A comparative analysis of mutual information methods for pairwise relationship detection in metagenomic data.

BACKGROUND: Construction of co-occurrence networks in metagenomic data often employs correlation to infer pairwise relationships between microbes. However, biological systems are complex and often display qualities non-linear in nature. Therefore, the reliance on correlation alone may overlook important relationships and fail to capture the full breadth of intricacies presented in underlying interaction networks. It is of interest to incorporate metrics that are not only robust in detecting linear relationships, but non-linear ones as well. RESULTS: In this paper, we explore the use of various mutual information (MI) estimation approaches for quantifying pairwise relationships in biological data and compare their performances against two traditional measures-Pearson's correlation coefficient, r, and Spearman's rank correlation coefficient, ρ. Metrics are tested on both simulated data designed to mimic pairwise relationships that may be found in ecological systems and real data from a previous study on C. diff infection. The results demonstrate that, in the case of asymmetric relationships, mutual information estimators can provide better detection ability than Pearson's or Spearman's correlation coefficients. Specifically, we find that these estimators have elevated performances in the detection of exploitative relationships, demonstrating the potential benefit of including them in future metagenomic studies. CONCLUSIONS: Mutual information (MI) can uncover complex pairwise relationships in biological data that may be missed by traditional measures of association. The inclusion of such relationships when constructing co-occurrence networks can result in a more comprehensive analysis than the use of correlation alone.

Metagenome-wide characterization of shared antimicrobial resistance genes in sympatric people and lemurs in rural Madagascar.

Background: Tracking the spread of antibiotic resistant bacteria is critical to reduce global morbidity and mortality associated with human and animal infections. There is a need to understand the role that wild animals in maintenance and transfer of antibiotic resistance genes (ARGs). Methods: This study used metagenomics to identify and compare the abundance of bacterial species and ARGs detected in the gut microbiomes from sympatric humans and wild mouse lemurs in a forest-dominated, roadless region of Madagascar near Ranomafana National Park. We examined the contribution of human geographic location toward differences in ARG abundance and compared the genomic similarity of ARGs between host source microbiomes. Results: Alpha and beta diversity of species and ARGs between host sources were distinct but maintained a similar number of detectable ARG alleles. Humans were differentially more abundant for four distinct tetracycline resistance-associated genes compared to lemurs. There was no significant difference in human ARG diversity from different locations. Human and lemur microbiomes shared 14 distinct ARGs with highly conserved in nucleotide identity. Synteny of ARG-associated assemblies revealed a distinct multidrug-resistant gene cassette carrying dfrA1 and aadA1 present in human and lemur microbiomes without evidence of geographic overlap, suggesting that these resistance genes could be widespread in this ecosystem. Further investigation into intermediary processes that maintain drug-resistant bacteria in wildlife settings is needed.

Metagenome comparison (MC): A new framework for detecting unique/enriched OMUs (operational metagenomic units) derived from whole-genome sequencing reads.

BACKGROUND: Current methods for comparing metagenomes, derived from whole-genome sequencing reads, include top-down metrics or parametric models such as metagenome-diversity, and bottom-up, non-parametric, model-free machine learning approaches like Naïve Bayes for k-mer-profiling. However, both types are limited in their ability to effectively and comprehensively identify and catalogue unique or enriched metagenomic genes, a critical task in comparative metagenomics. This challenge is significant and complex due to its NP-hard nature, which means computational time grows exponentially, or even faster, with the problem size, rendering it impractical for even the fastest supercomputers without heuristic approximation algorithms. METHOD: In this study, we introduce a new framework, MC (Metagenome-Comparison), designed to exhaustively detect and catalogue unique or enriched metagenomic genes (MGs) and their derivatives, including metagenome functional gene clusters (MFGC), or more generally, the operational metagenomic unit (OMU) that can be considered the counterpart of the OTU (operational taxonomic unit) from amplicon sequencing reads. The MC is essentially a heuristic search algorithm guided by pairs of new metrics (termed MG-specificity or OMU-specificity, MG-specificity diversity or OMU-specificity diversity). It is further constrained by statistical significance (P-value) implemented as a pair of statistical tests. RESULTS: We evaluated the MC using large metagenomic datasets related to obesity, diabetes, and IBD, and found that the proportions of unique and enriched metagenomic genes ranged from 0.001% to 0.08 % and 0.08%-0.82 % respectively, and less than 10 % for the MFGC. CONCLUSION: The MC provides a robust method for comparing metagenomes at various scales, from baseline MGs to various function/pathway clusters of metagenomes, collectively termed OMUs.

