Shotgun metagenomics, from sampling to analysis

A systematic review on current approaches in bat virus discovered between 2018 and 2022

Zoonotic viruses are widely seen as the primary threat for future pandemics. Bats are the most diverse group of mammals, with more than 1400 species distributed across most habitats on Earth. So far, 31 known virus families were associated with bats, although the understanding of most viruses were insufficient. Continuous efforts to discover, understand and monitor these bats viruses, is thereby an area of public health interest. This systematic review was designed to catalogue publications reporting novel bat virus discoveries within PubMed, SCOPUS, and Web of Science databases, within a 5-year period from 2018 to 2022. Various experimental parameters, including sampling locations, methodology, bat species diversity, similarity to known viruses, species demarcation of new viruses, and genomic sequencing strategies, were extracted from 41 publications and analyzed. In total, 72 novel viruses from 19 virus families were identified between 2018 and 2022, particularly from Genomoviridae (DNA viruses) and Coronaviridae (RNA viruses). That said, only a limited number of bat families featured extensively despite noticeable shift towards next generation sequencing methods and metagenomics pipeline for virus identification across different sampling methods. This review aims to provide a comprehensive analysis of the global efforts made over the past five years to identify and characterize emerging viruses in bat species, and to provide a detailed overview of the current technologies and methodologies used in these studies.

Relevance of harmful intratumoral microbiota in cancer progression and its clinical application

Microorganisms are closely related to human health, and changes in the microbiome can lead to the occurrence of diseases. With advances in sequencing technology and research, it has been discovered that intratumoral microbiota exists in various cancer tissues and differs in various cancers. Microorganism can colonize tumor tissues through intestine of damaged mucosal barrier, proximity to normal tissues and bloodstream circulation. Increasing evidence suggests that intratumoral microbiota promotes tumor progression by increasing genomic instability, affecting host immune systems, promoting tumor migration, and regulating tumor signaling pathways. This review article summarizes the latest progress in intratumoral microbiome research, including the development history of intratumoral microbiota, their composition and sources within tumors, their distribution in various cancer tissues, as well as their role in cancer development. Furthermore, the application of intratumoral microbiota in clinical settings is emphasized and we innovatively summarize the clinical trials involving microbial applications for cancer diagnosis and treatment across different countries.

Field and laboratory guidelines for reliable bioinformatic and statistical analysis of bacterial shotgun metagenomic data

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.

Effects of iron-carbon on nitrogen metabolism of floc and aerobic granular sludge

The aerobic granular sludge (AGS) process had been extensively studied for its simultaneous nitrification and denitrification (SND) capabilities. Iron-carbon (IC) had enhanced AGS nitrogen removal efficiency, but the mechanism remained unclear. In this study, four reactors had been added with 50, 30, 10, and 0 g/L of IC. Total nitrogen removal efficiency increased with IC dosage under the same operation mode. IC enhanced sludge ammonia oxidation rate, denitrification rate, and specific oxygen uptake rate, allowing SND to complete 60 min earlier, potentially reducing wastewater treatment costs. Notably, IC eliminated nitrite accumulation in conventional AGS effluent. IC decreased the abundance of genes and enzyme activities related to NOR expression, while increasing those related to NOS, which may mitigate the potential for nitrous oxide formation by microorganisms. In this study, IC acted as an enzymatic reaction activator, affecting granules more than flocs, with the activity gap gradually decreasing with the IC dosage.

Nanopore R10.4 metagenomic detection of blaCTX-M/blaDHA antimicrobial resistance genes and their genetic environments in stool

The increasing prevalence of gut colonization with CTX-M extended-spectrum β-lactamase- and/or DHA plasmid-mediated AmpC-producing Escherichia coli is a concern. Here, we evaluate Nanopore-shotgun metagenomic sequencing (Nanopore-SMS) latest V14 chemistry to detect blaCTX-M and blaDHA genes from healthy stools. We test 25 paired samples characterized with culture-based methods (native and pre-enriched). Antimicrobial resistant genes (ARGs) are detected from reads and meta-assembled genomes (MAGs) to determine their associated genetic environments (AGEs). Sensitivity and specificity of native Nanopore-SMS are 61.1% and 100%, compared to 81.5% and 75% for pre-enriched Nanopore-SMS, respectively. Native Nanopore-SMS identifies only one sample with an AGE, whereas pre-enriched Nanopore-SMS recognizes 9/18 plasmids and 5/9 E. coli chromosomes. Pre-enriched Nanopore-SMS identifies more ARGs than native Nanopore-SMS (p < 0.001). Notably, blaCTX-Ms and blaDHAs AGEs (plasmid and chromosomes) are identified within 1 hour of sequencing. Furthermore, microbiota analyses show that pre-enriched Nanopore-SMS results in more E. coli classified reads (47% vs. 3.1%), higher differential abundance (5.69 log2 fold) and lower Shannon diversity index (p < 0.0001). Nanopore-SMS has the potential to be used for intestinal colonization screening. However, sample pre-enrichment is necessary to increase sensitivity. Further computational improvements are needed to reduce the turnaround time for clinical applications.

