To assemble or not to assemble: metagenomic profiling of microbially mediated biogeochemical pathways in complex communities.

High-throughput profiling of microbial functional traits involved in various biogeochemical cycling pathways using shotgun metagenomic sequencing has been routinely applied in microbial ecology and environmental science. Multiple bioinformatics data processing approaches are available, including assembly-based (single-sample assembly and multi-sample assembly) and read-based (merged reads and raw data). However, it remains not clear how these different approaches may differ in data analyses and affect result interpretation. In this study, using two typical shotgun metagenome datasets recovered from geographically distant coastal sediments, the performance of different data processing approaches was comparatively investigated from both technical and biological/ecological perspectives. Microbially mediated biogeochemical cycling pathways, including nitrogen cycling, sulfur cycling and B12 biosynthesis, were analyzed. As a result, multi-sample assembly provided the most amount of usable information for targeted functional traits, at a high cost of computational resources and running time. Single-sample assembly and read-based analysis were comparable in obtaining usable information, but the former was much more time- and resource-consuming. Critically, different approaches introduced much stronger variations in microbial profiles than biological differences. However, community-level differences between the two sampling sites could be consistently observed despite the approaches being used. In choosing an appropriate approach, researchers shall balance the trade-offs between multiple factors, including the scientific question, the amount of usable information, computational resources and time cost. This study is expected to provide valuable technical insights and guidelines for the various approaches used for metagenomic data analysis.

Distinct Ecological Processes Mediate Domain-Level Differentiation in Microbial Spatial Scaling.

The spatial scaling of biodiversity, such as the taxa-area relationship (TAR) and distance-decay relationship (DDR), is a typical ecological pattern that is followed by both microbes and macrobes in natural ecosystems. Previous studies focusing on microbes mainly aimed to address whether and how different types of microbial taxa differ in spatial scaling patterns, leaving the underlying mechanisms largely untouched. In this study, the spatial scaling of different microbial domains and their associated ecological processes in an intertidal zone were comparatively investigated. The significant spatial scaling of biodiversity could be observed across all microbial domains, including archaea, bacteria, fungi, and protists. Among them, archaea and fungi were found with much stronger DDR slopes than those observed in bacteria and protists. For both TAR and DDR, rare subcommunities were mainly responsible for the observed spatial scaling patterns, except for the DDR of protists and bacteria. This was also evidenced by extending the TAR and DDR diversity metrics to Hill numbers. Further statistical analyses demonstrated that different microbial domains were influenced by different environmental factors and harbored distinct local community assembly processes. Of these, drift was mainly responsible for the compositional variations of bacteria and protists. Archaea were shaped by strong homogeneous selection, whereas fungi were more affected by dispersal limitation. Such differing ecological processes resulted in the domain-level differentiation of microbial spatial scaling. This study links ecological processes with microbial spatial scaling and provides novel mechanistic insights into the diversity patterns of microbes that belong to different trophic levels. IMPORTANCE As the most diverse and numerous life form on Earth, microorganisms play indispensable roles in natural ecological processes. Revealing their diversity patterns across space and through time is of essential importance to better understand the underlying ecological mechanisms controlling the distribution and assembly of microbial communities. However, the diversity patterns and their underlying ecological mechanisms for different microbial domains and/or trophic levels require further exploration. In this study, the spatial scaling of different microbial domains and their associated ecological processes in a mudflat intertidal zone were investigated. The results showed different spatial scaling patterns for different microbial domains. Different ecological processes underlie the domain-level differentiation of microbial spatial scaling. This study links ecological processes with microbial spatial scaling to provide novel mechanistic insights into the diversity patterns of microorganisms that belong to different trophic levels.

Random sampling in metagenomic sequencing leads to overestimated spatial scaling of microbial diversity.

