There is evidence that nonalcoholic fatty liver disease (NAFLD) is affected by gut microbiota. Therefore, we investigated its modifications in pediatric NAFLD patients using targeted metagenomics and metabolomics. Stools were collected from 61 consecutive patients diagnosed with nonalcoholic fatty liver (NAFL), nonalcoholic steatohepatitis (NASH), or obesity and 54 healthy controls (CTRLs), matched in a case‐control fashion. Operational taxonomic units were pyrosequenced targeting 16S ribosomal RNA and volatile organic compounds determined by solid‐phase microextraction gas chromatography‐mass spectrometry. The α‐diversity was highest in CTRLs, followed by obese, NASH, and NAFL patients; and β‐diversity distinguished between patients and CTRLs but not NAFL and NASH. Compared to CTRLs, in NAFLD patients Actinobacteria were significantly increased and Bacteroidetes reduced. There were no significant differences among the NAFL, NASH, and obese groups. Overall NAFLD patients had increased levels of Bradyrhizobium, Anaerococcus, Peptoniphilus, Propionibacterium acnes, Dorea, and Ruminococcus and reduced proportions of Oscillospira and Rikenellaceae compared to CTRLs. After reducing metagenomics and metabolomics data dimensionality, multivariate analyses indicated a decrease of Oscillospira in NAFL and NASH groups and increases of Ruminococcus, Blautia, and Dorea in NASH patients compared to CTRLs. Of the 292 volatile organic compounds, 26 were up‐regulated and 2 down‐regulated in NAFLD patients. Multivariate analyses found that combination of Oscillospira, Rickenellaceae, Parabacteroides, Bacteroides fragilis, Sutterella, Lachnospiraceae, 4‐methyl‐2‐pentanone, 1‐butanol, and 2‐butanone could discriminate NAFLD patients from CTRLs. Univariate analyses found significantly lower levels of Oscillospira and higher levels of 1‐pentanol and 2‐butanone in NAFL patients compared to CTRLs. In NASH, lower levels of Oscillospira were associated with higher abundance of Dorea and Ruminococcus and higher levels of 2‐butanone and 4‐methyl‐2‐pentanone compared to CTRLs. Conclusion: An Oscillospira decrease coupled to a 2‐butanone up‐regulation and increases in Ruminococcus and Dorea were identified as gut microbiota signatures of NAFL onset and NAFL‐NASH progression, respectively. (Hepatology 2017;65:451‐464)

The gut microbiota is composed of a huge number of different bacteria, which produce a large amount of compounds playing a key role in microbe selection and in the construction of a metabolic signaling network. The microbial activity is affected by environmental stimuli leading to the generation of a wide number of compounds, which influence the host metabolome and human health. Indeed, metabolic profiles related to the gut microbiota can offer deep insights on the impact of lifestyle and dietary factors on chronic and acute diseases. Metagenomics, metaproteomics and metabolomics are some of the meta-omics approaches to study the modulation of the gut microbiota. Metabolomic research applied to biofluids allows to: define the metabolic profile; identify and quantify classes and compounds of interest; characterize small molecules produced by intestinal microbes; and define the biochemical pathways of metabolites. Mass spectrometry and nuclear magnetic resonance spectroscopy are the principal technologies applied to metabolomics in terms of coverage, sensitivity and quantification. Moreover, the use of biostatistics and mathematical approaches coupled with metabolomics play a key role in the extraction of biologically meaningful information from wide datasets. Metabolomic studies in gut microbiota-related research have increased, focusing on the generation of novel biomarkers, which could lead to the development of mechanistic hypotheses potentially applicable to the development of nutritional and personalized therapies.