Understanding the gastrointestinal microbiome in systemic sclerosis: methodological advancements and emerging research

PURPOSE OF REVIEW

This review highlights the role of the gastrointestinal (GI) microbiome in systemic sclerosis (SSc). We describe techniques for evaluating the GI microbiome in humans, and emerging research linking GI microbiome alterations (i.e., dysbiosis) and distinct SSc clinical manifestations. We also address the evolving treatment landscape targeting dysbiosis in SSc.

RECENT FINDINGS

Recent literature brings into focus the complex relationship between the GI microbiome and SSc pathogenesis. Advanced techniques (e.g., shotgun metagenomics, meta-transcriptomics) provide deeper insights into microbial taxonomy and active gene expression, exposing dysbiosis as a potential driver of SSc. New studies demonstrate that SSc patients who possess specific SSc clinical features, (e.g., interstitial lung disease), have unique GI microbiome profiles.

SUMMARY

Dysbiosis is associated with specific clinical features in patients with SSc. New tools for studying the GI microbiome have furthered our understanding of the relationship between dysbiosis and SSc complications. Therapeutic avenues such as dietary adjustments, probiotics, antibiotics, mindfulness practices, and fecal transplants offer potential for managing SSc and preventing its progression through GI microbiome modulation. By clarifying what is known about the relationship between the GI dysbiosis, GI dysfunction, and SSc, this review enhances our understanding of SSc pathogenesis and proposes targeted interventions.

Metapresence: a tool for accurate species detection in metagenomics based on the genome-wide distribution of mapping reads

Shotgun metagenomics allows comprehensive sampling of the genomic information of microbes in a given environment and is a tool of choice for studying complex microbial systems. Mapping sequencing reads against a set of reference or metagenome-assembled genomes is in principle a simple and powerful approach to define the species-level composition of the microbial community under investigation. However, despite the widespread use of this approach, there is no established way to properly interpret the alignment results, with arbitrary relative abundance thresholds being routinely used to discriminate between present and absent species. Such an approach can be affected by significant biases, especially in the identification of rare species. Therefore, it is important to develop new metrics to overcome these biases. Here, we present Metapresence, a new tool to perform reliable identification of the species in metagenomic samples based on the distribution of mapped reads on the reference genomes. The analysis is based on two metrics describing the breadth of coverage and the genomic distance between consecutive reads. We demonstrate the high precision and wide applicability of the tool using data from various synthetic communities, a real mock community, and the gut microbiome of healthy individuals and antibiotic-associated-diarrhea patients. Overall, our results suggest that the proposed approach has a robust performance in hard-to-analyze microbial communities containing contaminated or closely related genomes in low abundance.IMPORTANCEDespite the prevalent use of genome-centric alignment-based methods to characterize microbial community composition, there lacks a standardized approach for accurately identifying the species within a sample. Currently, arbitrary relative abundance thresholds are commonly employed for this purpose. However, due to the inherent complexity of genome structure and biases associated with genome-centric approaches, this practice tends to be imprecise. Notably, it introduces significant biases, particularly in the identification of rare species. The method presented here addresses these limitations and contributes significantly to overcoming inaccuracies in precisely defining community composition, especially when dealing with rare members.

Melon: metagenomic long-read-based taxonomic identification and quantification using marker genes

Long-read sequencing holds great potential for characterizing complex microbial communities, yet taxonomic profiling tools designed specifically for long reads remain lacking. We introduce Melon, a novel marker-based taxonomic profiler that capitalizes on the unique attributes of long reads. Melon employs a two-stage classification scheme to reduce computational time and is equipped with an expectation-maximization-based post-correction module to handle ambiguous reads. Melon achieves superior performance compared to existing tools in both mock and simulated samples. Using wastewater metagenomic samples, we demonstrate the applicability of Melon by showing it provides reliable estimates of overall genome copies, and species-level taxonomic profiles.

Navigating the complex relationship between human gut microbiota and breast cancer: Physiopathological, prognostic and therapeutic implications

The human body represents the habitat of trillions of symbiotic microorganisms, collectively known as human microbiota, approximately half of which residing in the gut. The development of next-generation sequencing techniques has boosted the profiling of human microbiota in recent years. A growing body of evidence seems to support a strict relationship between the disruption of the mutualistic relationship between the microbiota and the host (i.e., dysbiosis) and the development of several diseases, including breast malignancies. Breast cancer still represents the most frequent cause of cancer-related death in women. Its complex relationship with gut microbiota is the object of a growing body of evidence. In fact, the interaction with the host immune system and a direct impact of gut microbiota on estrogen, lipid and polyphenols metabolism, seem to potentially affect breast tumor development, progression and response to treatments. In this review, in an attempt to help oncologists navigating this rapidly-evolving research field, we provide an essential overview on the taxonomy, main analytical techniques and terminology most commonly adopted. We discuss what is currently known regarding the interaction between gut microbiota and breast cancer and potential efforts to harness this complex interplay for therapeutic purposes, and revise main ongoing studies. We also briefly provide an overview on breast cancer intratumoral microbiota and its potential role beyond gut microbiota.

