Metagenomic insight into taxonomic composition, environmental filtering and functional redundancy for shaping worldwide modern non-lithifying microbial mats.

Modern microbial mats are relictual communities mostly found in extreme environments worldwide. Despite their significance as representatives of the ancestral Earth and their important roles in biogeochemical cycling, research on microbial mats has largely been localized, focusing on site-specific descriptions and environmental change experiments. Here, we present a global comparative analysis of non-lithifying microbial mats, integrating environmental measurements with metagenomic data from 62 samples across eight sites, including two new samples from the recently discovered Archaean Domes from Cuatro Ciénegas, Mexico. Our results revealed a notable influence of environmental filtering on both taxonomic and functional compositions of microbial mats. Functional redundancy appears to confer resilience to mats, with essential metabolic pathways conserved across diverse and highly contrasting habitats. We identified six highly correlated clusters of taxa performing similar ecological functions, suggesting niche partitioning and functional specialization as key mechanisms shaping community structure. Our findings provide insights into the ecological principles governing microbial mats, and lay the foundation for future research elucidating the intricate interplay between environmental factors and microbial community dynamics.

Seasonal dynamics and environmental drivers of tissue and mucus microbiomes in the staghorn coral Acropora pulchra.

Background: Rainfall-induced coastal runoff represents an important environmental impact in near-shore coral reefs that may affect coral-associated bacterial microbiomes. Shifts in microbiome community composition and function can stress corals and ultimately cause mortality and reef declines. Impacts of environmental stress may be site specific and differ between coral microbiome compartments (e.g., tissue versus mucus). Coastal runoff and associated water pollution represent a major stressor for near-shore reef-ecosystems in Guam, Micronesia. Methods: Acropora pulchra colonies growing on the West Hagåtña reef flat in Guam were sampled over a period of 8 months spanning the 2021 wet and dry seasons. To examine bacterial microbiome diversity and composition, samples of A. pulchra tissue and mucus were collected during late April, early July, late September, and at the end of December. Samples were collected from populations in two different habitat zones, near the reef crest (farshore) and close to shore (nearshore). Seawater samples were collected during the same time period to evaluate microbiome dynamics of the waters surrounding coral colonies. Tissue, mucus, and seawater microbiomes were characterized using 16S DNA metabarcoding in conjunction with Illumina sequencing. In addition, water samples were collected to determine fecal indicator bacteria (FIB) concentrations as an indicator of water pollution. Water temperatures were recorded using data loggers and precipitation data obtained from a nearby rain gauge. The correlation structure of environmental parameters (temperature and rainfall), FIB concentrations, and A. pulchra microbiome diversity was evaluated using a structural equation model. Beta diversity analyses were used to investigate spatio-temporal trends of microbiome composition. Results: Acropora pulchra microbiome diversity differed between tissues and mucus, with mucus microbiome diversity being similar to the surrounding seawater. Rainfall and associated fluctuations of FIB concentrations were correlated with changes in tissue and mucus microbiomes, indicating their role as drivers of A. pulchra microbiome diversity. A. pulchra tissue microbiome composition remained relatively stable throughout dry and wet seasons; tissues were dominated by Endozoicomonadaceae, coral endosymbionts and putative indicators of coral health. In nearshore A. pulchra tissue microbiomes, Simkaniaceae, putative obligate coral endosymbionts, were more abundant than in A. pulchra colonies growing near the reef crest (farshore). A. pulchra mucus microbiomes were more diverse during the wet season than the dry season, a distinction that was also associated with drastic shifts in microbiome composition. This study highlights the seasonal dynamics of coral microbiomes and demonstrates that microbiome diversity and composition may differ between coral tissues and the surface mucus layer.

Metagenomic analysis of the bacterial microbiome, resistome and virulome distinguishes Portuguese Serra da Estrela PDO cheeses from similar non-PDO cheeses: An exploratory approach.

