The spatial variation of soil bacterial community assembly processes affects the accuracy of source tracking in ten major Chinese cities

Drought-resistant trait of different crop genotypes determines assembly patterns of soil and phyllosphere microbial communities

Crop microbiomes are widely recognized to play a role in crop stress resistance, but the ecological processes that shape crop microbiomes under water stress are unclear. Therefore, we investigated the bacterial communities of two oat (Avena sativa) and two wheat (Triticum aestivum) genotypes under different water stress conditions. Our results show that the microbial assemblage was determined by the crop compartment niche. Host selection pressure on the bacterial community increased progressively from soil to epiphyte to endophyte pathways, leading to a decrease in bacterial community diversity and network complexity. Source tracing shows that soil is the primary source of crop microbial communities and that bulk soil is the main potential source of crop microbiota. It filters gradually through the different compartment niches of the crop. We found that the phyla Actinobacteria, Proteobacteria, Gemmatimonadota, and Myxococcota were significantly enriched in bacterial communities associated with crop-resistance enzyme activity. Crop genotype influenced the composition of the rhizosphere soil microbial community, and the composition of the phylloplane microbial community was affected by water stress. IMPORTANCE In this paper, we investigated the assembly of the plant microbiome in response to water stress. We found that the determinant of microbiome assembly under water stress was the host type and that microbial communities were progressively filtered and enriched as they moved from soil to epiphyte to endophyte communities, with the main potential source being bulk soil. We also screened for bacterial communities that were significantly associated with crop enzyme activity. Our research provides insights into the manipulation of microbes in response to crop resistance to water stress.

Rhizosphere microbial community assembly and association networks strongly differ based on vegetation type at a local environment scale

Introduction

Rhizosphere microbes perform critical functions for their hosts, and their structure is strongly influenced by vegetation type. Although studies on the effects of vegetation on rhizosphere microbial community structure have been conducted at large and global environment scales, studies at local environment scales would eliminate numerous external factors such as climate and soil type, while highlighting the potential influence of local vegetation type.

Methods

Here, we compared rhizosphere microbial communities using 54 samples under three vegetation types (herb, shrubs, and arbors, with bulk soil as the control) at the campus of Henan University. 16S rRNA and ITS amplicons were sequenced using Illumina high throughput sequencing.

Results and Discussion

Rhizosphere bacterial and fungal community structures were influenced considerably by vegetation type. Bacterial alpha diversity under herbs was significantly different from that under arbors and shrubs. The abundance of phyla such as Actinobacteria was extremely higher in bulk soil than in the rhizosphere soils. Herb rhizosphere harbored more unique species than other vegetation type soils. Furthermore, bacterial community assembly in bulk soil was more dominated by deterministic process, whereas the rhizosphere bacterial community assembly was dominated by stochasticity and the construction of fungal communities was all dominated by deterministic processes. In addition, rhizosphere microbial networks were less complex than bulk soil networks, and their keystone species differed based on vegetation type. Notably, bacterial community dissimilarities were strongly correlated with plant phylogenetic distance. Exploring rhizosphere microbial community patterns under different vegetation types could enhance our understanding of the role of rhizosphere microbes in ecosystem function and service provision, as well as basic information that could facilitate plant and microbial diversity conservation at the local environment scale.

Trends in forensic microbiology: From classical methods to deep learning

Forensic microbiology has been widely used in the diagnosis of causes and manner of death, identification of individuals, detection of crime locations, and estimation of postmortem interval. However, the traditional method, microbial culture, has low efficiency, high consumption, and a low degree of quantitative analysis. With the development of high-throughput sequencing technology, advanced bioinformatics, and fast-evolving artificial intelligence, numerous machine learning models, such as RF, SVM, ANN, DNN, regression, PLS, ANOSIM, and ANOVA, have been established with the advancement of the microbiome and metagenomic studies. Recently, deep learning models, including the convolutional neural network (CNN) model and CNN-derived models, improve the accuracy of forensic prognosis using object detection techniques in microorganism image analysis. This review summarizes the application and development of forensic microbiology, as well as the research progress of machine learning (ML) and deep learning (DL) based on microbial genome sequencing and microbial images, and provided a future outlook on forensic microbiology.

