Unabsorbed proteins reach the colon and are fermented by the microbiota, yielding a variety of harmful metabolites. In the present study, a 16S rRNA gene survey identified the bacterial taxa flourishing in 11 batch fermentations with proteins and peptones as the sole fermentable substrates, inoculated with the feces of six healthy adults. Organic acids, ammonia, and indole resulting from protein breakdown and fermentation accumulated in all of the cultures. Analysis of differential abundances among time-points identified Enterobacteriaceae, Burkholderiaceae, and Desulfovibrionaceae (including Esherichia-Shigella, Sutterella, Parasutterella, and Bilophila) among the bacteria that especially in the cultures with low inoculation load. Lachnospiraceae and Ruminococcaceae also encompassed many taxa that significantly expanded, mainly in cultures inoculated with high inoculation load, and showed the strongest correlation with the production of ammonium, indole, and p-cresol. Anaerotruncus, Dorea, Oscillibacter, Eubacterium oxidoreducens, Lachnoclostridium, Paeniclostridium, and Rombutsia were among them. Other Firmicutes (e.g., Roseburia, Ruminococcus, Lachnospira, Dialister, Erysipelotrichaceae, and Streptococcaceae) and many Bacteroidetes (e.g., Barnesiellaceae, Prevotellaceae, and Rickenelliaceae) decreased. Sequences attributed to Bacteroides, unresolved at the level of species, presented opposite contributions, resulting in no significant changes in the genus. This study sheds light on the multitude of bacterial taxa putatively participating in protein catabolism in the colon. Protein fermentation was confirmed as unfavorable to health, due to both the production of toxic metabolites and the blooming of opportunistic pathogens and pro-inflammatory bacteria.

Background: In the last decade, increasing evidence has shown that changes in human gut microbiota are associated with diseases, such as obesity. The excreted/secreted proteins (secretome) of the gut microbiota affect the microbial composition, altering its colonization and persistence. Furthermore, it influences microbiota-host interactions by triggering inflammatory reactions and modulating the host's immune response. The metatranscriptome is essential to elucidate which genes are expressed under diseases. In this regard, little is known about the expressed secretome in the microbiome. Here, we use a metatranscriptomic approach to delineate the secretome of the gut microbiome of Mexican children with normal weight (NW) obesity (O) and obesity with metabolic syndrome (OMS). Additionally, we performed the 16S rRNA profiling of the gut microbiota. Results: Out of the 115,712 metatranscriptome genes that codified for proteins, 30,024 (26%) were predicted to be secreted, constituting the Secrebiome of the gut microbiome. The 16S profiling confirmed an increased abundance in Firmicutes and decreased in Bacteroidetes in the obesity groups, and a significantly higher richness and diversity than the normal weight group. We found novel biomarkers for obesity with metabolic syndrome such as increased Coriobacteraceae, Collinsela, and Collinsella aerofaciens; Erysipelotrichaceae, Catenibacterium and Catenibacterium sp., and decreased Parabacteroides distasonis, which correlated with clinical and anthropometric parameters associated to obesity and metabolic syndrome. Related to the Secrebiome, 16 genes, homologous to F. prausniitzi, were overexpressed for the obese and 15 genes homologous to Bacteroides, were overexpressed in the obesity with metabolic syndrome. Furthermore, a significant enrichment of CAZy enzymes was found in the Secrebiome. Additionally, significant differences in the antigenic density of the Secrebiome were found between normal weight and obesity groups. Conclusions: These findings show, for the first time, the role of the Secrebiome in the functional human-microbiota interaction. Our results highlight the importance of metatranscriptomics to provide novel information about the gut microbiome’s functions that could help us understand the impact of the Secrebiome on the homeostasis of its human host. Furthermore, the metatranscriptome and 16S profiling confirmed the importance of treating obesity and obesity with metabolic syndrome as separate conditions to better understand the interplay between microbiome and disease.