Global Marine Cold Seep Metagenomes Reveal Diversity of Taxonomy, Metabolic Function, and Natural Products.

Cold seeps in the deep sea are closely linked to energy exploration as well as global climate change. The alkane-dominated chemical energy-driven model makes cold seeps an oasis of deep-sea life, showcasing an unparalleled reservoir of microbial genetic diversity. Here, by analyzing 113 metagenomes collected from 14 global sites across 5 cold seep types, we present a comprehensive Cold Seep Microbiomic Database (CSMD) to archive the genomic and functional diversity of cold seep microbiomes. The CSMD includes over 49 million non-redundant genes and 3175 metagenome-assembled genomes, which represent 1895 species spanning 105 phyla. In addition, beta diversity analysis indicates that both the sampling site and cold seep type have a substantial impact on the prokaryotic microbiome community composition. Heterotrophic and anaerobic metabolisms are prevalent in microbial communities, accompanied by considerable mixotrophs and facultative anaerobes, highlighting the versatile metabolic potential in cold seeps. Furthermore, secondary metabolic gene cluster analysis indicates that at least 98.81% of the sequences potentially encode novel natural products, with ribosomally synthesized and post-translationally modified peptides being the predominant type widely distributed in archaea and bacteria. Overall, the CSMD represents a valuable resource that would enhance the understanding and utilization of global cold seep microbiomes.

Global diversity and ecological functions of viruses inhabiting oil reservoirs.

Oil reservoirs, being one of the significant subsurface repositories of energy and carbon, host diverse microbial communities affecting energy production and carbon emissions. Viruses play crucial roles in the ecology of microbiomes, however, their distribution and ecological significance in oil reservoirs remain undetermined. Here, we assemble a catalogue encompassing viral and prokaryotic genomes sourced from oil reservoirs. The catalogue comprises 7229 prokaryotic genomes and 3,886 viral Operational Taxonomic Units (vOTUs) from 182 oil reservoir metagenomes. The results show that viruses are widely distributed in oil reservoirs, and 85% vOTUs in oil reservoir are detected in less than 10% of the samples, highlighting the heterogeneous nature of viral communities within oil reservoirs. Through combined microcosm enrichment experiments and bioinformatics analysis, we validate the ecological roles of viruses in regulating the community structure of sulfate reducing microorganisms, primarily through a virulent lifestyle. Taken together, this study uncovers a rich diversity of viruses and their ecological functions within oil reservoirs, offering a comprehensive understanding of the role of viral communities in the biogeochemical cycles of the deep biosphere.

Metapresence: a tool for accurate species detection in metagenomics based on the genome-wide distribution of mapping reads.

Shotgun metagenomics allows comprehensive sampling of the genomic information of microbes in a given environment and is a tool of choice for studying complex microbial systems. Mapping sequencing reads against a set of reference or metagenome-assembled genomes is in principle a simple and powerful approach to define the species-level composition of the microbial community under investigation. However, despite the widespread use of this approach, there is no established way to properly interpret the alignment results, with arbitrary relative abundance thresholds being routinely used to discriminate between present and absent species. Such an approach can be affected by significant biases, especially in the identification of rare species. Therefore, it is important to develop new metrics to overcome these biases. Here, we present Metapresence, a new tool to perform reliable identification of the species in metagenomic samples based on the distribution of mapped reads on the reference genomes. The analysis is based on two metrics describing the breadth of coverage and the genomic distance between consecutive reads. We demonstrate the high precision and wide applicability of the tool using data from various synthetic communities, a real mock community, and the gut microbiome of healthy individuals and antibiotic-associated-diarrhea patients. Overall, our results suggest that the proposed approach has a robust performance in hard-to-analyze microbial communities containing contaminated or closely related genomes in low abundance.IMPORTANCEDespite the prevalent use of genome-centric alignment-based methods to characterize microbial community composition, there lacks a standardized approach for accurately identifying the species within a sample. Currently, arbitrary relative abundance thresholds are commonly employed for this purpose. However, due to the inherent complexity of genome structure and biases associated with genome-centric approaches, this practice tends to be imprecise. Notably, it introduces significant biases, particularly in the identification of rare species. The method presented here addresses these limitations and contributes significantly to overcoming inaccuracies in precisely defining community composition, especially when dealing with rare members.