This study aimed to evaluate the microbiome, resistome and virulome of two types of Portuguese cheese using high throughput sequencing (HTS). Culture-dependent chromogenic methods were also used for certain groups/microorganisms. Eight samples of raw ewe's milk cheese were obtained from four producers: two producers with cheeses with a PDO (Protected Designation of Origin) label and the other two producers with cheeses without a PDO label. Agar-based culture methods were used to quantify total mesophiles, Enterobacteriaceae, Escherichia coli, Staphylococcus, Enterococcus and lactic acid bacteria. The presence of Listeria monocytogenes and Salmonella was also investigated. The selected isolates were identified by 16S rRNA gene sequencing and evaluated to determine antibiotic resistance and the presence of virulence genes. The eight cheese samples analyzed broadly complied with EC regulations in terms of the microbiological safety criteria. The HTS results demonstrated that Leuconostoc mesenteroides, Lactococcus lactis, Lactobacillus plantarum, Lacticaseibacillus rhamnosus, Enterococcus durans and Lactobacillus coryniformis were the most prevalent bacterial species in cheeses. The composition of the bacterial community varied, not only between PDO and non-PDO cheeses, but also between producers, particularly between the two non-PDO cheeses. Alpha-diversity analyses showed that PDO cheeses had greater bacterial diversity than non-PDO cheeses, demonstrating that the diversity of spontaneously fermented foods is significantly higher in cheeses produced without the addition of food preservatives and dairy ferments. Despite complying with microbiological regulations, both PDO and non-PDO cheeses harbored potential virulence genes as well as antibiotic resistance genes. However, PDO cheeses exhibited fewer of these virulence and antibiotic resistance genes compared to non-PDO cheeses. Therefore, the combination of conventional microbiological methods and the metagenomic approach could contribute to improving the attribution of the PDO label to this type of cheese.

Co-evolution of vaginal microbiome and cervical cancer.

BACKGROUND: Exploration of adaptive evolutionary changes at the genetic level in vaginal microbial communities during different stages of cervical cancer remains limited. This study aimed to elucidate the mutational profile of the vaginal microbiota throughout the progression of cervical disease and subsequently establish diagnostic models. METHODS: This study utilized a metagenomic dataset consisting of 151 subjects classified into four categories: invasive cervical cancer (CC) (n = 42), cervical intraepithelial neoplasia (CIN) (n = 43), HPV-infected (HPVi) patients without cervical lesions (n = 34), and healthy controls (n = 32). The analysis focused on changes in microbiome abundance and extracted information on genetic variation. Consequently, comprehensive multimodal microbial signatures associated with CC, encompassing taxonomic alterations, mutation signatures, and enriched metabolic functional pathways, were identified. Diagnostic models for predicting CC were established considering gene characteristics based on single nucleotide variants (SNVs). RESULTS: In this study, we screened and analyzed the abundances of 18 key microbial strains during CC progression. Additionally, 71,6358 non-redundant mutations were identified, predominantly consisting of SNVs that were further annotated into 25,773 genes. Altered abundances of SNVs and mutation types were observed across the four groups. Specifically, there were 9847 SNVs in the HPV-infected group and 14,892 in the CC group. Furthermore, two distinct mutation signatures corresponding to the benign and malignant groups were identified. The enriched metabolic pathways showed limited similarity with only two overlapping pathways among the four groups. HPVi patients exhibited active nucleotide biosynthesis, whereas patients with CC demonstrated a significantly higher abundance of signaling and cellular-associated protein families. In contrast, healthy controls showed a distinct enrichment in sugar metabolism. Moreover, biomarkers based on microbial SNV abundance displayed stronger diagnostic capability (cc.AUC = 0.87) than the species-level biomarkers (cc.AUC = 0.78). Ultimately, the integration of multimodal biomarkers demonstrated optimal performance for accurately identifying different cervical statuses (cc.AUC = 0.86), with an acceptable performance (AUC = 0.79) in the external testing set. CONCLUSIONS: The vaginal microbiome exhibits specific SNV evolution in conjunction with the progression of CC, and serves as a specific biomarker for distinguishing between different statuses of cervical disease.

Nanopore-Enabled Microbiome Analysis: Investigating Environmental and Host-Associated Samples in Rainbow Trout Aquaculture.

