Updated RDP taxonomy and RDP Classifier for more accurate taxonomic classification.

The RDP Classifier is one of the most popular machine learning approaches for taxonomic classification due to its robustness and relatively high accuracy. Both the RDP taxonomy and RDP Classifier have been updated to incorporate newly described taxa and recent changes to prokaryotic nomenclature.

Permafrost thaw with warming reduces microbial metabolic capacities in subsurface soils.

Microorganisms are major constituents of the total biomass in permafrost regions, whose underlain soils are frozen for at least two consecutive years. To understand potential microbial responses to climate change, here we examined microbial community compositions and functional capacities across four soil depths in an Alaska tundra site. We showed that a 5-year warming treatment increased soil thaw depth by 25.7% (p = .011) within the deep organic layer (15-25 cm). Concurrently, warming reduced 37% of bacterial abundance and 64% of fungal abundances in the deep organic layer, while it did not affect microbial abundance in other soil layers (i.e., 0-5, 5-15, and 45-55 cm). Warming treatment altered fungal community composition and microbial functional structure (p < .050), but not bacterial community composition. Using a functional gene array, we found that the relative abundances of a variety of carbon (C)-decomposing, iron-reducing, and sulphate-reducing genes in the deep organic layer were decreased, which was not observed by the shotgun sequencing-based metagenomics analysis of those samples. To explain the reduced metabolic capacities, we found that warming treatment elicited higher deterministic environmental filtering, which could be linked to water-saturated time, soil moisture, and soil thaw duration. In contrast, plant factors showed little influence on microbial communities in subsurface soils below 15 cm, despite a 25.2% higher (p < .05) aboveground plant biomass by warming treatment. Collectively, we demonstrate that microbial metabolic capacities in subsurface soils are reduced, probably arising from enhanced thaw by warming.

MetaFunPrimer: an Environment-Specific, High-Throughput Primer Design Tool for Improved Quantification of Target Genes.

Genes belonging to the same functional group may include numerous and variable gene sequences, making characterizing and quantifying difficult. Therefore, high-throughput design tools are needed to simultaneously create primers for improved quantification of target genes. We developed MetaFunPrimer, a bioinformatic pipeline, to design primers for numerous genes of interest. This tool also enables gene target prioritization based on ranking the presence of genes in user-defined references, such as environment-specific metagenomes. Given inputs of protein and nucleotide sequences for gene targets of interest and an accompanying set of reference metagenomes or genomes, MetaFunPrimer generates primers for ranked genes of interest. To demonstrate the usage and benefits of MetaFunPrimer, a total of 78 primer pairs were designed to target observed ammonia monooxygenase subunit A (amoA) genes of ammonia-oxidizing bacteria (AOB) in 1,550 publicly available soil metagenomes. We demonstrate computationally that these amoA-AOB primers can cover 94% of the amoA-AOB genes observed in the 1,550 soil metagenomes compared with a 49% estimated coverage by previously published primers. Finally, we verified the utility of these primer sets in incubation experiments that used long-term nitrogen fertilized or unfertilized soils. High-throughput quantitative PCR (qPCR) results and statistical analyses showed significant differences in relative quantification patterns between the two soils, and subsequent absolute quantifications also confirmed that target genes enumerated by six selected primer pairs were significantly more abundant in the nitrogen-fertilized soils. This new tool gives microbial ecologists a new approach to assess functional gene abundance and related microbial community dynamics quickly and affordably. IMPORTANCE Amplification-based gene characterization allows for sensitive and specific quantification of functional genes. There is often a large diversity of genes represented for functional gene groups, and multiple primers may be necessary to target associated genes. Current primer design tools are limited to designing primers for only a few genes of interest. MetaFunPrimer allows for high-throughput primer design for various genes of interest and also allows for ranking gene targets by their presence and abundance in environmental data sets. Primers designed by this tool improve the characterization and quantification of functional genes in broad gene amplification platforms and can be powerful with high-throughput qPCR approaches.

Long-read sequencing revealed cooccurrence, host range, and potential mobility of antibiotic resistome in cow feces.

