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BACKGROUND: Despite being among the most abundant biological entities on earth, bacteriophage (phage) remain an understudied component of host-associated systems. One limitation to studying host-associated phage is the lack of consensus on methods for sampling phage communities. Here, we compare paired total metagenomes and viral size fraction metagenomes (viromes) as methods for investigating the dsDNA viral communities associated with the GI tract of two bee species: the European honey bee Apis mellifera and the eastern bumble bee Bombus impatiens. RESULTS: We find that viromes successfully enriched for phage, thereby increasing phage recovery, but only in honey bees. In contrast, for bumble bees, total metagenomes recovered greater phage diversity. Across both bee species, viromes better sampled low occupancy phage, while total metagenomes were biased towards sampling temperate phage. Additionally, many of the phage captured by total metagenomes were absent altogether from viromes. Comparing between bees, we show that phage communities in commercially reared bumble bees are significantly reduced in diversity compared to honey bees, likely reflecting differences in bacterial titer and diversity. In a broader context, these results highlight the complementary nature of total metagenomes and targeted viromes, especially when applied to host-associated environments. CONCLUSIONS: Overall, we suggest that studies interested in assessing total communities of host-associated phage should consider using both approaches. However, given the constraints of virome sampling, total metagenomes may serve to sample phage communities with the understanding that they will preferentially sample dominant and temperate phage. Video Abstract.

UNLABELLED: Gastrointestinal diseases are the most frequently reported clinical problems in captive common marmosets (Callithrix jacchus), often affecting the health and welfare of the animal and ultimately their use as a research subject. The microbiome has been shown to be intimately connected to diet and gastrointestinal health. Here, we use shotgun metagenomics and untargeted metabolomics in fecal samples of common marmosets collected before, during, and after a dietary transition from a biscuit to a gel diet. The overall health of marmosets, measured as weight recovery and reproductive outcome, improved after the diet transition. Moreover, each marmoset pair had significant shifts in the microbiome and metabolome after the diet transition. In general, we saw a decrease in Escherichia coli and Prevotella species and an increase in Bifidobacterium species. Untargeted metabolic profiles indicated that polyamine levels, specifically cadaverine and putrescine, were high after diet transition, suggesting either an increase in excretion or a decrease in intestinal reabsorption at the intestinal level. In conclusion, our data suggest that Bifidobacterium species could potentially be useful as probiotic supplements to the laboratory marmoset diet. Future studies with a larger sample size will be beneficial to show that this is consistent with the diet change. IMPORTANCE: Appropriate diet and health of the common marmoset in captivity are essential both for the welfare of the animal and to improve experimental outcomes. Our study shows that a gel diet compared to a biscuit diet improves the health of a marmoset colony, is linked to increases in Bifidobacterium species, and increases the removal of molecules associated with disease. The diet transition had an influence on the molecular changes at both the pair and time point group levels, but only at the pair level for the microbial changes. It appears to be more important which genes and functions present changed rather than specific microbes. Further studies are needed to identify specific components that should be considered when choosing an appropriate diet and additional supplementary foods, as well as to validate the benefits of providing probiotics. Probiotics containing Bifidobacterium species appear to be useful as probiotic supplements to the laboratory marmoset diet, but additional work is needed to validate these findings.

The gut microbiota (GM) can regulate bone mass, but its association with incident fractures is unknown. We used Cox regression models to determine whether the GM composition is associated with incident fractures in the large FINRISK 2002 cohort (n = 7043, 1092 incident fracture cases, median follow-up time 18 years) with information on GM composition and functionality from shotgun metagenome sequencing. Higher alpha diversity was associated with decreased fracture risk (hazard ratio [HR] 0.92 per standard deviation increase in Shannon index, 95% confidence interval 0.87-0.96). For beta diversity, the first principal component was associated with fracture risk (Aitchison distance, HR 0.90, 0.85-0.96). In predefined phyla analyses, we observed that the relative abundance of Proteobacteria was associated with increased fracture risk (HR 1.14, 1.07-1.20), while the relative abundance of Tenericutes was associated with decreased fracture risk (HR 0.90, 0.85-0.96). Explorative sub-analyses within the Proteobacteria phylum showed that higher relative abundance of Gammaproteobacteria was associated with increased fracture risk. Functionality analyses showed that pathways related to amino acid metabolism and lipopolysaccharide biosynthesis associated with fracture risk. The relative abundance of Proteobacteria correlated with pathways for amino acid metabolism, while the relative abundance of Tenericutes correlated with pathways for butyrate synthesis. In conclusion, the overall GM composition was associated with incident fractures. The relative abundance of Proteobacteria, especially Gammaproteobacteria, was associated with increased fracture risk, while the relative abundance of Tenericutes was associated with decreased fracture risk. Functionality analyses demonstrated that pathways known to regulate bone health may underlie these associations.

BACKGROUND: Chronic exposure to microorganisms inside homes can impact respiratory health. Few studies have used advanced sequencing methods to examine adult respiratory outcomes, especially continuous measures. We aimed to identify metagenomic profiles in house dust related to the quantitative traits of pulmonary function and airway inflammation in adults. Microbial communities, 1264 species (389 genera), in vacuumed bedroom dust from 779 homes in a US cohort were characterized by whole metagenome shotgun sequencing. We examined two overall microbial diversity measures: richness (the number of individual microbial species) and Shannon index (reflecting both richness and relative abundance). To identify specific differentially abundant genera, we applied the Lasso estimator with high-dimensional inference methods, a novel framework for analyzing microbiome data in relation to continuous traits after accounting for all taxa examined together. RESULTS: Pulmonary function measures (forced expiratory volume in one second (FEV1), forced vital capacity (FVC), and FEV1/FVC ratio) were not associated with overall dust microbial diversity. However, many individual microbial genera were differentially abundant (p-value < 0.05 controlling for all other microbial taxa examined) in relation to FEV1, FVC, or FEV1/FVC. Similarly, fractional exhaled nitric oxide (FeNO), a marker of airway inflammation, was unrelated to overall microbial diversity but associated with differential abundance for many individual genera. Several genera, including Limosilactobacillus, were associated with a pulmonary function measure and FeNO, while others, including Moraxella to FEV1/FVC and Stenotrophomonas to FeNO, were associated with a single trait. CONCLUSIONS: Using state-of-the-art metagenomic sequencing, we identified specific microorganisms in indoor dust related to pulmonary function and airway inflammation. Some were previously associated with respiratory conditions; others were novel, suggesting specific environmental microbial components contribute to various respiratory outcomes. The methods used are applicable to studying microbiome in relation to other continuous outcomes. Video Abstract.

Historically neglected by microbial ecologists, soil viruses are now thought to be critical to global biogeochemical cycles. However, our understanding of their global distribution, activities and interactions with the soil microbiome remains limited. Here we present the Global Soil Virus Atlas, a comprehensive dataset compiled from 2,953 previously sequenced soil metagenomes and composed of 616,935 uncultivated viral genomes and 38,508 unique viral operational taxonomic units. Rarefaction curves from the Global Soil Virus Atlas indicate that most soil viral diversity remains unexplored, further underscored by high spatial turnover and low rates of shared viral operational taxonomic units across samples. By examining genes associated with biogeochemical functions, we also demonstrate the viral potential to impact soil carbon and nutrient cycling. This study represents an extensive characterization of soil viral diversity and provides a foundation for developing testable hypotheses regarding the role of the virosphere in the soil microbiome and global biogeochemistry.

