A Scale-Free, Fully Connected Global Transition Network Underlies Known Microbiome Diversity

Uncovering the complexity of childhood undernutrition through strain-level analysis of the gut microbiome

BACKGROUND

Undernutrition (UN) is a critical public health issue that threatens the lives of children under five in developing countries. While evidence indicates the crucial role of the gut microbiome (GM) in UN pathogenesis, the strain-level inspection and bacterial co-occurrence network investigation in the GM of UN children are lacking.

RESULTS

This study examines the strain compositions of the GM in 61 undernutrition patients (UN group) and 36 healthy children (HC group) and explores the topological features of GM co-occurrence networks using a complex network strategy. The strain-level annotation reveals that the differentially enriched species between the UN and HC groups are due to discriminated strain compositions. For example, Prevotella copri is mainly composed of P. copri ASM1680343v1 and P. copri ASM345920v1 in the HC group, but it is composed of P. copri ASM346549v1 and P. copri ASM347465v1 in the UN group. In addition, the UN-risk model constructed at the strain level demonstrates higher accuracy (AUC = 0.810) than that at the species level (AUC = 0.743). With complex network analysis, we further discovered that the UN group had a more complex GM co-occurrence network, with more hub bacteria and a higher clustering coefficient but lower information transfer efficiencies. Moreover, the results at the strain level suggested the inaccurate and even false conclusions obtained from species level analysis.

CONCLUSIONS

Overall, this study highlights the importance of examining the GM at the strain level and investigating bacterial co-occurrence networks to advance our knowledge of UN pathogenesis.

Unveiling metabotype clustering in resveratrol, daidzein, and ellagic acid metabolism: Prevalence, associated gut microbiomes, and their distinctive microbial networks

The gut microbiota (GM) produces different polyphenol-derived metabolites, yielding high interindividual variability and hampering consistent health effects. GM metabotypes associated with ellagic acid (urolithin metabotypes A (UMA), B (UMB), and 0 (UM0)), resveratrol (lunularin -producers (LP) and non-producers (LNP)), and daidzein (equol-producers (EP) and non-producers (ENP)) are known. However, individual polyphenol-related metabotypes do not occur individually. In contrast, different combinations coexist (i.e., metabotype clusters, MCs). We report here for the first time these MCs, their distribution, and their associated GM in adult humans (n = 127) after consuming for 7 days a nutraceutical (pomegranate, Polygonum cuspidatum, and red clover extracts) containing ellagitannins + ellagic acid, resveratrol, and isoflavones. Urine metabolites (UHPLC-QTOF-MS) and fecal microbiota (16S rRNA sequencing) were analyzed. Ten MCs were identified: LP + UMB + ENP (22.7%), LP + UMA + ENP (21.3%), LP + UMA + EP (16.7%), LP + UMB + EP (16%), LNP + UMA + ENP (11.3%), LNP + UMB + ENP (5.3%), LNP + UMA + EP (3.3%), LNP + UMB + EP (2%), LNP + UM0 + EP (0.7%), and LNP + UM0 + ENP (0.7%). Sex, BMI, and age did not affect the distribution of metabotypes or MCs. Multivariate analysis (MaAslin2) revealed genera differentially present in individual metabotypes and MCs. Network analysis (MENA) showed the taxa acting as module hubs and connectors. Compositional and functional profiling, alpha and beta diversities, topological network features, and GM modulation by the nutraceutical differed depending on whether the entire cohort or each MC was considered. The nutraceutical did not change the composition of LP + UMA + EP (the most robust GM with the most associated functions) but increased its network connectors. This pioneering approach, joining GM's compositional, functional, and network features in polyphenol metabolism, paves the way for identifying personalized GM-targeted strategies to improve polyphenol health benefits.

Comprehensive Assessment of 16S rRNA Gene Amplicon Sequencing for Microbiome Profiling across Multiple Habitats

The 16S rRNA gene works as a rapid and effective marker for the identification of microorganisms in complex communities; hence, a huge number of microbiomes have been surveyed by 16S amplicon-based sequencing. The resolution of the 16S rRNA gene is always considered only at the genus level; however, it has not been verified on a wide range of microbes yet. To fully explore the ability and potential of the 16S rRNA gene in microbial profiling, here, we propose Qscore, a comprehensive method to evaluate the performance of amplicons by integrating the amplification rate, multitier taxonomic annotation, sequence type, and length. Our in silico assessment by a "global view" of 35,889 microbe species across multiple reference databases summarizes the optimal sequencing strategy for 16S short reads. On the other hand, since microbes are unevenly distributed according to their habitats, we also provide the recommended configuration for 16 typical ecosystems based on the Qscores of 157,390 microbiomes in the Microbiome Search Engine (MSE). Detailed data simulation further proves that the 16S amplicons produced with Qscore-suggested parameters exhibit high precision in microbiome profiling, which is close to that of shotgun metagenomes under CAMI metrics. Therefore, by reconsidering the precision of 16S-based microbiome profiling, our work not only enables the high-quality reusability of massive sequence legacy that has already been produced but is also significant for guiding microbiome studies in the future. We have implemented the Qscore as an online service at http://qscore.single-cell.cn to parse the recommended sequencing strategy for specific habitats or expected microbial structures. IMPORTANCE 16S rRNA has long been used as a biomarker to identify distinct microbes from complex communities. However, due to the influence of the amplification region, sequencing type, sequence processing, and reference database, the accuracy of 16S rRNA has not been fully verified on a global range. More importantly, the microbial composition of different habitats varies greatly, and it is necessary to adopt different strategies according to the corresponding target microbes to achieve optimal analytical performance. Here, we developed Qscore, which evaluates the comprehensive performance of 16S amplicons from multiple perspectives, thus providing the best sequencing strategies for common ecological environments by using big data.

Application of a maximal-clique based community detection algorithm to gut microbiome data reveals driver microbes during influenza A virus infection

Influenza A Virus (IAV) infection followed by bacterial pneumonia often leads to hospitalization and death in individuals from high risk groups. Following infection, IAV triggers the process of viral RNA replication which in turn disrupts healthy gut microbial community, while the gut microbiota plays an instrumental role in protecting the host by evolving colonization resistance. Although the underlying mechanisms of IAV infection have been unraveled, the underlying complex mechanisms evolved by gut microbiota in order to induce host immune response following IAV infection remain evasive. In this work, we developed a novel Maximal-Clique based Community Detection algorithm for Weighted undirected Networks (MCCD-WN) and compared its performance with other existing algorithms using three sets of benchmark networks. Moreover, we applied our algorithm to gut microbiome data derived from fecal samples of both healthy and IAV-infected pigs over a sequence of time-points. The results we obtained from the real-life IAV dataset unveil the role of the microbial families Ruminococcaceae, Lachnospiraceae, Spirochaetaceae and Prevotellaceae in the gut microbiome of the IAV-infected cohort. Furthermore, the additional integration of metaproteomic data enabled not only the identification of microbial biomarkers, but also the elucidation of their functional roles in protecting the host following IAV infection. Our network analysis reveals a fast recovery of the infected cohort after the second IAV infection and provides insights into crucial roles of Desulfovibrionaceae and Lactobacillaceae families in combating Influenza A Virus infection. Source code of the community detection algorithm can be downloaded from https://github.com/AniBhar84/MCCD-WN.