Soil microbiome predictability increases with spatial and taxonomic scale

Depth effects on bacterial community altitudinal patterns and assembly processes in the warm-temperate montane forests of China

Soil bacterial communities are essential for ecosystem function, yet their response along altitudinal gradients in different soil strata remains unclear. Understanding bacterial community co-occurrence networks and assembly patterns in mountain ecosystems is crucial for comprehending microbial ecosystem functions. We utilized Illumina MiSeq sequencing to study bacterial diversity and assembly patterns of surface and subsurface soils across a range of elevations (700 to 2100 m) on Dongling Mountain. Our results showed significant altitudinal distribution patterns concerning bacterial diversity and structure in the surface soil. The bacterial diversity exhibited a consistent decrease, while specific taxa demonstrated unique patterns along the altitudinal gradient. However, no altitudinal dependence was observed for bacterial diversity and community structure in the subsurface soil. Additionally, a shift in bacterial ecological groups is evident with changing soil depth. Copiotrophic taxa thrive in surface soils characterized by higher carbon and nutrient content, while oligotrophic taxa dominate in subsurface soils with more limited resources. Bacterial community characteristics exhibited strong correlations with soil organic carbon in both soil layers, followed by pH in the surface soil and soil moisture in the subsurface soil. With increasing depth, there is an observable increase in taxa-taxa interaction complexity and network structure within bacterial communities. The surface soil exhibits greater sensitivity to environmental perturbations, leading to increased modularity and an abundance of positive relationships in its community networks compared to the subsurface soil. Furthermore, the bacterial community at different depths was influenced by combining deterministic and stochastic processes, with stochasticity (homogenizing dispersal and undominated) decreasing and determinism (heterogeneous selection) increasing with soil depth.

Effect of long-term in-row branch covering on soil microorganisms in pear orchards

Branches covering (BC) is a way to reuse the pruned branches and save the cost of ground cloth. This study investigated the effects of BC and ground-cloth covering on the soil microcosm environment by measuring the chemical properties and microbial communities at different soil depths for 6 years. The results revealed that BC significantly improved soil chemical properties, increased the abundance of bacterial microbial communities and the diversity and homogeneity of bacteria and fungi, while decreased the abundance of fungal microbial communities. There was a threshold value for the regulation of microbial communities by BC, which decreased the high-abundance communities (Proteobacteria, Ascomycota, etc.) and increased the low-abundance communities (Acidobacteriota, Basidiomycota, etc.). Fungi were more sensitive to BC than bacteria. The stability and homogeneity of microorganisms were stronger in the 15-25 cm soil layer. The bacterial phyla were dominated by Proteobacteria, with the top 10 phyla accounting for more than 80% of the relative abundance; the genera were dominated by MND1, with the top 10 genera accounting for about 10%. The fungal phyla were dominated by Ascomycota, with the top 10 phyla accounting for 50-90%; the genera were dominated by unidentified Pyronemataceae sp., with the top 10 genera accounting for 30-60%. The phyla that differed significantly between treatments were mainly Proteobacteria, Ascomycota, Acidobacteriota, and Basidiomycota. In addition, metabolism was the predominant function in bacteria, while Saprotroph was the predominant function in fungi. Bacteroidota correlated strongly with soil chemical properties and bacterial functions, while Chytridiomycota correlated strongly with soil chemical properties and Pathogen-Saprotroph-Symbiotroph. In conclusion, BC can improve soil nutrient content and optimize microbial community structure and function. Through initially assessing the effects of BC on soil nutrients and microorganisms in pear orchard rows, this study provides a reference for excavating key microorganisms and updating the soil row management model.

Elevated methane flux in a tropical peatland post-fire is linked to depth-dependent changes in peat microbiome assembly

Fires in tropical peatlands extend to depth, transforming them from carbon sinks into methane sources and severely limit forest recovery. Peat microbiomes influence carbon transformations and forest recovery, yet our understanding of microbiome shifts post-fire is currently limited. Our previous study highlighted altered relationships between the peat surface, water table, aboveground vegetation, and methane flux after fire in a tropical peatland. Here, we link these changes to post-fire shifts in peat microbiome composition and assembly processes across depth. We report kingdom-specific and depth-dependent shifts in alpha diversity post-fire, with large differences at deeper depths. Conversely, we found shifts in microbiome composition across all depths. Compositional shifts extended to functional groups involved in methane turnover, with methanogens enriched and methanotrophs depleted at mid and deeper depths. Finally, we show that community shifts at deeper depths result from homogeneous selection associated with post-fire changes in hydrology and aboveground vegetation. Collectively, our findings provide a biological basis for previously reported methane fluxes after fire and offer new insights into depth-dependent shifts in microbiome assembly processes, which ultimately underlie ecosystem function predictability and ecosystem recovery.

