Melon: metagenomic long-read-based taxonomic identification and quantification using marker genes.

Long-read sequencing holds great potential for characterizing complex microbial communities, yet taxonomic profiling tools designed specifically for long reads remain lacking. We introduce Melon, a novel marker-based taxonomic profiler that capitalizes on the unique attributes of long reads. Melon employs a two-stage classification scheme to reduce computational time and is equipped with an expectation-maximization-based post-correction module to handle ambiguous reads. Melon achieves superior performance compared to existing tools in both mock and simulated samples. Using wastewater metagenomic samples, we demonstrate the applicability of Melon by showing it provides reliable estimates of overall genome copies, and species-level taxonomic profiles.

Characterization of Siderophore Produced by Bacillus velezensis YL2021 and Its Application in Controlling Rice Sheath Blight and Rice Blast.

Bacillus velezensis YL2021 has extensive antimicrobial activities against phytopathogens, and its genome harbors a catechol-type siderophore biosynthesis gene cluster. Here, we describe the characterization of siderophore produced by strain YL2021 and its antimicrobial activity in vitro and in vivo. A few types of siderophores were detected by chrome azurol S plates coupled with Arnow's test, purified and identified by Reversed-phase high-performance liquid chromatography (RP-HPLC). We found that strain YL2021 can produce different antimicrobial compounds under low-iron M9 medium or iron-sufficient LB medium although antimicrobial activities can be easily observed on the two media as described above in vitro. Strain YL2021 can produce at least three catechol-type siderophores in low-iron M9 medium while no siderophore was produced in LB medium. Among them, the main antimicrobial siderophore produced by strain YL2021 was bacillibactin, with m/z of 882, based on the liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis, which has broad-spectrum antimicrobial activities against Gram-positive, Gram-negative bacteria, the oomycete Phytophthora capsici and phytopathogenic fungi. Moreover, the antifungal activity of siderophore including bacillibactin observed in vitro was correlated with control efficacies against rice sheath blight disease caused by Rhizoctonia solani and rice blast disease caused by Magnaporthe oryzae in vivo. Collectively, the results demonstrate that siderophore including bacillibactin produced by Bacillus velezensis YL2021 is a promising biocontrol agent for application in rice disease control.

ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences.

BACKGROUND: Emergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the key components to define bacterial resistance and their spread in different environments. Identification of ARGs, particularly from high-throughput sequencing data of the specimens, is the state-of-the-art method for comprehensively monitoring their spread and evolution. Current computational methods to identify ARGs mainly rely on alignment-based sequence similarities with known ARGs. Such approaches are limited by choice of reference databases and may potentially miss novel ARGs. The similarity thresholds are usually simple and could not accommodate variations across different gene families and regions. It is also difficult to scale up when sequence data are increasing. RESULTS: In this study, we developed ARGNet, a deep neural network that incorporates an unsupervised learning autoencoder model to identify ARGs and a multiclass classification convolutional neural network to classify ARGs that do not depend on sequence alignment. This approach enables a more efficient discovery of both known and novel ARGs. ARGNet accepts both amino acid and nucleotide sequences of variable lengths, from partial (30-50 aa; 100-150 nt) sequences to full-length protein or genes, allowing its application in both target sequencing and metagenomic sequencing. Our performance evaluation showed that ARGNet outperformed other deep learning models including DeepARG and HMD-ARG in most of the application scenarios especially quasi-negative test and the analysis of prediction consistency with phylogenetic tree. ARGNet has a reduced inference runtime by up to 57% relative to DeepARG. CONCLUSIONS: ARGNet is flexible, efficient, and accurate at predicting a broad range of ARGs from the sequencing data. ARGNet is freely available at https://github.com/id-bioinfo/ARGNet , with an online service provided at https://ARGNet.hku.hk . Video Abstract.

Metagenomic absolute quantification of antibiotic resistance genes and virulence factor genes-carrying bacterial genomes in anaerobic digesters.