While it is well recognized that the environmental resistome is global, diverse, and augmented by human activities, it has been difficult to assess risk because of the inability to culture many environmental organisms, and it is difficult to evaluate risk from current sequence-based environmental methods. The four most important criteria to determine risk are whether the antibiotic-resistance genes (ARGs) are a complete, potentially functional complement; if they are linked with other resistances; whether they are mobile; and the identity of their host. Long-read sequencing fills this important gap between culture and short sequence-based methods. To address these criteria, we collected feces from a ceftiofur-treated cow, enriched the samples in the presence of antibiotics to favor ARG functionality, and sequenced long reads using Nanopore and PacBio technologies. Multidrug-resistance genes comprised 58% of resistome abundance, but only 0.8% of them were plasmid associated; fluroquinolone-, aminoglycoside-, macrolide-lincosamide-streptogramin (MLS)-, and ß-lactam-resistance genes accounted for 2.7 to 12.3% of resistome abundance but with 19 to 78% located on plasmids. A variety of plasmid types were assembled, some of which share low similarity to plasmids in current databases. Enterobacteriaceae were dominant hosts of antibiotic-resistant plasmids; physical linkage of extended-spectrum ß-lactamase genes (CTX-M, TEM, CMY, and CARB) was largely found with aminoglycoside-, MLS-, tetracycline-, trimethoprim-, phenicol-, sulfonamide-, and mercury-resistance genes. A draft circular chromosome of Vagococcus lutrae was assembled; it carries MLS-, tetracycline- (including tetM and tetL on an integrative conjugative element), and trimethoprim-resistance genes flanked by many transposase genes and insertion sequences, implying that they remain transferrable.

Metagenomic analysis reveals the shared and distinct features of the soil resistome across tundra, temperate prairie, and tropical ecosystems.

BACKGROUND: Soil is an important reservoir of antibiotic resistance genes (ARGs), but their potential risk in different ecosystems as well as response to anthropogenic land use change is unknown. We used a metagenomic approach and datasets with well-characterized metadata to investigate ARG types and amounts in soil DNA of three native ecosystems: Alaskan tundra, US Midwestern prairie, and Amazon rainforest, as well as the effect of conversion of the latter two to agriculture and pasture, respectively. RESULTS: High diversity (242 ARG subtypes) and abundance (0.184-0.242 ARG copies per 16S rRNA gene copy) were observed irrespective of ecosystem, with multidrug resistance and efflux pump the dominant class and mechanism. Ten regulatory genes were identified and they accounted for 13-35% of resistome abundances in soils, among them arlR, cpxR, ompR, vanR, and vanS were dominant and observed in all studied soils. We identified 55 non-regulatory ARGs shared by all 26 soil metagenomes of the three ecosystems, which accounted for more than 81% of non-regulatory resistome abundance. Proteobacteria, Firmicutes, and Actinobacteria were primary ARG hosts, 7 of 10 most abundant ARGs were found in all of them. No significant differences in both ARG diversity and abundance were observed between native prairie soil and adjacent long-term cultivated agriculture soil. We chose 12 clinically important ARGs to evaluate at the sequence level and found them to be distinct from those in human pathogens, and when assembled they were even more dissimilar. Significant correlation was found between bacterial community structure and resistome profile, suggesting that variance in resistome profile was mainly driven by the bacterial community composition. CONCLUSIONS: Our results identify candidate background ARGs (shared in all 26 soils), classify ARG hosts, quantify resistance classes, and provide quantitative and sequence information suggestive of very low risk but also revealing resistance gene variants that might emerge in the future. Video abstract.

Winter warming rapidly increases carbon degradation capacities of fungal communities in tundra soil: Potential consequences on carbon stability.

High-latitude tundra ecosystems are increasingly affected by climate warming. As an important fraction of soil microorganisms, fungi play essential roles in carbon degradation, especially the old, chemically recalcitrant carbon. However, it remains obscure how fungi respond to climate warming and whether fungi, in turn, affect carbon stability of tundra. In a 2-year winter soil warming experiment of 2°C by snow fences, we investigated responses of fungal communities to warming in the active layer of an Alaskan tundra. Although fungal community composition, revealed by the 28S rRNA gene amplicon sequencing, remained unchanged (p > .05), fungal functional gene composition, revealed by a microarray named GeoChip, was altered (p < .05). Changes in functional gene composition were linked to winter soil temperature, thaw depth, soil moisture, and gross primary productivity (canonical correlation analysis, p < .05). Specifically, relative abundances of fungal genes encoding invertase, xylose reductase and vanillin dehydrogenase significantly increased (p < .05), indicating higher carbon degradation capacities of fungal communities under warming. Accordingly, we detected changes in fungal gene networks under warming, including higher average path distance, lower average clustering coefficient and lower percentage of negative links, indicating that warming potentially changed fungal interactions. Together, our study reveals higher carbon degradation capacities of fungal communities under short-term warming and highlights the potential impacts of fungal communities on tundra ecosystem respiration, and consequently future carbon stability of high-latitude tundra.

