Predicting disease occurrence with high accuracy based on soil macroecological patterns of Fusarium wilt

The Advance of Fusarium Wilt Tropical Race 4 in Musaceae of Latin America and the Caribbean: Current Situation

The fungus Fusarium oxysporum f. sp. cubense tropical race 4 (syn. Fusarium odoratissimum) (Foc TR4) causes vascular wilt in Musaceae plants and is considered the most lethal for these crops. In Latin America and the Caribbean (LAC), it was reported for the first time in Colombia (2019), later in Peru (2021), and recently declared in Venezuela (2023). This work aimed to analyze the evolution of Foc TR4 in Musaceae in LAC between 2018 and 2022. This perspective contains a selection of topics related to Foc TR4 in LAC that address and describe (i) the threat of Foc TR4 in LAC, (ii) a bibliometric analysis of the scientific production of Foc TR4 in LAC, (iii) the current situation of Foc TR4 in Colombia, Peru, and Venezuela, (iv) medium-term prospects in LAC member countries, and (v) export trade and local food security. In this study, the presence of Foc TR4 in Venezuela and the possible consequences of the production of Musaceae in the long term were reported for the first time. In conclusion, TR4 is a major threat to banana production in Latin America and the world, and it is important to take measures to control the spread of the fungus and minimize its impact on the banana industry. It is important to keep working on the control of Foc TR4, which requires the participation of the local and international industry, researchers, and consumers, among others, to prevent the disappearance of bananas.

EasyAmplicon: An easy-to-use, open-source, reproducible, and community-based pipeline for amplicon data analysis in microbiome research

It is difficult for beginners to learn and use amplicon analysis software because there are so many software tools to choose from, and all of them need multiple steps of operation. Herein, we provide a cross-platform, open-source, and community-supported analysis pipeline EasyAmplicon. EasyAmplicon has most of the modules needed for an amplicon analysis, including data quality control, merging of paired-end reads, dereplication, clustering or denoising, chimera detection, generation of feature tables, taxonomic diversity analysis, compositional analysis, biomarker discovery, and publication-quality visualization. EasyAmplicon includes more than 30 cross-platform modules and R packages commonly used in the field. All steps of the pipeline are integrated into RStudio, which reduces learning costs, keeps the flexibility of the analysis process, and facilitates personalized analysis. The pipeline is maintained and updated by the authors and editors of WeChat official account "Meta-genome." Our team will regularly release the latest tutorials both in Chinese and English, read the feedback from users, and provide help to them in the WeChat account and GitHub. The pipeline can be deployed on various platforms, and the installation time is less than half an hour. On an ordinary laptop, the whole analysis process for dozens of samples can be completed within 3 h. The pipeline is available at GitHub (https://github.com/YongxinLiu/EasyAmplicon) and Gitee (https://gitee.com/YongxinLiu/EasyAmplicon).

Machine learning in fermentative biohydrogen production: Advantages, challenges, and applications

Hydrogen can be produced in an environmentally friendly manner through biological processes using a variety of organic waste and biomass as feedstock. However, the complexity of biological processes limits their predictability and reliability, which hinders the scale-up and dissemination. This article reviews contemporary research and perspectives on the application of machine learning in biohydrogen production technology. Several machine learning algorithems have recently been implemented for modeling the nonlinear and complex relationships among operational and performance parameters in biohydrogen production as well as predicting the process performance and microbial population dynamics. Reinforced machine learning methods exhibited precise state prediction and retrieved the underlying kinetics effectively. Machine-learning based prediction was also improved by using microbial sequencing data as input parameters. Further research on machine learning could be instrumental in designing a process control tool to maintain reliable hydrogen production performance and identify connection between the process performance and the microbial population.

Biodiesel Co-Product enhances microbial stability and beneficial microbial communities along a gradient of soil water content

Biodiesel Co-Product (BCP) is a complex carbonaceous liquid formed during the commercial production of biodiesel. Previously, BCP was shown to decrease nitrogen (N) leaching from the soil, but the effects of BCP on the diversity, composition, and structure of soil microbial communities are not well understood. Here, we applied 1.5 mg BCP-C to acidic soil (pH 3.5) at a range of different water contents (from 40 % to 100 % water holding capacity) to investigate the interactions between BCP and increasing water holding capacity on the diversity, composition, and interactions of soil microbial communities. Distance-based multivariate linear model (DistLM) and non-metric multidimensional scaling (NMDS) analyses showed that BCP caused larger changes in fungal than bacterial communities, while soil water content had a greater effect on bacterial communities relative to fungal communities. Co-occurrence network analyses indicated that BCP amendment produced more robust and complex bacterial networks and more stable fungal ones. BCP significantly increased the OTU numbers of beneficial microbes (e.g., Trichoderma spp.) in all water contents, with fewer OTU numbers of putative pathogenetic species (Fusarium spp. and Aspergillus spp.). These findings indicate that BCP addition may be conducive to the health and stability of soil ecosystems.

Effects of nitric acid rain stress on soil nitrogen fractions and fungal communities in a northern subtropical forest, China

Acid rain has severely negatively impacted terrestrial ecosystems and biogeochemical cycles. However, the potential impacts of nitric acid rain (NAR) on soil nitrogen (N) fractions and fungal community diversity in northern subtropical forest soils remain largely unevaluated. In this study, treatments of NAR at pH = 4.5 (AR4.5), pH = 3.5 (AR3.5), and pH = 2.5 (AR2.5) were randomly sprayed in a typical Quercus acutissima Carruth. stand in northern subtropical China. The soil N fractions and soil fungal communities were analyzed after a 12-month experimental period. The results revealed that compared to the control, the soil total N (TN), microbial biomass N (MBN), hydrolysable ammonium N (HAN), amino-sugar N (ASN) and amino-acid N (AAN) contents decreased significantly by 19.61-13.07 %, 20.10-9.04 %, 60.41-28.87 %, 74.10-62.25 %, and 65.69-45.64 % under stronger acidity inputs (i.e., AR2.5 and AR3.5), respectively. Besides, the AR2.5 and AR3.5 treatments increased the α-diversity indices of soil fungal communities and altered the soil fungal community structure. Moreover, the NAR treatments represented an increase in the relative abundance of Ascomycota and Mortierellomycota and a decrease in that of Basidiomycota. Mortierella, Penicillium, and Tomentella can be used as indicator genera for changes in soil fungal community structures under NAR stress. Furthermore, AAN was the main environmental factor affecting soil fungal community at the phylum and genus levels. Cumulatively, findings from this research provide valuable insight into NAR's effects on N cycling and microbial communities in forest soils.

Inflammatory microbes and genes as potential biomarkers of Parkinson's disease

As the second-largest neurodegenerative disease in the world, Parkinson's disease (PD) has brought a severe economic and medical burden to our society. Growing evidence in recent years suggests that the gut microbiome may influence PD, but the exact pathogenesis of PD remains unclear. In addition, the current diagnosis of PD could be inaccurate and expensive. In this study, the largest meta-analysis currently of the gut microbiome in PD was analyzed, including 2269 samples by 16S rRNA gene and 236 samples by shotgun metagenomics, aiming to reveal the connection between PD and gut microbiome and establish a model to predict PD. The results showed that the relative abundances of potential pro-inflammatory bacteria, genes and pathways were significantly increased in PD, while potential anti-inflammatory bacteria, genes and pathways were significantly decreased. These changes may lead to a decrease in potential anti-inflammatory substances (short-chain fatty acids) and an increase in potential pro-inflammatory substances (lipopolysaccharides, hydrogen sulfide and glutamate). Notably, the results of 16S rRNA gene and shotgun metagenomic analysis have consistently identified five decreased genera (Roseburia, Faecalibacterium, Blautia, Lachnospira, and Prevotella) and five increased genera (Streptococcus, Bifidobacterium, Lactobacillus, Akkermansia, and Desulfovibrio) in PD. Furthermore, random forest models performed well for PD prediction based on 11 genera (accuracy > 80%) or 6 genes (accuracy > 90%) related to inflammation. Finally, a possible mechanism was presented to explain the pathogenesis of inflammation leading to PD. Our results provided further insights into the prediction and treatment of PD based on inflammation.

