Nitrogen-cycling processes under long-term compound heavy metal(loids) pressure around a gold mine: Stimulation of nitrite reduction.

Mining and tailings deposition can cause serious heavy metal(loids) pollution to the surrounding soil environment. Soil microorganisms adapt their metabolism to such conditions, driving alterations in soil function. This study aims to elucidate the response patterns of nitrogen-cycling microorganisms under long-term heavy metal(loids) exposure. The results showed that the diversity and abundance of nitrogen-cycling microorganisms showed negative feedback to heavy metal(loids) concentrations. Denitrifying microorganisms were shown to be the dominant microorganisms with over 60% of relative abundance and a complex community structure including 27 phyla. Further, the key bacterial species in the denitrification process were calculated using a random forest model, where the top three key species (Pseudomonas stutzei, Sphingobium japonicum and Leifsonia rubra) were found to play a prominent role in nitrite reduction. Functional gene analysis and qPCR revealed that nirK, which is involved in nitrite reduction, significantly accumulated in the most metal-rich soil with the increase of absolute abundance of 63.86%. The experimental results confirmed that the activity of nitrite reductase (Nir) encoded by nirK in the soil was increased at high concentrations of heavy metal(loids). Partial least squares-path model identified three potential modes of nitrite reduction processes being stimulated by heavy metal(loids), the most prominent of which contributed to enhanced nirK abundance and soil Nir activity through positive stimulation of key species. The results provide new insights and preliminary evidence on the stimulation of nitrite reduction processes by heavy metal(loids).

A trait-based ecological perspective on the soil microbial antibiotic-related genetic machinery.

Antibiotic resistance crisis dictates the need for resistance monitoring and the search for new antibiotics. The development of monitoring protocols is hindered by the great diversity of resistance factors, while the "streetlight effect" denies the possibility of discovering novel drugs based on existing databases. In this study, we address these challenges using high-throughput environmental screening viewed from a trait-based ecological perspective. Through an in-depth analysis of the metagenomes of 658 topsoil samples spanning Europe, we explored the distribution of 241 prokaryotic and fungal genes responsible for producing metabolites with antibiotic properties and 485 antibiotic resistance genes. We analyzed the diversity of these gene collections at different levels and modeled the distribution of each gene across environmental gradients. Our analyses revealed several nonparallel distribution patterns of the genes encoding sequential steps of enzymatic pathways synthesizing large antibiotic groups, pointing to gaps in existing databases and suggesting potential for discovering new analogues of known antibiotics. We show that agricultural activity caused a continental-scale homogenization of microbial antibiotic-related machinery, emphasizing the importance of maintaining indigenous ecosystems within the landscape mosaic. Based on the relationships between the proportion of the genes in the metagenomes with the main predictors (soil pH, land cover type, climate temperature and humidity), we illustrate how the properties of chemical structures dictate the distribution of the genes responsible for their synthesis across environments. With this understanding, we propose general principles to facilitate the discovery of antibiotics, including principally new ones, establish abundance baselines for antibiotic resistance genes, and predict their dissemination.

DeePhafier: a phage lifestyle classifier using a multilayer self-attention neural network combining protein information.

Bacteriophages are the viruses that infect bacterial cells. They are the most diverse biological entities on earth and play important roles in microbiome. According to the phage lifestyle, phages can be divided into the virulent phages and the temperate phages. Classifying virulent and temperate phages is crucial for further understanding of the phage-host interactions. Although there are several methods designed for phage lifestyle classification, they merely either consider sequence features or gene features, leading to low accuracy. A new computational method, DeePhafier, is proposed to improve classification performance on phage lifestyle. Built by several multilayer self-attention neural networks, a global self-attention neural network, and being combined by protein features of the Position Specific Scoring Matrix matrix, DeePhafier improves the classification accuracy and outperforms two benchmark methods. The accuracy of DeePhafier on five-fold cross-validation is as high as 87.54% for sequences with length >2000bp.

Microbial communities and functions are structured by vertical geochemical zones in a northern peatland.