Fairy: fast approximate coverage for multi-sample metagenomic binning

BACKGROUND

Metagenomic binning, the clustering of assembled contigs that belong to the same genome, is a crucial step for recovering metagenome-assembled genomes (MAGs). Contigs are linked by exploiting consistent signatures along a genome, such as read coverage patterns. Using coverage from multiple samples leads to higher-quality MAGs; however, standard pipelines require all-to-all read alignments for multiple samples to compute coverage, becoming a key computational bottleneck.

RESULTS

We present fairy ( https://github.com/bluenote-1577/fairy ), an approximate coverage calculation method for metagenomic binning. Fairy is a fast k-mer-based alignment-free method. For multi-sample binning, fairy can be > 250 × faster than read alignment and accurate enough for binning. Fairy is compatible with several existing binners on host and non-host-associated datasets. Using MetaBAT2, fairy recovers 98.5 % of MAGs with > 50 % completeness and < 5 % contamination relative to alignment with BWA. Notably, multi-sample binning with fairy is always better than single-sample binning using BWA ( > 1.5 × more > 50 % complete MAGs on average) while still being faster. For a public sediment metagenome project, we demonstrate that multi-sample binning recovers higher quality Asgard archaea MAGs than single-sample binning and that fairy's results are indistinguishable from read alignment.

CONCLUSIONS

Fairy is a new tool for approximately and quickly calculating multi-sample coverage for binning, resolving a computational bottleneck for metagenomics. Video Abstract.

Functional prediction based on 16S rRNA metagenome data from bacterial microbiota associated with macroalgae from the Peruvian coast

Macroalgae are vital reservoirs for essential epibiotic microorganisms. Among these are growth-promoting bacteria that support the growth and healthy development of their host macroalgae, and these macroalgae can be utilized in agriculture as biostimulants, offering an alternative to traditional agrochemicals. However, to date, no comparative studies have been conducted on the functional profile and bacterial diversity associated with coastal macroalgae of Peru. In this study, we employed amplicon sequencing of the V3-V4 region of 16S rRNA gene in twelve host macroalgae collected from two rocky shores in central Peru to compare their bacterial communities. The results revealed high bacterial diversity across both sites, but differences in microbial composition were noted. The phyla Bacteroidota and Pseudomonadota were predominant. The functional prediction highlighted 44 significant metabolic pathways associated with the bacterial microbiota when comparing host macroalgae. These active pathways are related to metabolism and genetic and cellular information processing. No direct association was detected between the macroalgal genera and the associated microbiota, suggesting that the bacterial community is largely influenced by their genetic functions than the taxonomic composition of their hosts. Furthermore, some species of Chlorophyta and Rhodophyta were observed to host growth-promoting bacteria, such as Maribacter sp. and Sulfitobacter sp.

Bacterial strain sharing between humans, animals, and the environment among urban households

Identifying bacterial transmission pathways is crucial to inform strategies aimed at curbing the spread of pathogenic and antibiotic-resistant bacteria, especially in rapidly urbanizing low- and middle-income countries. In this study, we assessed bacterial strain-sharing and dissemination of antibiotic resistance across humans, domesticated poultry, canines, household soil, and drinking water in urban informal settlements in Nairobi, Kenya. We collected 321 samples from 50 households and performed Pooling Isolated Colonies-seq (PIC-seq) by sequencing pools of up to five Escherichia coli colonies per sample to capture strain diversity, strain-sharing patterns, and overlap of antibiotic-resistant genes (ARGs). Bacterial strains isolated from the household environment carried clinically relevant ARGs, reinforcing the role of the environment in antibiotic resistance dissemination. Strain-sharing rates and resistome similarities across sample types were strongly correlated within households, suggesting clonal spread of bacteria is a main driver of dissemination of ARGs in the domestic urban environment. Within households, E. coli strain-sharing was rare between humans and animals but more frequent between humans and drinking water. E. coli contamination in stored drinking water was also associated with higher strain-sharing between humans in the same household. Our study demonstrates that contaminated drinking water facilitates human to human strain sharing and water treatment can disrupt transmission.

Microbiota-gut-brain axis in perioperative neurocognitive and depressive disorders: Pathogenesis to treatment

An increasing number of people undergo anesthesia and surgery. Perioperative neurocognitive and depressive disorders are common central nervous system complications with similar pathogeneses. These conditions pose a deleterious threat to human health and a significant societal burden. In recent years, numerous studies have focused on the role of the gut microbiota and its metabolites in the central nervous system via the gut-brain axis. Its involvement in perioperative neurocognitive and depressive disorders has attracted considerable attention. This review aimed to elucidate the role of the gut microbiota and its metabolites in the pathogenesis of perioperative neurocognitive and depressive disorders, as well as the value of targeted interventions and treatments.

