Building up a clinical microbiota profiling: a quality framework proposal

Metabolites: a converging node of host and microbe to explain meta-organism

Meta-organisms encompassing the host and resident microbiota play a significant role in combatting diseases and responding to stress. Hence, there is growing traction to build a knowledge base about this ecosystem, particularly to characterize the bidirectional relationship between the host and microbiota. In this context, metabolomics has emerged as the major converging node of this entire ecosystem. Systematic comprehension of this resourceful omics component can elucidate the organism-specific response trajectory and the communication grid across the ecosystem embodying meta-organisms. Translating this knowledge into designing nutraceuticals and next-generation therapy are ongoing. Its major hindrance is a significant knowledge gap about the underlying mechanisms maintaining a delicate balance within this ecosystem. To bridge this knowledge gap, a holistic picture of the available information has been presented with a primary focus on the microbiota-metabolite relationship dynamics. The central theme of this article is the gut-brain axis and the participating microbial metabolites that impact cerebral functions.

Multicenter evaluation of gut microbiome profiling by next-generation sequencing reveals major biases in partial-length metabarcoding approach

Next-generation sequencing workflows, using either metabarcoding or metagenomic approaches, have massively contributed to expanding knowledge of the human gut microbiota, but methodological bias compromises reproducibility across studies. Where these biases have been quantified within several comparative analyses on their own, none have measured inter-laboratory reproducibility using similar DNA material. Here, we designed a multicenter study involving seven participating laboratories dedicated to partial- (P1 to P5), full-length (P6) metabarcoding, or metagenomic profiling (MGP) using DNA from a mock microbial community or extracted from 10 fecal samples collected at two time points from five donors. Fecal material was collected, and the DNA was extracted according to the IHMS protocols. The mock and isolated DNA were then provided to the participating laboratories for sequencing. Following sequencing analysis according to the laboratories' routine pipelines, relative taxonomic-count tables defined at the genus level were provided and analyzed. Large variations in alpha-diversity between laboratories, uncorrelated with sequencing depth, were detected among the profiles. Half of the genera identified by P1 were unique to this partner and two-thirds of the genera identified by MGP were not detected by P3. Analysis of beta-diversity revealed lower inter-individual variance than inter-laboratory variances. The taxonomic profiles of P5 and P6 were more similar to those of MGP than those obtained by P1, P2, P3, and P4. Reanalysis of the raw sequences obtained by partial-length metabarcoding profiling, using a single bioinformatic pipeline, harmonized the description of the bacterial profiles, which were more similar to each other, except for P3, and closer to the profiles obtained by MGP. This study highlights the major impact of the bioinformatics pipeline, and primarily the database used for taxonomic annotation. Laboratories need to benchmark and optimize their bioinformatic pipelines using standards to monitor their effectiveness in accurately detecting taxa present in gut microbiota.

Changes in the Gut Microbiota after the Use of Herbal Medicines in Overweight and Obese Individuals: A Systematic Review

BACKGROUND

Herbal medicine is a low-cost treatment and has been increasingly applied in obesity treatment. Gut microbiota (GM) is strongly associated with obesity pathogenesis.

METHODS

We conducted a systematic review guided by the question: "Does the use of herbal medicine change the GM composition in obese individuals?" Randomized clinical trials with obese individuals assessing the effects of herbal medicine intervention in GM were retrieved from the Medline, Embase, Scopus, Web of Science, and Cochrane Library databases, including the Cochrane Controlled Trials Register. Two reviewers independently extracted data using standardized piloted data extraction forms and assessed the study-level risk of bias using an Excel template of the Cochrane "Risk of bias" tool 2-RoB 2.

RESULTS

We identified 1094 articles in the databases. After removing duplicates and reading the title and abstract, 14 publications were fully evaluated, of which seven publications from six studies were considered eligible. The herbs analyzed were Moringa oleifera, Punica granatum, Scutellaria baicalensis, Schisandra chinensis, W-LHIT and WCBE. The analysis showed that Schisandra chinensis and Scutellaria baicalensis had significant effects on weight loss herbal intervention therapy composed by five Chinese herbal medicines Ganoderma lucidum, Coptis chinensis, Astragalus membranaceus, Nelumbo nucifera gaertn, and Fructus aurantii (W-LHIT) and white common bean extract (WCBE) on GM, but no significant changes in anthropometry and laboratory biomarkers.

CONCLUSIONS

Herbal medicine modulates GM and is associated with increased genera in obese individuals.

Meta-analysis of caries microbiome studies can improve upon disease prediction outcomes

As one of the most prevalent infective diseases worldwide, it is crucial that we not only know the constituents of the oral microbiome in dental caries but also understand its functionality. Herein, we present a reproducible meta-analysis to effectively report the key components and the associated functional signature of the oral microbiome in dental caries. Publicly available sequencing data were downloaded from online repositories and subjected to a standardized analysis pipeline before analysis. Meta-analyses identified significant differences in alpha and beta diversities of carious microbiomes when compared to healthy ones. Additionally, machine learning and receiver operator characteristic analysis showed an ability to discriminate between healthy and disease microbiomes. We identified from importance values, as derived from random forest analyses, a group of genera, notably containing Selenomonas, Aggregatibacter, Actinomyces and Treponema, which can be predictive of dental caries. Finally, we propose the most appropriate study design for investigating the microbiome of dental caries by synthesizing the studies, which had the most accurate differentiation based on random forest modelling. In conclusion, we have developed a non-biased, reproducible pipeline, which can be applied to microbiome meta-analyses of multiple diseases, but importantly we have derived from our meta-analysis a key group of organisms that can be used to identify individuals at risk of developing dental caries based on oral microbiome inhabitants.

Clinical Parasitology and Parasitome Maps as Old and New Tools to Improve Clinical Microbiomics

A growing body of evidence shows that dysbiotic gut microbiota may correlate with a wide range of disorders; hence, the clinical use of microbiota maps and fecal microbiota transplantation (FMT) can be exploited in the clinic of some infectious diseases. Through direct or indirect ecological and functional competition, FMT may stimulate decolonization of pathogens or opportunistic pathogens, modulating immune response and colonic inflammation, and restoring intestinal homeostasis, which reduces host damage. Herein, we discuss how diagnostic parasitology may contribute to designing clinical metagenomic pipelines and FMT programs, especially in pediatric subjects. The consequences of more specialized diagnostics in the context of gut microbiota communities may improve the clinical parasitology and extend its applications to the prevention and treatment of several communicable and even noncommunicable disorders.