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The composition of the gut microbiota is a known factor in various diseases and has proven to be a strong basis for automatic classification of disease state. A need for a better understanding of microbiota data on the functional scale has since been voiced, as it would enhance these approaches' biological interpretability. In this paper, we have developed a computational pipeline for integrating the functional annotation of the gut microbiota into an automatic classification process and facilitating downstream interpretation of its results. The process takes as input taxonomic composition data, which can be built from 16S or whole genome sequencing, and links each component to its functional annotations through interrogation of the UniProt database. A functional profile of the gut microbiota is built from this basis. Both profiles, microbial and functional, are used to train Random Forest classifiers to discern unhealthy from control samples. SPARTA ensures full reproducibility and exploration of inherent variability by extending state-of-the-art methods in three dimensions: increased number of trained random forests, selection of important variables with an iterative process, repetition of full selection process from different seeds. This process shows that the translation of the microbiota into functional profiles gives non-significantly different performances when compared to microbial profiles on 5 of 6 datasets. This approach's main contribution however stems from its interpretability rather than its performance: through repetition, it also outputs a robust subset of discriminant variables. These selections were shown to be more consistent than those obtained by a state-of-the-art method, and their contents were validated through a manual bibliographic research. The interconnections between selected taxa and functional annotations were also analyzed and revealed that important annotations emerge from the cumulated influence of non-selected taxa., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Ruiz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Aim: The current study investigated the performance of 4 widely used DNA extraction kits using different types of high (stool) and low biomass samples (chyme, broncho alveolar lavage and sputum)., Methods: Qiagen Powerfecal Pro DNA kit, Macherey Nucleospin Soil kit, Macherey Nucleospin Tissue Kit and MagnaPure LC DNA isolation kit III were evaluated in terms of DNA quantity, quality, diversity and composition profiles., Results: The quantity and quality of DNA varied among the four kits. The microbiota of the stool samples showed similar diversity and compositional profiles for the 4 kits., Conclusion: Despite differences in DNA quality and quantity, the 4 kits yielded similar results for stool samples, while all kits were not sensitive enough for low biomass samples., (© 2023 The Authors.)

The role of intestinal bacterial microbiota has been described as key in the pathophysiology of Crohn's disease (CD). CD is characterized by frequent relapses after periods of remission which are not entirely understood. In this paper, we investigate whether the heterogeneity in microbiota profiles in CD patients could be a suitable predictor for these relapses. This prospective observational study involved 259 CD patients, in which 41 provided an additional total of 62 consecutive fecal samples, with an average interval of 25 weeks in between each of these samples. Fecal microbiota was analyzed by massive genomic sequencing through 16 S rRNA amplicon sampling. We found that our 259 CD patients could be split into three distinct subgroups of microbiota (G1, G2, G3). From G1 to G3, we noticed a progressive decrease in alpha diversity (p ≤ 0.0001) but no change in the fecal calprotectin (FC) level. Focusing on the 103 consecutive samples from 41 CD patients, we showed that the patients microbiota profiles were remarkably stable over time and associated with increasing symptom severity. Investigating further this microbiota/severity association revealed that the first signs of aggravation are (1) a loss of the main anti-inflammatory Short-Chain Fatty Acids (SCFAs) Roseburia, Eubacterium, Subdoligranumum, Ruminococcus (P < 0.05), (2) an increase in pro-inflammatory pathogens Proteus, Finegoldia (P < 0.05) while (3) an increase of other minor SCFA producers such as Ezakiella, Anaerococcus, Megasphaera, Anaeroglobus, Fenollaria (P < 0.05). Further aggravation of clinical signs is significantly linked to the subsequent loss of these minor SCFAs species and to an increase in other proinflammatory Proteobacteria such as Klebsiella, Pseudomonas, Salmonella, Acinetobacter, Hafnia and proinflammatory Firmicutes such as Staphylococcus, Enterococcus, Streptococcus. (P < 0.05). To our knowledge, this is the first study (1) specifically identifying subgroups of microbiota profiles in CD patients, (2) relating these groups to the evolution of symptoms over time and (3) showing a two-step process in CD symptoms' worsening. This paves the way towards a better understanding of patient-to-patient heterogeneity, as well as providing early warning signals of future aggravation of the symptoms and eventually adapting empirically treatments., (© 2022. The Author(s).)

Background and Objectives: Patients with Hirschsprung's disease are at risk of developing Hirschsprung-associated enterocolitis, especially in the first 2 years of life. The pathophysiology of this inflammatory disease remains unclear, and intestinal dysbiosis has been proposed in the last decade. The primary objective of this study was to evaluate in a large cohort if Hirschsprung-associated enterocolitis was associated with alterations of fecal bacterial composition compared with HD without enterocolitis in different age groups., Methods: We analyzed the fecal microbiota structure of 103 Hirschsprung patients from 3 months to 16 years of age, all of whom had completed definitive surgery for rectosigmoid Hirschsprung. 16S rRNA gene sequencing allowed us to compare the microbiota composition between Hirschsprung's disease patients with (HAEC group) or without enterocolitis (HD group) in different age groups (0-2, 2-6, 6-12, and 12-16 years)., Results: Richness and diversity increased with age group but did not differ between HD and HAEC patients, irrespective of the age group. Relative abundance of Actinobacteria was lower in HAEC than in HD patients under 2 years of age (-66%, P = 0.045). Multivariate analysis by linear models (MaAsLin) considering sex, medications, birth mode, breast-feeding, and the Bristol stool scale, as well as surgery parameters, highlighted Flavonifractor plautii and Eggerthella lenta , as well as Ruminococcus gnavus group, as positively associated with Hirschsprung-associated enterocolitis in the 0-2 years age group., Conclusion: Hirschsprung-associated enterocolitis was associated with features of intestinal dysbiosis in infants (0-2 years) but not in older patients. This could explain the highest rate of enterocolitis in this age group., Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT02857205, MICROPRUNG, NCT02857205, 02/08/2016., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Arnaud, Cousin, Schmitt, Petit, Parmentier, Levard, Podevin, Guinot, DeNapoli, Hervieux, Flaum, De Vries, Randuineau, David-Le Gall, Buffet-Bataillon and Boudry.)

Metagenomics analysis is now routinely used for clinical diagnosis in several diseases, and we need confidence in interpreting metagenomics analysis of microbiota. Particularly from the side of clinical microbiology, we consider that it would be a major milestone to further advance microbiota studies with an innovative and significant approach consisting of processing steps and quality assessment for interpreting metagenomics data used for diagnosis. Here, we propose a methodology for taxon identification and abundance assessment of shotgun sequencing data of microbes that are well fitted for clinical setup. Processing steps of quality controls have been developed in order (i) to avoid low-quality reads and sequences, (ii) to optimize abundance thresholds and profiles, (iii) to combine classifiers and reference databases for best classification of species and abundance profiles for both prokaryotic and eukaryotic sequences, and (iv) to introduce external positive control. We find that the best strategy is to use a pipeline composed of a combination of different but complementary classifiers such as Kraken2/Bracken and Kaiju. Such improved quality assessment will have a major impact on the robustness of biological and clinical conclusions drawn from metagenomic studies.