A critical assessment of gene catalogs for metagenomic analysis

Exploitation of rhizosphere microbiome biodiversity in plant breeding

Climate change-induced stresses are perceived by plants at the root-soil interface, where they are alleviated through interactions between the host plant and the rhizosphere microbiome. The recruitment of specific microbiomes helps mitigate stress, increases resistance to pathogens, and promotes plant growth, development, and reproduction. The structure of the rhizosphere microbiome is shaped by crop domestication and variations in ploidy levels. Here we list key genes that regulate rhizosphere microbiomes and host genetic traits. We also discuss the prospects for rigorous analysis of symbiotic interactions, research needs, and strategies for systematically utilizing microbe-crop interactions to improve crop performance. Finally, we highlight challenges of maintaining live rhizosphere microbiome collections and mining heritable variability to enhance interactions between host plants and their rhizosphere microbiomes.

Quorum sensing regulates the efficiency of a microcystin-degrading microbial consortium

Microbial biodegradation represents an environmentally friendly solution for microcystin (MC) removal. However, the regulatory factors influencing MC biodegradation within microbial communities remain poorly understood. We hypothesized that a consortium of MC-degrading microorganisms can synergistically enhance MC biodegradation efficiency under quorum sensing (QS) signal regulation. Initially, analysis of publicly available data identified a widespread correlation between QS signals and MC-degrading genes. Subsequent laboratory studies confirmed that acyl-homoserine lactones (AHLs) represent the predominant QS signal type during the degradation of MCs by microbial consortia. A significant positive correlation was found among the AHL signal, MC degradation genes, and microbial members of the degradation process. Finally, we found that the absence of the AHL system reduced both the efficiency of MC degradation and the expression of mlr cluster genes in the microbial consortium, confirming the regulatory role of the AHL system in MC degradation at the community level. The mutualistic cooperation mechanisms were also demonstrated by metatranscriptomic and qRT-PCR analyses. These findings underscore the significant role played by the QS system in microbial community-mediated MC degradation and suggest that the manipulation of QS signals could be a promising strategy for enhancing MC treatment efficiency. Harnessing microbial cooperation through QS offers a sustainable approach for mitigating MC contamination and safeguarding water health.

Development of a quantitative metagenomic approach to establish quantitative limits and its application to viruses

Quantitative metagenomic methods are maturing but continue to lack clearly-defined analytical limits. Here, we developed a computational tool, QuantMeta, to determine the absolute abundance of targets in metagenomes spiked with synthetic DNA standards. The tool establishes (i) entropy-based detection thresholds to confidently determine the presence of targets, and (ii) an approach to identify and correct read mapping or assembly errors and thus improve the quantification accuracy. Together this allows for an approach to confidently quantify absolute abundance of targets, be they microbial populations, genes, contigs, or metagenome-assembled genomes. We applied the approach to quantify single- and double-stranded DNA viruses in wastewater viral metagenomes, including pathogens and bacteriophages. Concentrations of total DNA viruses in wastewater influent and effluent were >108 copies/ml using QuantMeta. Human-associated DNA viruses were detected and quantifiable with QuantMeta thresholds, including polyomavirus, papillomavirus, and crAss-like phages, at concentrations similar to previous reports that utilized quantitative polymerase chain reaction (PCR)-based assays. Our results highlight the higher detection thresholds of quantitative metagenomics (approximately 500 copies/μl) as compared to PCR-based quantification (approximately 10 copies/μl) despite a sequencing depth of 200 million reads per sample. The QuantMeta approach, applicable to both viral and cellular metagenomes, advances quantitative metagenomics by improving the accuracy of measured target absolute abundances.

A comprehensive catalog encompassing 1376 species-level genomes reveals the core community and functional diversity of anammox microbiota