Revealing the spatial scaling patterns of microbial diversity is of special interest in microbial ecology. One critical question is whether the observed spatial turnover rate truly reflect the actual spatial patterns of extremely diverse microbial communities. Using simulated mock communities, this study suggested that the currently observed microbial spatial turnover rates were overestimated by random sampling processes associated with high-throughput metagenomic sequencing. The observed z values were largely contributed by accumulated microbial taxa due to cumulative number of samples. This is a crucial issue because microbial communities already have very low spatial turnover rate due to the small size and potential cosmopolitism nature of microorganisms. Further investigations suggested a linear relationship between the observed and expected z values, which can be applied to remove random sampling noises from the observed z values. Adjustment of z values for data sets from six American forests showed much lower spatial turnover rate than that before adjustment. However, the patterns of z values among these six forests remained unchanged. This study suggested that our current understanding of microbial taxa-area relationships could be inaccurate. Therefore, cautions and efforts should be made for more accurate estimation and interpretation of microbial spatial patterns.

NCycDB: a curated integrative database for fast and accurate metagenomic profiling of nitrogen cycling genes.

MOTIVATION: The nitrogen (N) cycle is a collection of important biogeochemical pathways in the Earth ecosystem and has gained extensive foci in ecology and environmental studies. Currently, shotgun metagenome sequencing has been widely applied to explore gene families responsible for N cycle processes. However, there are problems in applying publically available orthology databases to profile N cycle gene families in shotgun metagenomes, such as inefficient database searching, unspecific orthology groups and low coverage of N cycle genes and/or gene (sub)families. RESULTS: To solve these issues, this study built a manually curated integrative database (NCycDB) for fast and accurate profiling of N cycle gene (sub)families from shotgun metagenome sequencing data. NCycDB contains a total of 68 gene (sub)families and covers eight N cycle processes with 84 759 and 219 146 representative sequences at 95 and 100% identity cutoffs, respectively. We also identified 1958 homologous orthology groups and included corresponding sequences in the database to avoid false positive assignments due to 'small database' issues. We applied NCycDB to characterize N cycle gene (sub)families in 52 shotgun metagenomes from the Global Ocean Sampling expedition. Further analysis showed that the structure and composition of N cycle gene families were most strongly correlated with latitude and temperature. NCycDB is expected to facilitate N cycle studies via shotgun metagenome sequencing approaches in various environments. The framework developed in this study can be served as a good reference to build similar knowledge-based functional gene databases in various processes and pathways. AVAILABILITY AND IMPLEMENTATION: NCycDB database files are available at https://github.com/qichao1984/NCyc. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Taxonomic and Functional Analyses of the Supragingival Microbiome from Caries-Affected and Caries-Free Hosts.

Caries is one of the most prevalent and costly infectious diseases affecting humans of all ages. It is initiated by cariogenic supragingival dental plaques forming on saliva-coated tooth surfaces, yet the etiology remains elusive. To determine which microbial populations may predispose a patient to caries, we report here an in-depth and comprehensive view of the microbial community associated with supragingival dental plaque collected from the healthy teeth of caries patients and healthy adults. We found that microbial communities from caries patients had a higher evenness and inter-individual variations but simpler ecological networks compared to healthy controls despite the overall taxonomic structure being similar. Genera including Selenomonas, Treponema, Atopobium, and Bergeriella were distributed differently between the caries and healthy groups with disturbed co-occurrence patterns. In addition, caries and healthy subjects carried different Treponema, Atopobium, and Prevotella species. Moreover, distinct populations of 13 function genes involved in organic acid synthesis, glycan biosynthesis, complex carbohydrate degradation, amino acid synthesis and metabolism, purine and pyrimidine metabolism, isoprenoid biosynthesis, lipid metabolism, and co-factor biosynthesis were present in each of the healthy and caries groups. Our results suggested that the fundamental differences in dental plaque ecology partially explained the patients' susceptibility to caries, and could be used for caries risk prediction in the future.

Dynamic Succession of Groundwater Sulfate-Reducing Communities during Prolonged Reduction of Uranium in a Contaminated Aquifer.