Cross-kingdom synthetic microbiota supports tomato suppression of Fusarium wilt disease

The role of rhizosphere microbiota in the resistance of tomato plant against soil-borne Fusarium wilt disease (FWD) remains unclear. Here, we showed that the FWD incidence was significantly negatively correlated with the diversity of both rhizosphere bacterial and fungal communities. Using the microbiological culturomic approach, we selected 205 unique strains to construct different synthetic communities (SynComs), which were inoculated into germ-free tomato seedlings, and their roles in suppressing FWD were monitored using omics approach. Cross-kingdom (fungi and bacteria) SynComs were most effective in suppressing FWD than those of Fungal or Bacterial SynComs alone. This effect was underpinned by a combination of molecular mechanisms related to plant immunity and microbial interactions contributed by the bacterial and fungal communities. This study provides new insight into the dynamics of microbiota in pathogen suppression and host immunity interactions. Also, the formulation and manipulation of SynComs for functional complementation constitute a beneficial strategy in controlling soil-borne disease.

Negative effects of abamectin on soil microbial communities in the short term

With the widespread use of abamectin in agriculture, there is increasing urgency to assess the effects of abamectin on soil microorganisms. Here, we treated plant-soil microcosms with abamectin at concentrations of 0.1 and 1.0 mg/kg and quantified the impacts of abamectin on bulk and rhizosphere soil microbial communities by shotgun metagenomics after 7 and 21 days of exposure. Although abamectin was reported to be easily degradable, it altered the composition of the soil microbial communities, disrupted microbial interactions, and decreased community complexity and stability after 7 days of exposure. After treatment with abamectin at a concentration of 1.0 mg/kg, some opportunistic human diseases, and soil-borne pathogens like Ralstonia were enriched in the soil. However, most ecological functions in soil, particularly the metabolic capacities of microorganisms, recovered within 21 days after abamectin treatment. The horizontal and vertical gene transfer under abamectin treatments increased the levels of antibiotic resistance genes dissemination. Overall, our findings demonstrated the negative effects of abamectin on soil ecosystems in the short-term and highlight a possible long-term risk to public and soil ecosystem health associated with antibiotic resistance genes dissemination.

Soil properties under different ecological restoration modes for the quarry in Yanshan mountains of Hebei province, China

The ecological environment of quarry mining area is fragile, and the vegetation restoration cycle is long and difficult, so scientific and appropriate artificial vegetation is of great significance to ecological restoration. The purpose of this study was to evaluate the herbaceous and woody vegetation restoration, including Medicago sativa (Me), artificial miscellaneous grass (Mg), Rhus typhina (Rh), fruit orchard (Or) and Pinus tabulaeformis (Pi), to investigate the soil physicochemical properties and the structure of the microbial communities, and to reveal the correlation between them. The results addressed that Medicago sativa and artificial miscellaneous grass had significant effect on soil remediation, which were conducive to scientific and efficient ecological restoration, and could promote ecological restoration in the damaged ecosystems. While, the modes of Rh and Pi were not suitable for ecological restoration in this study area because they had strong allelopathy. Another arborous restoration mode of Or showed a better improvement effect (including soil nutrients, soil microbial diversity, etc.) than that of Rh and Pi. The findings also indicated that the herbaceous vegetation restoration modes of Me and Mg significantly increased the relative abundance of Proteobacteria, Acidobacteria, Actinobacteria bacteria, Ascomycota and Mortierllomycota fungi, and reduced the relative abundance of Firmicutes bacteria and Basidiomycota fungi. This study also revealed that the trend of bacterial localization in the fruit orchard, artificial miscellaneous grass and Medicago sativa was more obvious. Among many soil abiotic factors, the contents of organic matter, available nitrogen and pH were the most important factors affecting soil microbial community.

Essential oil from Sabina chinensis leaves: A promising green control agent against Fusarium sp

Sabina chinensis is a woody plant with important ecological functions in different regions of China, but its essential oils (EO) against plant pathogenic fungi remain largely undetermined. The purpose of our study was to assess the chemical composition and antifungal activity of S. chinensis EO based on optimization of the extraction process. In this study, an actionable and effective model with the experimental results and identified optimum conditions (crushing degree of 20 mesh, liquid-solid ratio of 10.1:1, immersion time of 9.1 h) was established successfully to achieve an extraction yield of 0.54%, which was basically consistent with the theoretical value. A total of 26 compounds were identified using headspace gas chromatography-mass spectrometry (GC-MS) and showed that the major constituent was β-phellandrene (26.64-39.26%), followed by terpinen-4-ol (6.53-11.89%), bornyl acetate (6.13-10.53%), etc. For Petri plate assays, our experiments found for the first time that S. chinensis EO revealed high and long-term antifungal activity against the tested strains, including Fusarium oxysporum and Fusarium incarnatum, at EC₅₀ values of 1.42 and 1.15 µL/mL, which especially reached approximately 76% and 90% growth inhibition at a dose of 0.2 µL/mL, respectively. Furthermore, the antifungal activity of EO from different harvest periods showed remarkable variation. The orthogonal partial least-squares discriminant analysis (OPLS-DA) method revealed 11 metabolites with chemical marker components, and 5 of its potential antifungal activities, terpinen-4-ol, α-terpineol, α-elemol, γ-eudesmol, and bornyl acetate, were strongly correlated with the mycelial inhibition rate. In total, this study explored the antifungal activity of EO against root rot fungus as a potential fungicide and provided valuable information into developing potential products from natural agents.

Bio-fertilizer Affects Structural Dynamics, Function, and Network Patterns of the Sugarcane Rhizospheric Microbiota

Fertilizers and microbial communities that determine fertilizer efficiency are key to sustainable agricultural development. Sugarcane is an important sugar cash crop in China, and using bio-fertilizers is important for the sustainable development of China's sugar industry. However, information on the effects of bio-fertilizers on sugarcane soil microbiota has rarely been studied. In this study, the effects of bio-fertilizer application on rhizosphere soil physicochemical indicators, microbial community composition, function, and network patterns of sugarcane were discussed using a high-throughput sequencing approach. The experimental design is as follows: CK: urea application (57 kg/ha), CF: compound fertilizer (450 kg/ha), BF1: bio-fertilizer (1500 kg/ha of bio-fertilizer + 57 kg/ha of urea), and BF2: bio-fertilizer (2250 kg/ha of bio-fertilizer + 57 kg/ha of urea). The results showed that the bio-fertilizer was effective in increasing sugarcane yield by 3-12% compared to the CF treatment group, while reducing soil acidification, changing the diversity of fungi and bacteria, and greatly altering the composition and structure of the inter-root microbial community. Variance partitioning canonical correspondence (VPA) analysis showed that soil physicochemical variables explained 80.09% and 73.31% of the variation in bacteria and fungi, respectively. Redundancy analysis and correlation heatmap showed that soil pH, total nitrogen, and available potassium were the main factors influencing bacterial community composition, while total soil phosphorus, available phosphorus, pH, and available nitrogen were the main drivers of fungal communities. Volcano plots showed that using bio-fertilizers contributed to the accumulation of more beneficial bacteria in the sugarcane rhizosphere level and the decline of pathogenic bacteria (e.g., Leifsonia), which may slow down or suppress the occurrence of diseases. Linear discriminant analysis (LDA) and effect size analysis (LEfSe) searched for biomarkers under different fertilizer treatments. Meanwhile, support vector machine (SVM) assessed the importance of the microbial genera contributing to the variability between fertilizers, of interest were the bacteria Anaerolineace, Vulgatibacter, and Paenibacillus and the fungi Cochliobolus, Sordariales, and Dothideomycetes between CF and BF2, compared to the other genera contributing to the variability. Network analysis (co-occurrence network) showed that the network structure of bio-fertilizers was closer to the network characteristics of healthy soils, indicating that bio-fertilizers can improve soil health to some extent, and therefore if bio-fertilizers can be used as an alternative to chemical fertilizers in the future alternative, it is important to achieve green soil development and improve the climate.

Changes in Microbial Community Structure in Response to Gummosis in Peach Tree Bark

Peach gummosis disease has been identified as a serious challenge in Korean agriculture and has developed to become a major cause of agricultural productivity losses. However, treatments for gummosis have not been systemically established and studies of the microbiome closely related to this plant disease are lacking. Therefore, we analyzed the bacterial and fungal communities in the bark and rhizosphere soil of healthy peach trees and those with gummosis. Through high-throughput sequencing, we obtained unprecedented insights into the bacterial and fungal dynamics of each group, including their diversity and taxonomic classification, as well as network analyses. We found that the presence of gummosis drives a significantly higher alpha diversity in the bark bacterial community. Peach gummosis bark mycobiomes included greater numbers of opportunistic pathogens such as Ascochyta, Botryosphaeria, Saccharomyces, Nectriaceae_NA, Trametes, and Valsaceae_NA. However, the microbiome also included bacteria beneficial to plant growth and the production of polysaccharides-namely, 1174-901-12, Catenibacterium, Cutibacterium, Friedmanniella, Methylobacterium-Methylorubrum, Pseudomonas, Rhodobacter, and Sphingomonas. Furthermore, we confirmed that gummosis induced a more complex structure in the bark microbiome network. We conclude that the findings of this study provide a valuable aid in profiling the overall peach tree microbial ecosystem, which can be utilized to develop precise biomarkers for the early diagnosis of gummosis.