Sewage treatment works have been considered as hotspots for the dissemination of antibiotic resistance genes (ARGs). Anaerobic digestion (AD) has emerged as a promising approach for controlling the spread of ARGs while destroying biomass in sludge. Evaluating the impact of AD on ARG removal relies on the absolute quantification of ARGs. In this study, we quantified the ARG concentrations in both full-scale and lab-scale AD systems using a cellular spike-ins based absolute quantification approach. Results demonstrated that AD effectively removed 68 ± 18 %, 55 ± 12 %, and 57 ± 19 % of total ARGs in semi-continuous AD digesters, with solid retention times of 15, 20, and 25 days, respectively. The removal efficiency of total ARGs increased as the AD process progressed in the batch digesters over 40 days. A significant negative correlation was observed between digestion time and the concentrations of certain ARG types, such as beta-lactam, sulfonamide, and tetracycline. However, certain potential pathogenic antibiotic resistant bacteria (PARB) and multi-resistant high-risk ARGs-carrying populations robustly persisted throughout the AD process, regardless of the operating conditions. This study highlighted the influence of the AD process and its operating parameters on ARG removal, and revealed the broad spectrum and persistence of PARB in AD systems. These findings provided critical insights for the management of microbial hazards.

Is ICE hot? A genomic comparative study reveals integrative and conjugative elements as "hot" vectors for the dissemination of antibiotic resistance genes.

IMPORTANCE: Different from other extensively studied mobile genetic elements (MGEs) whose discoveries were initiated decades ago (1950s-1980s), integrative and conjugative elements (ICEs), a diverse array of more recently identified elements that were formally termed in 2002, have aroused increasing concern for their crucial contribution to the dissemination of antibiotic resistance genes (ARGs). However, the comprehensive understanding on ICEs' ARG profile across the bacterial tree of life is still blurred. Through a genomic study by comparison with two key MGEs, we, for the first time, systematically investigated the ARG profile as well as the host range of ICEs and also explored the MGE-specific potential to facilitate ARG propagation across phylogenetic barriers. These findings could serve as a theoretical foundation for risk assessment of ARGs mediated by distinct MGEs and further to optimize therapeutic strategies aimed at restraining antibiotic resistance crises.

Establishing reference material for the quest towards standardization in environmental microbial metagenomic studies.

Breakthroughs in DNA-based technologies, especially in metagenomic sequencing, have drastically enhanced researchers' ability to explore environmental microbiome and the associated interplays within. However, as new methodologies are being actively developed for improvements in different aspects, metagenomic workflows become diversified and heterogeneous. Through a single-variable control approach, we quantified the microbial profiling variations arising from 6 common technical variables associated with metagenomic workflows for both simple and complex samples. The incurred variations were constantly the lowest in replicates of DNA isolation and DNA sequencing library construction. Different DNA extraction kits often caused the highest variation among all the tested variables. Additionally, sequencing run batch was an important source of variability for targeted platforms. As such, the development of an environmental reference material for complex environmental samples could be beneficial in benchmarking accrued non-biological variability within and between protocols and insuring reliable and reproducible sequencing outputs immediately upstream of bioinformatic analysis. To develop an environment reference material, sequencing of a well-homogenized environmental sample composed of activated sludge was performed using different pre-analytical assays in replications. In parallel, a certified mock community was processed and sequenced. Assays were ranked based on the reconstruction of the theoretical mock community profile. The reproducibility of the best-performing assay and the microbial profile of the reference material were further ascertained. We propose the adoption of our complex environmental reference material, which could reflect the degree of diversity in environmental microbiome studies, to facilitate accurate, reproducible, and comparable environmental metagenomics-based studies.

Toward a Universal Unit for Quantification of Antibiotic Resistance Genes in Environmental Samples.