To further understand the diversity and dynamics of SRB in response to substrate amendment, we sequenced genes coding for the dissimilatory sulfite reductase (dsrA) in groundwater samples collected after an emulsified vegetable oil (EVO) amendment, which sustained U(VI)-reducing conditions for one year in a fast-flowing aquifer. EVO amendment significantly altered the composition of groundwater SRB communities. Sequences having no closely related-described species dominated (80%) the indigenous SRB communities in nonamended wells. After EVO amendment, Desulfococcus, Desulfobacterium, and Desulfovibrio, known for long-chain-fatty-acid, short-chain-fatty-acid and H2 oxidation and U(VI) reduction, became dominant accounting for 7 ± 2%, 21 ± 8%, and 55 ± 8% of the SRB communities, respectively. Succession of these SRB at different bioactivity stages based on redox substrates/products (acetate, SO4-2, U(VI), NO3-, Fe(II), and Mn(II)) was observed. Desulfovibrio and Desulfococcus dominated SRB communities at 4-31 days, whereas Desulfobacterium became dominant at 80-140 days. By the end of the experiment (day 269), the abundance of these SRB decreased but the overall diversity of groundwater SRB was still higher than non-EVO controls. Up to 62% of the SRB community changes could be explained by groundwater geochemical variables, including those redox substrates/products. A significant (P < 0.001) correlation was observed between groundwater U(VI) concentrations and Desulfovibrio abundance. Our results showed that the members of SRB and their dynamics were correlated significantly with slow EVO biodegradation, electron donor production and maintenance of U(VI)-reducing conditions in the aquifer.

HuMiChip2 for strain level identification and functional profiling of human microbiomes.

With the massive data generated by the Human Microbiome Project, how to transform such data into useful information and knowledge remains challenging. Here, with currently available sequencing information (reference genomes and metagenomes), we have developed a comprehensive microarray, HuMiChip2, for strain-level identification and functional characterization of human microbiomes. HuMiChip2 was composed of 29,467 strain-specific probes targeting 2063 microbial strains/species and 133,924 sequence- and group-specific probes targeting 157 key functional gene families involved in various metabolic pathways and host-microbiome interaction processes. Computational evaluation of strain-specific probes suggested that they were not only specific to mock communities of sequenced microorganisms and metagenomes from different human body sites but also to non-sequenced microbial strains. Experimental evaluation of strain-specific probes using single strains/species and mock communities suggested a high specificity of these probes with their corresponding targets. Application of HuMiChip2 to human gut microbiome samples showed the patient microbiomes of alcoholic liver cirrhosis significantly (p < 0.05) shifted their functional structure from the healthy individuals, and the relative abundance of 21 gene families significantly (p < 0.1) differed between the liver cirrhosis patients and healthy individuals. At the strain level, five Bacteroides strains were significantly (p < 0.1) and more frequently detected in liver cirrhosis patients. These results suggest that the developed HuMiChip2 is a useful microbial ecological microarray for both strain-level identification and functional profiling of human microbiomes.

Gene content dissimilarity for subclassification of highly similar microbial strains.

BACKGROUND: Identification and classification of highly similar microbial strains is a challenging issue in microbiology, ecology and evolutionary biology. Among various available approaches, gene content analysis is also at the core of microbial taxonomy. However, no threshold has been determined for grouping microorgnisms to different taxonomic levels, and it is still not clear that to what extent genomic fluidity should occur to form a microbial taxonomic group. RESULTS: By taking advantage of the eggNOG database for orthologous groups, we calculated gene content dissimilarity among different microbial strains based on the orthologous gene profiles and tested the possibility of applying gene content dissimilarity as a quantitative index in classifying microbial taxonomic groups, as well as its potential application in subclassification of highly similar microbial strains. Evaluation of gene content dissimilarity to completed microbial genomes at different taxonomic levels suggested that cutoffs of 0.2 and 0.4 can be respectively used for species and family delineation, and that 0.2 gene content dissimilarity cutoff approximately corresponded to 98 % 16S rRNA gene identity and 94 % ANI for microbial species delineation. Furthermore, application of gene content dissimilarity to highly similar microbial strains suggested it as an effective approach in classifying closely related microorganisms into subgroups. CONCLUSIONS: This approach is especially useful in identifying pathogens from commensals in clinical microbiology. It also provides novel insights into how genomic fluidity is linked with microbial taxonomy.

Network succession reveals the importance of competition in response to emulsified vegetable oil amendment for uranium bioremediation.