Study of the effect of bacterial-mediated legume plant growth using bacterial strain Serratia marcescens N1.14 X-45

Soil microorganisms play an indispensable role in plant growth and are widely used to promote plant growth. However, poor microbial strains are homogeneous. The heavy application of chemical fertilizers and pesticides to agricultural soil has adversely affected the soil flora, necessitating the regulation of the soil flora to maintain soil health. In this study, X-45, a highly efficient and phosphorus-dissolving strain of the lysogenic bacterium Serratia marcescens N1.14 was isolated from bare rock slope soil samples from Yueyang Avenue, Hunan Province, China. We observed that microbial strain X-45 could release P from the rocks into solution when the sample rocks were used as the only phosphorus source. Furthermore, we observed that the P content in media increased by 3.08 X compared to the control. After applying X-45 as a bacterial fertilizer, the growth of potted Indigofera pseudotinctoria plants significantly increased, the soil physicochemical properties were significantly improved, and the relative abundance of Bradyrhizobium in the soil increased significantly from 1 to 42%. Besides, Bradyrhizobium became the most dominant genus in the soil. The indirect promotion of another beneficial microorganism by X-45 further revealed the intrinsic mechanism by which X-45 exerted its effect on plant promotion and soil improvement. Using this bacteria, the hypothesis of the superposition effect of legume plant promotion was also confirmed.

Specific metabolites drive the deterministic assembly of diseased rhizosphere microbiome through weakening microbial degradation of autotoxin

BACKGROUND

Process and function that underlie the assembly of a rhizosphere microbial community may be strongly linked to the maintenance of plant health. However, their assembly processes and functional changes in the deterioration of soilborne disease remain unclear. Here, we investigated features of rhizosphere microbiomes related to Fusarium wilt disease and assessed their assembly by comparison pair of diseased/healthy sequencing data. The untargeted metabolomics was employed to explore potential community assembly drivers, and shotgun metagenome sequencing was used to reveal the mechanisms of metabolite-mediated process after soil conditioning.

RESULTS

Results showed the deterministic assembly process associated with diseased rhizosphere microbiomes, and this process was significantly correlated to five metabolites (tocopherol acetate, citrulline, galactitol, octadecylglycerol, and behenic acid). Application of the metabolites resulted in a deterministic assembly of microbiome with the high morbidity of watermelon. Furthermore, metabolite conditioning was found to weaken the function of autotoxin degradation undertaken by specific bacterial group (Bradyrhizobium, Streptomyces, Variovorax, Pseudomonas, and Sphingomonas) while promoting the metabolism of small-molecule sugars and acids initiated from another bacterial group (Anaeromyxobacter, Bdellovibrio, Conexibacter, Flavobacterium, and Gemmatimonas). Video Abstract CONCLUSION: These findings strongly suggest that shifts in a metabolite-mediated microbial community assembly process underpin the deterministic establishment of soilborne Fusarium wilt disease and reveal avenues for future research focusing on ameliorating crop loss due to this pathogen.

Responses of soil fungal taxonomic attributes and enzyme activities to copper and cadmium co-contamination in paddy soils

Excess heavy metals, especially copper (Cu) and cadmium (Cd), are common in paddy soils in the red soil hilly areas of southern China. Microorganisms are regulators of soil organic matter accumulation and pollutant transformation. Clarifying the effects of Cu and Cd accumulation on microbial community composition and function is a prerequisite for bioremediation of paddy soil contamination. However, it remains unclear how Cu and Cd contamination affects soil fungal taxonomic attributes and microbial-mediated biogeochemical processes in paddy soils. Here, soil heavy metals, fungal community composition, and soil enzyme activities were determined in paddy fields downstream of a typical mining area in southern China, and the effects of Cu and Cd co-contamination on fungal community diversity and co-occurrence networks, as well as the associations between them were assessed. The concentrations of Cu and Cd in paddy soils decreased from upstream to downstream of the river, and were positively correlated with the Shannon index of fungal communities. Soil Cu and Cd concentrations exhibited a greater impact on the structure and assembly of fungal communities than soil general properties. Increases in soil Cu and Cd concentrations were correlated with drastic changes in the cumulative relative abundance of ecological clusters in fungal co-occurrence networks. Soil Cu and Cd concentrations were positively correlated with the relative abundances of Eurotiomycetes, Pezizomycetes, Ustilaginomycetes, and Kickxellomycetes, respectively, whereas negatively correlated with hydrolase activities related to carbon, nitrogen, and phosphorus cycles. These results confirmed in the field that long-term Cu and Cd enrichment significantly altered the structure and diversity of fungal communities in the subtropical paddy soils, thereby affecting soil nutrient transformation and organic matter accumulation. This can also provide a basis for the bioremediation of heavy metal pollution in paddy soils.

Functional Guilds, Community Assembly, and Co-occurrence Patterns of Fungi in Metalliferous Mine Tailings Ponds in Mainland China

Metalliferous mine tailings ponds are generally characterized by low levels of nutrient elements, sustained acidic conditions, and high contents of toxic metals. They represent one kind of extreme environments that are believed to resemble the Earth's early environmental conditions. There is increasing evidence that the diversity of fungi inhabiting mine tailings ponds is much higher than previously thought. However, little is known about functional guilds, community assembly, and co-occurrence patterns of fungi in such habitats. As a first attempt to address this critical knowledge gap, we employed high-throughput sequencing to characterize fungal communities in 33 mine tailings ponds distributed across 18 provinces of mainland China. A total of 5842 fungal phylotypes were identified, with saprotrophic fungi being the major functional guild. The predictors of fungal diversity in whole community and sub-communities differed considerably. Community assembly of the whole fungal community and individual functional guilds were primarily governed by stochastic processes. Total soil nitrogen and total phosphorus mediated the balance between stochastic and deterministic processes of the fungal community assembly. Co-occurrence network analysis uncovered a high modularity of the whole fungal community. The observed main modules largely consisted of saprotrophic fungi as well as various phylotypes that could not be assigned to known functional guilds. The richness of core fungal phylotypes, occupying vital positions in co-occurrence network, was positively correlated with edaphic properties such as soil enzyme activity. This indicates the important roles of core fungal phylotypes in soil organic matter decomposition and nutrient cycling. These findings improve our understanding of fungal ecology of extreme environments.

Shared Core Microbiome and Functionality of Key Taxa Suppressive to Banana Fusarium Wilt

Microbial contributions to natural soil suppressiveness have been reported for a range of plant pathogens and cropping systems. To disentangle the mechanisms underlying suppression of banana Panama disease caused by Fusarium oxysporum f. sp. cubense tropical race 4 (Foc4), we used amplicon sequencing to analyze the composition of the soil microbiome from six separate locations, each comprised of paired orchards, one potentially suppressive and one conducive to the disease. Functional potentials of the microbiomes from one site were further examined by shotgun metagenomic sequencing after soil suppressiveness was confirmed by greenhouse experiments. Potential key antagonists involved in disease suppression were also isolated, and their activities were validated by a combination of microcosm and pot experiments. We found that potentially suppressive soils shared a common core community with relatively low levels of F. oxysporum and relatively high proportions of Myxococcales, Pseudomonadales, and Xanthomonadales, with five genera, Anaeromyxobacter, Kofleria, Plesiocystis, Pseudomonas, and Rhodanobacter being significantly enriched. Further, Pseudomonas was identified as a potential key taxon linked to pathogen suppression. Metagenomic analysis showed that, compared to the conducive soil, the microbiome in the disease suppressive soil displayed a significantly greater incidence of genes related to quorum sensing, biofilm formation, and synthesis of antimicrobial compounds potentially active against Foc4. We also recovered a higher frequency of antagonistic Pseudomonas isolates from disease suppressive experimental field sites, and their protective effects against banana Fusarium wilt disease were demonstrated under greenhouse conditions. Despite differences in location and soil conditions, separately located suppressive soils shared common characteristics, including enrichment of Myxococcales, Pseudomonadales, and Xanthomonadales, and enrichment of specific Pseudomonas populations with antagonistic activity against the pathogen. Moreover, changes in functional capacity toward an increase in quorum sensing, biofilm formation, and antimicrobial compound synthesizing involve in disease suppression.