From soil to sequence: filling the critical gap in genome-resolved metagenomics is essential to the future of soil microbial ecology

Soil microbiomes are heterogeneous, complex microbial communities. Metagenomic analysis is generating vast amounts of data, creating immense challenges in sequence assembly and analysis. Although advances in technology have resulted in the ability to easily collect large amounts of sequence data, soil samples containing thousands of unique taxa are often poorly characterized. These challenges reduce the usefulness of genome-resolved metagenomic (GRM) analysis seen in other fields of microbiology, such as the creation of high quality metagenomic assembled genomes and the adoption of genome scale modeling approaches. The absence of these resources restricts the scale of future research, limiting hypothesis generation and the predictive modeling of microbial communities. Creating publicly available databases of soil MAGs, similar to databases produced for other microbiomes, has the potential to transform scientific insights about soil microbiomes without requiring the computational resources and domain expertise for assembly and binning.

Bilateral Perivascular Chorioretinal Atrophy Resembling Pigmented Paravenous Chorioretinal Atrophy Post COVID-19 Infection: A Case Report and Comprehensive Immune Profiling

The pandemic of COVID-19 caused by the SARS-CoV-2 virus is ongoing and a serious menace to global public health. An ocular manifestation is an initial sign of the infection. To date, a comprehensive immune profile of patients with mild COVID-19 has not been well developed. Here, we report a 53-year-old female who noticed a sudden decrease in visual acuity (VA) in both eyes on the fourth day after COVID-19 infection. At presentation (acute phase), the best-corrected VA (BCVA) on the decimal chart was 0.5 in both the right and left eyes. Color fundus photography showed perivascular chorioretinal atrophy with peripheral pigment loss, similar to the fundus appearance of pigmented paravenous chorioretinal atrophy (PPCRA) in the inferior arcade vessels of both eyes. Optical coherence tomography indicated thinning and blurred boundaries of the outer retina in the lesion sites, implying anatomical destruction. She was followed up without any systemic medications. After approximately 15 weeks (remission phase), the BCVA recovered to 0.6 in the right eye and 0.8 in the left. Systemic immune profiles were analyzed using mass cytometry. In the acute phase, monocytes and basophils were dominantly elevated, which suggested the activation of innate immune responses to SARS-CoV-2 and allergic inflammation. In the remission phase, Th2-like cells, plasmablasts, and neutrophils increased predominantly, implying the maturation of adaptive immunity and the preparedness of innate immunity to combat the infection. Our findings indicate that perivascular chorioretinal atrophy resembling PPCRA is a clinical feature of the ocular phenotype of COVID-19, caused by systemic immune responses.

Metagenome-assembled genomes (MAGs) suggest an acetate-driven protective role in gut microbiota disrupted by Clostridioides difficile

Clostridioides difficile may have a negative impact on gut microbiota composition in terms of diversity and abundance, thereby triggering functional changes supported by the differential presence of genes involved in significant metabolic pathways, such as short-chain fatty acids (SCFA). This work has evaluated shotgun metagenomics data regarding 48 samples from four groups classified according to diarrhea acquisition site (community- and healthcare facility-onset) and positive or negative Clostridioides difficile infection (CDI) result. The metagenomic-assembled genomes (MAGs) obtained from each sample were taxonomically assigned for preliminary comparative analysis concerning differences in composition among groups. The predicted genes involved in metabolism, transport, and signaling remained constant in microbiota members; characteristic patterns were observed in MAGs and genes involved in SCFA butyrate and acetate metabolic pathways for each study group. A decrease in genera and species, as well as relative MAG abundance with the presence of the acetate metabolism-related gene, was evident in the HCFO/- group. Increased antibiotic resistance markers (ARM) were observed in MAGs along with the genes involved in acetate metabolism. The results highlight the need to explore the role of acetate in greater depth as a potential protector of the imbalances produced by CDI, as occurs in other inflammatory intestinal diseases.

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

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

Oral specimens as a tool for accurate metagenomic analysis: A pilot study

OBJECTIVES

Acute oral mucosal damage, as well as other inflammatory processes seem to be related to dysbiosis of the oral microbiome. The need to study changes in the oral microbiome led us to hypothesize what type of sample would provide the most representative picture of the entire human oral microbiome.

MATERIALS AND METHODS

An observational, and cross-sectional study was carried out. Six healthy adult participants provided 3 different sample types each, that included saliva, oral rinse and mucosal biopsy tissue. We performed 16S rRNA sequencing of the V3-V4 region of the 18 samples using Illumina MiSeq technology.

RESULTS

Participants were 27 ± 6,3 years old. Bacterial alpha diversity was higher in oral rinse samples compared to whole unstimulated saliva and oral mucosa tissue (p = 0,005). However, saliva specimens showed a 56 % relative abundance of identified species followed by a 30 % in oral rinse and only 1 % in tissue samples.