Surveillance of antibiotic resistance genes (ARGs) has been increasingly conducted in environmental sectors to complement the surveys in human and animal sectors under the "One-Health" framework. However, there are substantial challenges in comparing and synthesizing the results of multiple studies that employ different test methods and approaches in bioinformatic analysis. In this article, we consider the commonly used quantification units (ARG copy per cell, ARG copy per genome, ARG density, ARG copy per 16S rRNA gene, RPKM, coverage, PPM, etc.) for profiling ARGs and suggest a universal unit (ARG copy per cell) for reporting such biological measurements of samples and improving the comparability of different surveillance efforts.

Polycyclic aromatic hydrocarbon (PAH) biodegradation capacity revealed by a genome-function relationship approach.

BACKGROUND: Polycyclic aromatic hydrocarbon (PAH) contamination has been a worldwide environmental issue because of its impact on ecosystems and human health. Biodegradation plays an important role in PAH removal in natural environments. To date, many PAH-degrading strains and degradation genes have been reported. However, a comprehensive PAH-degrading gene database is still lacking, hindering a deep understanding of PAH degraders in the era of big data. Furthermore, the relationships between the PAH-catabolic genotype and phenotype remain unclear. RESULTS: Here, we established a bacterial PAH-degrading gene database and explored PAH biodegradation capability via a genome-function relationship approach. The investigation of functional genes in the experimentally verified PAH degraders indicated that genes encoding hydratase-aldolase could serve as a biomarker for preliminarily identifying potential degraders. Additionally, a genome-centric interpretation of PAH-degrading genes was performed in the public genome database, demonstrating that they were ubiquitous in Proteobacteria and Actinobacteria. Meanwhile, the global phylogenetic distribution was generally consistent with the culture-based evidence. Notably, a few strains affiliated with the genera without any previously known PAH degraders (Hyphomonas, Hoeflea, Henriciella, Saccharomonospora, Sciscionella, Tepidiphilus, and Xenophilus) also bore a complete PAH-catabolic gene cluster, implying their potential of PAH biodegradation. Moreover, a random forest analysis was applied to predict the PAH-degrading trait in the complete genome database, revealing 28 newly predicted PAH degraders, of which nine strains encoded a complete PAH-catabolic pathway. CONCLUSIONS: Our results established a comprehensive PAH-degrading gene database and a genome-function relationship approach, which revealed several potential novel PAH-degrader lineages. Importantly, this genome-centric and function-oriented approach can overcome the bottleneck of conventional cultivation-based biodegradation research and substantially expand our current knowledge on the potential degraders of environmental pollutants.

Global environmental resistome: Distinction and connectivity across diverse habitats benchmarked by metagenomic analyses.

The widely distributed antibiotic resistance genes (ARGs) were unevenly proliferated in various habitats. Great endeavors are needed to resolve the resistome features that can differentiate or connect different habitats. This study retrieved a broad spectrum of resistome profiles from 1723 metagenomes categorized into 13 habitats, encompassing industrial, urban, agricultural, and natural environments, and spanning most continents and oceans. The resistome features (ARG types, subtypes, indicator ARGs, and emerging mobilizable ARGs: mcr and tet(X)) in these habitats were benchmarked via a standardized workflow. We found that wastewater and wastewater treatment works were characterized to be reservoirs of more diverse genotypes of ARGs than any other habitats including human and livestock fecal samples, while fecal samples were with higher ARG abundance. Bacterial taxonomy composition was significantly correlated with resistome composition across most habitats. Moreover, the source-sink connectivities were disentangled by developing the resistome-based microbial attribution prediction model. Environmental surveys with standardized bioinformatic workflow proposed in this study will help comprehensively understand the transfer of ARGs in the environment, thus prioritizing the critical environments with high risks for intervention to tackle the problem of ARGs.

QMRA of beach water by Nanopore sequencing-based viability-metagenomics absolute quantification.