Background: Epidemiology of celiac disease (CD) is increasing. CD mainly presents in early childhood with small intestinal villous atrophy and signs of malabsorption. Compared to healthy individuals, CD patients seemed to be characterized by higher numbers of Gram-negative bacteria and lower numbers Gram-positive bacteria.Results: This study aimed at investigating the microbiota and metabolome of 19 celiac disease children under gluten-free diet (treated celiac disease, T-CD) and 15 non-celiac children (HC). PCR-denaturing gradient gel electrophoresis (DGGE) analyses by universal and group-specific primers were carried out in duodenal biopsies and faecal samples. Based on the number of PCR-DGGE bands, the diversity ofEubacteriawas the higher in duodenal biopsies of T-CD than HC children.Bifidobacteriawere only found in faecal samples. With a few exceptions, PCR-DGGE profiles of faecal samples forLactobacillusandBifidobacteriadiffered between T-CD and HC. As shown by culture-dependent methods, the levels ofLactobacillus,EnterococcusandBifidobacteriawere confirmed to be significantly higher (P= 0.028;P= 0.019; andP= 0.023, respectively) in fecal samples of HC than in T-CD children. On the contrary, cell counts (CFU/ml) of presumptiveBacteroides,Staphylococcus,Salmonella,ShighellaandKlebsiellawere significantly higher (P= 0.014) in T-CD compared to HC children.Enterococcus faeciumandLactobacillus plantarumwere the species most diffusely identified. This latter species was also found in all duodenal biopsies of T-CD and HC children. Other bacterial species were identified only in T-CD or HC faecal samples. As shown by Randomly Amplified Polymorphic DNA-PCR analysis, the percentage of strains identified as lactobacilli significantly (P= 0.011) differed between T-CD (ca. 26.5%) and HC (ca. 34.6%) groups. The metabolome of T-CD and HC children was studied using faecal and urine samples which were analyzed by gas-chromatography mass spectrometry-solid-phase microextraction and¹H-Nuclear Magnetic Resonance. As shown by Canonical Discriminant Analysis of Principal Coordinates, the levels of volatile organic compounds and free amino acids in faecal and/or urine samples were markedly affected by CD.Conclusion: As shown by the parallel microbiology and metabolome approach, the gluten-free diet lasting at least two years did not completely restore the microbiota and, consequently, the metabolome of CD children. Some molecules (e.g., ethyl-acetate and octyl-acetate, some short chain fatty acids and free amino acids, and glutamine) seems to be metabolic signatures of CD.

Beef chops were stored at 4°C under different conditions: in air (A), modified-atmosphere packaging (MAP), vacuum packaging (V), or bacteriocin-activated antimicrobial packaging (AV). After 0 to 45 days of storage, analyses were performed to determine loads of spoilage microorganisms, microbial metabolites (by solid-phase microextraction [SPME]-gas chromatography [GC]-mass spectrometry [MS] and proton nuclear magnetic resonance [ ¹ H NMR]), and microbial diversity (by PCR–denaturing gradient gel electrophoresis [DGGE] and pyrosequencing). The microbiological shelf life of meat increased with increasing selectivity of storage conditions. Culture-independent analysis by pyrosequencing of DNA extracted directly from meat showed that Brochothrix thermosphacta dominated during the early stages of storage in A and MAP, while Pseudomonas spp. took over during further storage in A. Many different bacteria, several of which are usually associated with soil rather than meat, were identified in V and AV; however, lactic acid bacteria (LAB) dominated during the late phases of storage, and Carnobacterium divergens was the most frequent microorganism in AV. Among the volatile metabolites, butanoic acid was associated with the growth of LAB under V and AV storage conditions, while acetoin was related to the other spoilage microbial groups and storage conditions. ¹ H NMR analysis showed that storage in air was associated with decreases in lactate, glycogen, IMP, and ADP levels and with selective increases in levels of 3-methylindole, betaine, creatine, and other amino acids. The meat microbiota is significantly affected by storage conditions, and its changes during storage determine complex shifts in the metabolites produced, with a potential impact on meat quality.

The human gut contains trillions of microbes that play a central role in host biology, including the provision of key nutrients from the diet. Food is a major source of precursors for metabolite production; in fact, diet modulates the gut microbiota (GM) as the nutrients, derived from dietary intake, reach the GM, affecting both the ecosystem and microbial metabolic profile. GM metabolic ability has an impact on human nutritional status from childhood. However, there is a wide variability of dietary patterns that exist among individuals. The study of interactions with the host via GM metabolic pathways is an interesting field of research in medicine, as microbiota members produce myriads of molecules with many bioactive properties. Indeed, much evidence has demonstrated the importance of metabolites produced by the bacterial metabolism from foods at the gut level that dynamically participate in various biochemical mechanisms of a cell as a reaction to environmental stimuli. Hence, the GM modulate homeostasis at the gut level, and the alteration in their composition can concur in disease onset or progression, including immunological, inflammatory, and metabolic disorders, as well as cancer. Understanding the gut microbe–nutrient interactions will increase our knowledge of how diet affects host health and disease, thus enabling personalized therapeutics and nutrition.