CONCLUSIONS

This study found differences on oral microbiome composition for each type of sample. Oral rinse should be chosen when higher alpha diversity is needed, whereas whole unstimulated saliva should be more appropriate for larger amount of bacterial DNA.

CLINICAL RELEVANCE

The results obtained demonstrate the importance of a correct choice of the optimal type of oral sample for microbiome studies due to the differences found in its composition.

Development of an anti-inflammatory diet for first-episode psychosis (FEP): a feasibility study protocol

Background

Evidence suggests inflammation plays a role in the pathophysiology of psychosis even in early illness, indicating a potential avenue for anti-inflammatory interventions that simultaneously address high rates of metabolic disease in this population. The aim of this study is to design a novel anti-inflammatory diet intervention (DI) that is feasible to implement in a first-episode psychosis (FEP) population.

Methods

Eligible FEP Participants are aged 15-30. The DI is currently being refined through a multi-phase process that includes the recruitment of focus groups that provide insight into feasibility of measures and nutritional education, as well as the implementation of the DI. The phases in the study are the Development Phase, Formative Phase, and the Feasibility Phase.

Results

The Development phase has resulted in the creation of a flexible DI for FEP based on existing research on nutritional health and informed by providers. This study has just completed the Formative phase, recruiting eligible participants to join focus groups that gleaned information about dietary habits, preferences, and food environments to further refine the DI.

Conclusion

Findings from earlier phases have advised the current Feasibility Phase in which this novel DI is being administered to a small cohort of FEP participants (N = 12) to determine acceptability of the DI from a lived experience perspective. Naturalistic changes in inflammatory biomarkers, metabolic health, and symptoms will also be measured.

Solving genomic puzzles: computational methods for metagenomic binning

Metagenomics involves the study of genetic material obtained directly from communities of microorganisms living in natural environments. The field of metagenomics has provided valuable insights into the structure, diversity and ecology of microbial communities. Once an environmental sample is sequenced and processed, metagenomic binning clusters the sequences into bins representing different taxonomic groups such as species, genera, or higher levels. Several computational tools have been developed to automate the process of metagenomic binning. These tools have enabled the recovery of novel draft genomes of microorganisms allowing us to study their behaviors and functions within microbial communities. This review classifies and analyzes different approaches of metagenomic binning and different refinement, visualization, and evaluation techniques used by these methods. Furthermore, the review highlights the current challenges and areas of improvement present within the field of research.

A permutable MLP-like architecture for disease prediction from gut metagenomic data

Metagenomic data plays a crucial role in analyzing the relationship between microbes and diseases. However, the limited number of samples, high dimensionality, and sparsity of metagenomic data pose significant challenges for the application of deep learning in data classification and prediction. Previous studies have shown that utilizing the phylogenetic tree structure to transform metagenomic abundance data into a 2D matrix input for convolutional neural networks (CNNs) improves classification performance. Inspired by the success of a Permutable MLP-like architecture in visual recognition, we propose Metagenomic Permutator (MetaP), which applied the Permutable MLP-like network structure to capture the phylogenetic information of microbes within the 2D matrix formed by phylogenetic tree. Our experiments demonstrate that our model achieved competitive performance compared to other deep neural networks and traditional machine learning, and has good prospects for multi-classification and large sample sizes. Furthermore, we utilize the SHAP (SHapley Additive exPlanations) method to interpret our model predictions, identifying the microbial features that are associated with diseases.

Strain tracking with uncertainty quantification

The ability to detect and quantify microbiota over time has a plethora of clinical, basic science, and public health applications. One of the primary means of tracking microbiota is through sequencing technologies. When the microorganism of interest is well characterized or known a priori , targeted sequencing is often used. In many applications, however, untargeted bulk (shotgun) sequencing is more appropriate; for instance, the tracking of infection transmission events and nucleotide variants across multiple genomic loci, or studying the role of multiple genes in a particular phenotype. Given these applications, and the observation that pathogens (e.g. Clostridioides difficile, Escherichia coli, Salmonella enterica ) and other taxa of interest can reside at low relative abundance in the gastrointestinal tract, there is a critical need for algorithms that accurately track low-abundance taxa with strain level resolution. Here we present a sequence quality- and time-aware model, ChronoStrain , that introduces uncertainty quantification to gauge low-abundance species and significantly outperforms the current state-of-the-art on both real and synthetic data. ChronoStrain leverages sequences' quality scores and the samples' temporal information to produce a probability distribution over abundance trajectories for each strain tracked in the model. We demonstrate Chronostrain's improved performance in capturing post-antibiotic Escherichia coli strain blooms among women with recurrent urinary tract infections (UTIs) from the UTI Microbiome (UMB) Project. Other strain tracking models on the same data either show inconsistent temporal colonization or can only track consistently using very coarse groupings. In contrast, our probabilistic outputs can reveal the relationship between low-confidence strains present in the sample that cannot be reliably assigned a single reference label (either due to poor coverage or novelty) while simultaneously calling high-confidence strains that can be unambiguously assigned a label. We also analyze samples from the Early Life Microbiota Colonisation (ELMC) Study demonstrating the algorithm's ability to correctly identify Enterococcus faecalis strains using paired sample isolates as validation.