Sugarcane Rhizosphere Bacteria Community Migration Correlates with Growth Stages and Soil Nutrient

Plants and rhizosphere bacterial microbiota have intimate relationships. As neighbors of the plant root system, rhizosphere microorganisms have a crucial impact on plant growth and health. In this study, we sampled rhizosphere soil of sugarcane in May (seedling), July (tillering), September (elongation) and November (maturity), respectively. We employ 16S rRNA amplicon sequencing to investigate seasonal variations in rhizosphere bacteria community structure and abundance, as well as their association with soil edaphic factors. The results demonstrate that soil pH, total nitrogen (TN) and available nitrogen (AN) decrease substantially with time. Rhizosphere bacteria diversity (Shannon) and the total enriched OTUs are also significantly higher in July relative to other months. Bacteria OTUs and functional composition exhibit a strong and significant correlation with soil temperature (Tem), suggesting that Tem was the potential determinant controlling rhizosphere bacteria diversity, enriched OTUs as well as functional composition. Redundancy analysis (RDA) point toward soil total potassium (TK), pH, TN, Tem and AN as principal determinant altering shifting bacteria community structure. Variation partitioning analysis (VPA) analysis further validate that a substantial proportion of variation (70.79%) detected in the rhizosphere bacteria community structure was attributed to edaphic factors. Mfuzz analysis classified the bacterial genera into four distinct clusters, with cluster two exhibiting a distinct and dramatic increase in July, predominantly occupied by Allocatelliglobosispora. The stochastic forest model found the key characteristic bacterial populations that can distinguish the four key growth periods of sugarcane. It may help us to answer some pending questions about the interaction of rhizosphere microorganisms with plants in the future.

Root exudate chemistry affects soil carbon mobilization via microbial community reassembly

Plant roots are one of the major mediators that allocate carbon captured from the atmosphere to soils as rhizodeposits, including root exudates. Although rhizodeposition regulates both microbial activity and the biogeochemical cycling of nutrients, the effects of particular exudate species on soil carbon fluxes and key rhizosphere microorganisms remain unclear. By combining high-throughput sequencing, q-PCR, and NanoSIMS analyses, we characterized the bacterial community structure, quantified total bacteria depending on root exudate chemistry, and analyzed the consequences on the mobility of mineral-protected carbon. Using well-controlled incubation experiments, we showed that the three most abundant groups of root exudates (amino acids, carboxylic acids, and sugars) have contrasting effects on the release of dissolved organic carbon (DOC) and bioavailable Fe in an Ultisol through the disruption of organo-mineral associations and the alteration of bacterial communities, thus priming organic matter decomposition in the rhizosphere. High resolution (down to 50 nm) NanoSIMS images of mineral particles indicated that iron and silicon co-localized significantly more organic carbon following amino acid inputs than treatments without exudates or with carboxylic acids. The application of sugar strongly reduced microbial diversity without impacting soil carbon mobilization. Carboxylic acids increased the prevalence of Actinobacteria and facilitated carbon mobilization, whereas amino acid addition increased the abundances of Proteobacteria that prevented DOC release. In summary, root exudate functions are defined by their chemical composition that regulates bacterial community composition and, consequently, the biogeochemical cycling of carbon in the rhizosphere.

Response of microbiomes with different abundances to removal of metal fractions by soil washing

Toxic metal contamination causes a great threat to soil ecosystem and human health. Soil washing is a fast practice for removing metals, but its influences on microbial diversity and the stability of soil ecosystem remain unknown. In this study, ethylenediaminetetraacetic acid (EDTA), citric acid (CA), and fermented pineapple peel residue (FPP) were used as representatives of chelates, low molecular organic acids and biological materials to wash Pb-polluted soils, and their impacts on microbial community were investigated. Washing with these agents effectively removed Pb, but altered microbial community structure. After washing with EDTA, CA, and FPP, 3-8 bacterial phyla and 1 fungal phylum greatly increased, while 7-20 bacterial and 0-6 fungal phyla severely decreased or even disappeared. The alterations of different microbiomes were closely related to soil metal fractions. The labile metal fraction had negative effects on most bacteria and fungi, but also showed positive influences on Actinobacteria, Patescibacteria, and Fusobacteria. The moderately stable and stable fractions were nontoxic to the most microbes, but still harmful to Patescibacteria and Deinococcus-Thermus. These findings provide new insights for the effects of soil washing remediation and toxicity of metal fractions on the microbiomes with different abundance.

Compare the performance of multiple binary classification models in microbial high-throughput sequencing datasets

The development of machine learning and deep learning provided solutions for predicting microbiota response on environmental change based on microbial high-throughput sequencing. However, there were few studies specifically clarifying the performance and practical of two types of binary classification models to find a better algorithm for the microbiota data analysis. Here, for the first time, we evaluated the performance, accuracy and running time of the binary classification models built by three machine learning methods - random forest (RF), support vector machine (SVM), logistic regression (LR), and one deep learning method - back propagation neural network (BPNN). The built models were based on the microbiota datasets that removed low-quality variables and solved the class imbalance problem. Additionally, we optimized the models by tuning. Our study demonstrated that dataset pre-processing was a necessary process for model construction. Among these 4 binary classification models, BPNN and RF were the most suitable methods for constructing microbiota binary classification models. Using these 4 models to predict multiple microbial datasets, BPNN showed the highest accuracy and the most robust performance, while the RF method was ranked second. We also constructed the optimal models by adjusting the epochs of BPNN and the n_estimators of RF for six times. The evaluation related to performances of models provided a road map for the application of artificial intelligence to assess microbial ecology.

Crop-type-driven changes in polyphenols regulate soil nutrient availability and soil microbiota

Crop rotation is a typical agronomic practice to mitigate soil deterioration caused by continuous cropping. However, the mechanisms of soil biotic and abiotic factors in response to different cropping patterns in acidic and polyphenol-rich tea nurseries remain unclear. In this study, the composition and function of microbial communities were comparatively investigated in soils of tea seedlings continuously planted for 2 years (AC: autumn-cutting; SC: summer-cutting) and in soils rotation with strawberries alternately for 3 years (AR: autumn-cutting). The results showed that AR significantly improved the survival of tea seedlings but greatly reduced the contents of soil polyphenols. The lower soil polyphenol levels in AR were associated with the decline of nutrients (SOC, TN, Olsen-P) availability, which stimulates the proliferation of nutrient cycling-related bacteria and mixed-trophic fungi, endophytic fungi and ectomycorrhizal fungi, thus further satisfying the nutrient requirements of tea seedlings. Moreover, lower levels of polyphenols facilitated the growth of plant beneficial microorganisms (Bacillus, Mortierella, etc.) and suppressed pathogenic fungi (Pseudopestalotiopsis, etc.), creating a more balanced microbial community that is beneficial to plant health. Our study broadens the understanding of the ecological role of plant secondary metabolites and provides new insights into the sustainability of tea breeding.

Machine learning predicts ecological risks of nanoparticles to soil microbial communities

With the rapid development of nanotechnology in agriculture, there is increasing urgency to assess the impacts of nanoparticles (NPs) on the soil environment. This study merged raw high-throughput sequencing (HTS) data sets generated from 365 soil samples to reveal the potential ecological effects of NPs on soil microbial community by means of metadata analysis and machine learning methods. Metadata analysis showed that treatment with nanoparticles did not have a significant impact on the alpha diversity of the microbial community, but significantly altered the beta diversity. Unfortunately, the abundance of several beneficial bacteria, such as Dyella, Methylophilus, Streptomyces, which promote the growth of plants, and improve pathogenic resistance, was reduced under the addition of synthetic nanoparticles. Furthermore, metadata demonstrated that nanoparticles treatment weakened the biosynthesis ability of cofactors, carriers, and vitamins, and enhanced the degradation ability of aromatic compounds, amino acids, etc. This is unfavorable for the performance of soil functions. Besides the soil heterogeneity, machine learning uncovered that a) the exposure time of nanoparticles was the most important factor to reshape the soil microbial community, and b) long-term exposure decreased the diversity of microbial community and the abundance of beneficial bacteria. This study is the first to use a machine learning model and metadata analysis to investigate the relationship between the properties of nanoparticles and the hazards to the soil microbial community from a macro perspective. This guides the rational use of nanoparticles for which the impacts on soil microbiota are minimized.