Microbiome sequencing is at the forefront of health management development, and as such, it is becoming of great interest to monitor the microbiome in the aquaculture industry as well. Oxford Nanopore Technologies (ONT) platforms are gaining popularity to study microbial communities, enabling faster sequencing, extended read length, and therefore, improved taxonomic resolution. Despite this, there is a lack of clear guidelines to perform a metabarcoding study, especially when dealing with samples from non-mammalian species, such as aquaculture-related samples. In this article, we provide general guidelines for sampling, nucleic acid extraction, and ONT-based library preparation for both environmental (water, sediment) and host-associated (gill or skin mucus, skin, gut content, or gut mucosa) microbiome analysis. Our procedures focus specifically on rainbow trout (Oncorhynchus mykiss) reared in experimental facilities. However, these protocols can also be transferred to alternative types of samples, such as environmental DNA (eDNA) monitoring from alternative water sources, or to different fish species. The additional challenge posed by the low biomass and limited bacterial diversity inherent in fish-associated microbiomes is addressed through the implementation of troubleshooting solutions. Furthermore, we describe a bioinformatic pipeline starting from raw reads and leading to taxonomic abundance tables using currently available tools and software. Finally, we provide a set of specific guidelines and considerations related to the strategic planning of a microbiome study within the context of aquaculture. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Environmental sample collection Basic Protocol 2: Host-associated sample collection Alternate Protocol: Host-associated sample collection: Alternative sample types Basic Protocol 3: Sample pre-treatment and nucleic acid extraction Basic Protocol 4: Quality control and preparation for 16S rRNA gene sequencing Support Protocol 1: Assessment of inhibition by quantitative PCR Support Protocol 2: Bioinformatic analysis from raw files to taxonomic abundance tables.

Microbial communities, antibiotic resistance genes, and virulence factors in urinary infectious stone-associated urinary tract infections.

Urinary infectious stones are challenging due to bacterial involvement, necessitating a comprehensive understanding of these conditions. Antibiotic-resistant urease-producing bacteria further complicate clinical management. In this study, analysis of urine and stone samples from urinary tract infection (UTI) patients revealed microbial shifts, gene enrichment in stones, and metabolic pathway disparities; antibiotic resistance gene trends were phylum-specific, urease-producing bacteria are at risk of acquiring AMR carried by Enterobacteriaceae under antibiotic, emphasizing potential AMR dissemination between them; Correlations of key pathogenic species in kidney stone and urine microbial communities highlight the need for targeted therapeutic strategies to manage complexities in UTIs; Stones and urine contain a variety of deleterious genes even before antibiotic use, and piperacillin/tazobactam better reduced the abundance of antibiotic resistance genes in stones and urine. The presence of diverse antibiotic resistance and virulence genes underscores challenges in clinical management and emphasizes the need for effective treatment strategies to mitigate risks associated with UTIs and urinary infectious stone formation. Ongoing research is vital for advancing knowledge and developing innovative approaches to address these urological conditions.

Exploring the microbiota difference of bronchoalveolar lavage fluid between community-acquired pneumonia with or without COPD based on metagenomic sequencing: a retrospective study.

BACKGROUND: Community-acquired pneumonia (CAP) patients with chronic obstructive pulmonary disease (COPD) have higher disease severity and mortality compared to those without COPD. However, deep investigation into microbiome distribution of lower respiratory tract of CAP with or without COPD was unknown. METHODS: So we used metagenomic next generation sequencing (mNGS) to explore the microbiome differences between the two groups. RESULTS: Thirty-six CAP without COPD and 11 CAP with COPD cases were retrieved. Bronchoalveolar lavage fluid (BALF) was collected and analyzed using untargeted mNGS and bioinformatic analysis. mNGS revealed that CAP with COPD group was abundant with Streptococcus, Prevotella, Bordetella at genus level and Cutibacterium acnes, Rothia mucilaginosa, Bordetella genomosp. 6 at species level. While CAP without COPD group was abundant with Ralstonia, Prevotella, Streptococcus at genus level and Ralstonia pickettii, Rothia mucilaginosa, Prevotella melaninogenica at species level. Meanwhile, both alpha and beta microbiome diversity was similar between groups. Linear discriminant analysis found that pa-raburkholderia, corynebacterium tuberculostearicum and staphylococcus hominis were more enriched in CAP without COPD group while the abundance of streptococcus intermedius, streptococcus constellatus, streptococcus milleri, fusarium was higher in CAP with COPD group. CONCLUSIONS: These findings revealed that concomitant COPD have an mild impact on lower airway microbiome of CAP patients.

Genetic control of rhizosphere microbiome of the cotton plants under field conditions.