Plant and fungal species interactions differ between aboveground and belowground habitats in mountain forests of eastern China

Plant and fungal species interactions drive many essential ecosystem properties and processes; however, how these interactions differ between aboveground and belowground habitats remains unclear at large spatial scales. Here, we surveyed 494 pairwise fungal communities in leaves and soils by Illumina sequencing, which were associated with 55 woody plant species across more than 2,000-km span of mountain forests in eastern China. The relative contributions of plant, climate, soil and space to the variation of fungal communities were assessed, and the plant-fungus network topologies were inferred. Plant phylogeny was the strongest predictor for fungal community composition in leaves, accounting for 19.1% of the variation. In soils, plant phylogeny, climatic factors and soil properties explained 9.2%, 9.0% and 8.7% of the variation in soil fungal community, respectively. The plant-fungus networks in leaves exhibited significantly higher specialization, modularity and robustness (resistance to node loss), but less complicated topology (e.g., significantly lower linkage density and mean number of links) than those in soils. In addition, host/fungus preference combinations and key species, such as hubs and connectors, in bipartite networks differed strikingly between aboveground and belowground samples. The findings provide novel insights into cross-kingdom (plant-fungus) species co-occurrence at large spatial scales. The data further suggest that community shifts of trees due to climate change or human activities will impair aboveground and belowground forest fungal diversity in different ways.

Advances in artificial intelligence-based microbiome for PMI estimation

Postmortem interval (PMI) estimation has always been a major challenge in forensic science. Conventional methods for predicting PMI are based on postmortem phenomena, metabolite or biochemical changes, and insect succession. Because postmortem microbial succession follows a certain temporal regularity, the microbiome has been shown to be a potentially effective tool for PMI estimation in the last decade. Recently, artificial intelligence (AI) technologies shed new lights on forensic medicine through analyzing big data, establishing prediction models, assisting in decision-making, etc. With the application of next-generation sequencing (NGS) and AI techniques, it is possible for forensic practitioners to improve the dataset of microbial communities and obtain detailed information on the inventory of specific ecosystems, quantifications of community diversity, descriptions of their ecological function, and even their application in legal medicine. This review describes the postmortem succession of the microbiome in cadavers and their surroundings, and summarizes the application, advantages, problems, and future strategies of AI-based microbiome analysis for PMI estimation.

Application of Microbiome in Forensics

Recent advances in next-generation sequencing technology and improvements in bioinformatics have expanded the scope of microbiome analysis as a forensic tool. Microbiome research is concerned with the study of the compositional profile and diversity of microbial flora as well as the interactions between microbes, hosts, and the environment. It has opened up many new possibilities for forensic analysis. In this review, we discuss various applications of microbiomes in forensics, including identification of individuals, geolocation inference, post-mortem interval (PMI) estimation, and others.

Meta-Omics-Supervised Characterization of Respiration Activities Associated with Microbial Immigrants in Anaerobic Sludge Digesters

Immigration has been recently recognized as an important ecological process that affects the microbial community structure in diverse ecosystems. However, the fate of microbial immigrants in the new environment and their involvement in the local biochemical network remain unclear. In this study, we performed meta-omics-supervised characterization of immigrants' activities in anaerobic sludge digesters. Metagenomic analyses revealed that immigrants from the feed sludge accounted for the majority of populations capable of anaerobic respiration in a digester. Electron acceptors that were predicted to be respired, including nitrate, nitrite, sulfate, and elemental sulfur, were added to digester sludge in batch tests. Consumption of up to 91% of the added electron acceptors was observed within the experiment period. 16S rRNA sequencing detected populations that were stimulated by the electron acceptors, largely overlapping with respiration-capable immigrants identified by metagenomic analysis. Metatranscriptomic analysis of the batch tests provided additional evidence for upregulated expression of respiration genes and concomitant suppressed expression of methanogenesis. Anaerobic respiration activity was further evaluated in full-scale digesters in nine wastewater treatment plants. Although nitrate and sulfate respiration were ubiquitous, the expression level of respiration genes was generally 2-3 orders of magnitude lower than the expression of methanogenesis in most digesters, suggesting marginal ecological roles by immigrants in full-scale digester ecosystems.