Research on the microbial community and function of the anammox process for environmentally friendly wastewater treatment has achieved certain success, which may mean more universal insights are needed. However, the comprehensive understanding of the anammox process is constrained by the limited taxonomic assignment and functional characterization of anammox microbiota, primarily due to the scarcity of high-quality genomes for most organisms. This study reported a global genome catalog of anammox microbiotas based on numerous metagenomes obtained from both lab- and full-scale systems. A total of 1376 candidate species from 7474 metagenome-assembled genomes were used to construct the genome catalog, providing extensive microbial coverage (averaged of 92.40 %) of anammox microbiota. Moreover, a total of 64 core genera and 44 core species were identified, accounting for approximately 64.25 % and 43.97 %, respectively, of anammox microbiota. The strict core genera encompassed not only functional bacteria (e.g., Brocadia, Desulfobacillus, Zeimonas, and Nitrosomonas) but also two candidate genera (UBA12294 and OLB14) affiliated with the order Anaerolineales. In particular, core denitrifying bacteria with observably taxonomic diversity exhibited diverse functional profiles; for instance, the potential of carbohydrate metabolism in Desulfobacillus and Zeimonas likely improves the mixotrophic lifestyle of anammox microbiota. Besides, a noteworthy association was detected between anammox microbiota and system type. Microbiota in coupling system exhibited complex diversity and interspecies interactions by limiting numerous core denitrifying bacteria. In summary, the constructed catalog substantially expands our understanding of the core community and their functions of anammox microbiota, providing a valuable resource for future studies on anammox systems.

Database size positively correlates with the loss of species-level taxonomic resolution for the 16S rRNA and other prokaryotic marker genes

For decades, the 16S rRNA gene has been used to taxonomically classify prokaryotic species and to taxonomically profile microbial communities. However, the 16S rRNA gene has been criticized for being too conserved to differentiate between distinct species. We argue that the inability to differentiate between species is not a unique feature of the 16S rRNA gene. Rather, we observe the gradual loss of species-level resolution for other nearly-universal prokaryotic marker genes as the number of gene sequences increases in reference databases. This trend was strongly correlated with how represented a taxonomic group was in the database and indicates that, at the gene-level, the boundaries between many species might be fuzzy. Through our study, we argue that any approach that relies on a single marker to distinguish bacterial taxa is fraught even if some markers appear to be discriminative in current databases.

Compendium of 5810 genomes of sheep and goat gut microbiomes provides new insights into the glycan and mucin utilization

BACKGROUND

Ruminant gut microbiota are critical in ecological adaptation, evolution, and nutrition utilization because it regulates energy metabolism, promotes nutrient absorption, and improves immune function. To study the functional roles of key gut microbiota in sheep and goats, it is essential to construct reference microbial gene catalogs and high-quality microbial genomes database.

RESULTS

A total of 320 fecal samples were collected from 21 different sheep and goat breeds, originating from 32 distinct farms. Metagenomic deep sequencing and binning assembly were utilized to construct a comprehensive microbial genome information database for the gut microbiota. We successfully generated the largest reference gene catalogs for gut microbiota in sheep and goats, containing over 162 million and 82 million nonredundant predicted genes, respectively, with 49 million shared nonredundant predicted genes and 1138 shared species. We found that the rearing environment has a greater impact on microbial composition and function than the host's species effect. Through subsequent assembly, we obtained 5810 medium- and high-quality metagenome-assembled genomes (MAGs), out of which 2661 were yet unidentified species. Among these MAGs, we identified 91 bacterial taxa that specifically colonize the sheep gut, which encode polysaccharide utilization loci for glycan and mucin degradation.

CONCLUSIONS

By shedding light on the co-symbiotic microbial communities in the gut of small ruminants, our study significantly enhances the understanding of their nutrient degradation and disease susceptibility. Our findings emphasize the vast potential of untapped resources in functional bacterial species within ruminants, further expanding our knowledge of how the ruminant gut microbiota recognizes and processes glycan and mucins. Video Abstract.

BASALT refines binning from metagenomic data and increases resolution of genome-resolved metagenomic analysis

Metagenomic binning is an essential technique for genome-resolved characterization of uncultured microorganisms in various ecosystems but hampered by the low efficiency of binning tools in adequately recovering metagenome-assembled genomes (MAGs). Here, we introduce BASALT (Binning Across a Series of Assemblies Toolkit) for binning and refinement of short- and long-read sequencing data. BASALT employs multiple binners with multiple thresholds to produce initial bins, then utilizes neural networks to identify core sequences to remove redundant bins and refine non-redundant bins. Using the same assemblies generated from Critical Assessment of Metagenome Interpretation (CAMI) datasets, BASALT produces up to twice as many MAGs as VAMB, DASTool, or metaWRAP. Processing assemblies from a lake sediment dataset, BASALT produces ~30% more MAGs than metaWRAP, including 21 unique class-level prokaryotic lineages. Functional annotations reveal that BASALT can retrieve 47.6% more non-redundant opening-reading frames than metaWRAP. These results highlight the robust handling of metagenomic sequencing data of BASALT.

Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing

Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.