Gene-informed decomposition model predicts lower soil carbon loss due to persistent microbial adaptation to warming.

Soil microbial respiration is an important source of uncertainty in projecting future climate and carbon (C) cycle feedbacks. However, its feedbacks to climate warming and underlying microbial mechanisms are still poorly understood. Here we show that the temperature sensitivity of soil microbial respiration (Q10) in a temperate grassland ecosystem persistently decreases by 12.0 ± 3.7% across 7 years of warming. Also, the shifts of microbial communities play critical roles in regulating thermal adaptation of soil respiration. Incorporating microbial functional gene abundance data into a microbially-enabled ecosystem model significantly improves the modeling performance of soil microbial respiration by 5-19%, and reduces model parametric uncertainty by 55-71%. In addition, modeling analyses show that the microbial thermal adaptation can lead to considerably less heterotrophic respiration (11.6 ± 7.5%), and hence less soil C loss. If such microbially mediated dampening effects occur generally across different spatial and temporal scales, the potential positive feedback of soil microbial respiration in response to climate warming may be less than previously predicted.

Winter warming in Alaska accelerates lignin decomposition contributed by Proteobacteria.

BACKGROUND: In a warmer world, microbial decomposition of previously frozen organic carbon (C) is one of the most likely positive climate feedbacks of permafrost regions to the atmosphere. However, mechanistic understanding of microbial mediation on chemically recalcitrant C instability is limited; thus, it is crucial to identify and evaluate active decomposers of chemically recalcitrant C, which is essential for predicting C-cycle feedbacks and their relative strength of influence on climate change. Using stable isotope probing of the active layer of Arctic tundra soils after depleting soil labile C through a 975-day laboratory incubation, the identity of microbial decomposers of lignin and, their responses to warming were revealed. RESULTS: The ß-Proteobacteria genus Burkholderia accounted for 95.1% of total abundance of potential lignin decomposers. Consistently, Burkholderia isolated from our tundra soils could grow with lignin as the sole C source. A 2.2 °C increase of warming considerably increased total abundance and functional capacities of all potential lignin decomposers. In addition to Burkholderia, α-Proteobacteria capable of lignin decomposition (e.g. Bradyrhizobium and Methylobacterium genera) were stimulated by warming by 82-fold. Those community changes collectively doubled the priming effect, i.e., decomposition of existing C after fresh C input to soil. Consequently, warming aggravates soil C instability, as verified by microbially enabled climate-C modeling. CONCLUSIONS: Our findings are alarming, which demonstrate that accelerated C decomposition under warming conditions will make tundra soils a larger biospheric C source than anticipated. Video Abstract.

Targeted assemblies of cas1 suggest CRISPR-Cas's response to soil warming.

There is an increasing interest in the clustered regularly interspaced short palindromic repeats CRISPR-associated protein (CRISPR-Cas) system to reveal potential virus-host dynamics. The universal and most conserved Cas protein, cas1 is an ideal marker to elucidate CRISPR-Cas ecology. We constructed eight Hidden Markov Models (HMMs) and assembled cas1 directly from metagenomes by a targeted-gene assembler, Xander, to improve detection capacity and resolve the diverse CRISPR-Cas systems. The eight HMMs were first validated by recovering all 17 cas1 subtypes from the simulated metagenome generated from 91 prokaryotic genomes across 11 phyla. We challenged the targeted method with 48 metagenomes from a tallgrass prairie in Central Oklahoma recovering 3394 cas1. Among those, 88 were near full length, 5 times more than in de-novo assemblies from the Oklahoma metagenomes. To validate the host assignment by cas1, the targeted-assembled cas1 was mapped to the de-novo assembled contigs. All the phylum assignments of those mapped contigs were assigned independent of CRISPR-Cas genes on the same contigs and consistent with the host taxonomies predicted by the mapped cas1. We then investigated whether 8 years of soil warming altered cas1 prevalence within the communities. A shift in microbial abundances was observed during the year with the biggest temperature differential (mean 4.16 °C above ambient). cas1 prevalence increased and even in the phyla with decreased microbial abundances over the next 3 years, suggesting increasing virus-host interactions in response to soil warming. This targeted method provides an alternative means to effectively mine cas1 from metagenomes and uncover the host communities.