Discerning network interactions among different species/populations in microbial communities has evoked substantial interests in recent years, but little information is available about temporal dynamics of microbial network interactions in response to environmental perturbations. Here, we modified the random matrix theory-based network approach to discern network succession in groundwater microbial communities in response to emulsified vegetable oil (EVO) amendment for uranium bioremediation. Groundwater microbial communities from one control and seven monitor wells were analysed with a functional gene array (GeoChip 3.0), and functional molecular ecological networks (fMENs) at different time points were reconstructed. Our results showed that the network interactions were dramatically altered by EVO amendment. Dynamic and resilient succession was evident: fairly simple at the initial stage (Day 0), increasingly complex at the middle period (Days 4, 17, 31), most complex at Day 80, and then decreasingly complex at a later stage (140-269 days). Unlike previous studies in other habitats, negative interactions predominated in a time-series fMEN, suggesting strong competition among different microbial species in the groundwater systems after EVO injection. Particularly, several keystone sulfate-reducing bacteria showed strong negative interactions with their network neighbours. These results provide mechanistic understanding of the decreased phylogenetic diversity during environmental perturbations.

Biogeographic patterns of soil diazotrophic communities across six forests in North America.

Soil diazotrophs play important roles in ecosystem functioning by converting atmospheric N2 into biologically available ammonium. However, the diversity and distribution of soil diazotrophic communities in different forests and whether they follow biogeographic patterns similar to macroorganisms still remain unclear. By sequencing nifH gene amplicons, we surveyed the diversity, structure and biogeographic patterns of soil diazotrophic communities across six North American forests (126 nested samples). Our results showed that each forest harboured markedly different soil diazotrophic communities and that these communities followed traditional biogeographic patterns similar to plant and animal communities, including the taxa-area relationship (TAR) and latitudinal diversity gradient. Significantly higher community diversity and lower microbial spatial turnover rates (i.e. z-values) were found for rainforests (~0.06) than temperate forests (~0.1). The gradient pattern of TARs and community diversity was strongly correlated (r(2)  > 0.5) with latitude, annual mean temperature, plant species richness and precipitation, and weakly correlated (r(2)  < 0.25) with pH and soil moisture. This study suggests that even microbial subcommunities (e.g. soil diazotrophs) follow general biogeographic patterns (e.g. TAR, latitudinal diversity gradient), and indicates that the metabolic theory of ecology and habitat heterogeneity may be the major underlying ecological mechanisms shaping the biogeographic patterns of soil diazotrophic communities.

The Diversity and Co-occurrence Patterns of N2-Fixing Communities in a CO2-Enriched Grassland Ecosystem.

Diazotrophs are the major organismal group responsible for atmospheric nitrogen (N2) fixation in natural ecosystems. The extensive diversity and structure of N2-fixing communities in grassland ecosystems and their responses to increasing atmospheric CO2 remain to be further explored. Through pyrosequencing of nifH gene amplicons and extraction of nifH genes from shotgun metagenomes, coupled with co-occurrence ecological network analysis approaches, we comprehensively analyzed the diazotrophic community in a grassland ecosystem exposed to elevated CO2 (eCO2) for 12 years. Long-term eCO2 increased the abundance of nifH genes but did not change the overall nifH diversity and diazotrophic community structure. Taxonomic and phylogenetic analysis of amplified nifH sequences suggested a high diversity of nifH genes in the soil ecosystem, the majority belonging to nifH clusters I and II. Co-occurrence ecological network analysis identified different co-occurrence patterns for different groups of diazotrophs, such as Azospirillum/Actinobacteria, Mesorhizobium/Conexibacter, and Bradyrhizobium/Acidobacteria. This indicated a potential attraction of non-N2-fixers by diazotrophs in the soil ecosystem. Interestingly, more complex co-occurrence patterns were found for free-living diazotrophs than commonly known symbiotic diazotrophs, which is consistent with the physical isolation nature of symbiotic diazotrophs from the environment by root nodules. The study provides novel insights into our understanding of the microbial ecology of soil diazotrophs in natural ecosystems.

Strain/species identification in metagenomes using genome-specific markers.