Rare genera differentiate urban green space soil bacterial communities in three cities across the world

Vegetation complexity is potentially important for urban green space designs aimed at fostering microbial biodiversity to benefit human health. Exposure to urban microbial biodiversity may influence human health outcomes via immune training and regulation. In this context, improving human exposure to microbiota via biodiversity-centric urban green space designs is an underused opportunity. There is currently little knowledge on the association between vegetation complexity (i.e. diversity and structure) and soil microbiota of urban green spaces. Here, we investigated the association between vegetation complexity and soil bacteria in urban green spaces in Bournemouth, UK; Haikou, China; and the City of Playford, Australia by sequencing the 16S rRNA V4 gene region of soil samples and assessing bacterial diversity. We characterized these green spaces as having ‘low’ or ‘high’ vegetation complexity and explored whether these two broad categories contained similar bacterial community compositions and diversity around the world. Within cities, we observed significantly different alpha and beta diversities between vegetation complexities; however, these results varied between cities. Rare genera (<1% relative abundance individually, on average 35% relative abundance when pooled) were most likely to be significantly different in sequence abundance between vegetation complexities and therefore explained much of the differences in microbial communities observed. Overall, general associations exist between soil bacterial communities and vegetation complexity, although these are not consistent between cities. Therefore, more in-depth work is required to be done locally to derive practical actions to assist the conservation and restoration of microbial communities in urban areas.

The Role Transition of Dietary Species Richness in Modulating the Gut Microbial Assembly and Postweaning Performance of a Generalist Herbivore

When facing a food shortage, generalist herbivores can respond by expanding their dietary species richness (DSR) to maximize energy collection, regardless of whether forages are preferred or not. Higher DSR usually indicates higher nutrient adequacy and better health. However, the high-DSR diet containing a large proportion of preferred species or a large proportion of less-preferred species means different things to an animal. It is still unknown how different shift patterns in DSR would affect distinctly the performance of animals via altering gut microbiota. We examined the gut microbial composition, diversity, community assembly processes, and performance of a generalist herbivore, Lasiopodomys brandtii, in a feeding experiment with increased levels of simulated DSR shifting from preferred plant species to less preferred ones. We found the survival rate and body growth of Brandt's voles showed a dome-shaped association with DSR: species performance increased initially with the increase of preferred plant species but declined with the increase of less-preferred food items. Several microbial taxa and functions closely related to the metabolism of amino acids and short-chain fatty acids also showed a dome-shaped association with DSR, which is consistent with the observation of performance change. However, the alpha diversities of gut microbiota increased linearly with DSR. The null model and phylogenetic analysis suggested that stochastic processes dominate at low DSR diets, whereas deterministic processes prevail at high DSR diets. These results suggest that the role of DSR in regulating animal performance by gut microbiota depends on the number of preferred forage items. IMPORTANCE The plant species diversity varies greatly under the influence of both climate change and human disturbance, which may negatively affect the productivity as well as the variability of organisms (e.g., small herbivores) at the next trophic level. It is still unknown how gut microbiota of small herbivores respond to such changes in dietary species richness. Our manipulative food experiment revealed that dietary species richness can affect the composition, functions, and community assembly of gut microbiota of Brandt's vole in a nonlinear way. Given the fast-growing interest in therapeutic diets to treat dysbiosis and to improve health conditions, our study highlights the need to consider not just the variety of consumed food but also the principles of rational nutrition.