The majority of the current regulatory practices for routine monitoring of beach water quality rely on the culture-based enumeration of faecal indicator bacteria (FIB) to develop criteria for promoting the general public's health. To address the limitations of culture methods and the arguable reliability of FIB in indicating health risks, we developed a Nanopore metagenomic sequencing-based viable cell absolute quantification workflow to rapidly and accurately estimate a broad range of microbes in beach waters by a combination of propidium monoazide (PMA) and cellular spike-ins. Using the simple synthetic bacterial communities mixed with viable and heat-killed cells, we observed near-complete relic DNA removal by PMA with minimal disturbance to the composition of viable cells, demonstrating the feasibility of PMA treatment in profiling viable cells by Nanopore sequencing. On a simple mock community comprised of 15 prokaryotic species, our results showed high accordance between the expected and estimated concentrations, suggesting the accuracy of our method in absolute quantification. We then further assessed the accuracy of our method for counting viable Escherichia coli and Vibrio spp. in beach waters by comparing to culture-based method, which were also in high agreement. Furthermore, we demonstrated that 1 Gb sequences obtained within 2 h would be sufficient to quantify a species having a concentration of ≥ 10 cells/mL in beach waters. Using our viability-resolved quantification workflow to assess the microbial risk of the beach water, we conducted (1) screening-level quantitative microbial risk assessment (QMRA) to investigate human illness risk and site-specific risk patterns that might guide risk management efforts and (2) metagenomics-based resistome risk assessment to evaluate another layer of risk caused by difficult illness treatment due to antimicrobial resistance (AMR). In summary, our metagenomic workflow for the rapid absolute quantification of viable bacteria demonstrated its great potential in paving new avenues toward holistic microbial risk assessment.

Ecological dynamics imposes fundamental challenges in community-based microbial source tracking.

Quantifying the contributions of possible environmental sources ("sources") to a specific microbial community ("sink") is a classical problem in microbiology known as microbial source tracking (MST). Solving the MST problem will not only help us understand how microbial communities were formed, but also have far-reaching applications in pollution control, public health, and forensics. MST methods generally fall into two categories: target-based methods (focusing on the detection of source-specific indicator species or chemicals); and community-based methods (using community structure to measure similarity between sink samples and potential source environments). As next-generation sequencing becomes a standard community-assessment method in microbiology, numerous community-based computational methods, referred to as MST solvers hereafter have been developed and applied to various real datasets to demonstrate their utility across different contexts. Yet, those MST solvers do not consider microbial interactions and priority effects in microbial communities. Here, we revisit the performance of several representative MST solvers. We show compelling evidence that solving the MST problem using existing MST solvers is impractical when ecological dynamics plays a role in community assembly. In particular, we clearly demonstrate that the presence of either microbial interactions or priority effects will render the MST problem mathematically unsolvable for MST solvers. We further analyze data from fecal microbiota transplantation studies, finding that the state-of-the-art MST solvers fail to identify donors for most of the recipients. Finally, we perform community coalescence experiments to demonstrate that the state-of-the-art MST solvers fail to identify the sources for most of the sinks. Our findings suggest that ecological dynamics imposes fundamental challenges in MST. Interpretation of results of existing MST solvers should be done cautiously.

Rapid absolute quantification of pathogens and ARGs by nanopore sequencing.

Compositional nature of relative abundance data in the current standard microbiome studies limits microbial dynamics interpretations and cross-sample comparisons. Here, we demonstrate the first rapid (1-h sequencing) method coupling Nanopore metagenomic sequencing with cellular spike-in to facilitate the absolute quantification and removal assessment of pathogens and antibiotic resistance genes (ARGs) in wastewater treatment plants (WWTPs). Nanopore sequencing-based quantification results for both simple mock community and complex real environmental samples showed a high consistency with those from the widely-used Illumina and culture-based approaches. Implementing such method, we quantified 46 predominant putative pathogenic species, and 361 ARGs in three WWTP sample sets. Though high log removals of dominant pathogens (2.23 logs) and ARGs (1.98 logs) were achieved, complete removal of all pathogens and ARGs were not achieved. Noticeably, Mycobacterium spp., Clostridium_P perfringens, and Borrelia hermsii exhibited low removal, and 13 ARGs even increased in absolute abundance after the treatment. Our proposed approach manifested its profound ability in providing absolute quantitation information guiding wastewater-based epidemiological surveillance and quantitative risk assessment facilitating microbial hazards management.

