The forecasting power of the microbiome

Impact of Soil Microbiomes on Mung Bean Cultivation: Insights from 16S rRNA Metagenomics

Cyclic nutrient processes, soil health maintenance, and plant development are contingent upon soil microbiomes. The microbial makeup of the soil of Maruthupandiyar College, Thanjavur, is assessed using 16S rRNA gene sequencing. QIIME2, in conjunction with the SILVA database, analyzed the sequencing data to examine microbial diversity and composition. The experimental results revealed a diverse array of bacteria in soil physicochemical properties. The alpha and beta diversity assessment revealed significant microbial community complexity and distribution patterns disparities. The research revealed bacterial groups associated with biological nitrogen fixing, suggesting their potential to enhance mung bean growth. The current study illustrates the significance of microbial interactions in soil for sustaining soil fertility and enhancing crop output. Research findings provide essential insights into improving the sustainability of tropical agriculture through intentional microbial management to create sustainable soil health systems.

Leveraging Fine-Scale Variation and Heterogeneity of the Wetland Soil Microbiome to Predict Nutrient Flux on the Landscape

Shifts in agricultural land use over the past 200 years have led to a loss of nearly 50% of existing wetlands in the USA, and agricultural activities contribute up to 65% of the nutrients that reach the Mississippi River Basin, directly contributing to biological disasters such as the hypoxic Gulf of Mexico "Dead" Zone. Federal efforts to construct and restore wetland habitats have been employed to mitigate the detrimental effects of eutrophication, with an emphasis on the restoration of ecosystem services such as nutrient cycling and retention. Soil microbial assemblages drive biogeochemical cycles and offer a unique and sensitive framework for the accurate evaluation, restoration, and management of ecosystem services. The purpose of this study was to elucidate patterns of soil bacteria within and among wetlands by developing diversity profiles from high-throughput sequencing data, link functional gene copy number of nitrogen cycling genes to measured nutrient flux rates collected from flow-through incubation cores, and predict nutrient flux using microbial assemblage composition. Soil microbial assemblages showed fine-scale turnover in soil cores collected across the topsoil horizon (0-5 cm; top vs bottom partitions) and were structured by restoration practices on the easements (tree planting, shallow water, remnant forest). Connections between soil assemblage composition, functional gene copy number, and nutrient flux rates show the potential for soil bacterial assemblages to be used as bioindicators for nutrient cycling on the landscape. In addition, the predictive accuracy of flux rates was improved when implementing deep learning models that paired connected samples across time.

Revealing microbial consortia that interfere with grapevine downy mildew through microbiome epidemiology

BACKGROUND

Plant and soil microbiomes can interfere with pathogen life cycles, but their influence on disease epidemiology remains understudied. Here, we analyzed the relationships between plant and soil microbiomes and long-term epidemiological records of grapevine downy mildew, a major disease caused by the oomycete Plasmopara viticola.

RESULTS

We found that certain microbial taxa were consistently more abundant in plots with lower disease incidence and severity and that the microbial community composition could predict disease incidence and severity. Microbial diversity was not strongly linked to epidemiological records, suggesting that disease incidence and severity is more related to the abundance of specific microbial taxa. These key taxa were identified in the topsoil, where the pathogen's oospores overwinter, and in the phyllosphere, where zoospores infect leaves. By contrast, the leaf endosphere, where the pathogen's mycelium develops, contained few taxa of interest. Surprisingly, the soil microbiota was a better predictor of disease incidence and severity than the leaf microbiota, suggesting that the soil microbiome could be a key indicator of the dynamics of this primarily aerial disease.

CONCLUSION

Our study integrates long-term epidemiological data with microbiome profiles of healthy plants to reveal fungi and bacteria relevant for the biocontrol of grapevine downy mildew. The resulting database provides a valuable resource for designing microbial consortia with potential biocontrol activity. The framework can be applied to other crop systems to guide the development of biocontrol strategies and reduce pesticide use in agriculture.

Quantifying uncertainty in microbiome-based prediction using Gaussian processes with microbial community dissimilarities

Summary

The human microbiome is closely associated with the health and disease of the human host. Machine learning models have recently utilized the human microbiome to predict health conditions and disease status. Quantifying predictive uncertainty is essential for the reliable application of these microbiome-based prediction models in clinical settings. However, uncertainty quantification in such prediction models remains unexplored. In this study, we have developed a probabilistic prediction model using a Gaussian process (GP) with a kernel function that incorporates microbial community dissimilarities. We evaluated the performance of probabilistic prediction across three regression tasks: chronological age, body mass index, and disease severity, using publicly available human gut microbiome datasets. The results demonstrated that our model outperformed existing methods in terms of probabilistic prediction accuracy. Furthermore, we found that the confidence levels closely matched the empirical coverage and that data points predicted with lower uncertainty corresponded to lower prediction errors. These findings suggest that GP regression models incorporating community dissimilarities effectively capture the characteristics of phylogenetic, high-dimensional, and sparse microbial abundance data. Our study provides a more reliable framework for microbiome-based prediction, potentially advancing the application of microbiome data in health monitoring and disease diagnosis in clinical settings.

Availability and implementation

The code is available at https://github.com/asahiadachi/gp4microbiome.