Bioavailability of clay-adsorbed dioxin to Sphingomonas wittichii RW1 and its associated genome-wide shifts in gene expression.

Polychlorinated dibenzo-p-dioxins and dibenzofurans are a group of chemically-related pollutants categorically known as dioxins. Some of their chlorinated congeners are among the most hazardous pollutants that persist in the environment. This persistence is due in part to the limited number of bacteria capable of metabolizing these compounds, but also to their limited bioavailability in soil. We used Sphingomonas wittichii strain RW1 (RW1), one of the few strains able to grow on dioxin, to characterize its ability to respond to and degrade clay-bound dioxin. We found that RW1 grew on and completely degraded dibenzo-p-dioxin (DD) intercalated into the smectite clay saponite (SAP). To characterize the effects of DD sorption on RW1 gene expression, we compared transcriptomes of RW1 grown with either free crystalline DD or DD intercalated clay, i.e. sandwiched between the clay interlayers (DDSAP). Free crystalline DD appeared to cause greater expression of toxicity and stress related functions. Genes coding for heat shock proteins, chaperones, as well as genes involved in DNA repair, and efflux were up-regulated during growth on crystalline dioxin compared to growth on intercalated dioxin. In contrast, growth on intercalated dioxin up-regulated genes that might be important in recognition and uptake mechanisms, as well as surface interaction/attachment/biofilm formation such as extracellular solute-binding protein and LuxR. These differences in gene expression may reflect the underlying adaptive mechanisms by which RW1 cells sense and deploy pathways to access dioxin intercalated into clay. These data show that intercalated DD remains bioavailable to the degrading bacterium with implications for bioremediation alternatives.

Warming-induced permafrost thaw exacerbates tundra soil carbon decomposition mediated by microbial community.

BACKGROUND: It is well-known that global warming has effects on high-latitude tundra underlain with permafrost. This leads to a severe concern that decomposition of soil organic carbon (SOC) previously stored in this region, which accounts for about 50% of the world's SOC storage, will cause positive feedback that accelerates climate warming. We have previously shown that short-term warming (1.5 years) stimulates rapid, microbe-mediated decomposition of tundra soil carbon without affecting the composition of the soil microbial community (based on the depth of 42684 sequence reads of 16S rRNA gene amplicons per 3 g of soil sample). RESULTS: We show that longer-term (5 years) experimental winter warming at the same site altered microbial communities (p < 0.040). Thaw depth correlated the strongest with community assembly and interaction networks, implying that warming-accelerated tundra thaw fundamentally restructured the microbial communities. Both carbon decomposition and methanogenesis genes increased in relative abundance under warming, and their functional structures strongly correlated (R2 > 0.725, p < 0.001) with ecosystem respiration or CH4 flux. CONCLUSIONS: Our results demonstrate that microbial responses associated with carbon cycling could lead to positive feedbacks that accelerate SOC decomposition in tundra regions, which is alarming because SOC loss is unlikely to subside owing to changes in microbial community composition. Video Abstract.

Review, Evaluation, and Directions for Gene-Targeted Assembly for Ecological Analyses of Metagenomes.