Northern peatlands are important carbon pools; however, differences in the structure and function of microbiomes inhabiting contrasting geochemical zones within these peatlands have rarely been emphasized. Using 16S rRNA gene sequencing, metagenomic profiling, and detailed geochemical analyses, we investigated the taxonomic composition and genetic potential across various geochemical zones of a typical northern peatland profile in the Changbai Mountains region (Northeastern China). Specifically, we focused on elucidating the turnover of organic carbon, sulfur (S), nitrogen (N), and methane (CH4). Three geochemical zones were identified and characterized according to porewater and solid-phase analyses: the redox interface (<10 cm), shallow peat (10-100 cm), and deep peat (>100 cm). The redox interface and upper shallow peat demonstrated a high availability of labile carbon, which decreased toward deeper peat. In deep peat, anaerobic respiration and methanogenesis were likely constrained by thermodynamics, rather than solely driven by available carbon, as the acetate concentrations reached 90 µmol·L-1. Both the microbial community composition and metabolic potentials were significantly different (p < 0.05) among the redox interface, shallow peat, and deep peat. The redox interface demonstrated a close interaction between N, S, and CH4 cycling, mainly driven by Thermodesulfovibrionia, Bradyrhizobium, and Syntrophorhabdia metagenome-assembled genomes (MAGs). The archaeal Bathyarchaeia were indicated to play a significant role in the organic carbon, N, and S cycling in shallow peat. Although constrained by anaerobic respiration and methanogenesis, deep peat exhibited a higher metabolic potential for organic carbon degradation, primarily mediated by Acidobacteriota. In terms of CH4 turnover, subsurface peat (10-20 cm) was a CH4 production hotspot, with a net turnover rate of ∼2.9 nmol·cm-3·d-1, while the acetoclastic, hydrogenotrophic, and methylotrophic methanogenic pathways all potentially contributed to CH4 production. The results of this study improve our understanding of biogeochemical cycles and CH4 turnover along peatland profiles.

Integrative genome-centric metagenomics for surface water surveillance: Elucidating microbiomes, antimicrobial resistance, and their associations.

Surface water ecosystems are intimately intertwined with anthropogenic activities and have significant public health implications as primary sources of irrigation water in agricultural production. Our extensive metagenomic analysis examined 404 surface water samples from four different geological regions in Chile and Brazil, spanning irrigation canals (n = 135), rivers (n = 121), creeks (n = 74), reservoirs (n = 66), and ponds (n = 8). Overall, 50.25 % of the surface water samples contained at least one of the pathogenic or contaminant bacterial genera (Salmonella: 29.21 %; Listeria: 6.19 %; Escherichia: 35.64 %). Furthermore, a total of 1,582 antimicrobial resistance (AMR) gene clusters encoding resistance to 25 antimicrobial classes were identified, with samples from Brazil exhibiting an elevated AMR burden. Samples from stagnant water sources were characterized by dominant Cyanobacteriota populations, resulting in significantly reduced biodiversity and more uniform community compositions. A significant association between taxonomic composition and the resistome was supported by a Procrustes analysis (p < 0.001). Notably, regional signatures were observed regarding the taxonomic and resistome profiles, as samples from the same region clustered together on both ordinates. Additionally, network analysis illuminated the intricate links between taxonomy and AMR at the contig level. Our deep sequencing efforts not only mapped the microbial landscape but also expanded the genomic catalog with newly characterized metagenome-assembled genomes (MAGs), boosting the classification of reads by 12.85 %. In conclusion, this study underscores the value of metagenomic approaches in surveillance of surface waters, enhancing our understanding of microbial and AMR dynamics with far-reaching public health and ecological ramifications.

Genomic and induction evidence for bacteriophage contributions to sargassum-bacteria symbioses.

BACKGROUND: Symbioses between primary producers and bacteria are crucial for nutrient exchange that fosters host growth and niche adaptation. Yet, how viruses that infect bacteria (phages) influence these bacteria-eukaryote interactions is still largely unknown. Here, we investigate the role of viruses on the genomic diversity and functional adaptations of bacteria associated with pelagic sargassum. This brown alga has dramatically increased its distribution range in the Atlantic in the past decade and is predicted to continue expanding, imposing severe impacts on coastal ecosystems, economies, and human health. RESULTS: We reconstructed 73 bacterial and 3963 viral metagenome-assembled genomes (bMAGs and vMAGs, respectively) from coastal Sargassum natans VIII and surrounding seawater. S. natans VIII bMAGs were enriched in prophages compared to seawater (28% and 0.02%, respectively). Rhodobacterales and Synechococcus bMAGs, abundant members of the S. natans VIII microbiome, were shared between the algae and seawater but were associated with distinct phages in each environment. Genes related to biofilm formation and quorum sensing were enriched in S. natans VIII phages, indicating their potential to influence algal association in their bacterial hosts. In-vitro assays with a bacterial community harvested from sargassum surface biofilms and depleted of free viruses demonstrated that these bacteria are protected from lytic infection by seawater viruses but contain intact and inducible prophages. These bacteria form thicker biofilms when growing on sargassum-supplemented seawater compared to seawater controls, and phage induction using mitomycin C was associated with a significant decrease in biofilm formation. The induced metagenomes were enriched in genomic sequences classified as temperate viruses compared to uninduced controls. CONCLUSIONS: Our data shows that prophages contribute to the flexible genomes of S. natans VIII-associated bacteria. These prophages encode genes with symbiotic functions, and their induction decreases biofilm formation, an essential capacity for flexible symbioses between bacteria and the alga. These results indicate that prophage acquisition and induction contribute to genomic and functional diversification during sargassum-bacteria symbioses, with potential implications for algae growth. Video Abstract.