IntroductionEmerging evidence links changes in the gut microbiome to late-onset Alzheimer's disease (LOAD), necessitating examination of AD mouse models with consideration of the microbiome.MethodsWe used shotgun metagenomics and untargeted metabolomics to study the human amyloid beta knock-in (hAβ-KI) murine model for LOAD compared to both wild-type (WT) mice and a model for early-onset AD (3xTg-AD).ResultsEighteen-month female (but not male) hAβ-KI microbiomes were distinct from WT microbiomes, with AD genotype accounting for 18% of the variance by permutational multivariate analysis of variance (PERMANOVA). Metabolomic diversity differences were observed in females, however no individual metabolites were differentially abundant. hAβ-KI mice microbiomes were distinguishable from 3xTg-AD animals (81% accuracy by random forest modeling), with separation primarily driven by Romboutsia ilealis and Turicibacter species. Microbiomes were highly cage specific, with cage assignment accounting for more than 40% of the PERMANOVA variance between the groups.DiscussionThese findings highlight a sex-dependent variation in the microbiomes of hAβ-KI mice and underscore the importance of considering the microbiome when designing studies that use murine models for AD.HighlightsMicrobial diversity and the abundance of several species differed in human amyloid beta knock-in (hAβ-KI) females but not males. Correlations to Alzheimer's disease (AD) genotype were stronger for the microbiome than the metabolome. Microbiomes from hAβ-KI mice were distinct from 3xTg-AD mice. Cage effects accounted for most of the variance in the microbiome and metabolome.

Bile acids (BA) are among the most abundant metabolites produced by the gut microbiome. Primary BAs produced in the liver are converted by gut bacterial 7-α-dehydroxylation into secondary BAs, which can differentially regulate host health via signaling based on their varying affinity for BA receptors. Despite the importance of secondary BAs in host health, the regulation of 7-α-dehydroxylation and the role of diet in modulating this process is incompletely defined. Understanding this process could lead to dietary guidelines that beneficially shift BA metabolism. Dietary fiber regulates gut microbial composition and metabolite production. We tested the hypothesis that feeding mice a diet rich in a fermentable dietary fiber, resistant starch (RS), would alter gut bacterial BA metabolism. Male and female wild-type mice were fed a diet supplemented with RS or an isocaloric control diet (IC). Metabolic parameters were similar between groups. RS supplementation increased gut luminal deoxycholic acid (DCA) abundance. However, gut luminal cholic acid (CA) abundance, the substrate for 7-α-dehydroxylation in DCA production, was unaltered by RS. Further, RS supplementation did not change the mRNA expression of hepatic BA producing enzymes or ileal BA transporters. Metagenomic assessment of gut bacterial composition revealed no change in the relative abundance of bacteria known to perform 7-α-dehydroxylation. P. ginsenosidimutans and P. multiformis were positively correlated with gut luminal DCA abundance and increased in response to RS supplementation. These data demonstrate that RS supplementation enriches gut luminal DCA abundance without increasing the relative abundance of bacteria known to perform 7-α-dehydroxylation.

The fields of Metagenomics and Metatranscriptomics involve the examination of complete nucleotide sequences, gene identification, and analysis of potential biological functions within diverse organisms or environmental samples. Despite the vast opportunities for discovery in metagenomics, the sheer volume and complexity of sequence data often present challenges in processing analysis and visualization. This article highlights the critical role of advanced visualization tools in enabling effective exploration, querying, and analysis of these complex datasets. Emphasizing the importance of accessibility, the article categorizes various visualizers based on their intended applications and highlights their utility in empowering bioinformaticians and non-bioinformaticians to interpret and derive insights from meta-omics data effectively.

Intraspecific genomic diversity brings the potential for an unreported and diverse reservoir of cryptic antibiotic resistance genes in pathogens, as cryptic resistance can occur without major mutations and horizontal transmission. Here, we predicted the differences in the types of antibiotics and genes that induce cryptic and latent resistance between micro-diverse Escherichia coli strains. For example, we hypothesize that known resistance genes will be the culprit of latent resistance within clinical strains. We used a modified functional metagenomics method to induce expression in eight E. coli strains. We found a total of 66 individual genes conferring phenotypic resistance to 11 out of 16 antibiotics. A total of 14 known antibiotic resistance genes comprised 21% of total identified genes, whereas the majority (52 genes) were unclassified cryptic resistance genes. Between the eight strains, 1.2% of core orthologous genes were positive (conferred resistance in at least one strain). Sixty-four percent of positive orthologous genes conferred resistance to only one strain, demonstrating high intraspecific variability of latent resistance genes. Cryptic resistance genes comprised most resistance genes among laboratory and clinical strains as well as natural, semisynthetic, and synthetic antibiotics. Known antibiotic resistance genes primarily conferred resistance to multiple antibiotics from varying origins and within multiple strains. Hence, it is uncommon for E. coli to develop cross-cryptic resistance to antibiotics from multiple origins or within multiple strains. We have uncovered prospective and previously unknown resistance genes as well as antibiotics that have the potential to trigger latent antibiotic resistance in E. coli strains from varying origins.IMPORTANCEIntraspecific genomic diversity may be a driving force in the emergence of adaptive antibiotic resistance. Adaptive antibiotic resistance enables sensitive bacterial cells to acquire temporary antibiotic resistance, creating an optimal window for the development of permanent mutational resistance. In this study, we investigate cryptic resistance, an adaptive resistance mechanism, and unveil novel (cryptic) antibiotic resistance genes that confer resistance when amplified within eight E. coli strains derived from clinical and laboratory origins. We identify the potential of cryptic resistance genes to confer cross-resistance to antibiotics from varying origins and within multiple strains. We discern antibiotic characteristics that promote latent resistance in multiple strains, considering intraspecific diversity. This study may help detect novel resistance genes and functional genes that could become responsible for cryptic resistance among diverse strains and antibiotics, thus also identifying potential novel antibiotic targets and mechanisms.

Ticks are increasingly important vectors of human and agricultural diseases. While many studies have focused on tick-borne bacteria, far less is known about tick-associated viruses and their roles in public health or tick physiology. To address this, we investigated patterns of bacterial and viral communities across two field populations of western black-legged ticks (Ixodes pacificus). Through metatranscriptomic analysis of 100 individual ticks, we quantified taxon prevalence, abundance, and co-occurrence with other members of the tick microbiome. In addition to commonly found tick-associated microbes, we assembled 11 novel RNA virus genomes from Rhabdoviridae, Chuviridae, Picornaviridae, Phenuiviridae, Reoviridae, Solemovidiae, Narnaviridae and two highly divergent RNA virus genomes lacking sequence similarity to any known viral families. We experimentally verified the presence of these in I. pacificus ticks across several life stages. We also unexpectedly identified numerous virus-like transcripts that are likely encoded by tick genomic DNA, and which are distinct from known endogenous viral element-mediated immunity pathways in invertebrates. Taken together, our work reveals that I. pacificus ticks carry a greater diversity of viruses than previously appreciated, in some cases resulting in evolutionarily acquired virus-like transcripts. Our findings highlight how pervasive and intimate tick-virus interactions are, with major implications for both the fundamental biology and vectorial capacity of I. pacificus ticks.ImportanceTicks are increasingly important vectors of disease, particularly in the United States where expanding tick ranges and intrusion into previously wild areas has resulted in increasing human exposure to ticks. Emerging human pathogens have been identified in ticks at an increasing rate, and yet little is known about the full community of microbes circulating in various tick species, a crucial first step to understanding how they interact with each and their tick host, as well as their ability to cause disease in humans. We investigated the bacterial and viral communities of the Western blacklegged tick in California and found 11 previously uncharacterized viruses circulating in this population.

Studies using 16S rRNA and shotgun metagenomics typically yield different results, usually attributed to PCR amplification biases. We introduce Greengenes2, a reference tree that unifies genomic and 16S rRNA databases in a consistent, integrated resource. By inserting sequences into a whole-genome phylogeny, we show that 16S rRNA and shotgun metagenomic data generated from the same samples agree in principal coordinates space, taxonomy and phenotype effect size when analyzed with the same tree.