Consistent predictors of microbial community composition across spatial scales in grasslands reveal low context-dependency

Environmental circumstances shaping soil microbial communities have been studied extensively. However, due to disparate study designs, it has been difficult to resolve whether a globally consistent set of predictors exists, or context-dependency prevails. Here, we used a network of 18 grassland sites (11 of those containing regional plant productivity gradients) to examine (i) if similar abiotic or biotic factors predict both large-scale (across sites) and regional-scale (within sites) patterns in bacterial and fungal community composition, and (ii) if microbial community composition differs consistently at two levels of regional plant productivity (low vs. high). Our results revealed that bacteria were associated with particular soil properties (such as base saturation) and both bacteria and fungi were associated with plant community composition across sites and within the majority of sites. Moreover, a discernible microbial community signal emerged, clearly distinguishing high and low-productivity soils across different grasslands independent of their location in the world. Hence, regional productivity differences may be typified by characteristic soil microbial communities across the grassland biome. These results could encourage future research aiming to predict the general effects of global changes on soil microbial community composition in grasslands and to discriminate fertile from infertile systems using generally applicable microbial indicators.

Unravelling Metabolic Heterogeneity of Chinese Baijiu Fermentation in Age-Gradient Vessels

Fermentation vessels affect the characteristics of food fermentation; however, we lack an approach to identify the biomarkers indicating fermentation. In this study, we applied metabolomics and high-throughput sequencing analysis to reveal the dynamic of metabolites and microbial communities in age-gradient fermentation vessels for baijiu production. Furthermore, we identified 64 metabolites during fermentation, and 19 metabolites significantly varied among the three vessels (p < 0.05). Moreover, the formation of these 19 metabolites were positively correlated with the core microbiota (including Aspergillus, Saccharomyces, Lactobacillus, and Bacillus). In addition, ethyl lactate or ethyl acetate were identified as the biomarkers for indicating the metabolism among age-gradient fermentation vessels by BP-ANN (R2 > 0.40). Therefore, this study combined the biological analysis and predictive model to identify the biomarkers indicating metabolism in different fermentation vessels, and it also provides a potential approach to assess the profiling of food fermentations.

Predicting microbial community compositions in wastewater treatment plants using artificial neural networks

BACKGROUND

Activated sludge (AS) of wastewater treatment plants (WWTPs) is one of the world's largest artificial microbial ecosystems and the microbial community of the AS system is closely related to WWTPs' performance. However, how to predict its community structure is still unclear.

RESULTS

Here, we used artificial neural networks (ANN) to predict the microbial compositions of AS systems collected from WWTPs located worldwide. The predictive accuracy R²_1:1 of the Shannon-Wiener index reached 60.42%, and the average R²_1:1 of amplicon sequence variants (ASVs) appearing in at least 10% of samples and core taxa were 35.09% and 42.99%, respectively. We also found that the predictability of ASVs was significantly positively correlated with their relative abundance and occurrence frequency, but significantly negatively correlated with potential migration rate. The typical functional groups such as nitrifiers, denitrifiers, polyphosphate-accumulating organisms (PAOs), glycogen-accumulating organisms (GAOs), and filamentous organisms in AS systems could also be well recovered using ANN models, with R²_1:1 ranging from 32.62% to 56.81%. Furthermore, we found that whether industry wastewater source contained in inflow (IndConInf) had good predictive abilities, although its correlation with ASVs in the Mantel test analysis was weak, which suggested important factors that cannot be identified using traditional methods may be highlighted by the ANN model.

CONCLUSIONS

We demonstrated that the microbial compositions and major functional groups of AS systems are predictable using our approach, and IndConInf has a significant impact on the prediction. Our results provide a better understanding of the factors affecting AS communities through the prediction of the microbial community of AS systems, which could lead to insights for improved operating parameters and control of community structure. Video Abstract.

Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies

The plant-associated microbiome is a key component of plant systems, contributing to their health, growth, and productivity. The application of machine learning (ML) in this field promises to help untangle the relationships involved. However, measurements of microbial communities by high-throughput sequencing pose challenges for ML. Noise from low sample sizes, soil heterogeneity, and technical factors can impact the performance of ML. Additionally, the compositional and sparse nature of these datasets can impact the predictive accuracy of ML. We review recent literature from plant studies to illustrate that these properties often go unmentioned. We expand our analysis to other fields to quantify the degree to which mitigation approaches improve the performance of ML and describe the mathematical basis for this. With the advent of accessible analytical packages for microbiome data including learning models, researchers must be familiar with the nature of their datasets.

Understory vegetation diversity, soil properties and microbial community response to different thinning intensities in Cryptomeria japonica var. sinensis plantations

Introduction

Soil microorganisms are the key factors in elucidating the effects of thinning on tree growth performance, but the effects of vegetation and soil on the species composition and function of soil microorganisms after thinning are still not well elaborated.

Methods

The effects of thinning on understory vegetation diversity, soil physicochemical properties and soil microbial community composition were investigated in a thinning trial plantation of Cryptomeria japonica var. sinensis, including four thinning intensities (control: 0%, LIT: 20%, MIT: 30% and HIT: 40%), and the relationships of the microbial community structure with the understory vegetation diversity and soil properties were assessed.

Results

The results showed that thinning had a greater effect on the diversity of the shrub layer than the herb layer. The soil bulk density and the contents of soil organic matter, total potassium and nitrogen increased with increasing thinning intensities. The Shannon and Chao indices of soil bacteria and fungi were significantly lower in the LIT, MIT and HIT treatments than in the control. Thinning can significantly increase the abundance of Proteobacteria and Actinobacteria, and higher thinning intensities led to a higher relative abundance of Ascomycota and a lower relative abundance of Basidiomycota, Rozellomycota, and Mortierellomycota. Redundancy analysis indicated that soil physicochemical properties rather than understory vegetation diversity were the main drivers of microbial communities, and fungi were more sensitive to soil properties than bacteria. Functional prediction showed that thinning significantly reduced the potential risk of human diseases and plant pathogens, and the nitrogen fixation capacity of bacteria was the highest in the HIT treatment. Thinning significantly increased the relative abundance of cellulolysis and soil saprotrophs in bacteria and fungi.

Conclusion

The findings provide important insights into the effects of thinning on C. japonica var. sinensis plantation ecosystems, which is essential for developing thinning strategies to promote their ecological and economic benefits.

Mussels and Local Conditions Interact to Influence Microbial Communities in Mussel Beds

Microbiomes are increasingly recognized as widespread regulators of function from individual organism to ecosystem scales. However, the manner in which animals influence the structure and function of environmental microbiomes has received considerably less attention. Using a comparative field study, we investigated the relationship between freshwater mussel microbiomes and environmental microbiomes. We used two focal species of unionid mussels, Amblema plicata and Actinonaias ligamentina, with distinct behavioral and physiological characteristics. Mussel microbiomes, those of the shell and biodeposits, were less diverse than both surface and subsurface sediment microbiomes. Mussel abundance was a significant predictor of sediment microbial community composition, but mussel species richness was not. Our data suggest that local habitat conditions which change dynamically along streams, such as discharge, water turnover, and canopy cover, work in tandem to influence environmental microbial community assemblages at discreet rather than landscape scales. Further, mussel burrowing activity and mussel shells may provide habitat for microbial communities critical to nutrient cycling in these systems.

The National Ecological Observatory Network's soil metagenomes: assembly and basic analysis

The largest dataset of soil metagenomes has recently been released by the National Ecological Observatory Network (NEON), which performs annual shotgun sequencing of soils at 47 sites across the United States. NEON serves as a valuable educational resource, thanks to its open data and programming tutorials, but there is currently no introductory tutorial for accessing and analyzing the soil shotgun metagenomic dataset. Here, we describe methods for processing raw soil metagenome sequencing reads using a bioinformatics pipeline tailored to the high complexity and diversity of the soil microbiome. We describe the rationale, necessary resources, and implementation of steps such as cleaning raw reads, taxonomic classification, assembly into contigs or genomes, annotation of predicted genes using custom protein databases, and exporting data for downstream analysis. The workflow presented here aims to increase the accessibility of NEON's shotgun metagenome data, which can provide important clues about soil microbial communities and their ecological roles.