Shotgun metagenome sequencing has become a fast, cheap and high-throughput technology for characterizing microbial communities in complex environments and human body sites. However, accurate identification of microorganisms at the strain/species level remains extremely challenging. We present a novel k-mer-based approach, termed GSMer, that identifies genome-specific markers (GSMs) from currently sequenced microbial genomes, which were then used for strain/species-level identification in metagenomes. Using 5390 sequenced microbial genomes, 8 770 321 50-mer strain-specific and 11 736 360 species-specific GSMs were identified for 4088 strains and 2005 species (4933 strains), respectively. The GSMs were first evaluated against mock community metagenomes, recently sequenced genomes and real metagenomes from different body sites, suggesting that the identified GSMs were specific to their targeting genomes. Sensitivity evaluation against synthetic metagenomes with different coverage suggested that 50 GSMs per strain were sufficient to identify most microbial strains with ≥0.25× coverage, and 10% of selected GSMs in a database should be detected for confident positive callings. Application of GSMs identified 45 and 74 microbial strains/species significantly associated with type 2 diabetes patients and obese/lean individuals from corresponding gastrointestinal tract metagenomes, respectively. Our result agreed with previous studies but provided strain-level information. The approach can be directly applied to identify microbial strains/species from raw metagenomes, without the effort of complex data pre-processing.

Fungal communities respond to long-term CO2 elevation by community reassembly.

Fungal communities play a major role as decomposers in the Earth's ecosystems. Their community-level responses to elevated CO2 (eCO2), one of the major global change factors impacting ecosystems, are not well understood. Using 28S rRNA gene amplicon sequencing and co-occurrence ecological network approaches, we analyzed the response of soil fungal communities in the BioCON (biodiversity, CO2, and N deposition) experimental site in Minnesota, USA, in which a grassland ecosystem has been exposed to eCO2 for 12 years. Long-term eCO2 did not significantly change the overall fungal community structure and species richness, but significantly increased community evenness and diversity. The relative abundances of 119 operational taxonomic units (OTU; ∼27% of the total captured sequences) were changed significantly. Significantly changed OTU under eCO2 were associated with decreased overall relative abundance of Ascomycota, but increased relative abundance of Basidiomycota. Co-occurrence ecological network analysis indicated that eCO2 increased fungal community network complexity, as evidenced by higher intermodular and intramodular connectivity and shorter geodesic distance. In contrast, decreased connections for dominant fungal species were observed in the eCO2 network. Community reassembly of unrelated fungal species into highly connected dense modules was observed. Such changes in the co-occurrence network topology were significantly associated with altered soil and plant properties under eCO2, especially with increased plant biomass and NH4 (+) availability. This study provided novel insights into how eCO2 shapes soil fungal communities in grassland ecosystems.

Phasing amplicon sequencing on Illumina Miseq for robust environmental microbial community analysis.

BACKGROUND: Although high-throughput sequencing, such as Illumina-based technologies (e.g. MiSeq), has revolutionized microbial ecology, adaptation of amplicon sequencing for environmental microbial community analysis is challenging due to the problem of low base diversity. RESULTS: A new phasing amplicon sequencing approach (PAS) was developed by shifting sequencing phases among different community samples from both directions via adding various numbers of bases (0-7) as spacers to both forward and reverse primers. Our results first indicated that the PAS method substantially ameliorated the problem of unbalanced base composition. Second, the PAS method substantially improved the sequence read base quality (an average of 10 % higher of bases above Q30). Third, the PAS method effectively increased raw sequence throughput (~15 % more raw reads). In addition, the PAS method significantly increased effective reads (9-47 %) and the effective read sequence length (16-96 more bases) after quality trim at Q30 with window 5. In addition, the PAS method reduced half of the sequencing errors (0.54-1.1 % less). Finally, two-step PCR amplification of the PAS method effectively ameliorated the amplification biases introduced by the long barcoded PCR primers. CONCLUSION: The developed strategy is robust for 16S rRNA gene amplicon sequencing. In addition, a similar strategy could also be used for sequencing other genes important to ecosystem functional processes.

Soil microbial community responses to a decade of warming as revealed by comparative metagenomics.