Identification of Soil Properties Associated with the Incidence of Banana Wilt Using Supervised Methods

Over the last few decades, a growing incidence of Banana Wilt (BW) has been detected in the banana-producing areas of the central zone of Venezuela. This disease is thought to be caused by a fungal−bacterial complex, coupled with the influence of specific soil properties. However, until now, there was no consensus on the soil characteristics associated with a high incidence of BW. The objective of this study was to identify the soil properties potentially associated with BW incidence, using supervised methods. The soil samples associated with banana plant lots in Venezuela, showing low (n = 29) and high (n = 49) incidence of BW, were collected during two consecutive years (2016 and 2017). On those soils, sixteen soil variables, including the percentage of sand, silt and clay, pH, electrical conductivity, organic matter, available contents of K, Na, Mg, Ca, Mn, Fe, Zn, Cu, S and P, were determined. The Wilcoxon test identified the occurrence of significant differences in the soil variables between the two groups of BW incidence. In addition, Orthogonal Least Squares Discriminant Analysis (OPLS-DA) and the Random Forest (RF) algorithm was applied to find soil variables capable of distinguishing banana lots showing high or low BW incidence. The OPLS-DA model showed a proper fitting of the data (R2Y: 0.61, p value < 0.01), and exhibited good predictive power (Q2: 0.50, p value < 0.01). The analysis of the Receiver Operating Characteristics (ROC) curves by RF revealed that the combination of Zn, Fe, Ca, K, Mn and Clay was able to accurately differentiate 84.1% of the banana lots with a sensitivity of 89.80% and a specificity of 72.40%. So far, this is the first study that identifies these six soil variables as possible new indicators associated with BW incidence in soils of lacustrine origin in Venezuela.

Predicting the humification degree of multiple organic solid waste during composting using a designated bacterial community

Microbes are the drivers for disposing of organic solid waste (OSW) during aerobic fermentation. Notwithstanding, the significance of microbes is underestimated in numerous studies on aerobic fermentation product assessments. Here, we investigated the humification degree (HD), and the humic acid content was assessed in terms of the bacterial community. The bacterial communities were useful indicators for making predictions and even correctly determined the categories of OSWs with 94% accuracy. The bacterial codes can also provide a better prediction of HD. Our results demonstrate that the bacteria code is a reliable biological method to assess HD effectively. Bacterial codes can be used as ecological and biological indicators to evaluate the quality of aerobic fermentation of different materials.

Pseudomonas spp. Enriched in Endophytic Community of Healthy Cotton Plants Inhibit Cotton Verticillium Wilt

The plant microbiome plays a fundamental role in plant growth and health. However, detailed information regarding the plant endophytic microbiome during the infection period of a pathogen is largely unknown. Here, we investigated the microbial community of healthy and diseased cotton plants and the root exudate profiles of susceptible and resistant cultivars utilizing high-throughput sequencing and metabolomics. The results showed that the pathogen infection reduced bacterial diversity and significantly affected the bacterial community composition. The microbiome assembly is shaped predominantly by cultivars. The endophytic microbiome of the infected plants showed greater complexity than the healthy plants in network analysis. The results displayed that a total of 76 compounds were significantly different in the two groups, with 18 compounds showing a higher relative abundance in the resistant cultivars and 58 compounds in the susceptible cultivars. Pathway enrichment analysis showed that pathways related to plant hormone signal transduction, biosynthesis of various secondary metabolites, and biosynthesis and metabolism of amino acids were prominently altered. We also demonstrate that plants inoculated with Pseudomonas sp. strains showed increased resistance to the cotton Verticillium wilt compared with the control plants in pot experiments. Overall, it showed that the pathogen infection affected the community composition, and healthy plants displayed an enriched beneficial microbiome to combat the plant disease. These findings significantly advance our understanding of the endophytic microbiome assembly under the pathogen infection and develop microbiome-based solutions for sustainable crop production systems.

The exacerbation of soil acidification correlates with structural and functional succession of the soil microbiome upon agricultural intensification

Agricultural intensification driven by land-use changes has caused continuous and cumulative soil acidification (SA) throughout the global agroecosystem. Microorganisms mediate acid-generating reactions; however, the microbial mechanisms responsible for exacerbating SA feedback remain largely unknown. To determine the microbial community composition and putative function associated with SA, we conducted a metagenomic analysis of soils across a chronosequence that has elapsed since the conversion of rice-wheat (RW) to rice-vegetable (RV) rotations. Compared to RW rotations, soil pH decreased by 0.50 and 1.56 units (p < 0.05) in response to 10-year and 20-year RV rotations, respectively. Additionally, acid saturation ratios were increased by 7.3% and 36.2% (p < 0.05), respectively. The loss of microbial beta-diversity was a key element that contributed to the exacerbation of SA in the RV. Notably, the 20-year RV-enriched microbial taxa were more hydrogen (H⁺)-, aluminium (Al³⁺)-, and nitrate nitrogen (NO₃^--N) -dependent and contained more genera exhibiting dehydrogenation functions than did RW-enriched taxa. "M00115, M00151, M00417, and M00004" and "M00531 and M00135" that are the "proton-pumping" and "proton-consuming" gene modules, respectively, were linked to the massive recruitment of acid-dependent biomarkers in 20-year RV soils, particularly Rhodanobacter, Gemmatirosa, Sphingomonas, and Streptomyces. Collectively, soils in long-term RV rotations were highly acidified and acid-sensitive, as the enrichment of microbial dehydrogenation genes allowing for soil buffering capacity is more vulnerable to H⁺ loading and consequently promotes the colonization of more acid-tolerant and acidogenic microbes, and ultimately provide new clues for researchers to elucidate the interaction between SA and the soil microbiome.

Machine learning predicts the impact of antibiotic properties on the composition and functioning of bacterial community in aquatic habitats

In the past decades, hundreds of antibiotics have been isolated from microbial metabolites or have been artificially synthesized for protecting humans, animals and crops from microbial infections. Their everlasting usage results in impacts on the microbial community composition and causes well-known collateral damage to the functioning of microbial communities. Nevertheless, the impact of different antibiotic properties on aquatic microbial communities have so far only poorly been disentangled. Here we characterized the environmental risk of 50 main kinds of antibiotics from 9 classes at a concentration of 10 μg/L for aquatic bacterial communities via metadata analysis combined with machine learning. Metadata analysis showed that the alpha diversity of the bacterial community increased only after treatment with aminoglycoside and β-lactam antibiotics, while its structure was changed by almost all tested antibiotics. The antibiotic treatment also disturbed the functions of the bacterial community, especially with regard to metabolic pathways, including amino acids, cofactors, vitamins, xenobiotics and carbohydrate metabolism. The critical characteristics (atom stereocenter count, number of hydrogen atoms in the antibiotic, and the adipose water coefficient) of antibiotics affecting the composition of the bacterial community in aquatic habitats were screened by machine learning. The key characteristics of antibiotics affecting the function bacterial communities were the number of hydrogen atoms, molecular weight and complexity. In summary, by developing machine learning models and by performing metadata analysis, this study provides the relationship between the properties of antibiotics and their adverse impacts on aquatic microbial communities from a macro perspective. The study also provides guidance for the rational design of antibiotics.

Metagenomics highlights the impact of climate and human activities on antibiotic resistance genes in China's estuaries

Estuarine environments faced with contaminations from coastal zones and the inland are vital sinks of antibiotic resistance genes (ARGs). However, little is known about the temporal-spatial pattern of ARGs and its predominant constraints in estuarine environments. Here, we leveraged metagenomics to investigate ARG profiles from 16 China's estuaries across 6 climate zones in dry and wet seasons, and disentangled their relationships with environmental constraints. Our results revealed that ARG abundance, richness, and diversity in dry season were higher than those in wet season, and ARG abundance exhibited an increasing trend with latitude. The prevalence of ARGs was significantly driven by human activities, mobile gene elements, microbial communities, antibiotic residuals, physicochemical properties, and climatic variables. Among which, climatic variables and human activities ranked the most important factors, contributing 44% and 36% of the total variance of observed ARGs, respectively. The most important climatic variable shaping ARGs is temperature, where increasing temperature is associated with decreased ARGs. Our results highlight that the prevalence of ARGs in estuarine environments would be co-driven by anthropogenic activities and climate, and suggest the dynamics of ARGs under future changing climate and socioeconomic development.