Shotgun metagenomics has greatly advanced our understanding of microbial communities over the last decade. Metagenomic analyses often include assembly and genome binning, computationally daunting tasks especially for big data from complex environments such as soil and sediments. In many studies, however, only a subset of genes and pathways involved in specific functions are of interest; thus, it is not necessary to attempt global assembly. In addition, methods that target genes can be computationally more efficient and produce more accurate assembly by leveraging rich databases, especially for those genes that are of broad interest such as those involved in biogeochemical cycles, biodegradation, and antibiotic resistance or used as phylogenetic markers. Here, we review six gene-targeted assemblers with unique algorithms for extracting and/or assembling targeted genes: Xander, MegaGTA, SAT-Assembler, HMM-GRASPx, GenSeed-HMM, and MEGAN. We tested these tools using two datasets with known genomes, a synthetic community of artificial reads derived from the genomes of 17 bacteria, shotgun sequence data from a mock community with 48 bacteria and 16 archaea genomes, and a large soil shotgun metagenomic dataset. We compared assemblies of a universal single copy gene (rplB) and two N cycle genes (nifH and nirK). We measured their computational efficiency, sensitivity, specificity, and chimera rate and found Xander and MegaGTA, which both use a probabilistic graph structure to model the genes, have the best overall performance with all three datasets, although MEGAN, a reference matching assembler, had better sensitivity with synthetic and mock community members chosen from its reference collection. Also, Xander and MegaGTA are the only tools that include post-assembly scripts tuned for common molecular ecology and diversity analyses. Additionally, we provide a mathematical model for estimating the probability of assembling targeted genes in a metagenome for estimating required sequencing depth.

Long-Term Warming in Alaska Enlarges the Diazotrophic Community in Deep Soils.

Tundra ecosystems are typically carbon (C) rich but nitrogen (N) limited. Since biological N2 fixation is the major source of biologically available N, the soil N2-fixing (i.e., diazotrophic) community serves as an essential N supplier to the tundra ecosystem. Recent climate warming has induced deeper permafrost thaw and adversely affected C sequestration, which is modulated by N availability. Therefore, it is crucial to examine the responses of diazotrophic communities to warming across the depths of tundra soils. Herein, we carried out one of the deepest sequencing efforts of nitrogenase gene (nifH) to investigate how 5 years of experimental winter warming affects Alaskan soil diazotrophic community composition and abundance spanning both the organic and mineral layers. Although soil depth had a stronger influence on diazotrophic community composition than warming, warming significantly (P < 0.05) enhanced diazotrophic abundance by 86.3% and aboveground plant biomass by 25.2%. Diazotrophic composition in the middle and lower organic layers, detected by nifH sequencing and a microarray-based tool (GeoChip), was markedly altered, with an increase of α-diversity. Changes in diazotrophic abundance and composition significantly correlated with soil moisture, soil thaw duration, and plant biomass, as shown by structural equation modeling analyses. Therefore, more abundant diazotrophic communities induced by warming may potentially serve as an important mechanism for supplementing biologically available N in this tundra ecosystem.IMPORTANCE With the likelihood that changes in global climate will adversely affect the soil C reservoir in the northern circumpolar permafrost zone, an understanding of the potential role of diazotrophic communities in enhancing biological N2 fixation, which constrains both plant production and microbial decomposition in tundra soils, is important in elucidating the responses of soil microbial communities to global climate change. A recent study showed that the composition of the diazotrophic community in a tundra soil exhibited no change under a short-term (1.5-year) winter warming experiment. However, it remains crucial to examine whether the lack of diazotrophic community responses to warming is persistent over a longer time period as a possibly important mechanism in stabilizing tundra soil C. Through a detailed characterization of the effects of winter warming on diazotrophic communities, we showed that a long-term (5-year) winter warming substantially enhanced diazotrophic abundance and altered community composition, though soil depth had a stronger influence on diazotrophic community composition than warming. These changes were best explained by changes in soil moisture, soil thaw duration, and plant biomass. These results provide crucial insights into the potential factors that may impact future C and N availability in tundra regions.

More replenishment than priming loss of soil organic carbon with additional carbon input.

Increases in carbon (C) inputs to soil can replenish soil organic C (SOC) through various mechanisms. However, recent studies have suggested that the increased C input can also stimulate the decomposition of old SOC via priming. Whether the loss of old SOC by priming can override C replenishment has not been rigorously examined. Here we show, through data-model synthesis, that the magnitude of replenishment is greater than that of priming, resulting in a net increase in SOC by a mean of 32% of the added new C. The magnitude of the net increase in SOC is positively correlated with the nitrogen-to-C ratio of the added substrates. Additionally, model evaluation indicates that a two-pool interactive model is a parsimonious model to represent the SOC decomposition with priming and replenishment. Our findings suggest that increasing C input to soils likely promote SOC accumulation despite the enhanced decomposition of old C via priming.