The Impact of Metolachlor Applications and Phytoremediation Processes on Soil Microorganisms: Insights from Functional Metagenomics Analysis

This study assessed the impact of phytoremediation on reducing the residual concentration of metolachlor in soil treated with doses of 530.7 and 1061.4 g/ha and its effect on microbial biodiversity in contaminated areas. For the plant species Avena sativa and Medicago sativa, a significant efficacy of 54.5 and 36.4% was observed in the dissipation of the herbicide, especially at higher doses. Although metolachlor application reduced soil microbial biodiversity, phytoremediating plants, especially M. sativa, promoted greater richness and distribution of microbial species, mitigating the negative effects of the herbicide. Principal component analysis revealed the influence of these plants and metolachlor on the composition of the microbial community. These results highlight the importance of phytoremediation in promoting soil biodiversity and reducing herbicide contamination, providing crucial insights for remediation strategies in contaminated areas.

Toward a Circular Bioeconomy: Designing Microbes and Polymers for Biodegradation

Polymer production is rapidly increasing, but there are no large-scale technologies available to effectively mitigate the massive accumulation of these recalcitrant materials. One potential solution is the development of a carbon-neutral polymer life cycle, where microorganisms convert plant biomass to chemicals, which are used to synthesize biodegradable materials that ultimately contribute to the growth of new plants. Realizing a circular carbon life cycle requires the integration of knowledge across microbiology, bioengineering, materials science, and organic chemistry, which itself has hindered large-scale industrial advances. This review addresses the biodegradation status of common synthetic polymers, identifying novel microbes and enzymes capable of metabolizing these recalcitrant materials and engineering approaches to enhance their biodegradation pathways. Design considerations for the next generation of biodegradable polymers are also reviewed, and finally, opportunities to apply findings from lignocellulosic biodegradation to the design and biodegradation of similarly recalcitrant synthetic polymers are discussed.

Shotgun metagenomic sequencing of swab samples from Japanese university campuses

We obtained shotgun metagenome sequences from swab samples obtained through 3-minute swabbing of different surfaces and the air within buildings at three university campuses in part of the Greater Tokyo Area in Japan. These data aid in understanding built environment microbial communities and elucidate various microbial profiles across different locations.

The virome of bubaline (Bubalus bubalis) tonsils reveals an unreported bubaline polyomavirus

Water buffalo (Bubalus bubalis) farming is increasing in many regions of the world due to the species' ability to thrive in environments where bovine cattle would struggle. Despite water buffaloes being known for their resistance to diseases, there is a lack of data about the diversity of the microbiome of the species. In this study, we examined the virome diversity in palatine tonsils collected from animals from the island of Marajó, northern Pará state, Brazil, which harbors the largest bubaline flock in the country. Tonsil fragments from 60 clinically healthy bubalines were randomly selected from a sample of 293 animals. The samples were purified, extracted, and randomly amplified with phi29 DNA polymerase. After amplification, the products were purified and sequenced. Circular DNA viruses were predominant in the tonsils' virome. Sequences of genome segments representative of members of the genera Alphapolyomavirus (including a previously unreported bubaline polyomavirus genome) and Gemycircularvirus were identified, along with other not yet classified circular virus genomes. As the animals were clinically healthy at the time of sampling, such viruses likely constitute part of the normal tonsillar virome of water buffaloes inhabiting the Ilha do Marajó biome.

Indigenous metal-tolerant mine water bacterial populations under varying metal stresses

The present study investigated the indigenous metal-tolerant bacterial populations in the mine-water microbiome. Our intention was to assess the effects of the metal concentrations in mine water on the bacterial community of mine waters. The bacterial communities in Vanadium and Gold mine-water samples were exposed to different heavy-metal Arsenic, Cadmium, Chromium, Nickel, Mercury and Vanadium at two different concentrations (5 and 25 mM). The 16S rRNA amplicon from mine waters were sequenced using the Illumina's NGS MiSeq platform. Data analysis revealed a high diversity in the bacterial populations associated with the different heavy metals at different concentrations. The taxonomic profiles obtained after the exposure were different in different salts, but mostly dominated by Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria and Firmicutes at variable relative abundance. Principal Component Analysis (PCoA) predicts the clear community shift after exposure with heavy metals salts and emergence of tolerant community depending upon the specific community present in the original mine water.

A practical introduction to holo-omics

Holo-omics refers to the joint study of non-targeted molecular data layers from host-microbiota systems or holobionts, which is increasingly employed to disentangle the complex interactions between the elements that compose them. We navigate through the generation, analysis, and integration of omics data, focusing on the commonalities and main differences to generate and analyze the various types of omics, with a special focus on optimizing data generation and integration. We advocate for careful generation and distillation of data, followed by independent exploration and analyses of the single omic layers to obtain a better understanding of the study system, before the integration of multiple omic layers in a final model is attempted. We highlight critical decision points to achieve this aim and flag the main challenges to address complex biological questions regarding the integrative study of host-microbiota relationships.