The Proportion of Soil-Borne Fungal Pathogens Increases with Elevated Organic Carbon in Agricultural Soils

Soil-borne fungal phytopathogens are important threats to soil and crop health. However, their community composition and environmental determinants remain unclear. Here, we explored the effects of agricultural fertilization regime (i.e., organic material application) on soil fungal phytopathogens, using data sets from a combination of field survey and long-term experiment. We found that soil organic carbon was the key factor that affected the diversity and relative abundance of fungal phytopathogens in agricultural soils. The dominant genera of phytopathogens including Monographella was also strongly associated with soil organic carbon. In addition, the elevated soil organic carbon enhanced the node proportion of phytopathogens and the positive interactions within the fungal community in the network. Results of the long-term experiment revealed that applications of crop straw and fresh livestock manure significantly increased the proportion of phytopathogens, which were associated with the elevated soil organic carbon. This work offers new insights into the occurrence and environmental factors of fungal phytopathogens in agricultural soils, which are fundamental to control their impacts on the soil and crop systems. IMPORTANCE Fungal phytopathogens are important threats to soil and crop health, but their community composition and environmental determinants remain unclear. We found that soil organic carbon is the key factor of the prevalence of fungal phytopathogens through a field survey, which is also supported by our long-term (6-year) experiment showing the applications of crop straw and fresh livestock manure significantly increased the proportion of fungal phytopathogens. These findings advance our understanding of the occurrence and environmental drivers of soil-borne fungal phytopathogens under agricultural fertilization regime and have important implications for the control of soil-borne pathogens.

Interfacing Machine Learning and Microbial Omics: A Promising Means to Address Environmental Challenges

Microbial communities are ubiquitous and carry an exceptionally broad metabolic capability. Upon environmental perturbation, microbes are also amongst the first natural responsive elements with perturbation-specific cues and markers. These communities are thereby uniquely positioned to inform on the status of environmental conditions. The advent of microbial omics has led to an unprecedented volume of complex microbiological data sets. Importantly, these data sets are rich in biological information with potential for predictive environmental classification and forecasting. However, the patterns in this information are often hidden amongst the inherent complexity of the data. There has been a continued rise in the development and adoption of machine learning (ML) and deep learning architectures for solving research challenges of this sort. Indeed, the interface between molecular microbial ecology and artificial intelligence (AI) appears to show considerable potential for significantly advancing environmental monitoring and management practices through their application. Here, we provide a primer for ML, highlight the notion of retaining biological sample information for supervised ML, discuss workflow considerations, and review the state of the art of the exciting, yet nascent, interdisciplinary field of ML-driven microbial ecology. Current limitations in this sphere of research are also addressed to frame a forward-looking perspective toward the realization of what we anticipate will become a pivotal toolkit for addressing environmental monitoring and management challenges in the years ahead.

Pathogen-Mediated Assembly of Plant-Beneficial Bacteria to Alleviate Fusarium Wilt in Pseudostellaria heterophylla

Fusarium wilt (FW) is a primary replant disease that affects Pseudostellaria heterophylla (Taizishen) and is caused by Fusarium oxysporum, which occurs widely in China under the continuous monocropping regime. However, the ternary interactions among the soil microbiota, P. heterophylla, and F. oxysporum remain unknown. We investigated the potential interaction relationship by which the pathogen-mediated P. heterophylla regulates the soil and the tuberous root microbiota via high-throughput sequencing. Plant-pathogen interaction assays were conducted to measure the arrival of F. oxysporum and Pseudomonas poae at the tuberous root via qPCR and subsequent seedling disease incidence. A growth assay was used to determine the effect of the tuberous root crude exudate inoculated with the pathogen on P. poae. We observed that pathogen-mediated P. heterophylla altered the diversity and the composition of the microbial communities in its rhizosphere soil and tuberous root. Beneficial microbe P. poae and pathogen F. oxysporum were significantly enriched in rhizosphere soil and within the tuberous root in the FW group with high severity. Correlation analysis showed that, accompanied with FW incidence, P. poae co-occurred with F. oxysporum. The aqueous extract of P. heterophylla tuberous root infected by F. oxysporum substantially promoted the growth of P. poae isolates (H1-3-A7, H2-3-B7, H4-3-C1, and N3-3-C4). These results indicated that the extracts from the tuberous root of P. heterophylla inoculated with F. oxysporum might attract P. poae and promote its growth. Furthermore, the colonization assay found that the gene copies of sucD in the P. poae and F. oxysporum treatment (up to 6.57 × 10¹⁰) group was significantly higher than those in the P. poae treatment group (3.29 × 10¹⁰), and a pathogen-induced attraction assay found that the relative copies of sucD of P. poae in the F. oxysporum treatment were significantly higher than in the H₂O treatment. These results showed that F. oxysporum promoted the colonization of P. poae on the tuberous root via F. oxysporum mediation. In addition, the colonization assay found that the disease severity index in the P. poae and F. oxysporum treatment group was significantly lower than that in the F. oxysporum treatment group, and a pathogen-induced attraction assay found that the disease severity index in the F. oxysporum treatment group was significantly higher than that in the H₂O treatment group. Together, these results suggest that pathogen-mediated P. heterophylla promoted and assembled plant-beneficial microbes against plant disease. Therefore, deciphering the beneficial associations between pathogen-mediated P. heterophylla and microbes can provide novel insights into the implementation and design of disease management strategies.

MicrobioSee: A Web-Based Visualization Toolkit for Multi-Omics of Microbiology

With the upgrade and development of the high-throughput sequencing technology, multi-omics data can be obtained at a low cost. However, mapping tools that existed for microbial multi-omics data analysis cannot satisfy the needs of data description and result in high learning costs, complex dependencies, and high fees for researchers in experimental biology fields. Therefore, developing a toolkit for multi-omics data is essential for microbiologists to save effort. In this work, we developed MicrobioSee, a real-time interactive visualization tool based on web technologies, which could visualize microbial multi-omics data. It includes 17 modules surrounding the major omics data of microorganisms such as the transcriptome, metagenome, and proteome. With MicrobioSee, methods for plotting are simplified in multi-omics studies, such as visualization of diversity, ROC, and enrichment pathways for DEGs. Subsequently, three case studies were chosen to represent the functional application of MicrobioSee. Overall, we provided a concise toolkit along with user-friendly, time-saving, cross-platform, and source-opening for researchers, especially microbiologists without coding experience. MicrobioSee is freely available at https://microbiosee.gxu.edu.cn.

Alteration of bacterial communities and co-occurrence networks as a legacy effect upon exposure to polyethylene residues under field environment

The use of plastic film mulch threatens the sustainability of the terrestrial environment because of the persistence of plastic residue. Identification of the potential long-term impacts of polyethylene (PE) residue on the soil microbiome has been overlooked in most studies. A long-term field experiment was conducted to expand this understanding by performing a co-occurrence network analysis of bacterial communities among different compartment niches (i.e. plastisphere, rhizosphere, and bulk soil) and three PE residue concentrations to determine the differential operational taxonomic units (OTUs) and keystone taxa. The specific set of bacterial microbes in the plastisphere was different from that of bulk soil and rhizosphere (R² = 0.372, P < 0.001, PERMANOVA). Totally, 215 and 257 differential OTUs were identified in response to the different compartment niches and PE residue concentrations, respectively. Among these, several hubs or keystone taxa responsible for the exposure to PE residues were further identified, most of which have potential biodegradation functions. Exposure to PE residues led to a reduced network complexity and microbiome stability in the soil ecosystem. This study provides a comprehensive evidence on the alteration of bacterial communities and co-occurrence networks in the terrestrial environment as a legacy effect when exposed to PE residues, and has potential implications for predicting the ecological functions of the soil ecosystem.

Toward understanding the genetic bases underlying plant-mediated "cry for help" to the microbiota

Canonical plant stress biology research has focused mainly on the dynamic regulation of internal genetic pathways in stress responses. Increasingly more studies suggest that plant-mediated timely reshaping of the microbiota could also confer benefits in responding to certain biotic and abiotic stresses. This has led to the "cry for help" hypothesis, which is supported by the identification of plant genetic regulators integrating biotic/abiotic stress signaling and microbiota sculpting. Although diverse genetic mutants have been reported to affect microbiota composition, it has been challenging to confirm the causal link between specific microbiota changes and plant phenotypic outputs (e.g., fitness benefits) due to the complexity of microbial community composition. This limits the understanding of the relevance of plant-mediated microbiota changes. We reviewed the genetic bases of host-mediated reshaping of beneficial microbiota in response to biotic and abiotic stresses, and summarized the practical approaches linking microbiota changes and "functional outputs" in plants. Further understanding of the key regulators and pathways governing the assembly of stress-alleviating microbiota would benefit the design of crops that could dynamically enlist beneficial microbiota under conditions of stress.