Measuring air metagenomic diversity in an agricultural ecosystem.

All species shed DNA during life or in death, providing an opportunity to monitor biodiversity via environmental DNA (eDNA). In recent years, combining eDNA, high-throughput sequencing technologies, bioinformatics, and increasingly complete sequence databases has promised a non-invasive and non-destructive environmental monitoring tool. Modern agricultural systems are often large monocultures and so are highly vulnerable to disease outbreaks. Pest and pathogen monitoring in agricultural ecosystems is key for efficient and early disease prevention, lower pesticide use, and better food security. Although the air is rich in biodiversity, it has the lowest DNA concentration of all environmental media and yet is the route for windborne spread of many damaging crop pathogens. Our work suggests that ecosystems can be monitored efficiently using airborne nucleic acid information. Here, we show that the airborne DNA of microbes can be recovered, shotgun sequenced, and taxonomically classified, including down to the species level. We show that by monitoring a field growing key crops we can identify the presence of agriculturally significant pathogens and quantify their changing abundance over a period of 1.5 months, often correlating with weather variables. We add to the evidence that aerial eDNA can be used as a source for biomonitoring in terrestrial ecosystems, specifically highlighting agriculturally relevant species and how pathogen levels correlate with weather conditions. Our ability to detect dynamically changing levels of species and strains highlights the value of airborne eDNA in agriculture, monitoring biodiversity changes, and tracking taxa of interest.

MAGs-centric crack: how long will, spore-positive Frankia and most Protofrankia, microsymbionts remain recalcitrant to axenic growth?

Nearly 50 years after the ground-breaking isolation of the primary Comptonia peregrina microsymbiont under axenic conditions, efforts to isolate a substantial number of Protofrankia and Frankia strains continue with enduring challenges and complexities. This study aimed to streamline genomic insights through comparative and predictive tools to extract traits crucial for isolating specific Frankia in axenic conditions. Pangenome analysis unveiled significant genetic diversity, suggesting untapped potential for cultivation strategies. Shared metabolic strategies in cellular components, central metabolic pathways, and resource acquisition traits offered promising avenues for cultivation. Ecological trait extraction indicated that most uncultured strains exhibit no apparent barriers to axenic growth. Despite ongoing challenges, potential caveats, and errors that could bias predictive analyses, this study provides a nuanced perspective. It highlights potential breakthroughs and guides refined cultivation strategies for these yet-uncultured strains. We advocate for tailored media formulations enriched with simple carbon sources in aerobic environments, with atmospheric nitrogen optionally sufficient to minimize contamination risks. Temperature adjustments should align with strain preferences-28-29°C for Frankia and 32-35°C for Protofrankia-while maintaining an alkaline pH. Given potential extended incubation periods (predicted doubling times ranging from 3.26 to 9.60 days, possibly up to 21.98 days), patience and rigorous contamination monitoring are crucial for optimizing cultivation conditions.

Distribution and community structure of antibiotic resistance genes in the Three Gorges Reservoir Area.