Microbes are essential for the functioning of all ecosystems, and as global warming and anthropogenic pollution threaten ecosystems, it is critical to understand how microbes respond to these changes. We investigated the climate response of Sphingomonas, a widespread gram-negative bacterial genus, during an 18-month microbial community reciprocal transplant experiment across a Southern California climate gradient. We hypothesized that after 18 months, the transplanted Sphingomonas clade and functional composition would correspond with site conditions and reflect the Sphingomonas composition of native communities. We extracted Sphingomonas sequences from metagenomic data across the gradient and assessed their clade and functional composition. Representatives of at least 12 major Sphingomonas clades were found at varying relative abundances along the climate gradient, and transplanted Sphingomonas clade composition shifted after 18 months. Site had a significant effect (PERMANOVA; P < 0.001) on the distribution of both Sphingomonas functional (R2 = 0.465) and clade composition (R2 = 0.400), suggesting that Sphingomonas composition depends on climate parameters. Additionally, for both Sphingomonas clade and functional composition, ordinations revealed that the transplanted communities shifted closer to the native Sphingomonas composition of the grassland site compared with the site they were transplanted into. Overall, our results indicate that climate and substrate collectively determine Sphingomonas clade and functional composition.IMPORTANCESphingomonas is the most abundant gram-negative bacterial genus in litter-degrading microbial communities of desert, grassland, shrubland, and forest ecosystems in Southern California. We aimed to determine whether Sphingomonas responds to climate change in the same way as gram-positive bacteria and whole bacterial communities in these ecosystems. Within Sphingomonas, both clade composition and functional genes shifted in response to climate and litter chemistry, supporting the idea that bacteria respond similarly to climate at different scales of genetic variation. This understanding of how microbes respond to perturbation across scales may aid in future predictions of microbial responses to climate change.

The infant gut microbiome is increasingly recognized as a reservoir of antibiotic resistance genes, yet the assembly of gut resistome in infants and its influencing factors remain largely unknown. We characterized resistome in 4132 metagenomes from 963 infants in six countries and 4285 resistance genes were observed. The inherent resistome pattern of healthy infants (N = 272) could be distinguished by two stages: a multicompound resistance phase (Months 0-7) and a tetracycline-mupirocin-β-lactam-dominant phase (Months 8-14). Microbial taxonomy explained 40.7% of the gut resistome of healthy infants, with Escherichia (25.5%) harboring the most resistance genes. In a further analysis with all available infants (N = 963), we found age was the strongest influencer on the resistome and was negatively correlated with the overall resistance during the first 3 years (p

MOTIVATION: Taxonomic classification of short reads and taxonomic profiling of metagenomic samples are well-studied yet challenging problems. The presence of species belonging to groups without close representation in a reference dataset is particularly challenging. While k-mer-based methods have performed well in terms of running time and accuracy, they tend to have reduced accuracy for such novel species. Thus, there is a growing need for methods that combine the scalability of k-mers with increased sensitivity. RESULTS: Here, we show that using locality-sensitive hashing (LSH) can increase the sensitivity of the k-mer-based search. Our method, which combines LSH with several heuristics techniques including soft lowest common ancestor labeling and voting, is more accurate than alternatives in both taxonomic classification of individual reads and abundance profiling. AVAILABILITY AND IMPLEMENTATION: CONSULT-II is implemented in C++, and the software, together with reference libraries, is publicly available on GitHub https://github.com/bo1929/CONSULT-II.

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

Survival and growth strategies of Antarctic endolithic microbes residing in Earth's driest and coldest desert remain virtually unknown. From 109 endolithic microbiomes, 4539 metagenome-assembled genomes were generated, 49.3 % of which were novel candidate bacterial species. We present evidence that trace gas oxidation and atmospheric chemosynthesis may be the prevalent strategies supporting metabolic activity and persistence of these ecosystems at the fringe of life and the limits of habitability.

BACKGROUND: Prior to soil formation, phosphate liberated by rock weathering is often sequestered into highly insoluble lanthanide phosphate minerals. Dissolution of these minerals releases phosphate and lanthanides to the biosphere. Currently, the microorganisms involved in phosphate mineral dissolution and the role of lanthanides in microbial metabolism are poorly understood. RESULTS: Although there have been many studies of soil microbiology, very little research has investigated microbiomes of weathered rock. Here, we sampled weathered granite and associated soil to identify the zones of lanthanide phosphate mineral solubilisation and genomically define the organisms implicated in lanthanide utilisation. We reconstructed 136 genomes from 11 bacterial phyla and found that gene clusters implicated in lanthanide-based metabolism of methanol (primarily xoxF3 and xoxF5) are surprisingly common in microbial communities in moderately weathered granite. Notably, xoxF3 systems were found in Verrucomicrobia for the first time, and in Acidobacteria, Gemmatimonadetes and Alphaproteobacteria. The xoxF-containing gene clusters are shared by diverse Acidobacteria and Gemmatimonadetes, and include conserved hypothetical proteins and transporters not associated with the few well studied xoxF systems. Given that siderophore-like molecules that strongly bind lanthanides may be required to solubilise lanthanide phosphates, it is notable that candidate metallophore biosynthesis systems were most prevalent in bacteria in moderately weathered rock, especially in Acidobacteria with lanthanide-based systems. CONCLUSIONS: Phosphate mineral dissolution, putative metallophore production and lanthanide utilisation by enzymes involved in methanol oxidation linked to carbonic acid production co-occur in the zone of moderate granite weathering. In combination, these microbial processes likely accelerate the conversion of granitic rock to soil.

We present six whole community shotgun metagenomic sequencing data sets of two types of biological soil crusts sampled at the ecotone of the Mojave Desert and Colorado Desert in California. These data will help us understand the diversity and function of biocrust microbial communities, which are essential for desert ecosystems.

Contamination of genomes is an increasingly recognized problem affecting several downstream applications, from comparative evolutionary genomics to metagenomics. Here we introduce ContScout, a precise tool for eliminating foreign sequences from annotated genomes. It achieves high specificity and sensitivity on synthetic benchmark data even when the contaminant is a closely related species, outperforms competing tools, and can distinguish horizontal gene transfer from contamination. A screen of 844 eukaryotic genomes for contamination identified bacteria as the most common source, followed by fungi and plants. Furthermore, we show that contaminants in ancestral genome reconstructions lead to erroneous early origins of genes and inflate gene loss rates, leading to a false notion of complex ancestral genomes. Taken together, we offer here a tool for sensitive removal of foreign proteins, identify and remove contaminants from diverse eukaryotic genomes and evaluate their impact on phylogenomic analyses.

Multi-omics approaches have been successfully applied to investigate pregnancy and health outcomes at a molecular and genetic level in several studies. As omics technologies advance, research areas are open to study further. Here we discuss overall trends and examples of successfully using omics technologies and techniques (e.g., genomics, proteomics, metabolomics, and metagenomics) to investigate the molecular epidemiology of pregnancy. In addition, we outline omics applications and study characteristics of pregnancy for understanding fundamental biology, causal health, and physiological relationships, risk and prediction modeling, diagnostics, and correlations.