Metatranscriptomics: A Tool for Clinical Metagenomics.

In the field of bioinformatics, amplicon sequencing of 16S rRNA genes has long been used to investigate community membership and taxonomic abundance in microbiome studies. As we can observe, shotgun metagenomics has become the dominant method in this field. This is largely owing to advancements in sequencing technology, which now allow for random sequencing of the entire genetic content of a microbiome. Furthermore, this method allows profiling both genes and the microbiome's membership. Although these methods have provided extensive insights into various microbiomes, they solely assess the existence of organisms or genes, without determining their active role within the microbiome. Microbiome scholarship now includes metatranscriptomics to decipher how a community of microorganisms responds to changing environmental conditions over a period of time. Metagenomic studies identify the microbes that make up a community but metatranscriptomics explores the diversity of active genes within that community, understanding their expression profile and observing how these genes respond to changes in environmental conditions. This expert review article offers a critical examination of the computational metatranscriptomics tools for studying the transcriptomes of microbial communities. First, we unpack the reasons behind the need for community transcriptomics. Second, we explore the prospects and challenges of metatranscriptomic workflows, starting with isolation and sequencing of the RNA community, then moving on to bioinformatics approaches for quantifying RNA features, and statistical techniques for detecting differential expression in a community. Finally, we discuss strengths and shortcomings in relation to other microbiome analysis approaches, pipelines, use cases and limitations, and contextualize metatranscriptomics as a tool for clinical metagenomics.

Metagenomic clustering links specific metabolic functions to globally relevant ecosystems.

Metagenomic sequencing has advanced our understanding of biogeochemical processes by providing an unprecedented view into the microbial composition of different ecosystems. While the amount of metagenomic data has grown rapidly, simple-to-use methods to analyze and compare across studies have lagged behind. Thus, tools expressing the metabolic traits of a community are needed to broaden the utility of existing data. Gene abundance profiles are a relatively low-dimensional embedding of a metagenome's functional potential and are, thus, tractable for comparison across many samples. Here, we compare the abundance of KEGG Ortholog Groups (KOs) from 6,539 metagenomes from the Joint Genome Institute's Integrated Microbial Genomes and Metagenomes (JGI IMG/M) database. We find that samples cluster into terrestrial, aquatic, and anaerobic ecosystems with marker KOs reflecting adaptations to these environments. For instance, functional clusters were differentiated by the metabolism of antibiotics, photosynthesis, methanogenesis, and surprisingly GC content. Using this functional gene approach, we reveal the broad-scale patterns shaping microbial communities and demonstrate the utility of ortholog abundance profiles for representing a rapidly expanding body of metagenomic data. IMPORTANCE: Metagenomics, or the sequencing of DNA from complex microbiomes, provides a view into the microbial composition of different environments. Metagenome databases were created to compile sequencing data across studies, but it remains challenging to compare and gain insight from these large data sets. Consequently, there is a need to develop accessible approaches to extract knowledge across metagenomes. The abundance of different orthologs (i.e., genes that perform a similar function across species) provides a simplified representation of a metagenome's metabolic potential that can easily be compared with others. In this study, we cluster the ortholog abundance profiles of thousands of metagenomes from diverse environments and uncover the traits that distinguish them. This work provides a simple to use framework for functional comparison and advances our understanding of how the environment shapes microbial communities.