An assessment of resistome and mobilome in wastewater treatment plants through temporal and spatial metagenomic analysis.

Wastewater treatment plants (WWTPs) are regarded as critical points in disseminating antibiotic resistance genes (ARGs). In particular, the discharging effluents from WWTPs generally bring downstream catchment areas exogenous ARGs and resistant bacteria. However, there lacks a sufficient assessment of the resistome and mobilome in effluents. In this study, a consecutive monthly sampling was conducted over 13 months in three Hong Kong (HK) WWTPs for metagenomic sequencing. Prevalence information of ARGs and mobile genetic elements (MGEs) was compared with counterparts in effluents from cities of North America, South America, Europe, and Asia. Moreover, a publicly accessible platform integrating the exposure ranking scheme, which was based on the global archive of ARG abundance, and a readily implementable online pipeline was developed to benefit communication in academia and government consultancy. Results demonstrated HK WWTPs were featured high ARG removal efficiency of 2.34-2.43 log reduction rate, and effluents were ranked in moderate levels of Level 2 and Level 3 in the exposure prioritizing scheme based on total ARG abundance. Moreover, absolute quantification of temporal variations of effluent resistome disclosed distinct changes over time among varied ARG types which were associated with prevalently used antibiotics, including quinolone and sulfonamide. This reinforces the need for real-time management of WWTP systems. Notably, ARGs of anthropogenic prevalence, high mobility, and potential pathogenicity were found to be present in HK effluents, drawing attention to the necessity for improved risk management. In addition, source tracking of effluent resistome and structural equation model analysis was conducted to explore the disparity in ARG abundance and diversity in different samples. The discovery of this study and the recommendation of a comprehensive exposure assessment will facilitate decision-making in resistome management in WWTPs to reduce the ARG and antibiotic resistant bacteria (ARB) contamination in the receiving environments.

Evidence for Long-Term Anthropogenic Pollution: The Hadal Trench as a Depository and Indicator for Dissemination of Antibiotic Resistance Genes.

Knowledge of the distribution and dissemination of antibiotic resistance genes (ARGs) is essential for understanding anthropogenic impacts on natural ecosystems. The transportation of ARGs via aquatic environments is significant and has received great attention, but whether there has been anthropogenic ARG pollution to the hadal ocean ecosystem has not been well explored. For investigating ecological health concerns, we profiled the ARG occurrence in sediments of the Mariana Trench (MT) (10 890 m), the deepest region of the ocean. Metagenomic-based ARG profiles showed a sudden increase of abundance and diversity in the surface layer of MT sediments reaching 2.73 × 10-2 copy/cell and 81 subtypes, and a high percentage of ∼63.6% anthropogenic pollution sources was predicted by the Bayesian-modeling classification method. These together suggested that ARG accumulation and anthropogenic impacts have already permeated into the bottom of the deepest corner on the earth. Moreover, six ARG-carrying draft genomes were retrieved using a metagenomic binning strategy, one of which assigned as Streptococcus was identified as a potential bacterial host to contribute to the ARG accumulation in MT, carrying ermF, tetM, tetQ, cfxA2, PBP-2X, and PBP-1A. We propose that the MT ecosystem needs further long-term monitoring for the assessment of human impacts, and our identified three biomarkers (cfxA2, ermF, and mefA) could be used for the rapid monitoring of anthropogenic pollution. Together our findings imply that anthropogenic pollution has penetrated into the deepest region of the ocean and urge for better pollution control to reduce the risk of ARG dissemination to prevent the consistent accumulation and potential threat to the natural environment.

Successional dynamics and alternative stable states in a saline activated sludge microbial community over 9 years.