Tracking the hologenome dynamics in aquatic invertebrates by the holo-2bRAD approach

The "hologenome" concept is an increasingly popular way of thinking about microbiome-host for marine organisms. However, it is challenging to track hologenome dynamics because of the large amount of material, with tracking itself usually resulting in damage or death of the research object. Here we show the simple and efficient holo-2bRAD approach for the tracking of hologenome dynamics in marine invertebrates (i.e., scallop and shrimp) from one holo-2bRAD library. The stable performance of our approach was shown with high genotyping accuracy of 99.91% and a high correlation of r > 0.99 for the species-level profiling of microorganisms. To explore the host-microbe association underlying mass mortality events of bivalve larvae, core microbial species changed with the stages were found, and two potentially associated host SNPs were identified. Overall, our research provides a powerful tool with various advantages (e.g., cost-effective, simple, and applicable for challenging samples) in genetic, ecological, and evolutionary studies.

16S rRNA, metagenomics and 2bRAD-M sequencing to decode human thanatomicrobiome

Microorganisms are essential in the decomposition of corpses and play a significant role in forensic science. However, previous studies have primarily focused on animal remains, specifically the gut, skin, and burial environment. Insufficient research has been conducted on the microbiota of human cadavers, especially in cases of advanced decomposition and additional tissues, resulting in a lack of relevant reference data. In this study, the microbiota of eight cadavers at different stages of decomposition were detected using 16S rRNA, metagenomic sequencing and 2bRAD-M sequencing. Nine different sites, including oral and nasal cavities, heart, liver, spleen, lung, kidney, muscle and gut, were analysed and the efficacy of these methods was evaluated. The results showed that 16S rRNA sequencing was the most cost-effective method for the study of cadavers in the early stages of decomposition, whereas for cadaveric tissues in the late stages of decomposition, 2bRAD-M could overcome host contamination more effectively than metagenomic sequencing. This paves the way for new opportunities in data retrieval and promotes in-depth investigations into the microbiota.

Integrating functional metagenomics to decipher microbiome-immune interactions

Microbial metabolites can be viewed as the cytokines of the microbiome, transmitting information about the microbial and metabolic environment of the gut to orchestrate and modulate local and systemic immune responses. Still, many immunology studies focus solely on the taxonomy and community structure of the gut microbiota rather than its functions. Early sequencing-based microbiota profiling approaches relied on PCR amplification of small regions of bacterial and fungal genomes to facilitate identification of the microbes present. However, recent microbiome analysis methods, particularly shotgun metagenomic sequencing, now enable culture-independent profiling of microbiome functions and metabolites in addition to taxonomic characterization. In this review, we showcase recent advances in functional metagenomics methods and applications and discuss the current limitations and potential avenues for future development. Importantly, we highlight a few examples of key areas of opportunity in immunology research where integrating functional metagenomic analyses of the microbiome can substantially enhance a mechanistic understanding of microbiome-immune interactions and their contributions to health and disease states.

Hijacking a rapid and scalable metagenomic method reveals subgenome dynamics and evolution in polyploid plants

Premise

The genomes of polyploid plants archive the evolutionary events leading to their present forms. However, plant polyploid genomes present numerous hurdles to the genome comparison algorithms for classification of polyploid types and exploring genome dynamics.

Methods

Here, the problem of intra- and inter-genome comparison for examining polyploid genomes is reframed as a metagenomic problem, enabling the use of the rapid and scalable MinHashing approach. To determine how types of polyploidy are described by this metagenomic approach, plant genomes were examined from across the polyploid spectrum for both k-mer composition and frequency with a range of k-mer sizes. In this approach, no subgenome-specific k-mers are identified; rather, whole-chromosome k-mer subspaces were utilized.

Results

Given chromosome-scale genome assemblies with sufficient subgenome-specific repetitive element content, literature-verified subgenomic and genomic evolutionary relationships were revealed, including distinguishing auto- from allopolyploidy and putative progenitor genome assignment. The sequences responsible were the rapidly evolving landscape of transposable elements. An investigation into the MinHashing parameters revealed that the downsampled k-mer space (genomic signatures) produced excellent approximations of sequence similarity. Furthermore, the clustering approach used for comparison of the genomic signatures is scrutinized to ensure applicability of the metagenomics-based method.

Discussion

The easily implementable and highly computationally efficient MinHashing-based sequence comparison strategy enables comparative subgenomics and genomics for large and complex polyploid plant genomes. Such comparisons provide evidence for polyploidy-type subgenomic assignments. In cases where subgenome-specific repeat signal may not be adequate given a chromosomes' global k-mer profile, alternative methods that are more specific but more computationally complex outperform this approach.