Predicting climate-change impacts on the global glacier-fed stream microbiome

The shrinkage of glaciers and the vanishing of glacier-fed streams (GFSs) are emblematic of climate change. However, forecasts of how GFS microbiome structure and function will change under projected climate change scenarios are lacking. Combining 2,333 prokaryotic metagenome-assembled genomes with climatic, glaciological, and environmental data collected by the Vanishing Glaciers project from 164 GFSs draining Earth's major mountain ranges, we here predict the future of the GFS microbiome until the end of the century under various climate change scenarios. Our model framework is rooted in a space-for-time substitution design and leverages statistical learning approaches. We predict that declining environmental selection promotes primary production in GFSs, stimulating both bacterial biomass and biodiversity. Concomitantly, predictions suggest that the phylogenetic structure of the GFS microbiome will change and entire bacterial clades are at risk. Furthermore, genomic projections reveal that microbiome functions will shift, with intensified solar energy acquisition pathways, heterotrophy and algal-bacterial interactions. Altogether, we project a 'greener' future of the world's GFSs accompanied by a loss of clades that have adapted to environmental harshness, with consequences for ecosystem functioning.

Engineering agricultural soil microbiomes and predicting plant phenotypes

Plant growth-promoting rhizobacteria (PGPR) can improve crop yields, nutrient use efficiency, plant tolerance to stressors, and confer benefits to future generations of crops grown in the same soil. Unlocking the potential of microbial communities in the rhizosphere and endosphere is therefore of great interest for sustainable agriculture advancements. Before plant microbiomes can be engineered to confer desirable phenotypic effects on their plant hosts, a deeper understanding of the interacting factors influencing rhizosphere community structure and function is needed. Dealing with this complexity is becoming more feasible using computational approaches. In this review, we discuss recent advances at the intersection of experimental and computational strategies for the investigation of plant-microbiome interactions and the engineering of desirable soil microbiomes.

Fecal and skin microbiota of two rescued Mediterranean monk seal pups during rehabilitation

IMPORTANCE

This study showed that during the rehabilitation of two rescued Mediterranean monk seal pups (Monachus monachus), the skin and fecal bacterial communities showed similar succession patterns between the two individuals. This finding means that co-housed pups share their microbiomes, and this needs to be considered in cases of infection outbreaks and their treatment. The housing conditions, along with the feeding scheme and care protocols, including the admission of antibiotics as prophylaxis, probiotics, and essential food supplements, resulted in bacterial communities with no apparent pathogenic bacteria. This is the first contribution to the microbiome of the protected seal species of M. monachus and contributes to the animal's conservation practices through its microbiome.

Biological and experimental factors that define the effectiveness of microbial inoculation on plant traits: a meta-analysis

Bacterial and fungal microbiomes associated with plants can significantly affect the host's phenotype. Inoculating plants with one or multiple bacterial and fungal species can affect specific plant traits, which is exploited in attempts to increase plant performance and stress tolerance by microbiome engineering. Currently, we lack a comprehensive synthesis on the generality of these effects related to different biological (e.g. plant models, plant traits, and microbial taxa) and experimental factors. In a meta-analysis, we showed that the plant trait under consideration and the microbial taxa used to inoculate plants significantly influenced the strength of the effect size. In a methodological context, experiments under sterilized conditions and short-term periods resulted in larger positive effects on plant traits than those of unsterilized and long-term experiments. We recommend that future studies should not only consider (short-term) laboratory experiments with sterilized plants and single inoculants but also and more often (long-term) field or greenhouse experiments with naturally occurring microbial communities associated with the plants and inoculated consortia including both bacteria and fungi.

Microbial degradation of naphthenic acids using constructed wetland treatment systems: metabolic and genomic insights for improved bioremediation of process-affected water

Naphthenic acids (NAs) are a complex mixture of organic compounds released during bitumen extraction from mined oil sands that are important contaminants of oil sands process-affected water (OSPW). NAs can be toxic to aquatic organisms and, therefore, are a main target compound for OSPW. The ability of microorganisms to degrade NAs can be exploited for bioremediation of OSPW using constructed wetland treatment systems (CWTS), which represent a possible low energy and low-cost option for scalable in situ NA removal. Recent advances in genomics and analytical chemistry have provided insights into a better understanding of the metabolic pathways and genes involved in NA degradation. Here, we discuss the ecology of microbial NA degradation with a focus on CWTS and summarize the current knowledge related to the metabolic pathways and genes used by microorganisms to degrade NAs. Evidence to date suggests that NAs are mostly degraded aerobically through ring cleavage via the beta-oxidation pathway, which can be combined with other steps such as aromatization, alpha-oxidation, omega-oxidation, or activation as coenzyme A (CoA) thioesters. Anaerobic NA degradation has also been reported via the production of benzoyl-CoA as an intermediate and/or through the involvement of methanogens or nitrate, sulfate, and iron reducers. Furthermore, we discuss how genomic, statistical, and modeling tools can assist in the development of improved bioremediation practices.

Tourism affects microbial assemblages in show caves

Anthropogenic disturbance on natural ecosystems is growing in frequency and magnitude affecting all ecosystems components. Understanding the response of different types of biocoenosis to human disturbance is urgently needed and it can be achieved by adopting a metacommunity framework. With the aid of advanced molecular techniques, we investigated sediment communities of Fungi, Bacteria and Archaea in four Italian show caves, aiming to disentangle the effects induced by tourism on their diversity and to highlight changes in the driving forces that shape their community composition. We modelled diversity measures against proxies of tourism pressure. With this approach we demonstrate that the cave tourism has a direct effect on the community of Bacteria and an indirect influence on Fungi and Archaea. By analysing the main driving forces influencing the community composition of the three microbial groups, we highlighted that stochastic factors override dispersal-related processes and environmental selection in show caves compared to undisturbed areas. Thanks to this approach, we provide new perspectives on the dynamics of microbial communities under human disturbance suggesting that a proper understanding of the underlying selective mechanisms requires a comprehensive and multi-taxonomic approach.