Understanding the extent of heritability of a plant-associated microbiome (phytobiome) is critically important for exploitation of phytobiomes in agriculture. Two crosses were made between pairs of cotton cultivars (Z2 and J11, L1 and Z49) with differential resistance to Verticillium wilt. F2 plants were grown in a field, together with the four parents to study the heritability of cotton rhizosphere microbiome. Amplicon sequencing was used to profile bacterial and fungal communities in the rhizosphere. F2 offspring plants of both crosses had higher average alpha diversity indices than the two parents; parents differed significantly from F2 offspring in Bray-Curtis beta diversity indices as well. Two types of data were used to study the heritability of rhizosphere microbiome: principal components (PCs) and individual top microbial operational taxonomic units (OTUs). For the L1 × Z49 cross, the variance among the F2 progeny genotypes (namely, genetic variance, VT) was significantly greater than the random variability (VE) for 12 and 34 out of top 100 fungal and bacterial PCs, respectively. For the Z2 × J11 cross, the corresponding values were 10 and 20 PCs. For 29 fungal OTUs and 10 bacterial OTUs out of the most abundant 100 OTUs, genetic variance (VT) was significantly greater than VE for the L1 × Z49 cross; the corresponding values for the Z2 × J11 cross were 24 and one. The estimated heritability was mostly in the range of 40% to 60%. These results suggested the existence of genetic control of polygenic nature for specific components of rhizosphere microbiome in cotton. KEY POINTS: • F2 offspring cotton plants differed significantly from parents in rhizosphere microbial diversity. • Specific rhizosphere components are likely to be genetically controlled by plants. • Common PCs and specific microbial groups are significant genetic components between the two crosses.

Colorectal cancer microbiome programs DNA methylation of host cells by affecting methyl donor metabolism.

BACKGROUND: Colorectal cancer (CRC) arises from complex interactions between host and environment, which include the gut and tissue microbiome. It is hypothesized that epigenetic regulation by gut microbiota is a fundamental interface by which commensal microbes dynamically influence intestinal biology. The aim of this study is to explore the interplay between gut and tissue microbiota and host DNA methylation in CRC. METHODS: Metagenomic sequencing of fecal samples was performed on matched CRC patients (n = 18) and healthy controls (n = 18). Additionally, tissue microbiome was profiled with 16S rRNA gene sequencing on tumor (n = 24) and tumor-adjacent normal (n = 24) tissues of CRC patients, while host DNA methylation was assessed through whole-genome bisulfite sequencing (WGBS) in a subset of 13 individuals. RESULTS: Our analysis revealed substantial alterations in the DNA methylome of CRC tissues compared to adjacent normal tissues. An extensive meta-analysis, incorporating publicly available and in-house data, identified significant shifts in microbial-derived methyl donor-related pathways between tumor and adjacent normal tissues. Of note, we observed a pronounced enrichment of microbial-associated CpGs within the promoter regions of genes in adjacent normal tissues, a phenomenon notably absent in tumor tissues. Furthermore, we established consistent and recurring associations between methylation patterns of tumor-related genes and specific bacterial taxa. CONCLUSIONS: This study emphasizes the pivotal role of the gut microbiota and pathogenic bacteria in dynamically shaping DNA methylation patterns, impacting physiological homeostasis, and contributing to CRC tumorigenesis. These findings provide valuable insights into the intricate host-environment interactions in CRC development and offer potential avenues for therapeutic interventions in this disease.

Deciphering the microbial landscape of lower respiratory tract infections: insights from metagenomics and machine learning.

Background: Lower respiratory tract infections represent prevalent ailments. Nonetheless, current comprehension of the microbial ecosystems within the lower respiratory tract remains incomplete and necessitates further comprehensive assessment. Leveraging the advancements in metagenomic next-generation sequencing (mNGS) technology alongside the emergence of machine learning, it is now viable to compare the attributes of lower respiratory tract microbial communities among patients across diverse age groups, diseases, and infection types. Method: We collected bronchoalveolar lavage fluid samples from 138 patients diagnosed with lower respiratory tract infections and conducted mNGS to characterize the lung microbiota. Employing various machine learning algorithms, we investigated the correlation of key bacteria in patients with concurrent bronchiectasis and developed a predictive model for hospitalization duration based on these identified key bacteria. Result: We observed variations in microbial communities across different age groups, diseases, and infection types. In the elderly group, Pseudomonas aeruginosa exhibited the highest relative abundance, followed by Corynebacterium striatum and Acinetobacter baumannii. Methylobacterium and Prevotella emerged as the dominant genera at the genus level in the younger group, while Mycobacterium tuberculosis and Haemophilus influenzae were prevalent species. Within the bronchiectasis group, dominant bacteria included Pseudomonas aeruginosa, Haemophilus influenzae, and Klebsiella pneumoniae. Significant differences in the presence of Pseudomonas phage JBD93 were noted between the bronchiectasis group and the control group. In the group with concomitant fungal infections, the most abundant genera were Acinetobacter and Pseudomonas, with Acinetobacter baumannii and Pseudomonas aeruginosa as the predominant species. Notable differences were observed in the presence of Human gammaherpesvirus 4, Human betaherpesvirus 5, Candida albicans, Aspergillus oryzae, and Aspergillus fumigatus between the group with concomitant fungal infections and the bacterial group. Machine learning algorithms were utilized to select bacteria and clinical indicators associated with hospitalization duration, confirming the excellent performance of bacteria in predicting hospitalization time. Conclusion: Our study provided a comprehensive description of the microbial characteristics among patients with lower respiratory tract infections, offering insights from various perspectives. Additionally, we investigated the advanced predictive capability of microbial community features in determining the hospitalization duration of these patients.