Antibiotic resistance genes (ARG) are widespread across various regions. While several studies have investigated the distribution of antibiotic resistance in natural environments, the occurrence and diversity of ARGs in the Three Gorges Reservoir have not been fully elucidated. In this study, we employed metagenomic sequencing techniques to investigate the abundance, diversity, and influencing factors of ARGs in the ecosystem of the Three Gorges Reservoir. A total of 874 ARGs, 20 antibiotic classes, and 6 resistance mechanisms were detected. The dominant ARG is the macB, the dominant antibiotic class is multidrug resistance (MDR), and the dominant resistance mechanism is antibiotic efflux. The microorganisms with the highest contribution to ARGs are Betaproteobacteria and Gammaproteobacteria. In this region, pH and NH4+ concentration were significantly negatively correlated with the relative abundance of most ARGs, while NO3- concentration and TN were significantly positively correlated with the relative abundance of most ARGs. The results indicate that the Three Gorges Reservoir constitutes a significant reservoir of ARGs. By studying the distribution of ARGs in the sediments of the Three Gorges Reservoir Area and the relationship between environmental factors and ARGs, we can more comprehensively understand the pollution status of ARGs in this area, and provide theoretical support for subsequent treatment.

Detection of novel orthoparamyxoviruses, orthonairoviruses and an orthohepevirus in European white-toothed shrews.

While the viromes and immune systems of bats and rodents have been extensively studied, comprehensive data are lacking for insectivores (order Eulipotyphla) despite their wide geographic distribution. Anthropogenic land use and outdoor recreational activities, as well as changes in the range of shrews, may lead to an expansion of the human-shrew interface with the risk of spillover infections, as reported for Borna disease virus 1. We investigated the virome of 45 individuals of 4 white-toothed shrew species present in Europe, using metagenomic RNA sequencing of tissue and intestine pools. Moderate to high abundances of sequences related to the families Paramyxoviridae, Nairoviridae, Hepeviridae and Bornaviridae were detected. Whole genomes were determined for novel orthoparamyxoviruses (n=3), orthonairoviruses (n=2) and an orthohepevirus. The novel paramyxovirus, tentatively named Hasua virus, was phylogenetically related to the zoonotic Langya virus and Mòjiang virus. The novel orthonairoviruses, along with the potentially zoonotic Erve virus, fall within the shrew-borne Thiafora virus genogroup. The highest viral RNA loads of orthoparamyxoviruses were detected in the kidneys, in well-perfused organs for orthonairoviruses and in the liver and intestine for orthohepevirus, indicating potential transmission routes. Notably, several shrews were found to be coinfected with viruses from different families. Our study highlights the virus diversity present in shrews, not only in biodiversity-rich regions but also in areas influenced by human activity. This study warrants further research to characterize and assess the clinical implications and risk of these viruses and the importance of shrews as reservoirs in European ecosystems.

Analysis of metagenome and metabolome disclosed the mechanisms of Dendrobium officinale polysaccharide on DSS-induced ulcerative colitis-affected mice.

Currently, there is no known cause for ulcerative colitis (UC), an inflammatory bowel disease that is difficult to treat. This assay aimed to investigate the protective effects and mechanisms of Dendrobium officinale polysaccharide (DOP) in mice with acute UC induced by dextran sulphate sodium (DSS). We found that DOP could improve weight loss, decrease the disease activity index (DAI), and regulate the release of interleukin 2 (IL-2), IL-4, IL-6, and IL-10 in DSS-induced acute UC mice. Additionally, DOP preserved the integrity of the intestinal barrier in UC mice by increasing goblet cell density and maintaining tight junctions. DOP significantly enhanced total antioxidant capacity (T-AOC), and reduced glutathione (GSH), nitric oxide (NO), and malondialdehyde (MDA) levels in the bloodstream. In terms of serum biochemistry, DOP markedly elevated levels of bilirubin (BIL), alkaline phosphatase (ALP), total bile acid (TBA), creatinine (Crea), and creative kinase isoenzyme (CKMB). Furthermore, DOP increased the relative abundance of Lactobacillales. DOP also improved intestinal health and stimulated the synthesis of potent anti-inflammatory and antiviral substances by regulating the metabolism of purines, prostaglandins, and leukotrienes. Therefore, DOP can be considered a functional dietary supplement for the treatment of UC, as it improves the condition of DSS-induced UC mice.

Green manuring combined with zeolite reduced nitrous oxide emissions in maize field by targeting microbial nitrogen transformations.