Primer set 2.0 for highly parallel qPCR array targeting antibiotic resistance genes and mobile genetic elements.

The high-throughput antibiotic resistance gene (ARG) qPCR array, initially published in 2012, is increasingly used to quantify resistance and mobile determinants in environmental matrices. Continued utility of the array; however, necessitates improvements such as removing or redesigning questionable primer sets, updating targeted genes and coverage of available sequences. Towards this goal, a new primer design tool (EcoFunPrimer) was used to aid in identification of conserved regions of diverse genes. The total number of assays used for diverse genes was reduced from 91 old primer sets to 52 new primer sets, with only a 10% loss in sequence coverage. While the old and new array both contain 384 primer sets, a reduction in old primer sets permitted 147 additional ARGs and mobile genetic elements to be targeted. Results of validating the updated array with a mock community of strains resulted in over 98% of tested instances incurring true positive/negative calls. Common queries related to sensitivity, quantification and conventional data analysis (e.g. Ct cutoff value, and estimated genomic copies without standard curves) were also explored. A combined list of new and previously used primer sets is provided with a recommended set based on redesign of primer sets and results of validation.

The Microbial Genomes Atlas (MiGA) webserver: taxonomic and gene diversity analysis of Archaea and Bacteria at the whole genome level.

The small subunit ribosomal RNA gene (16S rRNA) has been successfully used to catalogue and study the diversity of prokaryotic species and communities but it offers limited resolution at the species and finer levels, and cannot represent the whole-genome diversity and fluidity. To overcome these limitations, we introduced the Microbial Genomes Atlas (MiGA), a webserver that allows the classification of an unknown query genomic sequence, complete or partial, against all taxonomically classified taxa with available genome sequences, as well as comparisons to other related genomes including uncultivated ones, based on the genome-aggregate Average Nucleotide and Amino Acid Identity (ANI/AAI) concepts. MiGA integrates best practices in sequence quality trimming and assembly and allows input to be raw reads or assemblies from isolate genomes, single-cell sequences, and metagenome-assembled genomes (MAGs). Further, MiGA can take as input hundreds of closely related genomes of the same or closely related species (a so-called 'Clade Project') to assess their gene content diversity and evolutionary relationships, and calculate important clade properties such as the pangenome and core gene sets. Therefore, MiGA is expected to facilitate a range of genome-based taxonomic and diversity studies, and quality assessment across environmental and clinical settings. MiGA is available at http://microbial-genomes.org/.

Interprofessional Collaborative Practice: How Could Dentistry Participate?

There is a remarkable phenomenon occurring among health professionals: the development of ongoing, routine collaboration, both in educating the next generation of providers and in delivering care. These new approaches, commonly referred to as interprofessional education and interprofessional collaborative practice, have been introduced into academic health settings and delivery systems throughout the U.S. and the rest of the world; however, the full integration of dentistry in health care teams remains unrealized. In academic settings, dentistry has found ways to collaborate with the other health professions, but most practicing dentists still find themselves on the margins of new models of care delivery. This article provides a perspective on the history and context of the evolution of collaborative approaches to health care and proposes ways in which dentistry can participate more fully in the future.

Biotic responses buffer warming-induced soil organic carbon loss in Arctic tundra.

Climate warming can result in both abiotic (e.g., permafrost thaw) and biotic (e.g., microbial functional genes) changes in Arctic tundra. Recent research has incorporated dynamic permafrost thaw in Earth system models (ESMs) and indicates that Arctic tundra could be a significant future carbon (C) source due to the enhanced decomposition of thawed deep soil C. However, warming-induced biotic changes may influence biologically related parameters and the consequent projections in ESMs. How model parameters associated with biotic responses will change under warming and to what extent these changes affect projected C budgets have not been carefully examined. In this study, we synthesized six data sets over 5 years from a soil warming experiment at the Eight Mile Lake, Alaska, into the Terrestrial ECOsystem (TECO) model with a probabilistic inversion approach. The TECO model used multiple soil layers to track dynamics of thawed soil under different treatments. Our results show that warming increased light use efficiency of vegetation photosynthesis but decreased baseline (i.e., environment-corrected) turnover rates of SOC in both the fast and slow pools in comparison with those under control. Moreover, the parameter changes generally amplified over time, suggesting processes of gradual physiological acclimation and functional gene shifts of both plants and microbes. The TECO model predicted that field warming from 2009 to 2013 resulted in cumulative C losses of 224 or 87 g/m2 , respectively, without or with changes in those parameters. Thus, warming-induced parameter changes reduced predicted soil C loss by 61%. Our study suggests that it is critical to incorporate biotic changes in ESMs to improve the model performance in predicting C dynamics in permafrost regions.