Microbial communities have evolved to colonize all ecosystems of the planet, from the deep sea to the human gut. Microbes survive by sensing, responding, and adapting to immediate environmental cues. This process is driven by signal transduction proteins such as histidine kinases, which use their sensing domains to bind or otherwise detect environmental cues and transduce signals to adjust internal processes. We hypothesized that an ecosystems unique stimuli leave a sensor fingerprint, able to identify and shed insight on ecosystem conditions. To test this, we collected 20,712 publicly available metagenomes from Host-associated, Environmental, and Engineered ecosystems across the globe. We extracted and clustered the collections nearly 18M unique sensory domains into 113,712 similar groupings with MMseqs2. We built gradient-boosted decision tree machine learning models and found we could classify the ecosystem type (accuracy: 87%) and predict the levels of different physical parameters (R2 score: 83%) using the sensor cluster abundance as features. Feature importance enables identification of the most predictive sensors to differentiate between ecosystems which can lead to mechanistic interpretations if the sensor domains are well annotated. To demonstrate this, a machine learning model was trained to predict patients disease state and used to identify domains related to oxygen sensing present in a healthy gut but missing in patients with abnormal conditions. Moreover, since 98.7% of identified sensor domains are uncharacterized, importance ranking can be used to prioritize sensors to determine what ecosystem function they may be sensing. Furthermore, these new predictive sensors can function as targets for novel sensor engineering with applications in biotechnology, ecosystem maintenance, and medicine.IMPORTANCEMicrobes infect, colonize, and proliferate due to their ability to sense and respond quickly to their surroundings. In this research, we extract the sensory proteins from a diverse range of environmental, engineered, and host-associated metagenomes. We trained machine learning classifiers using sensors as features such that it is possible to predict the ecosystem for a metagenome from its sensor profile. We use the optimized models feature importance to identify the most impactful and predictive sensors in different environments. We next use the sensor profile from human gut metagenomes to classify their disease states and explore which sensors can explain differences between diseases. The sensors most predictive of environmental labels here, most of which correspond to uncharacterized proteins, are a useful starting point for the discovery of important environment signals and the development of possible diagnostic interventions.

Western Eurasia witnessed several large-scale human migrations during the Holocene1-5. Here, to investigate the cross-continental effects of these migrations, we shotgun-sequenced 317 genomes-mainly from the Mesolithic and Neolithic periods-from across northern and western Eurasia. These were imputed alongside published data to obtain diploid genotypes from more than 1,600 ancient humans. Our analyses revealed a 'great divide' genomic boundary extending from the Black Sea to the Baltic. Mesolithic hunter-gatherers were highly genetically differentiated east and west of this zone, and the effect of the neolithization was equally disparate. Large-scale ancestry shifts occurred in the west as farming was introduced, including near-total replacement of hunter-gatherers in many areas, whereas no substantial ancestry shifts happened east of the zone during the same period. Similarly, relatedness decreased in the west from the Neolithic transition onwards, whereas, east of the Urals, relatedness remained high until around 4,000 BP, consistent with the persistence of localized groups of hunter-gatherers. The boundary dissolved when Yamnaya-related ancestry spread across western Eurasia around 5,000 BP, resulting in a second major turnover that reached most parts of Europe within a 1,000-year span. The genetic origin and fate of the Yamnaya have remained elusive, but we show that hunter-gatherers from the Middle Don region contributed ancestry to them. Yamnaya groups later admixed with individuals associated with the Globular Amphora culture before expanding into Europe. Similar turnovers occurred in western Siberia, where we report new genomic data from a 'Neolithic steppe' cline spanning the Siberian forest steppe to Lake Baikal. These prehistoric migrations had profound and lasting effects on the genetic diversity of Eurasian populations.

Functional traits influence the assembly of microbial communities, but identifying these traits in the environment has remained challenging. We studied ectomycorrhizal fungal (EMF) communities inhabiting Populus trichocarpa roots distributed across a precipitation gradient in the Pacific Northwest, USA. We profiled these communities using taxonomic (meta-barcoding) and functional (metagenomic) approaches. We hypothesized that genes involved in fungal drought-stress tolerance and fungal mediated plant water uptake would be most abundant in drier soils. We were unable to detect support for this hypothesis; instead, the abundance of genes involved in melanin synthesis, hydrophobins, aquaporins, trehalose-synthases, and other gene families exhibited no significant shifts across the gradient. Finally, we studied variation in sequence homology for certain genes, finding that fungal communities in dry soils are composed of distinct aquaporin and hydrophobin gene sequences. Altogether, our results suggest that while EMF communities exhibit significant compositional shifts across this gradient, coupled functional turnover, at least as inferred using community metagenomics is limited. Accordingly, the consequences of these distinct EMF communities on plant water uptake remain critically unknown, and future studies targeting the expression of genes involved in drought stress tolerance are required.

Although long-read sequencing has enabled obtaining high-quality and complete genomes from metagenomes, many challenges still remain to completely decompose a metagenome into its constituent prokaryotic and viral genomes. This study focuses on decomposing an estuarine metagenome to obtain a more accurate estimate of microbial diversity. To achieve this, we developed a new bead-based DNA extraction method, a novel bin refinement method, and obtained 150 Gbp of Nanopore sequencing. We estimate that there are ~500 bacterial and archaeal species in our sample and obtained 68 high-quality bins (>90% complete, 100 kbp, and all ribosomal and tRNA genes). We also obtained many contigs of picoeukaryotes, environmental DNA of larger eukaryotes such as mammals, and complete mitochondrial and chloroplast genomes and detected ~40,000 viral populations. Our analysis indicates that there are only a few strains that comprise most of the species abundances. Ocean and estuarine microbiomes play critical roles in global element cycling and ecosystem function. Despite the importance of these microbial communities, many species still have not been cultured in the lab. Environmental sequencing is the primary way the function and population dynamics of these communities can be studied. Long-read sequencing provides an avenue to overcome limitations of short-read technologies to obtain complete microbial genomes but comes with its own technical challenges, such as needed sequencing depth and obtaining high-quality DNA. We present here new sampling and bioinformatics methods to attempt decomposing an estuarine microbiome into its constituent genomes. Our results suggest there are only a few strains that comprise most of the species abundances from viruses to picoeukaryotes, and to fully decompose a metagenome of this diversity requires 1 Tbp of long-read sequencing. We anticipate that as long-read sequencing technologies continue to improve, less sequencing will be needed.

Comparative analysis of (meta)genomes necessitates aggregation, integration, and synthesis of well-annotated data using standards. The Genomic Standards Consortium (GSC) collaborates with the research community to develop and maintain the Minimum Information about any (x) Sequence (MIxS) reporting standard for genomic data. To facilitate the use of the GSC's MIxS reporting standard, we provide a description of the structure and terminology, how to navigate ontologies for required terms in MIxS, and demonstrate practical usage through a soil metagenome example.

Culture-based microbial natural product discovery strategies fail to realize the extraordinary biosynthetic potential detected across earth's microbiomes. Here we introduce Small Molecule In situ Resin Capture (SMIRC), a culture-independent method to obtain natural products directly from the environments in which they are produced. We use SMIRC to capture numerous compounds including two new carbon skeletons that were characterized using NMR and contain structural features that are, to the best of our knowledge, unprecedented among natural products. Applications across diverse marine habitats reveal biome-specific metabolomic signatures and levels of chemical diversity in concordance with sequence-based predictions. Expanded deployments, in situ cultivation, and metagenomics facilitate compound discovery, enhance yields, and link compounds to candidate producing organisms, although microbial community complexity creates challenges for the later. This compound-first approach to natural product discovery provides access to poorly explored chemical space and has implications for drug discovery and the detection of chemically mediated biotic interactions.

Giant viruses (GVs) are key players in ecosystem functioning, biogeochemistry, and eukaryotic genome evolution. GV diversity and abundance in aquatic systems can exceed that of prokaryotes, but their diversity and ecology in lakes, especially polar ones, remain poorly understood. We conducted a comprehensive survey and meta-analysis of GV diversity across 20 lakes, spanning polar to temperate regions, combining our extensive lake metagenome database from the Canadian Arctic and subarctic with publicly available datasets. Leveraging a novel GV genome identification tool, we identified 3304 GV metagenome-assembled genomes, revealing lakes as untapped GV reservoirs. Phylogenomic analysis highlighted their dispersion across all Nucleocytoviricota orders. Strong GV population endemism emerged between lakes from similar regions and biomes (Antarctic and Arctic), but a polar/temperate barrier in lacustrine GV populations and differences in their gene content could be observed. Our study establishes a robust genomic reference for future investigations into lacustrine GV ecology in fast changing polar environments.