Phylogeny and disease associations of a widespread and ancient intestinal bacteriophage lineage.

Viruses are core components of the human microbiome, impacting health through interactions with gut bacteria and the immune system. Most human microbiome viruses are bacteriophages, which exclusively infect bacteria. Until recently, most gut virome studies focused on low taxonomic resolution (e.g., viral operational taxonomic units), hampering population-level analyses. We previously identified an expansive and widespread bacteriophage lineage in inhabitants of Amsterdam, the Netherlands. Here, we study their biodiversity and evolution in various human populations. Based on a phylogeny using sequences from six viral genome databases, we propose the Candidatus order Heliusvirales. We identify heliusviruses in 82% of 5441 individuals across 39 studies, and in nine metagenomes from humans that lived in Europe and North America between 1000 and 5000 years ago. We show that a large lineage started to diversify when Homo sapiens first appeared some 300,000 years ago. Ancient peoples and modern hunter-gatherers have distinct Ca. Heliusvirales populations with lower richness than modern urbanized people. Urbanized people suffering from type 1 and type 2 diabetes, as well as inflammatory bowel disease, have higher Ca. Heliusvirales richness than healthy controls. We thus conclude that these ancient core members of the human gut virome have thrived with increasingly westernized lifestyles.

Exploring Microorganisms Associated to Acute Febrile Illness and Severe Neurological Disorders of Unknown Origin: A Nanopore Metagenomics Approach.

Acute febrile illness (AFI) and severe neurological disorders (SNDs) often present diagnostic challenges due to their potential origins from a wide range of infectious agents. Nanopore metagenomics is emerging as a powerful tool for identifying the microorganisms potentially responsible for these undiagnosed clinical cases. In this study, we aim to shed light on the etiological agents underlying AFI and SND cases that conventional diagnostic methods have not been able to fully elucidate. Our approach involved analyzing samples from fourteen hospitalized patients using a comprehensive nanopore metagenomic approach. This process included RNA extraction and enrichment using the SMART-9N protocol, followed by nanopore sequencing. Subsequent steps involved quality control, host DNA/cDNA removal, de novo genome assembly, and taxonomic classification. Our findings in AFI cases revealed a spectrum of disease-associated microbes, including Escherichia coli, Streptococcus sp., Human Immunodeficiency Virus 1 (Subtype B), and Human Pegivirus. Similarly, SND cases revealed the presence of pathogens such as Escherichia coli, Clostridium sp., and Dengue virus type 2 (Genotype-II lineage). This study employed a metagenomic analysis method, demonstrating its efficiency and adaptability in pathogen identification. Our investigation successfully identified pathogens likely associated with AFI and SNDs, underscoring the feasibility of retrieving near-complete genomes from RNA viruses. These findings offer promising prospects for advancing our understanding and control of infectious diseases, by facilitating detailed genomic analysis which is critical for developing targeted interventions and therapeutic strategies.

METASEED: a novel approach to full-length 16S rRNA gene reconstruction from short read data.

BACKGROUND: With the emergence of Oxford Nanopore technology, now the on-site sequencing of 16S rRNA from environments is available. Due to the error level and structure, the analysis of such data demands some database of reference sequences. However, many taxa from complex and diverse environments, have poor representation in publicly available databases. In this paper, we propose the METASEED pipeline for the reconstruction of full-length 16S sequences from such environments, in order to improve the reference for the subsequent use of on-site sequencing. RESULTS: We show that combining high-precision short-read sequencing of both 16S and full metagenome from the same samples allow us to reconstruct high-quality 16S sequences from the more abundant taxa. A significant novelty is the carefully designed collection of metagenome reads that matches the 16S amplicons, based on a combination of uniqueness and abundance. Compared to alternative approaches this produces superior results. CONCLUSION: Our pipeline will facilitate numerous studies associated with various unknown microorganisms, thus allowing the comprehension of the diverse environments. The pipeline is a potential tool in generating a full length 16S rRNA gene database for any environment.

A permutable MLP-like architecture for disease prediction from gut metagenomic data.