Soil microbial communities are extremely complex, being composed of thousands of low-abundance species (<0.1% of total). How such complex communities respond to natural or human-induced fluctuations, including major perturbations such as global climate change, remains poorly understood, severely limiting our predictive ability for soil ecosystem functioning and resilience. In this study, we compared 12 whole-community shotgun metagenomic data sets from a grassland soil in the Midwestern United States, half representing soil that had undergone infrared warming by 2°C for 10 years, which simulated the effects of climate change, and the other half representing the adjacent soil that received no warming and thus, served as controls. Our analyses revealed that the heated communities showed significant shifts in composition and predicted metabolism, and these shifts were community wide as opposed to being attributable to a few taxa. Key metabolic pathways related to carbon turnover, such as cellulose degradation (∼13%) and CO2 production (∼10%), and to nitrogen cycling, including denitrification (∼12%), were enriched under warming, which was consistent with independent physicochemical measurements. These community shifts were interlinked, in part, with higher primary productivity of the aboveground plant communities stimulated by warming, revealing that most of the additional, plant-derived soil carbon was likely respired by microbial activity. Warming also enriched for a higher abundance of sporulation genes and genomes with higher G+C content. Collectively, our results indicate that microbial communities of temperate grassland soils play important roles in mediating feedback responses to climate change and advance the understanding of the molecular mechanisms of community adaptation to environmental perturbations.

The Thermoanaerobacter glycobiome reveals mechanisms of pentose and hexose co-utilization in bacteria.

Thermoanaerobic bacteria are of interest in cellulosic-biofuel production, due to their simultaneous pentose and hexose utilization (co-utilization) and thermophilic nature. In this study, we experimentally reconstructed the structure and dynamics of the first genome-wide carbon utilization network of thermoanaerobes. The network uncovers numerous novel pathways and identifies previously unrecognized but crucial pathway interactions and the associated key junctions. First, glucose, xylose, fructose, and cellobiose catabolism are each featured in distinct functional modules; the transport systems of hexose and pentose are apparently both regulated by transcriptional antiterminators of the BglG family, which is consistent with pentose and hexose co-utilization. Second, glucose and xylose modules cooperate in that the activity of the former promotes the activity of the latter via activating xylose transport and catabolism, while xylose delays cell lysis by sustaining coenzyme and ion metabolism. Third, the vitamin B12 pathway appears to promote ethanologenesis through ethanolamine and 1, 2-propanediol, while the arginine deiminase pathway probably contributes to cell survival in stationary phase. Moreover, by experimentally validating the distinct yet collaborative nature of glucose and xylose catabolism, we demonstrated that these novel network-derived features can be rationally exploited for product-yield enhancement via optimized timing and balanced loading of the carbon supply in a substrate-specific manner. Thus, this thermoanaerobic glycobiome reveals novel genetic features in carbon catabolism that may have immediate industrial implications and provides novel strategies and targets for fermentation and genome engineering.

The mechanism of low blue light-induced leaf senescence mediated by GmCRY1s in soybean.

Leaf senescence is a crucial trait that has a significant impact on crop quality and yield. Previous studies have demonstrated that light is a key factor in modulating the senescence process. However, the precise mechanism by which plants sense light and control senescence remains largely unknown, particularly in crop species. In this study, we reveal that the reduction in blue light under shading conditions can efficiently induce leaf senescence in soybean. The blue light receptors GmCRY1s rather than GmCRY2s, primarily regulate leaf senescence in response to blue light signals. Our results show that GmCRY1s interact with DELLA proteins under light-activated conditions, stabilizing them and consequently suppressing the transcription of GmWRKY100 to delay senescence. Conversely, LBL reduces the interaction between GmCRY1s and the DELLA proteins, leading to their degradation and premature senescence of leaves. Our findings suggest a GmCRY1s-GmDELLAs-GmWRKY100 regulatory cascade that is involved in mediating LBL-induced leaf senescence in soybean, providing insight into the mechanism of how light signals regulate leaf senescence. Additionally, we generate GmWRKY100 knockout soybeans that show delayed leaf senescence and improved yield under natural field conditions, indicating potential applications in enhancing soybean production by manipulating the leaf senescence trait.

Beyond water and soil: Air emerges as a major reservoir of human pathogens.