Statistical and computational methods for integrating microbiome, host genomics, and metabolomics data.

Large-scale microbiome studies are progressively utilizing multiomics designs, which include the collection of microbiome samples together with host genomics and metabolomics data. Despite the increasing number of data sources, there remains a bottleneck in understanding the relationships between different data modalities due to the limited number of statistical and computational methods for analyzing such data. Furthermore, little is known about the portability of general methods to the metagenomic setting and few specialized techniques have been developed. In this review, we summarize and implement some of the commonly used methods. We apply these methods to real data sets where shotgun metagenomic sequencing and metabolomics data are available for microbiome multiomics data integration analysis. We compare results across methods, highlight strengths and limitations of each, and discuss areas where statistical and computational innovation is needed.

Exploration of mobile genetic elements in the ruminal microbiome of Nellore cattle.

Metagenomics has made it feasible to elucidate the intricacies of the ruminal microbiome and its role in the differentiation of animal production phenotypes of significance. The search for mobile genetic elements (MGEs) has taken on great importance, as they play a critical role in the transfer of genetic material between organisms. Furthermore, these elements serve a dual purpose by controlling populations through lytic bacteriophages, thereby maintaining ecological equilibrium and driving the evolutionary progress of host microorganisms. In this study, we aimed to identify the association between ruminal bacteria and their MGEs in Nellore cattle using physical chromosomal links through the Hi-C method. Shotgun metagenomic sequencing and the proximity ligation method ProxiMeta were used to analyze DNA, getting 1,713,111,307 bp, which gave rise to 107 metagenome-assembled genomes from rumen samples of four Nellore cows maintained on pasture. Taxonomic analysis revealed that most of the bacterial genomes belonged to the families Lachnospiraceae, Bacteroidaceae, Ruminococcaceae, Saccharofermentanaceae, and Treponemataceae and mostly encoded pathways for central carbon and other carbohydrate metabolisms. A total of 31 associations between host bacteria and MGE were identified, including 17 links to viruses and 14 links to plasmids. Additionally, we found 12 antibiotic resistance genes. To our knowledge, this is the first study in Brazilian cattle that connect MGEs with their microbial hosts. It identifies MGEs present in the rumen of pasture-raised Nellore cattle, offering insights that could advance biotechnology for food digestion and improve ruminant performance in production systems.

Microbiome and epigenetic variation in wild fish with low genetic diversity.

Non-genetic sources of phenotypic variation, such as the epigenome and the microbiome, could be important contributors to adaptive variation for species with low genetic diversity. However, little is known about the complex interaction between these factors and the genetic diversity of the host, particularly in wild populations. Here, we examine the skin microbiome composition of two closely-related mangrove killifish species with different mating systems (self-fertilising and outcrossing) under sympatric and allopatric conditions. This allows us to partition the influence of the genotype and the environment on their microbiome and (previously described) epigenetic profiles. We find the diversity and community composition of the skin microbiome are strongly shaped by the environment and, to a lesser extent, by species-specific influences. Heterozygosity and microbiome alpha diversity, but not epigenetic variation, are associated with the fluctuating asymmetry of traits related to performance (vision) and behaviour (aggression). Our study identifies that a proportion of the epigenetic diversity and microbiome differentiation is unrelated to genetic variation, and we find evidence for an associative relationship between microbiome and epigenetic diversity in these wild populations. This suggests that both mechanisms could potentially contribute to variation in species with low genetic diversity.