Metagenomic data plays a crucial role in analyzing the relationship between microbes and diseases. However, the limited number of samples, high dimensionality, and sparsity of metagenomic data pose significant challenges for the application of deep learning in data classification and prediction. Previous studies have shown that utilizing the phylogenetic tree structure to transform metagenomic abundance data into a 2D matrix input for convolutional neural networks (CNNs) improves classification performance. Inspired by the success of a Permutable MLP-like architecture in visual recognition, we propose Metagenomic Permutator (MetaP), which applied the Permutable MLP-like network structure to capture the phylogenetic information of microbes within the 2D matrix formed by phylogenetic tree. Our experiments demonstrate that our model achieved competitive performance compared to other deep neural networks and traditional machine learning, and has good prospects for multi-classification and large sample sizes. Furthermore, we utilize the SHAP (SHapley Additive exPlanations) method to interpret our model predictions, identifying the microbial features that are associated with diseases.

MAGinator enables accurate profiling of de novo MAGs with strain-level phylogenies.

Metagenomic sequencing has provided great advantages in the characterisation of microbiomes, but currently available analysis tools lack the ability to combine subspecies-level taxonomic resolution and accurate abundance estimation with functional profiling of assembled genomes. To define the microbiome and its associations with human health, improved tools are needed to enable comprehensive understanding of the microbial composition and elucidation of the phylogenetic and functional relationships between the microbes. Here, we present MAGinator, a freely available tool, tailored for profiling of shotgun metagenomics datasets. MAGinator provides de novo identification of subspecies-level microbes and accurate abundance estimates of metagenome-assembled genomes (MAGs). MAGinator utilises the information from both gene- and contig-based methods yielding insight into both taxonomic profiles and the origin of genes and genetic content, used for inference of functional content of each sample by host organism. Additionally, MAGinator facilitates the reconstruction of phylogenetic relationships between the MAGs, providing a framework to identify clade-level differences.

Diversity and potential functional role of phyllosphere-associated actinomycetota isolated from cupuassu (Theobroma grandiflorum) leaves: implications for ecosystem dynamics and plant defense strategies.

Exploring the intricate relationships between plants and their resident microorganisms is crucial not only for developing new methods to improve disease resistance and crop yields but also for understanding their co-evolutionary dynamics. Our research delves into the role of the phyllosphere-associated microbiome, especially Actinomycetota species, in enhancing pathogen resistance in Theobroma grandiflorum, or cupuassu, an agriculturally valuable Amazonian fruit tree vulnerable to witches' broom disease caused by Moniliophthora perniciosa. While breeding resistant cupuassu genotypes is a possible solution, the capacity of the Actinomycetota phylum to produce beneficial metabolites offers an alternative approach yet to be explored in this context. Utilizing advanced long-read sequencing and metagenomic analysis, we examined Actinomycetota from the phyllosphere of a disease-resistant cupuassu genotype, identifying 11 Metagenome-Assembled Genomes across eight genera. Our comparative genomic analysis uncovered 54 Biosynthetic Gene Clusters related to antitumor, antimicrobial, and plant growth-promoting activities, alongside cutinases and type VII secretion system-associated genes. These results indicate the potential of phyllosphere-associated Actinomycetota in cupuassu for inducing resistance or antagonism against pathogens. By integrating our genomic discoveries with the existing knowledge of cupuassu's defense mechanisms, we developed a model hypothesizing the synergistic or antagonistic interactions between plant and identified Actinomycetota during plant-pathogen interactions. This model offers a framework for understanding the intricate dynamics of microbial influence on plant health. In conclusion, this study underscores the significance of the phyllosphere microbiome, particularly Actinomycetota, in the broader context of harnessing microbial interactions for plant health. These findings offer valuable insights for enhancing agricultural productivity and sustainability.

AFITbin: a metagenomic contig binning method using aggregate l-mer frequency based on initial and terminal nucleotides.