Database size positively correlates with the loss of species-level taxonomic resolution for the 16S rRNA and other prokaryotic marker genes

For decades, the 16S rRNA gene has been used to taxonomically classify prokaryotic species and to taxonomically profile microbial communities. The 16S rRNA gene has been criticized for being too conserved to differentiate between distinct species. We argue that the inability to differentiate between species is not a unique feature of the 16S rRNA gene. Rather, we observe the gradual loss of species-level resolution for other marker genes as the number of gene sequences increases in reference databases. We demonstrate this effect through the analysis of three commonly used databases of nearly-universal prokaryotic marker genes: the SILVA 16S rRNA gene database, the Genome Taxonomy Database (GTDB), and a set of 40 taxonomically-informative single-copy genes. Our results reflect a more fundamental property of the taxonomies themselves and have broad implications for bioinformatic analyses beyond taxonomic classification. Effective solutions for fine-level taxonomic classification require a more precise, and operationally-relevant, definition of the taxonomic labels being sought, and the use of combinations of genomic markers in the classification process.

Importance

The use of reference databases for assigning taxonomic labels to genomic and metagenomic sequences is a fundamental bioinformatic task in the characterization of microbial communities. The increasing accessibility of high throughput sequencing has led to a rapid increase in the size and number of sequences in databases. This has been beneficial for improving our understanding of the global microbial genetic diversity. However, there is evidence that as the microbial diversity is more densely sampled, increasingly longer genomic segments are needed to differentiate between distinct species. The scientific community needs to be aware of this issue and needs to develop methods that better account for it when assigning taxonomic labels to metagenomic sequences from microbial communities.

SCRAPT: an iterative algorithm for clustering large 16S rRNA gene data sets

16S rRNA gene sequence clustering is an important tool in characterizing the diversity of microbial communities. As 16S rRNA gene data sets are growing in size, existing sequence clustering algorithms increasingly become an analytical bottleneck. Part of this bottleneck is due to the substantial computational cost expended on small clusters and singleton sequences. We propose an iterative sampling-based 16S rRNA gene sequence clustering approach that targets the largest clusters in the data set, allowing users to stop the clustering process when sufficient clusters are available for the specific analysis being targeted. We describe a probabilistic analysis of the iterative clustering process that supports the intuition that the clustering process identifies the larger clusters in the data set first. Using real data sets of 16S rRNA gene sequences, we show that the iterative algorithm, coupled with an adaptive sampling process and a mode-shifting strategy for identifying cluster representatives, substantially speeds up the clustering process while being effective at capturing the large clusters in the data set. The experiments also show that SCRAPT (Sample, Cluster, Recruit, AdaPt and iTerate) is able to produce operational taxonomic units that are less fragmented than popular tools: UCLUST, CD-HIT and DNACLUST. The algorithm is implemented in the open-source package SCRAPT. The source code used to generate the results presented in this paper is available at https://github.com/hsmurali/SCRAPT.

Quantifying Shared and Unique Gene Content across 17 Microbial Ecosystems

Measuring microbial diversity is traditionally based on microbe taxonomy. Here, in contrast, we aimed to quantify heterogeneity in microbial gene content across 14,183 metagenomic samples spanning 17 ecologies, including 6 human associated, 7 nonhuman host associated, and 4 in other nonhuman host environments. In total, we identified 117,629,181 nonredundant genes. The vast majority of genes (66%) occurred in only one sample (i.e., "singletons"). In contrast, we found 1,864 sequences present in every metagenome, but not necessarily every bacterial genome. Additionally, we report data sets of other ecology-associated genes (e.g., abundant in only gut ecosystems) and simultaneously demonstrated that prior microbiome gene catalogs are both incomplete and inaccurately cluster microbial genetic life (e.g., at gene sequence identities that are too restrictive). We provide our results and the sets of environmentally differentiating genes described above at http://www.microbial-genes.bio. IMPORTANCE The amount of shared genetic elements has not been quantified between the human microbiome and other host- and non-host-associated microbiomes. Here, we made a gene catalog of 17 different microbial ecosystems and compared them. We show that most species shared between environment and human gut microbiomes are pathogens and that prior gene catalogs described as "nearly complete" are far from it. Additionally, over two-thirds of all genes only appear in a single sample, and only 1,864 genes (0.001%) are found in all types of metagenomes. These results highlight the large diversity between metagenomes and reveal a new, rare class of genes, those found in every type of metagenome, but not every microbial genome.

Addressing antibiotic resistance: computational answers to a biological problem?