Nonpareil 3: Fast Estimation of Metagenomic Coverage and Sequence Diversity.

Estimations of microbial community diversity based on metagenomic data sets are affected, often to an unknown degree, by biases derived from insufficient coverage and reference database-dependent estimations of diversity. For instance, the completeness of reference databases cannot be generally estimated since it depends on the extant diversity sampled to date, which, with the exception of a few habitats such as the human gut, remains severely undersampled. Further, estimation of the degree of coverage of a microbial community by a metagenomic data set is prohibitively time-consuming for large data sets, and coverage values may not be directly comparable between data sets obtained with different sequencing technologies. Here, we extend Nonpareil, a database-independent tool for the estimation of coverage in metagenomic data sets, to a high-performance computing implementation that scales up to hundreds of cores and includes, in addition, a k-mer-based estimation as sensitive as the original alignment-based version but about three hundred times as fast. Further, we propose a metric of sequence diversity (Nd ) derived directly from Nonpareil curves that correlates well with alpha diversity assessed by traditional metrics. We use this metric in different experiments demonstrating the correlation with the Shannon index estimated on 16S rRNA gene profiles and show that Nd additionally reveals seasonal patterns in marine samples that are not captured by the Shannon index and more precise rankings of the magnitude of diversity of microbial communities in different habitats. Therefore, the new version of Nonpareil, called Nonpareil 3, advances the toolbox for metagenomic analyses of microbiomes. IMPORTANCE Estimation of the coverage provided by a metagenomic data set, i.e., what fraction of the microbial community was sampled by DNA sequencing, represents an essential first step of every culture-independent genomic study that aims to robustly assess the sequence diversity present in a sample. However, estimation of coverage remains elusive because of several technical limitations associated with high computational requirements and limiting statistical approaches to quantify diversity. Here we described Nonpareil 3, a new bioinformatics algorithm that circumvents several of these limitations and thus can facilitate culture-independent studies in clinical or environmental settings, independent of the sequencing platform employed. In addition, we present a new metric of sequence diversity based on rarefied coverage and demonstrate its use in communities from diverse ecosystems.

ARGs-OAP v2.0 with an expanded SARG database and Hidden Markov Models for enhancement characterization and quantification of antibiotic resistance genes in environmental metagenomes.

Motivation: Much global attention has been paid to antibiotic resistance in monitoring its emergence, accumulation and dissemination. For rapid characterization and quantification of antibiotic resistance genes (ARGs) in metagenomic datasets, an online analysis pipeline, ARGs-OAP has been developed consisting of a database termed Structured Antibiotic Resistance Genes (the SARG) with a hierarchical structure (ARGs type-subtype-reference sequence). Results: The new release of the database, termed SARG version 2.0, contains sequences not only from CARD and ARDB databases, but also carefully selected and curated sequences from the latest protein collection of the NCBI-NR database, to keep up to date with the increasing number of ARG deposited sequences. SARG v2.0 has tripled the sequences of the first version and demonstrated improved coverage of ARGs detection in metagenomes from various environmental samples. In addition to annotation of high-throughput raw reads using a similarity search strategy, ARGs-OAP v2.0 now provides model-based identification of assembled sequences using SARGfam, a high-quality profile Hidden Markov Model (HMM), containing profiles of ARG subtypes. Additionally, ARGs-OAP v2.0 improves cell number quantification by using the average coverage of essential single copy marker genes, as an option in addition to the previous method based on the 16S rRNA gene. Availability and implementation: ARGs-OAP can be accessed through http://smile.hku.hk/SARGs. The database could be downloaded from the same site. Source codes for this study can be downloaded from https://github.com/xiaole99/ARGs-OAP-v2.0. Supplementary information: Supplementary data are available at Bioinformatics online.