BACKGROUND: Microbial communities in both natural and applied settings reliably carry out myriads of functions, yet how stable these taxonomically diverse assemblages can be and what causes them to transition between states remains poorly understood. We studied monthly activated sludge (AS) samples collected over 9 years from a full-scale wastewater treatment plant to answer how complex AS communities evolve in the long term and how the community functions change when there is a disturbance in operational parameters. RESULTS: Here, we show that a microbial community in activated sludge (AS) system fluctuated around a stable average for 3 years but was then abruptly pushed into an alternative stable state by a simple transient disturbance (bleaching). While the taxonomic composition rapidly turned into a new state following the disturbance, the metabolic profile of the community and system performance remained remarkably stable. A total of 920 metagenome-assembled genomes (MAGs), representing approximately 70% of the community in the studied AS ecosystem, were recovered from the 97 monthly AS metagenomes. Comparative genomic analysis revealed an increased ability to aggregate in the cohorts of MAGs with correlated dynamics that are dominant after the bleaching event. Fine-scale analysis of dynamics also revealed cohorts that dominated during different periods and showed successional dynamics on seasonal and longer time scales due to temperature fluctuation and gradual changes in mean residence time in the reactor, respectively. CONCLUSIONS: Our work highlights that communities can assume different stable states under highly similar environmental conditions and that a specific disturbance threshold may lead to a rapid shift in community composition. Video Abstract.

An omics-based framework for assessing the health risk of antimicrobial resistance genes.

Antibiotic resistance genes (ARGs) are widespread among bacteria. However, not all ARGs pose serious threats to public health, highlighting the importance of identifying those that are high-risk. Here, we developed an 'omics-based' framework to evaluate ARG risk considering human-associated-enrichment, gene mobility, and host pathogenicity. Our framework classifies human-associated, mobile ARGs (3.6% of all ARGs) as the highest risk, which we further differentiate as 'current threats' (Rank I; 3%) - already present among pathogens - and 'future threats' (Rank II; 0.6%) - novel resistance emerging from non-pathogens. Our framework identified 73 'current threat' ARG families. Of these, 35 were among the 37 high-risk ARGs proposed by the World Health Organization and other literature; the remaining 38 were significantly enriched in hospital plasmids. By evaluating all pathogen genomes released since framework construction, we confirmed that ARGs that recently transferred into pathogens were significantly enriched in Rank II ('future threats'). Lastly, we applied the framework to gut microbiome genomes from fecal microbiota transplantation donors. We found that although ARGs were widespread (73% of genomes), only 8.9% of genomes contained high-risk ARGs. Our framework provides an easy-to-implement approach to identify current and future antimicrobial resistance threats, with potential clinical applications including reducing risk of microbiome-based interventions.

Impact of chicken litter pre-application treatment on the abundance, field persistence, and transfer of antibiotic resistant bacteria and antibiotic resistance genes to vegetables.

Treatment of manures prior to land application can potentially reduce the abundance of antibiotic resistance genes and thus the risk of contaminating crops or water resources. In this study, raw and composted chicken litter were applied to field plots that were cropped to carrots, lettuce and radishes. Vegetables were washed per normal culinary practice before downstream analysis. The impact of composting on manure microbial composition, persistence of antibiotic resistant bacteria in soil following application, and distribution of antibiotic resistance genes and bacteria on washed vegetables were determined. A subset of samples that were thought likely to reveal the most significant effects were chosen for shotgun sequencing. The absolute abundance of all target genes detected by qPCR decreased after composting except sul1, intI1, incW and erm(F) that remained stable. The shotgun sequencing revealed that some integron integrases were enriched by composting. Composting significantly reduced the abundance of enteric bacteria, including those carrying antibiotic resistance. Manure-amended soil showed significantly higher abundances of sul1, str(A), str(B), erm(B), aad(A), intI1 and incW compared to unmanured soil. At harvest, those genes that were detected in soil samples before the application of manure (intI1, sul1, strA and strB) were quantifiable by qPCR on vegetables, with a larger number of gene targets detected on the radishes than in the carrots or lettuce. Shotgun metagenomic sequencing suggested that the increase of antibiotic resistance genes on radishes produced in soil receiving raw manure may be due to changes to soil microbial communities following manure application, rather than transfer to the radishes of enteric bacteria. Overall, under field conditions there was limited evidence for transfer of antibiotic resistance genes from composted or raw manure to vegetables that then persisted through washing.