BACKGROUND: Using next-generation sequencing technologies, scientists can sequence complex microbial communities directly from the environment. Significant insights into the structure, diversity, and ecology of microbial communities have resulted from the study of metagenomics. The assembly of reads into longer contigs, which are then binned into groups of contigs that correspond to different species in the metagenomic sample, is a crucial step in the analysis of metagenomics. It is necessary to organize these contigs into operational taxonomic units (OTUs) for further taxonomic profiling and functional analysis. For binning, which is synonymous with the clustering of OTUs, the tetra-nucleotide frequency (TNF) is typically utilized as a compositional feature for each OTU. RESULTS: In this paper, we present AFIT, a new l-mer statistic vector for each contig, and AFITBin, a novel method for metagenomic binning based on AFIT and a matrix factorization method. To evaluate the performance of the AFIT vector, the t-SNE algorithm is used to compare species clustering based on AFIT and TNF information. In addition, the efficacy of AFITBin is demonstrated on both simulated and real datasets in comparison to state-of-the-art binning methods such as MetaBAT 2, MaxBin 2.0, CONCOT, MetaCon, SolidBin, BusyBee Web, and MetaBinner. To further analyze the performance of the purposed AFIT vector, we compare the barcodes of the AFIT vector and the TNF vector. CONCLUSION: The results demonstrate that AFITBin shows superior performance in taxonomic identification compared to existing methods, leveraging the AFIT vector for improved results in metagenomic binning. This approach holds promise for advancing the analysis of metagenomic data, providing more reliable insights into microbial community composition and function. AVAILABILITY: A python package is available at: https://github.com/SayehSobhani/AFITBin .

Metagenomic and paleopathological analyses of a historic documented collection explore ancient dental calculus as a diagnostic tool.

Dental calculus is a microbial biofilm that contains biomolecules from oral commensals and pathogens, including those potentially related to cause of death (CoD). To assess the utility of calculus as a diagnostically informative substrate, in conjunction with paleopathological analysis, calculus samples from 39 individuals in the Smithsonian Institution's Robert J. Terry Collection with CoDs of either syphilis or tuberculosis were assessed via shotgun metagenomic sequencing for the presence of Treponema pallidum subsp. pallidum and Mycobacterium tuberculosis complex (MTBC) DNA. Paleopathological analysis revealed that frequencies of skeletal lesions associated with these diseases were partially inconsistent with diagnostic criteria. Although recovery of T. p. pallidum DNA from individuals with a syphilis CoD was elusive, MTBC DNA was identified in at least one individual with a tuberculosis CoD. The authenticity of MTBC DNA was confirmed using targeted quantitative PCR assays, MTBC genome enrichment, and in silico bioinformatic analyses; however, the lineage of the MTBC strain present could not be determined. Overall, our study highlights the utility of dental calculus for molecular detection of tuberculosis in the archaeological record and underscores the effect of museum preparation techniques and extensive handling on pathogen DNA preservation in skeletal collections.

Strain-specific gut microbial signatures in type 2 diabetes identified in a cross-cohort analysis of 8,117 metagenomes.

The association of gut microbial features with type 2 diabetes (T2D) has been inconsistent due in part to the complexity of this disease and variation in study design. Even in cases in which individual microbial species have been associated with T2D, mechanisms have been unable to be attributed to these associations based on specific microbial strains. We conducted a comprehensive study of the T2D microbiome, analyzing 8,117 shotgun metagenomes from 10 cohorts of individuals with T2D, prediabetes, and normoglycemic status in the United States, Europe, Israel and China. Dysbiosis in 19 phylogenetically diverse species was associated with T2D (false discovery rate < 0.10), for example, enriched Clostridium bolteae and depleted Butyrivibrio crossotus. These microorganisms also contributed to community-level functional changes potentially underlying T2D pathogenesis, for example, perturbations in glucose metabolism. Our study identifies within-species phylogenetic diversity for strains of 27 species that explain inter-individual differences in T2D risk, such as Eubacterium rectale. In some cases, these were explained by strain-specific gene carriage, including loci involved in various mechanisms of horizontal gene transfer and novel biological processes underlying metabolic risk, for example, quorum sensing. In summary, our study provides robust cross-cohort microbial signatures in a strain-resolved manner and offers new mechanistic insights into T2D.