Green manure is a crucial strategy for increasing cereal yield and mitigating environmental burden while reducing chemical N fertilizer. To effectively tackle climate change, finding ways to reduce nitrous oxide (N2O) emissions from green manuring systems is vital. Herein, field and 15N labeled microcosm experiments were arranged to investigate the effect and mechanisms of green manuring and zeolite application on N2O emission. Both experiments comprised four treatments: conventional chemical N (N100), 70 % chemical N (N70), N70 with green manure (N70 + CV), and N70 + CV combined with zeolite (N70 + CV + Z). Compared with N100, both N70 + CV and N70 + CV + Z maintained maize yield, cumulative N2O emissions decreased by 37.7 % and 34.9 % in N70 + CV + Z in 2022-yr and 2023-yr, and by 12.8 % in N70 + CV in 2022-yr. Moreover, the reduction of N2O emission primarily occurred after incorporating green manure. The N100 and N70 + CV demonstrated a similar transformed proportion of chemical N to N2O (i.e., 4.9 % and 4.7 %) while reducing it to 2.7 % in N70 + CV + Z. Additionally, a mere 0.7 % of green manure N was transformed to N2O in both N70 + CV and N70 + CV + Z treatments. Compared with N100, both N70 + CV and N70 + CV + Z decreased the relative abundances of ammonia oxidation microbes, increased denitrifier and the ratios of (nirK + nirS)/nosZ and norBC/nosZ. Furthermore, compared with N70 + CV, N70 + CV + Z decreased the relative abundances of N2O-producer and the ratios of (nirK + nirS)/nosZ and norBC/nosZ in denitrification. These findings revealed that the reduction of N2O emissions resulting from green manure replaced chemical N was mainly due to weakened nitrification, while zeolite reduced N2O emissions attributed to enhanced conversion of N2O to N2. Moreover, certain key N-cycling functional bacteria, such as Phycisphaerae, Rubrobacteria, and Thermoflexia, were positively correlated with N2O emission. In contrast, Dehalococcoidia, Gammaproteobacteria, and Betaproteobacteria were negatively correlated with N2O emission. This investigation uncovered the underlying mechanisms for effectively reducing N2O emissions through green manure combined with zeolite.

MICROPHERRET: MICRObial PHEnotypic tRait ClassifieR using Machine lEarning Techniques.

BACKGROUND: In recent years, there has been a rapid increase in the number of microbial genomes reconstructed through shotgun sequencing, and obtained by newly developed approaches including metagenomic binning and single-cell sequencing. However, our ability to functionally characterize these genomes by experimental assays is orders of magnitude less efficient. Consequently, there is a pressing need for the development of swift and automated strategies for the functional classification of microbial genomes. RESULTS: The present work leverages a suite of supervised machine learning algorithms to establish a range of 86 metabolic and other ecological functions, such as methanotrophy and plastic degradation, starting from widely obtainable microbial genome annotations. Tests performed on independent datasets demonstrated robust performance across complete, fragmented, and incomplete genomes above a 70% completeness level for most of the considered functions. Application of the algorithms to the Biogas Microbiome database yielded predictions broadly consistent with current biological knowledge and correctly detecting functionally-related nuances of archaeal genomes. Finally, a case study focused on acetoclastic methanogenesis demonstrated how the developed machine learning models can be refined or expanded with models describing novel functions of interest. CONCLUSIONS: The resulting tool, MICROPHERRET, incorporates a total of 86 models, one for each tested functional class, and can be applied to high-quality microbial genomes as well as to low-quality genomes derived from metagenomics and single-cell sequencing. MICROPHERRET can thus aid in understanding the functional role of newly generated genomes within their micro-ecological context.

The microbial community structure and nitrogen cycle of high-altitude pristine saline lakes on the Qinghai-Tibetan plateau.

The nitrogen (N) cycle is the foundation of the biogeochemistry on Earth and plays a crucial role in global climate stability. It is one of the most important nutrient cycles in high-altitude lakes. The biogeochemistry of nitrogen is almost entirely dependent on redox reactions mediated by microorganisms. However, the nitrogen cycling of microbial communities in the high-altitude saline lakes of the Qinghai-Tibet Plateau (QTP), the world's "third pole" has not been investigated extensively. In this study, we used a metagenomic approach to investigate the microbial communities in four high-altitude pristine saline lakes in the Altun mountain on the QTP. We observed that Proteobacteria, Bacteroidota, and Actinobacteriota were dominant in these lakes. We reconstructed 1,593 bacterial MAGs and 8 archaeal MAGs, 1,060 of which were found to contain nitrogen cycle related genes. Our analysis revealed that nitrite reduction, nitrogen fixation, and assimilatory nitrate reduction processes might be active in the lakes. Denitrification might be a major mechanism driving the potential nitrogen loss, while nitrification might be inactive. A wide variety of microorganisms in the lake, dominated by Proteobacteria, participate together in the nitrogen cycle. The prevalence of the dominant taxon Yoonia in these lakes may be attributed to its well-established nitrogen functions and the coupled proton dynamics. This study is the first to systematically investigate the structure and nitrogen function of the microbial community in the high-altitude pristine saline lakes in the Altun mountain on the QTP. As such, it contributes to a better comprehension of biogeochemistry of high-altitude saline lakes.

