Namco: a microbiome explorer

A specific microbial consortium enhances Th1 immunity, improves LCMV viral clearance but aggravates LCMV disease pathology in mice

Anti-viral immunity can vary tremendously from individual to individual but mechanistic understanding is still scarce. Here, we show that a defined, low complex bacterial community (OMM12) but not the general absence of microbes in germ-free mice leads to a more potent immune response compared to the microbiome of specific-pathogen-free (SPF) mice after a systemic viral infection with LCMV Clone-13. Consequently, gnotobiotic mice colonized with OMM12 have more severe LCMV-induced disease pathology but also enhance viral clearance in the intestinal tract. Mechanistically, single-cell RNA sequencing analysis of adoptively transferred virus-specific T helper cells and endogenous T helper cells in the intestinal tract reveal a stronger pro-inflammatory Th1 profile and a more vigorous expansion in OMM12 than SPF mice. Altogether, our work highlights the causative function of the intestinal microbiome for shaping adaptive anti-viral immunity with implications for vaccination strategies and anti-cancer treatment regimens.

Comparison of plant microbiota in diseased and healthy rice reveals methylobacteria as health signatures with biocontrol capabilities

Introduction

Rice (Oryza sativa) is a staple food worldwide, but its production is under constant pressure from both abiotic and biotic stresses, resulting in high use of agrochemicals. The plant microbiome harbours microorganisms that can benefit plant health and provide alternatives to the use of agrochemicals. The composition of plant microbiomes depends on many factors (soil composition, age, and health) and is considered a primary driver of future plant health. To identify plant microbiomes that protect against disease, we hypothesised that asymptomatic rice plants in fields under high pathogen pressure (i.e., healthy islands of plants among predominantly diseased plants) might harbour a microbiota that protects them from disease.

Material and Methods

We sampled healthy and leaf-diseased plants in rice fields with high disease incidence in Cambodia and profiled their microbiota at leaf, root, and rhizosphere levels using 16S V3V4 and 18S V4 amplicon barcoding sequencing.

Results

Comparison of amplicon sequence variants (ASV) of the microbiota of healthy and diseased samples revealed both disease and healthy signatures (significant enrichment or depletion at ASV/species/genus level) in both fields. The genera Methylobacterium and Methylorubrum were identified health taxa signatures with several species significantly enriched in healthy leaf samples (Methylobacterium indicum, Methylobacterium komagatae, Methylobacterium aerolatum, and Methylorubrum rhodinum). A cultivation approach on rice samples led to the isolation of bacterial strains of these two genera, which were further tested as bioinoculants on rice leaves under controlled conditions, showing for some of them a significant reduction (up to 77%) in symptoms induced by Xanthomonas oryzae pv. oryzae infection.

Discussion

We validated the hypothesis that healthy plants in fields under high disease occurrence can host specific microbiota with biocontrol capacities. This strategy could help identify new microbes with biocontrol potential for sustainable rice production.

Rapid biotransformation of STW 5 constituents by human gut microbiome from IBS- and non-IBS donors

STW 5, a blend of nine medicinal plant extracts, exhibits promising efficacy in treating functional gastrointestinal disorders, notably irritable bowel syndrome (IBS). Nonetheless, its effects on the gastrointestinal microbiome and the role of microbiota on the conversion of its constituents are still largely unexplored. This study employed an experimental ex vivo model to investigate STW 5's differential effects on fecal microbial communities and metabolite production in samples from individuals with and without IBS. Using 560 fecal microcosms (IBS patients, n = 6; healthy controls, n = 10), we evaluated the influence of pre-digested STW 5 and controls on microbial and metabolite composition at time points 0, 0.5, 4, and 24 h. Our findings demonstrate the potential of this ex vivo platform to analyze herbal medicine turnover within 4 h with minimal microbiome shifts due to abiotic factors. While only minor taxonomic disparities were noted between IBS- and non-IBS samples and upon treatment with STW 5, rapid metabolic turnover of STW 5 components into specific degradation products, such as 18β-glycyrrhetinic acid, davidigenin, herniarin, 3-(3-hydroxyphenyl)propanoic acid, and 3-(2-hydroxy-4-methoxyphenyl)propanoic acid occurred. For davidigenin, 3-(3-hydroxyphenyl)propanoic acid and 18β-glycyrrhetinic acid, anti-inflammatory, cytoprotective, or spasmolytic activities have been previously described. Notably, the microbiome-driven metabolic transformation did not induce a global microbiome shift, and the detected metabolites were minimally linked to specific taxa. Observed biotransformations were independent of IBS diagnosis, suggesting potential benefits for IBS patients from biotransformation products of STW 5.

IMPORTANCE

STW 5 is an herbal medicinal product with proven clinical efficacy in the treatment of functional gastrointestinal disorders, like functional dyspepsia and irritable bowel syndrome (IBS). The effects of STW 5 on fecal microbial communities and metabolite production effects have been studied in an experimental model with fecal samples from individuals with and without IBS. While only minor taxonomic disparities were noted between IBS- and non-IBS samples and upon treatment with STW 5, rapid metabolic turnover of STW 5 components into specific degradation products with reported anti-inflammatory, cytoprotective, or spasmolytic activities was observed, which may be relevant for the pharmacological activity of STW 5.

