (FMT) treatment protocol, including anaerobic sample preparation, induces remission of active ulcerative colitis. The promising results are another piece in the puzzle, but it is not yet possible to draw conclusions and implement the procedure in clinical practice.

Bacteriocins are bactericidal peptides, ribosomally synthesized, with an inhibitory activity against diverse groups of undesirable microorganisms. Bacteriocins are produced by both gram-positive and gram-negative bacteria, and to a lesser extent by some archaea. Bacteriophages are viruses that are able to infect bacterial cells and force them to produce viral components, using a lytic or lysogenic cycle. They constitute a large community in the human gut called the phageome, the most abundant part of the gut virome. Bacteriocins and bacteriophages may have an influence on both human health and diseases, thanks to their ability to modulate the gut microbiota and regulate the competitive relationship among the different microorganisms, strains and cells living in the human intestine. In this review, we explore the role of bacteriocins and bacteriophages in the most frequent gastrointestinal diseases by dissecting their interaction with the complex environment of the human gut, analyzing a possible link with extra-intestinal diseases, and speculating on their possible therapeutic application with the end goal of promoting gut health.

The worldwide prevalence of obesity more than doubled between 1980 and 2014. The obesity pandemic is tightly linked to an increase in energy availability, sedentariness and greater control of ambient temperature that have paralleled the socioeconomic development of the past decades. The most frequent cause which leads to the obesity development is a dysbalance between energy intake and energy expenditure. The gut microbiota as an environmental factor which influence whole-body metabolism by affecting energy balance but also inflammation and gut barrier function, integrate peripheral and central food intake regulatory signals and thereby increase body weight. Probiotics have physiologic functions that contribute to the health of gut microbiota, can affect food intake and appetite, body weight and composition and metabolic functions through gastrointestinal pathways and modulation of the gut bacterial community.

Preliminary studies suggested a possible correlation of microbiota with Barrett's esophagus (BE) and esophageal adenocarcinoma (EAC), where the need for tools to ameliorate its poor prognosis is mandatory. We explored the potential signature of esophageal microbiota and its predicted functional profile along the continuous spectrum from BE to EAC. We analyzed through 16S-based amplicon sequencing the mucosal microbiota and the microbiota-related functional predictions in 10 BE and 6 EAC patients compared with 10 controls, exploring also potential differences between the metaplastic mucosa (BEM) and the adjacent normal areas of BE patients (BEU). BEM and EAC showed a higher level of α and β-diversity. BEM evidenced a decrease of Streptococcus and an increase of Prevotella, Actinobacillus, Veillonella, and Leptotrichia. EAC displayed a striking reduction of Streptococcus, with an increase of Prevotella, Veillonella and Leptotrichia. LefSe analysis identified Leptotrichia as the main taxa distinguishing EAC. BEM showed a decreased α-diversity compared with BEU and a reduction of Bacteroidetes, Prevotella and Fusobacterium. Functional predictions identified peculiar profiles for each group with a high potential for replication and repair in BEM; an upregulated energy, replication and signaling metabolisms, with the fatty-acids biosynthesis and nitrogen and D-alanine pathways down-regulated in EAC. Our pilot study identifies a unique microbial structure and function profile for BE and EAC, as well as for metaplastic and near-normal areas. It proposes a new concept for BE, which could be intended not only as the histological, but, also, as the microbial closest precursor of EAC. This requires further larger follow-up studies, but opens intriguing horizons towards innovative diagnostic and therapeutic options for EAC.

The stomach is inhabited by diverse microbial communities, co-existing in a dynamic balance. Long-term use of drugs such as proton pump inhibitors (PPIs), or bacterial infection such as Helicobacter pylori , cause significant microbial alterations. Yet, studies revealing how the commensal bacteria re-organize, due to these perturbations of the gastric environment, are in early phase and rely principally on linear techniques for multivariate analysis. Here we disclose the importance of complementing linear dimensionality reduction techniques with nonlinear ones to unveil hidden patterns that remain unseen by linear embedding. Then, we prove the advantages to complete multivariate pattern analysis with differential network analysis, to reveal mechanisms of bacterial network re-organizations which emerge from perturbations induced by a medical treatment (PPIs) or an infectious state ( H. pylori ). Finally, we show how to build bacteria-metabolite multilayer networks that can deepen our understanding of the metabolite pathways significantly associated to the perturbed microbial communities. Drug use or bacterial infection can cause significant alterations of gastric microbiome. Here, the authors show how advanced pattern recognition by nonlinear machine intelligence can help disclose a bacteria-metabolite network which enlightens mechanisms behind such perturbations.