Python for Population Genomics (PyPop) is a software package that processes genotype and allele data and performs large-scale population genetic analyses on highly polymorphic multi-locus genotype data. In particular, PyPop tests data conformity to Hardy-Weinberg equilibrium expectations, performs Ewens-Watterson tests for selection, estimates haplotype frequencies, measures linkage disequilibrium, and tests significance. Standardized means of performing these tests is key for contemporary studies of evolutionary biology and population genetics, and these tests are central to genetic studies of disease association as well. Here, we present PyPop 1.0.0, a new major release of the package, which implements new features using the more robust infrastructure of GitHub, and is distributed via the industry-standard Python Package Index. New features include implementation of the asymmetric linkage disequilibrium measures and, of particular interest to the immunogenetics research communities, support for modern nomenclature, including colon-delimited allele names, and improvements to meta-analysis features for aggregating outputs for multiple populations. Code available at: https://zenodo.org/records/10080668 and https://github.com/alexlancaster/pypop.

Cyanobacteria form diverse communities and are important primary producers in Antarctic freshwater environments, but their geographic distribution patterns in Antarctica and globally are still unresolved. There are however few genomes of cultured cyanobacteria from Antarctica available and therefore metagenome-assembled genomes (MAGs) from Antarctic cyanobacteria microbial mats provide an opportunity to explore distribution of uncultured taxa. These MAGs also allow comparison with metagenomes of cyanobacteria enriched communities from a range of habitats, geographic locations, and climates. However, most MAGs do not contain 16S rRNA gene sequences, making a 16S rRNA gene-based biogeography comparison difficult. An alternative technique is to use large-scale k-mer searching to find genomes of interest in public metagenomes. This paper presents the results of k-mer based searches for 5 Antarctic cyanobacteria MAGs from Lake Fryxell and Lake Vanda, assigned the names Phormidium pseudopriestleyi FRX01, Microcoleus sp. MP8IB2.171, Leptolyngbya sp. BulkMat.35, Pseudanabaenaceae cyanobacterium MP8IB2.15, and Leptolyngbyaceae cyanobacterium MP9P1.79 in 498,942 unassembled metagenomes from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). The Microcoleus sp. MP8IB2.171 MAG was found in a wide variety of environments, the P. pseudopriestleyi MAG was found in environments with challenging conditions, the Leptolyngbyaceae cyanobacterium MP9P1.79 MAG was only found in Antarctica, and the Leptolyngbya sp. BulkMat.35 and Pseudanabaenaceae cyanobacterium MP8IB2.15 MAGs were found in Antarctic and other cold environments. The findings based on metagenome matches and global comparisons suggest that these Antarctic cyanobacteria have distinct distribution patterns ranging from locally restricted to global distribution across the cold biosphere and other climatic zones.

Summary Microbiota have co‐evolved with plants over millions of years and are intimately linked to plants, ranging from symbiosis to pathogenesis. However, our understanding of the existence of a shared core microbiota across phylogenetically diverse plants remains limited. A common garden field experiment was conducted to investigate the rhizosphere microbial communities of phylogenetically contrasting herbaceous families. Through a combination of metagenomic sequencing, analysis of plant economic traits, and soil biochemical properties, we aimed to elucidate the eco‐evolutionary role of the core rhizosphere microbiota in light of plant economic strategies. We identified a conserved core microbiota consisting of 278 taxa that was closely associated with the phylogeny of the plants studied. This core microbiota actively participated in multiple nitrogen metabolic processes and showed a strong correlation with the functional potential of rhizosphere nitrogen cycling, thereby serving as an extended trait in the plant nitrogen acquisition. Furthermore, our examination of simulated species loss revealed the crucial role of the core microbiota in maintaining the rhizosphere community's network stability. Our study highlighted that the core microbiota, which exhibited a phylogenetically conserved association with plants, potentially represented an extension of the plant phenotype and played an important role in nitrogen acquisition. These findings held implications for the utilization of microbiota‐mediated plant functions. [ABSTRACT FROM AUTHOR]

Background: Clean energy hydrogen (H2) produced from abundant lignocellulose is an alternative to fossil energy. As an essential influencing factor, there is a lack of comparison between constant temperatures (35, 55 and 65 °C) and gradient heating temperature (35 to 65 °C) on the H2 production regulation potential from lignocellulose-rich straw via high-solid anaerobic digestion (HS-AD). More importantly, the microbial mechanism of temperature regulating H2 accumulation needs to be investigated. Results: Constant 65 °C led to the lowest lignin residue (1.93%) and the maximum release of cellulose and hemicellulose, and the highest H2 production (26.01 mL/g VS). H2 production at 35 and 55 °C was only 14.56 and 24.13 mL/g VS, respectively. In order to further explore the potential of ultra-high temperature (65 °C), HS-AD was performed by gradient heating conditions (35 to 65 °C). However, compared to constant 65 °C, gradient heating conditions led to higher lignin residue (2.49%) and lower H2 production (13.53 mL/g VS) than gradient heating conditions (47.98%). In addition, metagenomic analysis showed the cellulose/hemicellulose hydrolyzing bacteria and genes (mainly Thermoclostridium, and xynA, xynB, abfA, bglB and xynD), H2-producing bacteria and related genes (mainly Thermoclostridium, and nifD, nifH and nifK), and microbial movement and metabolic functions were enriched at 65 °C. However, the enrichment of two-component systems under gradient heating conditions resulted in a lack of highly-enriched ultra-high-temperature cellulose/hemicellulose hydrolyzing genera and related genes but rather enriched H2 consumption genera and genes (mainly Acetivibrio, and hyaB and hyaA) resulting in a weaker H2 production. Conclusions: The lignin degradation process does not directly determine H2 accumulation, which was actually regulated by bacteria/genes contributing to H2 production/consumption. In addition, it is temperature that enhances the hydrolysis process of lignin rather than lignin-degrading enzymes, bacteria and genes by promoting microbial material transfer and metabolism. In terms of temperature, one of the key parameters of HS-AD for H2 production, we developed an important regulatory strategy, enriched the theoretical basis of temperature regulation for H2 production to further expanded the research horizon in this field. ABqfqNejHM5MQBNj5XnjcN Video Abstract [ABSTRACT FROM AUTHOR]

Hepatolenticular degeneration (HLD), also known as Wilson's disease (WD), is a rare autosomal recessive disorder regarding copper metabolism. Whether gut microbiota imbalance is involved in developing HLD remains unknown. A comprehensive 16S rRNA amplicon sequencing, metagenomic sequencing, and metabonomic analysis were undertaken in patients with WD to analyze the composition and function profiles of gut microbiota in patients with WD. The data demonstrated differences in gut microbiota and metabolic pathways between WD patients and normal individuals, significantly decreasing bacterial richness and diversity. The levels of Selenomonaceae and Megamonas in WD patients are significantly higher than those in healthy individuals. The relative abundances of Roseburia inulinivorans in patients with WD are lower than in healthy individuals. Compared with healthy people, the level of metabolites in patients with WD is abnormal. Leucylproline, 5-Phenylvaleric Acid and N-Desmethylclobazam, which have nutritional and protective effects, are significantly reduced fecal metabolites in patients with WD. D-Gluconic acid, which can chelate metal ions, may be a potential treatment for WD. The positive correlation it demonstrates with Alistipes indistinctus and Prevotella stercora indicates potential bacteria able to treat WD. These metabolites are mainly related to the biosynthesis of antibiotics, alpha-linolenic acid metabolism, one carbon pool by folate, nicotinate and nicotinamide metabolism. In conclusion, the data from this study elucidate novel mechanisms describing how abnormal gut miccrobiota contribute to the pathogenesis of WD and outlines new molecules for the treatment of WD. [ABSTRACT FROM AUTHOR]