Pyricularia sp. jiangsuensis, a new cryptic rice panicle blast pathogen from rice fields in Jiangsu Province, China.

Pyricularia oryzae is a multi-host pathogen causing cereal disease, including the devastating rice blast. Panicle blast is a serious stage, leading to severe yield loss. Thirty-one isolates (average 4.1%) were collected from the rice panicle lesions at nine locations covering Jiangsu province from 2010 to 2017. These isolates were characterized as Pyricularia sp. jiangsuensis distinct from known Pyricularia species. The representative strain 18-2 can infect rice panicle, root and five kinds of grasses. Intriguingly, strain 18-2 can co-infect rice leaf with P. oryzae Guy11. The whole genome of P. sp. jiangsuensis 18-2 was sequenced. Nine effectors were distributed in translocation or inversion region, which may link to the rapid evolution of effectors. Twenty-one homologues of known blast-effectors were identified in strain 18-2, seven effectors including the homologues of SLP1, BAS2, BAS113, CDIP2/3, MoHEG16 and Avr-Pi54, were upregulated in the sample of inoculated panicle with strain 18-2 at 24 hpi compared with inoculation at 8 hpi. Our results provide evidences that P. sp. jiangsuensis represents an addition to the mycobiota of blast disease. This study advances our understanding of the pathogenicity of P. sp. jiangsuensis to hosts, which sheds new light on the adaptability in the co-evolution of pathogen and host.

Microbiome assembly for sulfonamide subsistence and the transfer of genetic determinants.

Antibiotic subsistence in bacteria represents an alternative resistance machinery, while paradoxically, it is also a cure for environmental resistance. Antibiotic-subsisting bacteria can detoxify antibiotic-polluted environments and prevent the development of antibiotic resistance in environments. However, progress toward efficient in situ engineering of antibiotic-subsisting bacteria is hindered by the lack of mechanistic and predictive understanding of the assembly of the functioning microbiome. By top-down manipulation of wastewater microbiomes using sulfadiazine as the single limiting source, we monitored the ecological selection process that forces the wastewater microbiome to perform efficient sulfadiazine subsistence. We found that the community-level assembly selects for the same three families rising to prominence across different initial pools of microbiomes. We further analyzed the assembly patterns using a linear model. Detailed inspections of the sulfonamide metabolic gene clusters in individual genomes of isolates and assembled metagenomes reveal limited transfer potential beyond the boundaries of the Micrococcaceae lineage. Our results open up new possibilities for engineering specialist bacteria for environmental applications.

The N-terminus of an Ustilaginoidea virens Ser-Thr-rich glycosylphosphatidylinositol-anchored protein elicits plant immunity as a MAMP.

Many pathogens infect hosts through specific organs, such as Ustilaginoidea virens, which infects rice panicles. Here, we show that a microbe-associated molecular pattern (MAMP), Ser-Thr-rich Glycosyl-phosphatidyl-inositol-anchored protein (SGP1) from U. virens, induces immune responses in rice leaves but not panicles. SGP1 is widely distributed among fungi and acts as a proteinaceous, thermostable elicitor of BAK1-dependent defense responses in N. benthamiana. Plants specifically recognize a 22 amino acid peptide (SGP1 N terminus peptide 22, SNP22) in its N-terminus that induces cell death, oxidative burst, and defense-related gene expression. Exposure to SNP22 enhances rice immunity signaling and resistance to infection by multiple fungal and bacterial pathogens. Interestingly, while SGP1 can activate immune responses in leaves, SGP1 is required for U. virens infection of rice panicles in vivo, showing it contributes to the virulence of a panicle adapted pathogen.