Machine Learning Predicts Biogeochemistry from Microbial Community Structure in a Complex Model System

Microbial community structure is influenced by the environment and in turn exerts control on many environmental parameters. We applied this concept in a bioreactor study to test whether microbial community structure contains information sufficient to predict the concentration of H₂S as the product of sulfate reduction. Microbial sulfate reduction is a major source of H₂S in many industrial and environmental systems and is often influenced by the existing physicochemical conditions. Production of H₂S in industrial systems leads to occupational hazards and adversely affects the quality of products. A long-term (148 days) experiment was conducted in upflow bioreactors to mimic sulfidogenesis, followed by inhibition with nitrate salts and a resumption of H₂S generation when inhibition was released. We determined microbial community structure in 731 samples across 20 bioreactors using 16S rRNA gene sequencing and applied a random forest algorithm to successfully predict different phases of sulfidogenesis and mitigation (accuracy = 93.17%) and sessile and effluent microbial communities (accuracy = 100%). Similarly derived regression models that also included cell abundances were able to predict H₂S concentration with remarkably high fidelity (R² > 0.82). Metabolic profiles based on microbial community structure were also found to be reliable predictors for H₂S concentration (R² = 0.78). These results suggest that microbial community structure contains information sufficient to predict sulfidogenesis in a closed system, with anticipated applications to microbially driven processes in open environments.

IMPORTANCE Microbial communities control many biogeochemical processes. Many of these processes are impractical or expensive to measure directly. Because the taxonomic structure of the microbial community is indicative of its function, it encodes information that can be used to predict biogeochemistry. Here, we demonstrate how a machine learning technique can be used to predict sulfidogenesis, a key biogeochemical process in a model system. A distinction of this research was the ability to predict H₂S production in a bioreactor from the effluent bacterial community structure without direct observations of the sessile community or other environmental conditions. This study establishes the ability to use machine learning approaches in predicting sulfide concentrations in a closed system, which can be further developed as a valuable tool for predicting biogeochemical processes in open environments. As machine learning algorithms continue to improve, we anticipate increased applications of microbial community structure to predict key environmental and industrial processes.

Co-composting of fresh tobacco leaves and soil: an exploration on the utilization of fresh tobacco waste in farmland

A large amount of fresh tobacco waste with high water content are produced in farmland, and it may cause environmental pollution if it is not properly treated. The fresh tobacco waste is not easily collected and transported, resulting in its centralized treatment is expensive. This study is to clarify whether it is feasible to treat fresh tobacco wastes by co-composting of them and soil in farmland and applied the obtained compost product into the soil instead of a part of tobacco-specific fertilizer. The results showed that, compared with that in original soil, the relative abundance of Pseudomonas, Azotobacter, and Coprinus of the co-composted products increased by roughly 244%, 323%, and 675%, respectively, and effective nitrogen and available potassium increased by roughly 157% and 132%, respectively. In addition, the nicotine content in co-composted products decreased dramatically compared with the discarded tobacco leaves. The application of the co-composted products and 20% fertilizer amount (15 g/plant) (YD5) exhibited the highest relative abundance of beneficial microbial communities in the soil and the best growth of tobacco plants. The co-composting of fresh tobacco waste and soil in farmland is an effective measure to treat the fresh tobacco waste, and its products increased beneficial microorganisms and stimulate the growth of tobacco plants by replacing an amount of the fertilizer.

Composition identification and functional verification of bacterial community in disease-suppressive soils by machine learning

It has been widely reported that probiotic consortia in the rhizosphere can enhance the plant resistance to pathogens. However, the general composition and functional profiles of bacterial community in soils which suppress multiple diseases for various plants remain largely unknown. Here, we combined metadata analysis with machine learning to identify the general patterns of bacterial-community composition in disease-suppressive soils. Disease-suppressive soils significantly enriched Firmicutes and Actinobacteria but showed a decrease in Proteobacteria and Bacteroidetes. Our machine-learning models accurately identified the disease-conducive and -suppressive soils with 54 biomarker genera, 28 of which were potentially beneficial. We further carried out a successive passaging experiment with the susceptible rps2 mutant of Arabidopsis thaliana invaded by Pseudomonas syringae pv. tomato DC3000 (avrRpt2) for functional verification of potential beneficial bacteria. The disease-suppressive ability of Kribbella, Nocardioides and Bacillus was confirmed, and they positively activated the pathogen-associated molecular patterns-triggered immunity pathway. Results also showed that chemical control by pesticides in agricultural production decreased the disease-suppressive ability of soil. This study provides a method for accurately predicting the occurrence of multiple diseases in soil and identified potential beneficial bacteria to guide a wide range of multiple-strain biological control strategies in agricultural management.

Detection of Pathogenic and Beneficial Microbes for Roselle Wilt Disease

Wilt disease of roselle (Hibiscus sabdariffa L.) is common in Taiwan; however, the causative agent remains unknown. The stems of wilted roselle are browned, slightly constricted, and covered by white aerial hyphae, suggesting that potential pathogens may originate from soil. To identify the potential pathogens, we conducted a rhizosphere microbiota survey in phenotypically healthy and diseased plants through fungal internal transcribed spacer (ITS) and bacterial 16S rRNA amplicon sequencing for uncovering the microbial compositions in the roselle rhizosphere. The fungal family Nectriaceae exhibited significantly higher abundance in diseased rhizospheres than in healthy rhizospheres, and this bacterial community was more specific to geography (i.e., plot-dependent) than to rhizosphere disease status. However, a few bacterial groups such as Bacilli were associated with the healthy rhizosphere. Fusarium species were the most dominant species of Nectriaceae in the survey and became the main target for potential pathogen isolation. We successfully isolated 119 strains from diseased plants in roselle fields. Koch’s postulates were used to evaluate the pathogenicity of these strains; our results indicated that Fusarium solani K1 (FsK1) can cause wilting and a rotted pith in roselles, which was consistent with observations in the fields. This is the first demonstration that F. solani can cause roselle wilt in Taiwan. Furthermore, these newly isolated strains are the most dominant operational taxonomic units detected in ITS amplicon sequencing in diseased rhizospheres, which serves as further evidence that F. solani is the main pathogen causing the roselle wilt disease. Administration of Bacillus velezensis SOI-3374, a strain isolated from a healthy roselle rhizosphere, caused considerable anti-FsK1 activity, and it can serve as a potential biocontrol agent against roselle wilt disease.

The Potential Effect of Microbiota in Predicting The Freshness of Chilled Chicken

1. The goals of this study were to analyse the changes in microbiota composition of chilled chicken during storage and identify microbial biomarkers related to meat freshness.2. The study used 16S rDNA sequencing to track the microbiota shift in chilled chicken during storage. Associations between microbiota composition and storage time were analysed and microbial biomarkers were identified.3. The results showed that microbial diversity of chilled chicken decreased with the storage time. A total of 27 and 24 microbial biomarkers were identified by using orthogonal partial least squares (OPLS) and the random forest regression approach, respectively. The receiver operating characteristic (ROC) curve analysis indicated that the OPLS regression approach had better performance in identifying freshness-related biomarkers. The multiple stepwise regression analysis identified four key microbial biomarkers, including Streptococcus, Carnobacterium, Serratia and Photobacterium genera and constructed a predictive model.4. The study provided microbial biomarkers and a model related to the freshness of chilled chicken. These findings provide a basis for developing detection methods of the freshness of chilled chicken.