Longitudinal multicompartment characterization of host-microbiota interactions in patients with acute respiratory failure.

Critical illness can significantly alter the composition and function of the human microbiome, but few studies have examined these changes over time. Here, we conduct a comprehensive analysis of the oral, lung, and gut microbiota in 479 mechanically ventilated patients (223 females, 256 males) with acute respiratory failure. We use advanced DNA sequencing technologies, including Illumina amplicon sequencing (utilizing 16S and ITS rRNA genes for bacteria and fungi, respectively, in all sample types) and Nanopore metagenomics for lung microbiota. Our results reveal a progressive dysbiosis in all three body compartments, characterized by a reduction in microbial diversity, a decrease in beneficial anaerobes, and an increase in pathogens. We find that clinical factors, such as chronic obstructive pulmonary disease, immunosuppression, and antibiotic exposure, are associated with specific patterns of dysbiosis. Interestingly, unsupervised clustering of lung microbiota diversity and composition by 16S independently predicted survival and performed better than traditional clinical and host-response predictors. These observations are validated in two separate cohorts of COVID-19 patients, highlighting the potential of lung microbiota as valuable prognostic biomarkers in critical care. Understanding these microbiome changes during critical illness points to new opportunities for microbiota-targeted precision medicine interventions.

Genome-resolved metagenomics reveals diverse taxa and metabolic complexity in Antarctic lake microbial structures.

Lake Untersee, a lake in Antarctica that is perennially covered with ice, is home to unique microbial structures that are not lithified. We have evaluated the structure of the community and its metabolic potential across the pigmented upper layers and the sediment-enriched deeper layers in these pinnacle and cone-shaped microbial structures using metagenomics. These microbial structures are inhabited by distinct communities. The upper layers of the cone-shaped structures have a higher abundance of the cyanobacterial MAG Microcoleus, while the pinnacle-shaped structures have a higher abundance of Elainellacea MAG. This suggests that cyanobacteria influence the morphologies of the mats. We identified stark contrasts in the composition of the community and its metabolic potential between the upper and lower layers of the mat. The upper layers of the mat, which receive light, have an increased abundance of photosynthetic pathways. In contrast, the lower layer has an increased abundance of heterotrophic pathways. Our results also showed that Lake Untersee is the first Antarctic lake with a substantial presence of ammonia-oxidizing Nitrospiracea and amoA genes. The genomic capacity for recycling biological molecules was prevalent across metagenome-assembled genomes (MAGs) that cover 19 phyla. This highlights the importance of nutrient scavenging in ultra-oligotrophic environments. Overall, our study provides new insights into the formation of microbial structures and the potential metabolic complexity of Antarctic laminated microbial mats. These mats are important environments for biodiversity that drives biogeochemical cycling in polar deserts.

Genome-resolved metagenomics reveals the effect of nutrient availability on bacterial genomic properties across 44 European freshwater lakes.

Understanding intricate microbial interactions in the environment is crucial. This is especially true for the relationships between nutrients and bacteria, as phosphorus, nitrogen and organic carbon availability are known to influence bacterial population dynamics. It has been suggested that low nutrient conditions prompt the evolutionary process of genome streamlining. This process helps conserve scarce nutrients and allows for proliferation. Genome streamlining is associated with genomic properties such as %GC content, genes encoding sigma factors, percent coding regions, gene redundancy, and functional shifts in processes like cell motility and ATP binding cassette transporters, among others. The current study aims to unveil the impact of nutrition on the genome size, %GC content, and functional properties of pelagic freshwater bacteria. We do this at finer taxonomic resolutions for many metagenomically characterized communities. Our study confirms the interplay of trophic level and genomic properties. It also highlights that different nutrient types, particularly phosphorus and nitrogen, impact these properties differently. We observed a covariation of functional traits with genome size. Larger genomes exhibit enriched pathways for motility, environmental interaction, and regulatory genes. ABC transporter genes reflect the availability of nutrients in the environment, with small genomes presumably relying more on metabolites from other organisms. We also discuss the distinct strategies different phyla adopt to adapt to oligotrophic environments. The findings contribute to our understanding of genomic adaptations within complex microbial communities.

Analysis of the Nasal Microbiota in Healthy and Diseased Pigs.