Diet changes due to urbanization in South Africa are linked to microbiome and metabolome signatures of Westernization and colorectal cancer

Transition from traditional high-fiber to Western diets in urbanizing communities of Sub-Saharan Africa is associated with increased risk of non-communicable diseases (NCD), exemplified by colorectal cancer (CRC) risk. To investigate how urbanization gives rise to microbial patterns that may be amenable by dietary intervention, we analyzed diet intake, fecal 16 S bacteriome, virome, and metabolome in a cross-sectional study in healthy rural and urban Xhosa people (South Africa). Urban Xhosa individuals had higher intakes of energy (urban: 3,578 ± 455; rural: 2,185 ± 179 kcal/d), fat and animal protein. This was associated with lower fecal bacteriome diversity and a shift from genera favoring degradation of complex carbohydrates (e.g., Prevotella) to taxa previously shown to be associated with bile acid metabolism and CRC. Urban Xhosa individuals had higher fecal levels of deoxycholic acid, shown to be associated with higher CRC risk, but similar short-chain fatty acid concentrations compared with rural individuals. Fecal virome composition was associated with distinct gut bacterial communities across urbanization, characterized by different dominant host bacteria (urban: Bacteriodota; rural: unassigned taxa) and variable correlation with fecal metabolites and dietary nutrients. Food and skin microbiota samples showed compositional differences along the urbanization gradient. Rural-urban dietary transition in South Africa is linked to major changes in the gut microbiome and metabolome. Further studies are needed to prove cause and identify whether restoration of specific components of the traditional diet will arrest the accelerating rise in NCDs in Sub-Saharan Africa.

A toolbox of machine learning software to support microbiome analysis

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.

Aircraft surveys for air eDNA: probing biodiversity in the sky

Air is a medium for dispersal of environmental DNA (eDNA) carried in bioaerosols, yet the atmosphere is mostly unexplored as a source of genetic material encompassing all domains of life. In this study, we designed and deployed a robust, sterilizable hardware system for airborne nucleic acid capture featuring active filtration of a quantifiable, controllable volume of air and a high-integrity chamber to protect the sample from loss or contamination. We used our hardware system on an aircraft across multiple height transects over major aerosolization sources to collect air eDNA, coupled with high-throughput amplicon sequencing using multiple DNA metabarcoding markers targeting bacteria, plants, and vertebrates to test the hypothesis of large-scale genetic presence of these bioaerosols throughout the planetary boundary layer in the lower troposphere. Here, we demonstrate that the multi-taxa DNA assemblages inventoried up to 2,500 m using our airplane-mounted hardware system are reflective of major aerosolization sources in the survey area and show previously unreported airborne species detections (i.e., Allium sativum L). We also pioneer an aerial survey flight grid standardized for atmospheric sampling of genetic material and aeroallergens using a light aircraft and limited resources. Our results show that air eDNA from terrestrial bacteria, plants, and vertebrates is detectable up to high altitude using our airborne air sampler and demonstrate the usefulness of light aircraft in monitoring campaigns. However, our work also underscores the need for improved marker choices and reference databases for species in the air column, particularly eukaryotes. Taken together, our findings reveal strong connectivity or mixing of terrestrial-associated eDNA from ground level aerosolization sources and the atmosphere, and we recommend that parameters and indices considering lifting action, atmospheric instability, and potential for convection be incorporated in future surveys for air eDNA. Overall, this work establishes a foundation for light aircraft campaigns to comprehensively and economically inventory bioaerosol emissions and impacts at scale, enabling transformative future opportunities in airborne DNA technology.

Contamination detection and microbiome exploration with GRIMER

BACKGROUND

Contamination detection is a important step that should be carefully considered in early stages when designing and performing microbiome studies to avoid biased outcomes. Detecting and removing true contaminants is challenging, especially in low-biomass samples or in studies lacking proper controls. Interactive visualizations and analysis platforms are crucial to better guide this step, to help to identify and detect noisy patterns that could potentially be contamination. Additionally, external evidence, like aggregation of several contamination detection methods and the use of common contaminants reported in the literature, could help to discover and mitigate contamination.

RESULTS

We propose GRIMER, a tool that performs automated analyses and generates a portable and interactive dashboard integrating annotation, taxonomy, and metadata. It unifies several sources of evidence to help detect contamination. GRIMER is independent of quantification methods and directly analyzes contingency tables to create an interactive and offline report. Reports can be created in seconds and are accessible for nonspecialists, providing an intuitive set of charts to explore data distribution among observations and samples and its connections with external sources. Further, we compiled and used an extensive list of possible external contaminant taxa and common contaminants with 210 genera and 627 species reported in 22 published articles.

CONCLUSION

GRIMER enables visual data exploration and analysis, supporting contamination detection in microbiome studies. The tool and data presented are open source and available at https://gitlab.com/dacs-hpi/grimer.