Gut Microbial Alterations in Diarrheal Baer's Pochards (Aythya baeri)

The structure and composition of gut microbiota correlate with the occurrence and development of host health and disease. Diarrhea can cause alterations in gut microbiota in animals, and the changes in the gut microbial structure and composition may affect the development of diarrhea. However, there is a scarcity of information on the effects of diarrhea on gut fungal composition and structure, particularly in Baer's pochard (Aythya baeri). The current study was performed for high-throughput sequencing of the fungal-specific internal transcribed spacer 1 (ITS-1) to detect the differences of gut mycobiota in healthy and diarrheal Baer's pochard. Results showed that the gut mycobiota not only decreased significantly in diversity but also in structure and composition. Statistical analysis between two groups revealed a significant decrease in the abundance of phylum Rozellomycota, Zoopagomycota, Mortierellomycota, and Kickxellomycota in diarrheal Baer's pochard. At the genus levels, fungal relative abundance changed significantly in 95 genera, with 56 fungal genera, such as Wickerhamomyces, Alternaria, Penicillium, Cystofilobasidium, and Filobasidium, increasing significantly in the gut of the diarrheal Baer's pochard. In conclusion, the current study revealed the discrepancy in the gut fungal diversity and community composition between the healthy and diarrheal Baer's pochard, laying the basis for elucidating the relationship between diarrhea and the gut mycobiota in Baer's pochard.

Impact of different types of anthropogenic pollution on bacterial community and metabolic genes in urban river sediments

Sediment bacterial communities play a crucial role in the biogeochemical cycle of nutrient elements in urban river. However, the distribution of nitrogen cycle genes on bacterial communities in urban rivers sediments is largely unknown. Here, 16S rRNA amplicon sequencing was used to analyze the composition, co-occurrence patterns and nitrogen cycle process of bacterial communities in urban river sediments under the influence of different exogenous pollution. The results revealed that bacterial communities had significant spatial heterogeneity in river sediments of different polluted areas, and the input of different exogenous pollutants shaped the abundance and distribution of nitrogen cycle-related genes in the sediments. In addition, denitrification process played a leading role in the nitrogen cycle of river sediments, and the genes associated with the nitrification process were rarely observed in all samples. The important bacterial taxonomic biomarkers of nitrogen cycling-related genes screened by random forest algorithm were Synergistia, WS6_Dojkabacteria and Caldisericia. Meanwhile, different co-occurrence patterns observed in different types of polluted areas clarified the impact of environmental filtration and niche differentiation on bacterial communities. In conclusion, this study reveals the nitrogen cycle process and the distribution of related genes mediated by bacterial communities under the impact of different anthropogenic contamination, and provides novel insights for the assembly of bacterial communities in urban river sediments.

Spatiotemporal Quantification of Plasmodiophora brassicae Inoculum in Relation to Clubroot Development Under Inoculated and Naturally Infested Field Conditions

Clubroot caused by Plasmodiophora brassicae is a destructive disease of cruciferous plants worldwide. A quantitative PCR (qPCR) system specific to P. brassicae was developed. Analysis of the qPCR sensitivity indicated that the lower limit of detection was 1 × 10¹ resting spores/ml, 1 × 10² spores/g of soil, and 1 × 10³ spores/g of roots and seeds. The regression curves generated from the qPCR data of different samples had a parallel relationship. The difference between the theoretical and actual concentrations was lowest at 1 × 10⁵ spores/g of sample, compared with other concentrations. The P. brassicae biomass in soil and plant root tissues after inoculated with different spore concentrations was correlated. A correlation analysis confirmed that the clubroot incidence and disease index at 6 weeks after inoculation increased as the spore concentration increased. Under field conditions, the natural inoculum density of the P. brassicae population decreased at the early stage and then increased, with P. brassicae mainly being detected at a soil depth of 0 to 50 cm. The horizontal distribution of P. brassicae varied in the field with occurrences of hot spots. This study established a qPCR-based method for quantitative detection of clubroot. The developed assay is useful for monitoring the spatiotemporal dynamics of P. brassicae in the field. It may also be applicable for clubroot forecasting as a part of proactive disease management.

Penicillium simplicissimum NL-Z1 Induced an Imposed Effect to Promote the Leguminous Plant Growth

It is found effective for phytoremediation of the guest soil spraying method by adding microbes to promote the growth of arbor leguminous plant on a high and steep rock slope. However, its underlying mechanisms remain elusive. Here, some experiments were conducted to explore the multifunctions of Penicillium simplicissimum NL-Z1 on rock weathering, nodule growth, and beneficial microbial regulation. The results show that P. simplicissimum NL-Z1 significantly increased the release of phosphorus, potassium, calcium, and magnesium from the rock by 226, 29, 24, and 95%, respectively, compared with that of the control. A significant increase of 153% in Indigofera pseudotinctoria Matsum nodule biomass, accompanied by an increase of 37% in the leguminous plant biomass was observed in the P. simplicissimum NL-Z1 treatment than in the control treatment. Interestingly, even though P. simplicissimum NL-Z1 itself became a minor microbial community in the soil, it induced a significant increase in Mortierella, which, as a beneficial microbe, can promote phosphate-solubilizing and plant growth. The results suggest that P. simplicissimum NL-Z1 could induce an imposed effect to promote leguminous plant growth, which may be conducive to the development of the phytoremediation technique for high and steep rock slope. The study provides a novel thought of using the indirect effect of microbes, i.e., promoting other beneficial microbes, to improve soil environment.

Gammaproteobacteria, a core taxon in the guts of soil fauna, are potential responders to environmental concentrations of soil pollutants

BACKGROUND

The ubiquitous gut microbiotas acquired from the environment contribute to host health. The gut microbiotas of soil invertebrates are gradually assembled from the microecological region of the soil ecosystem which they inhabit, but little is known about their characteristics when the hosts are under environmental stress. The rapid development of high-throughput DNA sequencing in the last decade has provided unprecedented insights and opportunities to characterize the gut microbiotas of soil invertebrates. Here, we characterized the core, transient, and rare bacterial taxa in the guts of soil invertebrates using the core index (CI) and developed a new theory of global microbial diversity of soil ecological microregions.

RESULTS

We found that the Gammaproteobacteria could respond indiscriminately to the exposure to environmental concentrations of soil pollutants and were closely associated with the physiology and function of the host. Meanwhile, machine-learning models based on metadata calculated that Gammaproteobacteria were the core bacteria with the highest colonization potential in the gut, and further identified that they were the best indicator taxon of the response to environmental concentrations of soil pollution. Gammaproteobacteria also closely correlated with the abundance of antibiotic resistance genes.

CONCLUSIONS

Our results determined that Gammaproteobacteria were an indicator taxon in the guts of the soil invertebrates that responded to environmental concentrations of soil pollutants, thus providing an effective theoretical basis for subsequent assessments of soil ecological risk. The results of the physiological and biochemical analyses of the host and the microbial-community functions, and the antibiotic resistance of Gammaproteobacteria, provide new insights for evaluating global soil ecological health. Video abstract.

Disease-induced changes in plant microbiome assembly and functional adaptation

BACKGROUND

The plant microbiome is an integral part of the host and increasingly recognized as playing fundamental roles in plant growth and health. Increasing evidence indicates that plant rhizosphere recruits beneficial microbes to the plant to suppress soil-borne pathogens. However, the ecological processes that govern plant microbiome assembly and functions in the below- and aboveground compartments under pathogen invasion are not fully understood. Here, we studied the bacterial and fungal communities associated with 12 compartments (e.g., soils, roots, stems, and fruits) of chili pepper (Capsicum annuum L.) using amplicons (16S and ITS) and metagenomics approaches at the main pepper production sites in China and investigated how Fusarium wilt disease (FWD) affects the assembly, co-occurrence patterns, and ecological functions of plant-associated microbiomes.

RESULTS

The amplicon data analyses revealed that FWD affected less on the microbiome of pepper reproductive organs (fruit) than vegetative organs (root and stem), with the strongest impact on the upper stem epidermis. Fungal intra-kingdom networks were less stable and their communities were more sensitive to FWD than the bacterial communities. The analysis of microbial interkingdom network further indicated that FWD destabilized the network and induced the ecological importance of fungal taxa. Although the diseased plants were more susceptible to colonization by other pathogenic fungi, their below- and aboveground compartments can also recruit potential beneficial bacteria. Some of the beneficial bacterial taxa enriched in the diseased plants were also identified as core taxa for plant microbiomes and hub taxa in networks. On the other hand, metagenomic analysis revealed significant enrichment of several functional genes involved in detoxification, biofilm formation, and plant-microbiome signaling pathways (i.e., chemotaxis) in the diseased plants.

CONCLUSIONS

Together, we demonstrate that a diseased plant could recruit beneficial bacteria and mitigate the changes in reproductive organ microbiome to facilitate host or its offspring survival. The host plants may attract the beneficial microbes through the modulation of plant-microbiome signaling pathways. These findings significantly advance our understanding on plant-microbiome interactions and could provide fundamental and important data for harnessing the plant microbiome in sustainable agriculture. Video abstract.