The duo Clostridium and Lactobacillus linked to hydrogen production from a lignocellulosic substrate.

The study aimed to identify interspecies interactions within a native microbial community present in a hydrogen-producing bioreactor fed with two wheat straw cultivars. The relationships between the microbial community members were studied building a canonical correspondence analysis and corroborated through in vitro assays. The results showed that the bioreactor reached a stable hydrogen production of ca. 86 mL/kg·d in which the cultivar change did not affect the average performance. Lactobacillus and Clostridium dominated throughout the whole operation period where butyric acid was the main metabolite. A canonical correspondence analysis correlated positively Lactobacillus with hydrogen productivity and hydrogen-producing bacteria like Clostridium and Ruminococaceae. Agar diffusion testing of isolated strains confirmed that Lactobacillus inhibited the growth of Enterococcus, but not of Clostridium. We suggest that the positive interaction between Lactobacillus and Clostridium is generated by a division of labor for degrading the lignocellulosic substrate in which Lactobacillus produces lactic acid from the sugar fermentation while Clostridium quickly uses this lactic acid to produce hydrogen and butyric acid. The significance of this work lies in the fact that different methodological approaches confirm a positive association in the duo Lactobacillus-Clostridium in a bioreactor with stable hydrogen production from a complex substrate.

Corrigendum: Human gut microbiome across different lifestyles: from hunter-gatherers to urban populations.

[This corrects the article DOI: 10.3389/fmicb.2022.843170.].

Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data.

Feature-based molecular networking (FBMN) is a popular analysis approach for liquid chromatography-tandem mass spectrometry-based non-targeted metabolomics data. While processing liquid chromatography-tandem mass spectrometry data through FBMN is fairly streamlined, downstream data handling and statistical interrogation are often a key bottleneck. Especially users new to statistical analysis struggle to effectively handle and analyze complex data matrices. Here we provide a comprehensive guide for the statistical analysis of FBMN results, focusing on the downstream analysis of the FBMN output table. We explain the data structure and principles of data cleanup and normalization, as well as uni- and multivariate statistical analysis of FBMN results. We provide explanations and code in two scripting languages (R and Python) as well as the QIIME2 framework for all protocol steps, from data clean-up to statistical analysis. All code is shared in the form of Jupyter Notebooks ( https://github.com/Functional-Metabolomics-Lab/FBMN-STATS ). Additionally, the protocol is accompanied by a web application with a graphical user interface ( https://fbmn-statsguide.gnps2.org/ ) to lower the barrier of entry for new users and for educational purposes. Finally, we also show users how to integrate their statistical results into the molecular network using the Cytoscape visualization tool. Throughout the protocol, we use a previously published environmental metabolomics dataset for demonstration purposes. Together, the protocol, code and web application provide a complete guide and toolbox for FBMN data integration, cleanup and advanced statistical analysis, enabling new users to uncover molecular insights from their non-targeted metabolomics data. Our protocol is tailored for the seamless analysis of FBMN results from Global Natural Products Social Molecular Networking and can be easily adapted to other mass spectrometry feature detection, annotation and networking tools.

Human Gut Microbiome Across Different Lifestyles: From Hunter-Gatherers to Urban Populations.

Human lifestyle and its relationship with the human microbiome has been a line of research widely studied. This is because, throughout human history, civilizations have experienced different environments and lifestyles that could have promoted changes in the human microbiome. The comparison between industrialized and non-industrialized human populations in several studies has allowed to observe variation in the microbiome structure due to the population lifestyle. Nevertheless, the lifestyle of human populations is a gradient where several subcategories can be described. Yet, it is not known how these different lifestyles of human populations affect the microbiome structure on a large scale. Therefore, the main goal of this work was the collection and comparison of 16S data from the gut microbiome of populations that have different lifestyles around the world. With the data obtained from 14 studies, it was possible to compare the gut microbiome of 568 individuals that represent populations of hunter-gatherers, agricultural, agropastoral, pastoral, and urban populations. Results showed that industrialized populations present less diversity than those from non-industrialized populations, as has been described before. However, by separating traditional populations into different categories, we were able to observe patterns that cannot be appreciated by encompassing the different traditional lifestyles in a single category. In this sense, we could confirm that different lifestyles exhibit distinct alpha and beta diversity. In particular, the gut microbiome of pastoral and agropastoral populations seems to be more similar to those of urban populations according to beta diversity analysis. Beyond that, beta diversity analyses revealed that bacterial composition reflects the different lifestyles, representing a transition from hunters-gatherers to industrialized populations. Also, we found that certain groups such as Bacteoidaceae, Lanchospiraceae, and Rickenellaceae have been favored in the transition to modern societies, being differentially abundant in urban populations. Thus, we could hypothesize that due to adaptive/ecological processes; multifunctional bacterial groups (e.g., Bacteroidaceae) could be replacing some functions lost in the transition to modern lifestyle.

Host genotype explains rhizospheric microbial community composition: the case of wild cotton metapopulations (Gossypium hirsutum L.) in Mexico.

The rhizosphere provides several benefits to the plant host being a strong determinant for its health, growth and productivity. Nonetheless, the factors behind the assembly of the microbial communities associated with the rhizosphere such as the role of plant genotypes are not completely understood. In this study, we tested the role that intraspecific genetic variation has in rhizospheric microbial community assemblages, using genetically distinct wild cotton populations as a model of study. We followed a common garden experiment including five wild cotton populations, controlling for plant genotypes, environmental conditions and soil microbial community inoculum, to test for microbial differences associated with genetic variation of the plant hosts. Microbial communities of the treatments were characterized by culture-independent 16S rRNA gene amplicon sequencing with Illumina MiSeq platform. We analyzed microbial community diversity (alpha and beta), and diversity structure of such communities, determined by co-occurrence networks. Results show that different plant genotypes select for different and specific microbial communities from a common inoculum. Although we found common amplicon sequence variants (ASVs) to all plant populations (235), we also found unique ASVs for different populations that could be related to potential functional role of such ASVs in the rhizosphere.