Genomic reconstruction of unclassified microorganisms: Analysis of CRISPR arrays and genes involved in defense mechanisms.

The constant battle between bacteria and viruses has led to the development of sophisticated antiviral defense strategies by bacteria to defend themselves against phages. This study analyzed a marshland metagenome to identify and characterize bacterial antiviral defense systems and phage interactions. We assembled 210 metagenome-assembled genomes (MAGs) from environmental DNA extracted from Pallikaranai marshland soil and 37 unclassified MAGs were filtered. MIMAG standards were followed, 2 high-quality and 15 medium-quality unclassified MAGs were picked. MINCED was used to identify 137 CRISPR arrays in the quality MAGs, and ViroBLAST was used to identify the phages that interact with the bacteria. About 242 spacer sequences were extracted from the CRISPR arrays, of which 54 had significant matches in the ViroBLAST database. 7 unverified bacteriophage species were also detected in the MAGs. The viral group of Caudoviricetes phage elements were identified as a frequent genome terminal repeat. The PADLOC identified 11 genes involved as a defense system in the MAGs. The PD-T4-6 defense system was found to be prevalent in 15 different unclassified MAGs. This study presents valuable insights intothe adaptations of unclassified bacteria to bacteriophages, as well as the genes used by these bacteria as a defense mechanism.

Microbial life in preferential flow paths in subsurface clayey till revealed by metataxonomy and metagenomics.

BACKGROUND: Subsurface microorganisms contribute to important ecosystem services, yet little is known about how the composition of these communities is affected by small scale heterogeneity such as in preferential flow paths including biopores and fractures. This study aimed to provide a more complete characterization of microbial communities from preferential flow paths and matrix sediments of a clayey till to a depth of 400 cm by using 16S rRNA gene and fungal ITS2 amplicon sequencing of environmental DNA. Moreover, shotgun metagenomics was applied to samples from fractures located 150 cm below ground surface (bgs) to investigate the bacterial genomic adaptations resulting from fluctuating exposure to nutrients, oxygen and water. RESULTS: The microbial communities changed significantly with depth. In addition, the bacterial/archaeal communities in preferential flow paths were significantly different from those in the adjacent matrix sediments, which was not the case for fungal communities. Preferential flow paths contained higher abundances of 16S rRNA and ITS gene copies than the corresponding matrix sediments and more aerobic bacterial taxa than adjacent matrix sediments at 75 and 150 cm bgs. These findings were linked to higher organic carbon and the connectivity of the flow paths to the topsoil as demonstrated by previous dye tracer experiments. Moreover, bacteria, which were differentially more abundant in the fractures than in the matrix sediment at 150 cm bgs, had higher abundances of carbohydrate active enzymes, and a greater potential for mixotrophic growth. CONCLUSIONS: Our results demonstrate that the preferential flow paths in the subsurface are unique niches that are closely connected to water flow and the fluctuating ground water table. Although no difference in fungal communities were observed between these two niches, hydraulically active flow paths contained a significantly higher abundance in fungal, archaeal and bacterial taxa. Metagenomic analysis suggests that bacteria in tectonic fractures have the genetic potential to respond to fluctuating oxygen levels and can degrade organic carbon, which should result in their increased participation in subsurface carbon cycling. This increased microbial abundance and activity needs to be considered in future research and modelling efforts of the soil subsurface.

Duck gut metagenome reveals the microbiome signatures linked to intestinal regional, temporal development, and rearing condition.