Assessing the risk of human pathogens in the environment is crucial for controlling the spread of diseases and safeguarding human health. However, conducting a thorough assessment of low-abundance pathogens in highly complex environmental microbial communities remains challenging. This study compiled a comprehensive catalog of 247 human-pathogenic bacterial taxa from global biosafety agencies and identified more than 78 million genome-specific markers (GSMs) from their 17,470 sequenced genomes. Subsequently, we analyzed these pathogens' types, abundance, and diversity within 474 shotgun metagenomic sequences obtained from diverse environmental sources. The results revealed that among the four habitats studied (air, water, soil, and sediment), the detection rate, diversity, and abundance of detectable pathogens in the air all exceeded those in the other three habitats. Air, sediment, and water environments exhibited identical dominant taxa, indicating that these human pathogens may have unique environmental vectors for their transmission or survival. Furthermore, we observed the impact of human activities on the environmental risk posed by these pathogens, where greater amounts of human activities significantly increased the abundance of human pathogenic bacteria, especially in water and air. These findings have remarkable implications for the environmental risk assessment of human pathogens, providing valuable insights into their presence and distribution across different habitats.

Metagenomic-based discovery and comparison of the lignin degrading potential of microbiomes in aquatic and terrestrial ecosystems via the LCdb database.

Lignin, as an abundant organic carbon, plays a vital role in the global carbon cycle. However, our understanding of the global lignin-degrading microbiome remains elusive. The greatest barrier has been absence of a comprehensive and accurate functional gene database. Here, we first developed a curated functional gene database (LCdb) for metagenomic profiling of lignin degrading microbial consortia. Via the LCdb, we draw a clear picture describing the global biogeography of communities with lignin-degrading potential. They exhibit clear niche differentiation at the levels of taxonomy and functional traits. The terrestrial microbiomes showed the highest diversity, yet the lowest correlations. In particular, there were few correlations between genes involved in aerobic and anaerobic degradation pathways, showing a clear functional redundancy property. In contrast, enhanced correlations, especially closer inter-connections between anaerobic and aerobic groups, were observed in aquatic consortia in response to the lower diversity. Specifically, dypB and dypA, are widespread on Earth, indicating their essential roles in lignin depolymerization. Estuarine and marine consortia featured the laccase and mnsod genes, respectively. Notably, the roles of archaea in lignin degradation were revealed in marine ecosystems. Environmental factors strongly influenced functional traits, but weakly shaped taxonomic groups. Null mode analysis further verified that composition of functional traits was deterministic, while taxonomic composition was highly stochastic, demonstrating that the environment selects functional genes rather than taxonomic groups. Our study not only develops a useful tool to study lignin degrading microbial communities via metagenome sequencing but also advances our understanding of ecological traits of these global microbiomes.

Temporal turnover of viral biodiversity and functional potential in intertidal wetlands.

As the central members of the microbiome networks, viruses regulate the composition of microbial communities and drive the nutrient cycles of ecosystems by lysing host cells. Therefore, uncovering the dynamic patterns and the underlying ecological mechanisms mediating the tiniest viral communities across space and through time in natural ecosystems is of crucial importance for better understanding the complex microbial world. Here, the temporal dynamics of intertidal viral communities were investigated via a time-series sampling effort. A total of 1911 viral operational taxonomic units were recovered from 36 bimonthly collected shotgun metagenomes. Functionally important auxiliary metabolic genes involved in carbohydrate, sulfur, and phosphorus metabolism were detected, some of which (e.g., cysH gene) were stably present within viral genomes over time. Over the sampling period, strong and comparable temporal turnovers were observed for intertidal viromes and their host microbes. Winter was determined as the pivotal point for the shifts in viral diversity patterns. Notably, the viral micro-diversity covaried with the macro-diversity, following similar temporal patterns. The relative abundances of viral taxa also covaried with their host prokaryotes. Meanwhile, the virus-host relationships at the whole community level were relatively stable. Further statistical analyses demonstrated that the dynamic patterns of viral communities were highly deterministic, for which temperature was the major driver. This study provided valuable mechanistic insights into the temporal turnover of viral communities in complex ecosystems such as intertidal wetlands.