Massive sequencing of a fragment of 16S rRNA gene allows the characterization of bacterial communities in different body sites: the microbiota. Nasal microbiota can be analyzed by DNA extraction from nasal swabs, amplification of the specific fragment of interest, and posterior sequencing. The raw sequences obtained need to go through a computational process to check their quality and then assign the taxonomy. Here, we will describe the complete process from sampling to get the microbial diversity of nasal microbiota in health and disease.

Organic fertilization co-selects genetically linked antibiotic and metal(loid) resistance genes in global soil microbiome.

Antibiotic resistance genes (ARGs) and metal(loid) resistance genes (MRGs) coexist in organic fertilized agroecosystems based on their correlations in abundance, yet evidence for the genetic linkage of ARG-MRGs co-selected by organic fertilization remains elusive. Here, an analysis of 511 global agricultural soil metagenomes reveals that organic fertilization correlates with a threefold increase in the number of diverse types of ARG-MRG-carrying contigs (AMCCs) in the microbiome (63 types) compared to non-organic fertilized soils (22 types). Metatranscriptomic data indicates increased expression of AMCCs under higher arsenic stress, with co-regulation of the ARG-MRG pairs. Organic fertilization heightens the coexistence of ARG-MRG in genomic elements through impacting soil properties and ARG and MRG abundances. Accordingly, a comprehensive global map was constructed to delineate the distribution of coexistent ARG-MRGs with virulence factors and mobile genes in metagenome-assembled genomes from agricultural lands. The map unveils a heightened relative abundance and potential pathogenicity risks (range of 4-6) for the spread of coexistent ARG-MRGs in Central North America, Eastern Europe, Western Asia, and Northeast China compared to other regions, which acquire a risk range of 1-3. Our findings highlight that organic fertilization co-selects genetically linked ARGs and MRGs in the global soil microbiome, and underscore the need to mitigate the spread of these co-resistant genes to safeguard public health.

Proof of Concept Study: Comparability of Microbiome Diversity in Self- and Physician-Collected HPV-Positive and HPV-Negative Cervicovaginal Samples.

Recent studies have revealed the impact of human papillomavirus (HPV) infections on the cervicovaginal microbiome; however, few have explored the utility of self-collected specimens (SCS) for microbiome detection, obtained using standardised methods for HPV testing. Here, we present a proof-of-concept analysis utilising Oxford Nanopore sequencing of the 16S rRNA gene in paired samples collected either by the patient using an Evalyn Brush or collected by a physician using liquid-based cytology (LBC). We found no significant differences in the α-diversity estimates between the SCS and LBC samples. Similarly, when analysing ß-diversity, we observed a close grouping of paired samples, indicating that both collection methods detected the same microbiome features. The identification of genera and Lactobacillus species in each sample allowed for their classification into community state types (CSTs). Notably, paired samples had the same CST, while HPV-positive and -negative samples belonged to distinct CSTs. As previously described in other studies, HPV-positive samples exhibited heightened bacterial diversity, reduced Lactobacillus abundance, and an increase in genera like Sneathia or Dialister. Altogether, this study showed comparable results between the SCS and LBC samples, underscoring the potential of self-sampling for analysing the microbiome composition in cervicovaginal samples initially collected for HPV testing in the context of cervical cancer screening.

Pinpointing the microbiota of tardigrades: What is really there?

Microbiota are considered significant in the biology of tardigrades, yet their diversity and distribution remain largely unexplored. This is partly due to the methodological challenges associated with studying the microbiota of small organisms that inhabit microbe-rich environments. In our study, we characterized the microbiota of 31 species of cultured tardigrades using 16S rRNA amplicon sequencing. We employed various sample preparation strategies and multiple types of controls and estimated the number of microbes in samples using synthetic DNA spike-ins. We also reanalysed data from previous tardigrade microbiome studies. Our findings suggest that the microbial communities of cultured tardigrades are predominantly composed of bacterial genotypes originating from food, medium, or reagents. Despite numerous experiments, we found it challenging to identify strains that were enriched in certain tardigrades, which would have indicated likely symbiotic associations. Putative tardigrade-associated microbes rarely constituted more than 20% of the datasets, although some matched symbionts identified in other studies. We also uncovered serious contamination issues in previous tardigrade microbiome studies, casting doubt on some of their conclusions. We concluded that tardigrades are not universally dependent on specialized microbes. Our work underscores the need for rigorous safeguards in studies of the microbiota of microscopic organisms and serves as a cautionary tale for studies involving samples with low microbiome abundance.