The increasing prevalence of infections caused by antibiotic-resistant bacteria is a global healthcare crisis. Understanding the spread of resistance is predicated on the surveillance of antibiotic resistance genes within an environment. Bioinformatics and artificial intelligence (AI) methods applied to metagenomic sequencing data offer the capacity to detect known and infer yet-unknown resistance mechanisms, and predict future outbreaks of antibiotic-resistant infections. Machine learning methods, in particular, could revive the waning antibiotic discovery pipeline by helping to predict the molecular structure and function of antibiotic resistance compounds, and optimising their interactions with target proteins. Consequently, AI has the capacity to play a central role in guiding antibiotic stewardship and future clinical decision-making around antibiotic resistance.

Genome-centric metagenomics insights into functional divergence and horizontal gene transfer of denitrifying bacteria in anammox consortia

Denitrifying bacteria with high abundances in anammox communities play crucial roles in achieving stable anammox-based systems. Despite the relative constant composition of denitrifying bacteria, their functional diversity remains to be explored in anammox communities. Herein, a total of 77 high-quality metagenome-assembled genomes (MAGs) of denitrifying bacteria were recovered from the anammox community in a full-scale swine wastewater treatment plant. Among these microbes, a total of 26 MAGs were affiliated with the seven dominant denitrifying genera that have total abundances higher than 1%. A meta-analysis of these species suggested that external organics reduced the abundances of genus Ignavibacterium and species MAG.305 of UTPRO2 in anammox communities. Comparative genome analysis revealed functional divergence across different denitrifying bacteria, largely owing to their distinct capabilities for carbohydrate (including endogenous and exogenous) utilization and vitamin (e.g., pantothenate and thiamine) biosynthesis. Serval microbes in this system contained fewer genes encoding biotin, pantothenate and methionine biosynthesis compared with their related species from other habitats. In addition, the genes encoding energy production and conversion (73 genes) and inorganic ion transport (53 genes) putatively transferred from other species to denitrifying bacteria, while these denitrifying bacteria (especially genera UTPRO2 and SCN-69-89) likely donated the genes encoding nutrients (e.g., inorganic ion and amino acid) transporter (64 genes) for other members to utilize new metabolites. Collectively, these findings highlighted the functional divergence of these denitrifying bacteria and speculated that the genetic interactions within anammox communities through horizontal gene transfer may be one of the reasons for their functional divergence.

Critical Assessment of Metagenome Interpretation: the second round of challenges

Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). The CAMI II challenge engaged the community to assess methods on realistic and complex datasets with long- and short-read sequences, created computationally from around 1,700 new and known genomes, as well as 600 new plasmids and viruses. Here we analyze 5,002 results by 76 program versions. Substantial improvements were seen in assembly, some due to long-read data. Related strains still were challenging for assembly and genome recovery through binning, as was assembly quality for the latter. Profilers markedly matured, with taxon profilers and binners excelling at higher bacterial ranks, but underperforming for viruses and Archaea. Clinical pathogen detection results revealed a need to improve reproducibility. Runtime and memory usage analyses identified efficient programs, including top performers with other metrics. The results identify challenges and guide researchers in selecting methods for analyses.

This study presents the results of the second round of the Critical Assessment of Metagenome Interpretation challenges (CAMI II), which is a community-driven effort for comprehensively benchmarking tools for metagenomics data analysis.

Inferring microbiota functions from taxonomic genes: a review

Deciphering microbiota functions is crucial to predict ecosystem sustainability in response to global change. High-throughput sequencing at the individual or community level has revolutionized our understanding of microbial ecology, leading to the big data era and improving our ability to link microbial diversity with microbial functions. Recent advances in bioinformatics have been key for developing functional prediction tools based on DNA metabarcoding data and using taxonomic gene information. This cheaper approach in every aspect serves as an alternative to shotgun sequencing. Although these tools are increasingly used by ecologists, an objective evaluation of their modularity, portability, and robustness is lacking. Here, we reviewed 100 scientific papers on functional inference and ecological trait assignment to rank the advantages, specificities, and drawbacks of these tools, using a scientific benchmarking. To date, inference tools have been mainly devoted to bacterial functions, and ecological trait assignment tools, to fungal functions. A major limitation is the lack of reference genomes-compared with the human microbiota-especially for complex ecosystems such as soils. Finally, we explore applied research prospects. These tools are promising and already provide relevant information on ecosystem functioning, but standardized indicators and corresponding repositories are still lacking that would enable them to be used for operational diagnosis.