Background: Antibiotic resistance is a critical global concern, posing significant challenges to human health and medical treatments. Studying antibiotic resistance genes (ARGs) is essential not only in clinical settings but also in diverse environmental contexts. However, ARGs in unique environments such as anchialine caves, which connect both fresh and marine water, remain largely unexplored despite their intriguing ecological characteristics. Results: We present the first study that comprehensively explores the occurrence and distribution of ARGs and mobile genetic elements (MGEs) within an anchialine cave. Utilizing metagenomic sequencing we uncovered a wide array of ARGs with the bacitracin resistance gene, bacA and multidrug resistance genes, being the most dominant. The cave's microbial community and associated resistome were significantly influenced by the salinity gradient. The discovery of novel β-lactamase variants revealed the cave's potential as a reservoir for previously undetected resistance genes. ARGs in the cave demonstrated horizontal transfer potential via plasmids, unveiling ecological implications. Conclusions: These findings highlight the need for further exploration of the resistome in unique environments like anchialine caves. The interconnected dynamics of ARGs and MGEs within anchialine caves offer valuable insights into potential reservoirs and mechanisms of antibiotic resistance in natural ecosystems. This study not only advances our fundamental understanding but also highlights the need for a comprehensive approach to address antibiotic resistance in diverse ecological settings. [ABSTRACT FROM AUTHOR]

Host‐associated microbiomes are thought to play a key role in host physiology and fitness, but this conclusion mainly derives from systems biased towards animal models and humans. While many studies on non‐model and wild animals have characterised the taxonomic diversity of their microbiomes, few have investigated the functional potential of these microbial communities. Functional ‘omics’ approaches, such as metagenomics, metatranscriptomics and metabolomics, represent promising techniques to probe the significance of host‐associated microbiomes in the wild. In this review, we propose to (1) briefly define the main available functional omics tools along with their strengths and limitations, (2) summarise the key advances enabled by omics tools to understand microbiome function in human and animal models, (3) showcase examples of how these methods have already brought invaluable insights into wild host microbiomes and (4) provide guidelines on how to implement these tools to address outstanding questions in the field of wild animal microbiomes. To conclude, we suggest that, building on knowledge derived from cheaper, more traditional approaches (e.g. 16S metabarcoding and qPCR), functional omics tools represent a promising approach to test hypotheses regarding the ecological and evolutionary significance of the resident microbiota in wild animals. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Our oceans are populated with a wide diversity of planktonic organisms that form complex dynamic communities at the base of marine trophic networks. Within such communities are phytoplankton, unicellular photosynthetic taxa that provide an estimated half of global primary production and support biogeochemical cycles, along with other essential ecosystem services. One of the major challenges for microbial ecologists has been to try to make sense of this complexity. While phytoplankton distributions can be well explained by abiotic factors such as temperature and nutrient availability, there is increasing evidence that their ecological roles are tightly linked to their metabolic interactions with other plankton members through complex mechanisms (e.g. competition and symbiosis). Therefore, unravelling phytoplankton metabolic interactions is the key for inferring their dependency on, or antagonism with, other taxa and better integrating them into the context of carbon and nutrient fluxes in marine trophic networks. In this review, we attempt to summarize the current knowledge brought by ecophysiology, organismal imaging, in silico predictions and co-occurrence networks using 'omics data, highlighting successful combinations of approaches that may be helpful for future investigations of phytoplankton metabolic interactions within their complex communities. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'. [ABSTRACT FROM AUTHOR]

Marine plankton communities form intricate networks of interacting organisms at the base of the food chain, and play a central role in regulating ocean biogeochemical cycles and climate. However, predicting plankton community shifts in response to climate change remains challenging. While species distribution models are valuable tools for predicting changes in species biogeography under climate change scenarios, they generally overlook the key role of biotic interactions, which can significantly shape ecological processes and ecosystem responses. Here, we introduce a novel statistical framework, association distribution modelling (ADM), designed to model and predict ecological associations distribution in space and time. Applied on a Tara Oceans genome-resolved metagenomics dataset, the present-day biogeography of ADM-inferred marine plankton associations revealed four major biogeographic biomes organized along a latitudinal gradient. We predicted the evolution of these biome-specific communities in response to a climate change scenario, highlighting differential responses to environmental change. Finally, we explored the functional potential of impacted plankton communities, focusing on carbon fixation, outlining the predicted evolution of its geographical distribution and implications for ecosystem function. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'. [ABSTRACT FROM AUTHOR]

The rhizosphere, the soil region influenced by plant roots, represents a dynamic microenvironment where intricate interactions between plants and microorganisms shape soil health, nutrient cycling, and plant growth. Soil microorganisms are integral players in the transformation of materials, the dynamics of energy flows, and the intricate cycles of biogeochemistry. Considerable research has been dedicated to investigating the abundance, diversity, and intricacies of interactions among different microbes, as well as the relationships between plants and microbes present in the rhizosphere. Metagenomics, a powerful suite of techniques, has emerged as a transformative tool for dissecting the genetic repertoire of complex microbial communities inhabiting the rhizosphere. The review systematically navigates through various metagenomic approaches, ranging from shotgun metagenomics, enabling unbiased analysis of entire microbial genomes, to targeted sequencing of the 16S rRNA gene for taxonomic profiling. Each approach's strengths and limitations are critically evaluated, providing researchers with a nuanced understanding of their applicability in different research contexts. A central focus of the review lies in the practical applications of rhizosphere metagenomics in various fields including agriculture. By decoding the genomic content of rhizospheric microbes, researchers gain insights into their functional roles in nutrient acquisition, disease suppression, and overall plant health. The review also addresses the broader implications of metagenomic studies in advancing our understanding of microbial diversity and community dynamics in the rhizosphere. It serves as a comprehensive guide for researchers, agronomists, and policymakers, offering a roadmap for harnessing metagenomic approaches to unlock the full potential of the rhizosphere microbiome in promoting sustainable agriculture. [ABSTRACT FROM AUTHOR]

This study aimed to compare the microbiome profiles of patients with colorectal cancer (CRC, n =380) and colorectal adenomas (CRA, n =110) against generally healthy participants (n =2,461) from various studies. The overarching objective was to conduct a real-life experiment and develop a robust machine learning model applicable to the general population. A total of 2,951 stool samples underwent a comprehensive analysis using the in-house MetaBakery pipeline. This included various data matrices such as microbial taxonomy, functional genes, enzymatic reactions, metabolic pathways, and predicted metabolites. The study found no statistically significant difference in microbial diversity among individuals. However, distinct clusters were identified for healthy, CRC, and CRA groups through linear discriminant analysis (LDA). Machine learning analysis demonstrated consistent model performance, indicating the potential of microbiome layers (microbial taxa, functional genes, enzymatic reactions, and metabolic pathways) as prediagnostic indicators for CRC and CRA. Notable biomarkers on the taxonomy level and microbial functionality (gene families, enzymatic reactions, and metabolic pathways) associated with CRC were identified. The research presents promising avenues for practical clinical applications, with potential validation on external clinical datasets in future studies. [ABSTRACT FROM AUTHOR]