The duck gastrointestinal tract (GIT) harbors an abundance of microorganisms that play an important role in duck health and production. Here, we constructed the first relatively comprehensive duck gut microbial gene catalog (24 million genes) and 4437 metagenome-assembled genomes using 375 GIT metagenomic samples from four different duck breeds across five intestinal segments under two distinct rearing conditions. We further characterized the intestinal region-specific microbial taxonomy and their assigned functions, as well as the temporal development and maturation of the duck gut microbiome. Our metagenomic analysis revealed the similarity within the microbiota of the foregut and hindgut compartments, but distinctive taxonomic and functional differences between distinct intestinal segments. In addition, we found a significant shift in the microbiota composition of newly hatched ducks (3 days), followed by increased diversity and enhanced stability across growth stages (14, 42, and 70 days), indicating that the intestinal microbiota develops into a relatively mature and stable community as the host duck matures. Comparing the impact of different rearing conditions (with and without water) on duck cecal microbiota communities and functions, we found that the bacterial capacity for lipopolysaccharide biosynthesis was significantly increased in ducks that had free access to water, leading to the accumulation of pathogenic bacteria and antibiotic-resistance genes. Taken together, our findings expand the understanding of the microbiome signatures linked to intestinal regional, temporal development, and rearing conditions in ducks, which highlight the significant impact of microbiota on poultry health and production.

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

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

Ruminal microbial metagenomes and host transcriptomes shed light on individual variability in the growth rate of lambs before weaning: the regulated mechanism and potential long-term effect on the host.

Weaning weight is a reflection of management during the breastfeeding phase and will influence animal performance in subsequent phases, considered important indicators within production systems. The aims of this study were as follows: (i) to investigate variability in the growth rate among individual lambs from ewes rearing single or twin lambs fed with two different diets and (ii) to explore the molecular mechanisms regulating the growth rate and the potential long-term effects on the host. No significant change in lamb average daily gain (ADG) was observed in litter size and diet treatment, and there were large variations among individual lambs (ranging from 0.13 to 0.41 kg/day). Further analysis was conducted on serum amino acids, rumen fermentation characteristics, rumen metagenomics and transcriptome, and hepatic transcriptome of lambs with extremely high (HA; n = 6) and low (LA; n = 6) ADG. We observed significant increases in serum lysine, leucine, alanine, and phenylalanine in the HA group. The metagenome revealed that the HA group presented a higher rumen propionate molar proportion via increasing gene abundance in the succinate pathway for propionate synthesis. For the rumen transcriptome, higher expressed gene sets in the HA group were mainly related to rumen epithelial growth, including cytokine-cytokine receptor interaction, Jak-STAT signaling pathway, and adherens junction. For the liver transcriptome, the upregulated KEGG pathways in the HA group were primarily associated with fatty acid degradation, glyoxylate and dicarboxylate metabolism, cholesterol metabolism, and the immune system. This research suggests that preweaning lambs with high ADG may benefit from rumen development and enhanced liver metabolic and immune function. IMPORTANCE: There is accumulating evidence indicating that the early-life rumen microbiome plays vital roles in rumen development and microbial fermentation, which subsequently affects the growth of young ruminants. The liver is also vital to regulate the metabolism and distribution of nutrients. Our results demonstrate that lambs with high average daily gain (ADG) enhanced microbial volatile fatty acid (VFA) metabolism toward rumen propionate and serum amino acid (AA) production to support host growth. The study highlights that high ADG in the preweaning period is beneficial for the rumen development and liver energy metabolism, leading to better growth later in life. Overall, this study explores the molecular mechanisms regulating the growth rate and the potential long-term effects of increased growth rate on the host metabolism, providing fundamental knowledge about nutrient manipulation in pre-weaning.

Metagenome-assembled microbial genomes from Parkinson's disease fecal samples.

The human gut microbiome composition has been linked to Parkinson's disease (PD). However, knowledge of the gut microbiota on the genome level is still limited. Here we performed deep metagenomic sequencing and binning to build metagenome-assembled genomes (MAGs) from 136 human fecal microbiomes (68 PD samples and 68 control samples). We constructed 952 non-redundant high-quality MAGs and compared them between PD and control groups. Among these MAGs, there were 22 different genomes of Collinsella and Prevotella, indicating high variability of those genera in the human gut environment. Microdiversity analysis indicated that Ruminococcus bromii was statistically significantly (p < 0.002) more diverse on the strain level in the control samples compared to the PD samples. In addition, by clustering all genes and performing presence-absence analysis between groups, we identified several control-specific (p < 0.05) related genes, such as speF and Fe-S oxidoreductase. We also report detailed annotation of MAGs, including Clusters of Orthologous Genes (COG), Cas operon type, antiviral gene, prophage, and secondary metabolites biosynthetic gene clusters, which can be useful for providing a reference for future studies.