Y ileocolic CD (n=11) were examined for the viral and microbial communities in their feces and, in one additional case, in the intestinal tissue. Using two different approaches, we compared the viral and microbial communities in several ways: by group (patients vs. controls), entity (viruses vs. bacteria), read assembly (unassembled vs. assembled reads), and methodology (our approach vs. an existing pipeline). Differences in the viral and microbial composition, and abundance between the two groups were analyzed to identify taxa that are under- or over-represented. RESULTS: A lower diversity but more variability between the CD samples in both virome and microbiome was found, with a clear distinction between groups based on the microbiome. Only ≈5% of the differential viral biomarkers are more represented in the CD group (Synechococcus phageS CBS1 andRetroviridaefamily viruses), compared with 95% in the control group. Unrelated patterns of bacteria and bacteriophages were observed. CONCLUSIONS: Our use of an extensive database is critical to retrieve more viral hits than in previous approaches. Unrelated patterns of bacteria and bacteriophages may be due to uneven representation of certain viruses in databases, among other factors. Further characterization ofRetroviridaeviruses in the CD group could be of interest, given their links with immunodeficiency and the immune responses. To conclude, some methodological considerations underlying the analysis of the viral community composition and abundance are discussed. *Correspondence: Andrés Moya, PhD, Departamento de Genética, Cavanilles Institute on Biodiversity and Evolutionary Biology, University of València, c/ José Beltrán 2, 46980 Paterna, Valencia, Spain or Genomics and Health Area, Centro Superior de Investigación en Salud Pública—Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunidad Valenciana (CSISP-FISABIO), Avenida de Cataluña 21, 46020 Valencia, Spain. E-mail: andres.moya@uv.es or moya_and@gva.es Received 30 November 2012; accepted 6 May 2013 published online 13 June 2013 © 2013 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The American College of Gastroenterology...

Background. The aim of this study was to survey the bacterial and viral communities in different types of samples from patients with Crohn's disease (CD) at dif

Background: The main limitations in the analysis of viral metagenomes are perhaps the high genetic variability and the lack of information in extant databases. To address these issues, several bioinformatic tools have been specifically designed or adapted for metagenomics by improving read assembly and creating more sensitive methods for homology detection. This study compares the performance of different available assemblers and taxonomic annotation software using simulated viral-metagenomic data. Results: We simulated two 454 viral metagenomes using genomes from NCBI's RefSeq database based on the list of actual viruses found in previously published metagenomes. Three different assembly strategies, spanning six assemblers, were tested for performance: overlap-layout-consensus algorithms Newbler, Celera and Minimo; de Bruijn graphs algorithms Velvet and MetaVelvet; and read probabilistic model Genovo. The performance of the assemblies was measured by the length of resulting contigs (using N50), the percentage of reads assembled and the overall accuracy when comparing against corresponding reference genomes. Additionally, the number of chimeras per contig and the lowest common ancestor were estimated in order to assess the effect of assembling on taxonomic and functional annotation. The functional classification of the reads was evaluated by counting the reads that correctly matched the functional data previously reported for the original genomes and calculating the number of over-represented functional categories in chimeric contigs. The sensitivity and specificity of tBLASTx, PhymmBL and the k-mer frequencies were measured by accurate predictions when comparing simulated reads against the NCBI Virus genomes RefSeq database. Conclusions: Assembling improves functional annotation by increasing accurate assignations and decreasing ambiguous hits between viruses and bacteria. However, the success is limited by the chimeric contigs occurring at all taxonomic levels. The assembler and its parameters should be selected based on the focus of each study. Minimo's non-chimeric contigs and Genovo's long contigs excelled in taxonomy assignation and functional annotation, respectively. tBLASTx stood out as the best approach for taxonomic annotation for virus identification. PhymmBL proved useful in datasets in which no related sequences are present as it uses genomic features that may help identify distant taxa. The k-frequencies underperformed in all viral datasets.

The interplay between shrimp immune system, its environment, and microbiota contributes to the organism’s homeostasis and optimal production. The metagenomic composition is typically studied using 16S rDNA profiling by clustering amplicon sequences into operational taxonomic units (OTUs) and, more recently, amplicon sequence variants (ASVs). Establish the compatibility of the taxonomy, α, and β diversity described by both methods is necessary to compare past and future shrimp microbiota studies. Here, we used identical sequences to survey the V3 16S hypervariable-region using 97% and 99% OTUs and ASVs to assess the hepatopancreas and intestine microbiota of L. vannamei from two ponds under standardized rearing conditions. We found that applying filters to retain clusters >0.1% of the total abundance per sample enabled a consistent taxonomy comparison while preserving >94% of the total reads. The three sets turned comparable at the family level, whereas the 97% identity OTU set produced divergent genus and species profiles. Interestingly, the detection of organ and pond variations was robust to the clustering method’s choice, producing comparable α and β-diversity profiles. For comparisons on shrimp microbiota between past and future studies, we strongly recommend that ASVs be compared at the family level to 97% identity OTUs or use 99% identity OTUs, both using tailored frequency filters.