Over the past decade, there has been a growing interest in common octopus aquaculture, prompted by several factors such as the increase in market demand, the decline in overall fisheries and the search for more sustainable food resources. Nevertheless, this interest has raised concerns about the potential impact of largescale production and intensified farming practices in the future. This study aims to establish a baseline understanding of the natural microbial communities that inhabit skin mucus of the common octopus, describe its core microbiota, and assess the impact of captive rearing on these communities and animal welfare. The skin microbiome of wild-captured octopuses was compared with that of reared in aquaculture using 16S ribosomal RNA metabarcoding. The findings show that the core microbiota of octopus mucosal skin is dominated by the phyla Bacteroidota and Pseudomonadota, with Aurantivirga, Pseudofulvibacter, and Rubritalea being the most abundant genera. Despite differences in abundance, the microbiota composition and diversity between wild and aquaculture octopuses were similar. Wild samples had an enrichment of Gammaproteobacteria, including potentially pathogenic species such as Vibrio spp., Photobacteriumswingsii, and Lactococcus garvieae, which were less prevalent or absent in aquaculture samples. KEGG functional pathways predictions indicated a higher enrichment in functional pathways related to xenobiotic remediation in wild samples, reflecting their need to adapt to a more variable and potentially contaminated environment. This is the first study to characterize the mucosal skin microbiome of the common octopus and to compare wild and aquaculture specimens. The results suggest that current aquaculture practices support animal welfare through the use of controlled hatchery environments and high-quality water conditions. These insights are valuable for the development of sustainable and responsible aquaculture practices, with the octopus microbiome serving as a potential biomarker for health status and animal welfare. [ABSTRACT FROM AUTHOR]

Background: Environmental reservoirs of antibiotic resistance pose a threat to human and animal health. Aquatic biofilms impacted by wastewater effluent (WW) are known environmental reservoirs for antibiotic resistance; however, the relative importance of biotic factors and abiotic factors from WW on the abundance of antibiotic resistance genes (ARGs) within aquatic biofilms remains unclear. Additionally, experimental evidence is limited within complex aquatic microbial communities as to whether genes bearing low sequence similarity to validated reference ARGs are functional as ARGs. Results: To disentangle the effects of abiotic and biotic factors on ARG abundances, natural biofilms were previously grown in flume systems with different proportions of stream water and either ultrafiltered or non-ultrafiltered WW. In this study, we conducted deep shotgun metagenomic sequencing of 75 biofilm, stream, and WW samples from these flume systems and compared the taxonomic and functional microbiome and resistome composition. Statistical analysis revealed an alignment of the resistome and microbiome composition and a significant association with experimental treatment. Several ARG classes exhibited an increase in normalized metagenomic abundances in biofilms grown with increasing percentages of non-ultrafiltered WW. In contrast, sulfonamide and extended-spectrum beta-lactamase ARGs showed greater abundances in biofilms grown in ultrafiltered WW compared to non-ultrafiltered WW. Overall, our results pointed toward the dominance of biotic factors over abiotic factors in determining ARG abundances in WW-impacted stream biofilms and suggested gene family-specific mechanisms for ARGs that exhibited divergent abundance patterns. To investigate one of these specific ARG families experimentally, we biochemically characterized a new beta-lactamase from the Planctomycetota (Phycisphaeraceae). This beta-lactamase displayed activity in the cleavage of cephalosporin analog despite sharing a low sequence identity with known ARGs. Conclusions: This discovery of a functional planctomycete beta-lactamase ARG is noteworthy, not only because it was the first beta-lactamase to be biochemically characterized from this phylum, but also because it was not detected by standard homology-based ARG tools. In summary, this study conducted a metagenomic analysis of the relative importance of biotic and abiotic factors in the context of WW discharge and their impact on both known and new ARGs in aquatic biofilms. DkJa1T-YWptaXXeH_hsDtB Video Abstract [ABSTRACT FROM AUTHOR]

The mosquito microbiome significantly influences vector competence, including in Aedes albopictus, a globally invasive vector. Describing the microbiome and Wolbachia strains of Ae. albopictus from different regions can guide area-specific control strategies. Mosquito samples from Spain and São Tomé were analyzed using 16S rRNA gene sequencing and metagenomic sequencing. Wolbachia infection patterns were observed by sex and population. Female mosquitoes were blood-fed, a factor considered in analyzing their microbiota. Results revealed a dominance of dual Wolbachia infections, strains A and B, in the microbiome of both populations of Ae. albopictus, especially among females. Both populations shared a core microbiome, although 5 and 9 other genera were only present in Spain and São Tomé populations, respectively. Genera like Pelomonas and Nevskia were identified for the first time in Aedes mosquitoes. This study is the first to describe the Ae. albopictus bacteriome in Spain and São Tomé, offering insights for the development of targeted mosquito control strategies. Understanding the specific microbiome composition can help in designing more effective interventions, such as microbiome manipulation and Wolbachia-based approaches, to reduce vector competence and transmission potential of these mosquitoes. [ABSTRACT FROM AUTHOR]

The North Temperate Lakes Long-Term Ecological Research (NTL-LTER) program has been extensively used to improve understanding of how aquatic ecosystems respond to environmental stressors, climate fluctuations, and human activities. Here, we report on the metagenomes of samples collected between 2000 and 2019 from Lake Mendota, a freshwater eutrophic lake within the NTL-LTER site. We utilized the distributed metagenome assembler MetaHipMer to coassemble over 10 terabases (Tbp) of data from 471 individual Illumina-sequenced metagenomes. A total of 95,523,664 contigs were assembled and binned to generate 1,894 non-redundant metagenome-assembled genomes (MAGs) with ≥50% completeness and ≤10% contamination. Phylogenomic analysis revealed that the MAGs were nearly exclusively bacterial, dominated by Pseudomonadota (Proteobacteria, N = 623) and Bacteroidota (N = 321). Nine eukaryotic MAGs were identified by eukCC with six assigned to the phylum Chlorophyta. Additionally, 6,350 high-quality viral sequences were identified by geNomad with the majority classified in the phylum Uroviricota. This expansive coassembled metagenomic dataset provides an unprecedented foundation to advance understanding of microbial communities in freshwater ecosystems and explore temporal ecosystem dynamics. [ABSTRACT FROM AUTHOR]

Kinema, a sticky-textured fermented soybean food with umami flavour, is originated in the historical Limbuwan region, now situated in the eastern districts of Nepal. It has since expanded to different areas in Darjeeling Hills and Sikkim in India and Bhutan. The Limboo community, one of the oldest indigenous groups in the Himalayas, has developed a method for fermenting soybeans to produce kinema. Bacillus is the main bacterial genus responsible for fermenting kinema, along with lactic acid bacteria, yeasts, and fungi. Kinema is full of nutrients like protein, fats, carbohydrates, amino acids, vitamins, and minerals and offers health benefits through its antioxidant, anti-inflammatory, antiobesity, and antidiabetic properties. A novel strain of Bacillus subtilis Tamang has been identified in kinema samples from Darjeeling Hills, India, exhibiting elevated levels of γ-PGA and various biomarker genes for health benefits and biological functionalities. Kinema contains secondary metabolites including several bioactive compounds, immunomodulators, vitamin B-complexes, and others that help promote good health. The transcriptome of the B. subtilis Tamang strain reveals genes associated with GABA production, Vitamin B12 synthesis, and fibrinolytic activity. Safety evaluations have demonstrated that the strain and the production of kinema do not pose any toxicity risks based on animal studies. Despite facing challenges in terms of its strong taste and slimy consistency for broader acceptance, the bioactive elements and safe starter cultures found in kinema hold promise for the development of functional food. [ABSTRACT FROM AUTHOR]