A non-methanogenic archaeon within the order Methanocellales.

Serpentinization, a geochemical process found on modern and ancient Earth, provides an ultra-reducing environment that can support microbial methanogenesis and acetogenesis. Several groups of archaea, such as the order Methanocellales, are characterized by their ability to produce methane. Here, we generate metagenomic sequences from serpentinized springs in The Cedars, California, and construct a circularized metagenome-assembled genome of a Methanocellales archaeon, termed Met12, that lacks essential methanogenesis genes. The genome includes genes for an acetyl-CoA pathway, but lacks genes encoding methanogenesis enzymes such as methyl-coenzyme M reductase, heterodisulfide reductases and hydrogenases. In situ transcriptomic analyses reveal high expression of a multi-heme c-type cytochrome, and heterologous expression of this protein in a model bacterium demonstrates that it is capable of accepting electrons. Our results suggest that Met12, within the order Methanocellales, is not a methanogen but a CO2-reducing, electron-fueled acetogen without electron bifurcation.

Utilizing co-abundances of antimicrobial resistance genes to identify potential co-selection in the resistome.

The rapid spread of antimicrobial resistance (AMR) is a threat to global health, and the nature of co-occurring antimicrobial resistance genes (ARGs) may cause collateral AMR effects once antimicrobial agents are used. Therefore, it is essential to identify which pairs of ARGs co-occur. Given the wealth of next-generation sequencing data available in public repositories, we have investigated the correlation between ARG abundances in a collection of 214,095 metagenomic data sets. Using more than 6.76∙108 read fragments aligned to acquired ARGs to infer pairwise correlation coefficients, we found that more ARGs correlated with each other in human and animal sampling origins than in soil and water environments. Furthermore, we argued that the correlations could serve as risk profiles of resistance co-occurring to critically important antimicrobials (CIAs). Using these profiles, we found evidence of several ARGs conferring resistance for CIAs being co-abundant, such as tetracycline ARGs correlating with most other forms of resistance. In conclusion, this study highlights the important ARG players indirectly involved in shaping the resistomes of various environments that can serve as monitoring targets in AMR surveillance programs. IMPORTANCE: Understanding the collateral effects happening in a resistome can reveal previously unknown links between antimicrobial resistance genes (ARGs). Through the analysis of pairwise ARG abundances in 214K metagenomic samples, we observed that the co-abundance is highly dependent on the environmental context and argue that these correlations can be used to show the risk of co-selection occurring in different settings.

Organic fertilization co-selects genetically linked antibiotic and metal(loid) resistance genes in global soil microbiome.

Antibiotic resistance genes (ARGs) and metal(loid) resistance genes (MRGs) coexist in organic fertilized agroecosystems based on their correlations in abundance, yet evidence for the genetic linkage of ARG-MRGs co-selected by organic fertilization remains elusive. Here, an analysis of 511 global agricultural soil metagenomes reveals that organic fertilization correlates with a threefold increase in the number of diverse types of ARG-MRG-carrying contigs (AMCCs) in the microbiome (63 types) compared to non-organic fertilized soils (22 types). Metatranscriptomic data indicates increased expression of AMCCs under higher arsenic stress, with co-regulation of the ARG-MRG pairs. Organic fertilization heightens the coexistence of ARG-MRG in genomic elements through impacting soil properties and ARG and MRG abundances. Accordingly, a comprehensive global map was constructed to delineate the distribution of coexistent ARG-MRGs with virulence factors and mobile genes in metagenome-assembled genomes from agricultural lands. The map unveils a heightened relative abundance and potential pathogenicity risks (range of 4-6) for the spread of coexistent ARG-MRGs in Central North America, Eastern Europe, Western Asia, and Northeast China compared to other regions, which acquire a risk range of 1-3. Our findings highlight that organic fertilization co-selects genetically linked ARGs and MRGs in the global soil microbiome, and underscore the need to mitigate the spread of these co-resistant genes to safeguard public health.