Background: When individuals infected with human immunodeficiency virus (HIV) experience pulmonary infections, they often exhibit severe symptoms and face a grim prognosis. Consequently, early, rapid, and accurate pathogen diagnosis is vital for informing effective treatment strategies. This study aimed to use metagenomic next-generation sequencing (mNGS) and targeted mNGS (tNGS) to elucidate the characteristics of pulmonary infections in HIV and non-HIV individuals. Methods: This study enrolled 90 patients with pulmonary infection at the Department of Infectious Diseases of The First Hospital of Jilin University from June 2022 to May 2023, and they were divided into HIV (n=46) and non-HIV (n=44) infection groups. Their bronchoalveolar lavage fluid (BALF) was collected for mNGS analysis to evaluate the differences in pulmonary infection pathogens, and tNGS detection was performed on BALF samples from 15 HIVinfected patients. Results: A total of 37 pathogens were identified in this study, including 21 bacteria, 5 fungi, 5 viruses, 5 mycobacteria, and 1 mycoplasma. The sensitivity of mNGS was 78.9% (71/90), which is significantly higher than that of conventional methods (CTM) (39/90, P=1.5E-8). The combination of mNGS with CTM can greatly enhance the sensitivity of pathogen detection. The prevalence of Pneumocystis jirovecii (82.6% vs. 9.1%), cytomegalovirus (CMV) (58.7% vs. 0%), and Epstein-Barr virus (EBV) (17.4% vs. 2.3%) was significantly higher in the HIV infection group than in the non-HIV infection group (P<0.05). Although no statistically significant difference was observed, the detection rate of Mycobacteria was higher in HIVinfected patients (17.4%) than in the non-HIV group (6.8%). Furthermore, the tNGS results of BALF from15 HIV-infected patients were not entirely consistent with the mNGS results., and the concordance rate of tNGS for the detection of main pathogens reached 86.7% (13/15). Conclusion: Next-generation sequencing (NGS) can accurately detect pathogens in the BALF of patients with pulmonary infection. The sensitivity of tNGS is comparable to that of mNGS. Therefore, this technique should be promoted in the clinic for better patient outcomes. [ABSTRACT FROM AUTHOR]

AbstractThis study documents the first report of halo blight of hop (Humulus lupulus L.), caused by the fungal pathogen Diaporthe humulicola, in Prince Edward Island (PE), Canada. This pathogen has been previously reported in Michigan, New York, Connecticut and Quebec. Disease symptoms were reported in hopyards in Harrington and Mount Albion, PE. Symptoms reported include both foliar lesions and severe necrosis of hop cones. Through a metagenomic approach using Nanopore sequencing of the full Internal Transcribed Spacer (ITS) region products, multiple fungal species were identified in infected hop cones. Many plant pathogens were present in the hop cones including species of Botrytis, Alternaria, Colletotrichum and Fusarium. Additionally, metagenomic sequencing identified reads that most closely matched D. humulicola. Symptomatic leaf tissue sections were cultured on amended PDA to isolate the pathogen, then colony PCR was conducted and ITS regions were sequenced, confirming the identity of D. humulicola. Pathogenicity was validated using Koch’s postulates on hop leaf disks, with lesion development occurring 7 days post-inoculation. Considering the humid conditions characteristic of eastern North American hop-growing zones, halo blight’s emergence in PE emphasizes the need for comprehensive monitoring and improved best management strategies to protect the PE hop industry. [ABSTRACT FROM AUTHOR]

Pulmonary invasive fungal infections (pIFI) disproportionately affect patients with haematological malignancies (HM). Establishing a rapid and accurate diagnosis of pIFI is challenging. Multiple guidelines recommend diagnostic testing of invasive fungal infections but lack consensus and may contribute to inconsistent diagnostic approaches. To identify key diagnostic challenges and review metagenomic sequencing data. PubMed, professional consortium, and scientific society websites search to identify relevant, published, evidence-based clinical guidelines within the past 5 years. PubMed searchs for papers describing clinically relevant novel diagnostic technologies. Current guidelines for patients with HM and suspected pIFI recommend chest computed tomography imaging and specimen testing with microscopic examination (including calcofluor white stain, histopathology, cytopathology, etc.), Aspergillus galactomannan, β-D-glucan, PCR, and culture, each with certain limitations. Emerging real-world data support the adjunctive use of metagenomic sequencing-based tests for the timely diagnosis of pIFI. High-quality evidence from robust clinical trials is needed to determine whether guidelines should be updated to include novel diagnostic technologies. Trials should ask whether the combination of powerful novel diagnostics, such as pathogen-agnostic metagenomic sequencing technologies in conjunction with conventional testing can optimize the diagnostic yield for all potential pIFI pathogens that impact the health of patients with HM. [ABSTRACT FROM AUTHOR]

Shotgun metagenomics is an increasingly cost-effective approach for profiling environmental and host-associated microbial communities. However, due to the complexity of both microbiomes and the molecular techniques required to analyze them, the reliability and representativeness of the results are contingent upon the field, laboratory, and bioinformatic procedures employed. Here, we consider 15 field and laboratory issues that critically impact downstream bioinformatic and statistical data processing, as well as result interpretation, in bacterial shotgun metagenomic studies. The issues we consider encompass intrinsic properties of samples, study design, and laboratory-processing strategies. We identify the links of field and laboratory steps with downstream analytical procedures, explain the means for detecting potential pitfalls, and propose mitigation measures to overcome or minimize their impact in metagenomic studies. We anticipate that our guidelines will assist data scientists in appropriately processing and interpreting their data, while aiding field and laboratory researchers to implement strategies for improving the quality of the generated results. [ABSTRACT FROM AUTHOR]

The build-up of arsenic in agricultural soil through contaminated irrigational groundwater is a concern. Metagenomic analysis of such contaminated sites may provide a remarkable opportunity to extensively investigate the responses and adaptation of microbial communities to different levels of arsenic. The study focuses on the comparative analysis of microbial community composition and associated functions in paddy soil samples with a gradient of arsenic contamination (4.88 to 43.67 mg kg−1). Actinobacteria was found to dominate the bacterial phyla in all four samples with abundance ranging from 39.77% to 49.39% followed by Proteobacteria (20.71–38.24%). Whereas the fungal phylum Ascomycota (92.42–95.29%) dominated all the samples studied. In the study, bacteria were found to be abundant in the mid-level (15.89 and 24.84 mg kg−1) of arsenic, which included genera Gaiella, Nocardioides, Solirubrobacter, Microvirga, and Nitrospira. Fungi Beauveria, Talaromyces, Aspergillus, Pyrenophora, and Valsa were higher in relative abundance corresponding to soil arsenic concentration. Verticillium, previously reported for Pb, Zn, and Cd removal, was found in the soil sample with the mid-arsenic concentration (15.89 mg kg−1). The relative abundance of the arsenic metabolizing/ transport genes of native microbial communities also varied with the soil arsenic gradient, the genes arsJ, arsM, aioR, arsH, and arsC being the most affected. The study is the first report of Gaiella, Solirubrobacter, Beauveria, and Verticillium present in arsenic-contaminated soil, and further studies are required to explore their potential role in arsenic bioremediation. [ABSTRACT FROM AUTHOR]

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Polluted drains across the globe are affected due to reckless disposal of untreated industrial effluents resulting in significant water pollution affecting microbial community structure/dynamics. To elucidate this, polluted samples were collected from Budha Nala (BN) drain, Tung Dhab (TD) drain, and wastewater treatment plant (WWTP) receiving an inflow of organic pollutants as well as heavy metals due to anthropogenic activities. The sample of unpolluted pristine soil (PS) was used as control, as there is no history of usage of organic chemicals at this site. The bacterial diversity of these samples was sequenced using the Illumina MiSeq platform by amplifying the V3/V4 region of 16S rRNA. The majority of operational taxonomic unit (OTUs) at polluted sites belonged to phyla Proteobacteria specifically Gammaproteobacteria class, followed by Actinobacteria, Bacteriodetes, Chloroflexi, Firmicutes, Planctomycetes, WS6, and TM7, whereas unpolluted site revealed the prevalence of Proteobacteria followed by Actinobacteria, Planctomycetes, Firmicutes, Acidobacteria, Chloroflexi, Bacteroidetes, Verrucomicrobia, and Nitrospirae. The data sets decode unclassified species of the phyla Proteobacteria, Bacteriodetes, Chloroflexi, Firmicutes, and WS6, along with some unclassified bacterial species. The study provided a comparative study of changed microbial community structure, their possible functions across diverse geographical locations, and identifying specific bacterial genera as pollution bio‐indicators of aged polluted drains. [ABSTRACT FROM AUTHOR]