Hybrid sequence-based analysis reveals the distribution of bacterial species and genes in the oral microbiome at a high resolution

Bacteria in the oral microbiome are poorly identified owing to the lack of established culture methods for them. Thus, this study aimed to use culture-free analysis techniques, including bacterial single-cell genome sequencing, to identify bacterial species and investigate gene distribution in saliva. Saliva samples from the same individual were classified as inactivated or viable and then analyzed using 16S rRNA sequencing, metagenomic shotgun sequencing, and bacterial single-cell sequencing. The results of 16S rRNA sequencing revealed similar microbiota structures in both samples, with Streptococcus being the predominant genus. Metagenomic shotgun sequencing showed that approximately 80 % of the DNA in the samples was of non-bacterial origin, whereas single-cell sequencing showed an average contamination rate of 10.4 % per genome. Single-cell sequencing also yielded genome sequences for 43 out of 48 wells for the inactivated samples and 45 out of 48 wells for the viable samples. With respect to resistance genes, four out of 88 isolates carried cfxA, which encodes a β-lactamase, and four isolates carried erythromycin resistance genes. Tetracycline resistance genes were found in nine bacteria. Metagenomic shotgun sequencing provided complete sequences of cfxA, ermF, and ermX, whereas other resistance genes, such as tetQ and tetM, were detected as fragments. In addition, virulence factors from Streptococcus pneumoniae were the most common, with 13 genes detected. Our average nucleotide identity analysis also suggested five single-cell-isolated bacteria as potential novel species. These data would contribute to expanding the oral microbiome data resource.

Antimicrobial resistance landscape in a metropolitan city context using open drain wastewater-based metagenomic analysis

One Health concept recognizes the inextricable interactions of diverse ecosystems and their subsequent effect on human, animal and plant health. Antimicrobial resistance (AMR) is a major One Health concern and is predicted to cause catastrophes if appropriate measures are not implemented. To understand the AMR landscape in a south Indian metropolitan city, metagenomic analysis of open drains was performed. The data suggests that in January 2022, macrolide class of antibiotics contributed the highest resistance of 40.1% in the city, followed by aminoglycoside- 24.4%, tetracycline- 11.3% and lincosamide- 6.7%. The 'mutations in the 23S rRNA gene conferring resistance to macrolide antibiotics' were the major contributor of resistance with a prevalence of 39.7%, followed by '16s rRNA with mutation conferring resistance to aminoglycoside antibiotics'- 22.2%, '16S rRNA with mutation conferring resistance to tetracycline derivatives'- 9.2%, and '23S rRNA with mutation conferring resistance to lincosamide antibiotics'- 6.7%. The most prevalent antimicrobial resistance gene (ARG) 'mutations in the 23S rRNA gene conferring resistance to macrolide antibiotics' was present in multiple pathogens including Escherichia coli, Campylobacter jejuni, Acinetobacter baumannii, Streptococcus pneumoniae, Pseudomonas aeruginosa, Neisseria gonorrhoeae, Klebsiella pneumoniae and Helicobacter pylori. Most of the geographical locations in the city showed a similar landscape for AMR. Considering human mobility and anthropogenic activities, such an AMR landscape could be common across other regions too. The data indicates that pathogens are evolving and acquiring antibiotic resistance genes to evade antibiotics of multiple major drug classes in diverse hosts. The outcomes of the study are relevant not only in understanding the resistance landscape at a broader level but are also important for identifying the resistant drug classes, the mechanisms of gaining resistance and for developing new drugs that target specific pathways. This kind of surveillance protocol can be extended to regions in other developing countries to assess and combat the problem of antimicrobial resistance.

Insights into the seasonal changes in the taxonomic and functional diversity of bacteria in the eastern Arabian Sea: Shotgun metagenomics approach

The eastern Arabian Sea (EAS) is known for its unique oceanographic features such as the seasonal monsoonal winds, upwelling of nutrient-rich waters and a significant increase in primary productivity during the monsoon season. In this study, we utilised the shotgun metagenomics approach to determine the seasonal variations in bacterial taxonomic and functional profiles during the non-monsoon and monsoon seasons in the EAS. Significant seasonal variations in the bacterial community structure were observed at the phylum and genera levels. These findings also correspond with seasonal shifts in the functional profiles of the bacterial communities based on the variations of genes encoding enzymes associated with different metabolic pathways. Pronounced seasonal variation of bacterial taxa was evident with an increased abundance of Idiomarina, Marinobacter, Psychrobacter and Alteromonas of Proteobacteria, Bacillus and Staphylococcus of Firmicutes during the non-monsoon season. These taxa were linked to elevated nucleotide and amino acid biosynthesis, amino acid and lipid degradation. Conversely, during the monsoon, the taxa composition changed with Alteromonas, Candidatus Pelagibacter of Proteobacteria and Cyanobacteria Synechococcus; contributing largely to the amino acid and lipid biosynthesis, fermentation and inorganic nutrient metabolism which was evident from functional analysis. Regression analysis confirmed that increased seasonal primary productivity significantly influenced the abundance of genes associated with carbohydrate, protein and lipid metabolism. These highlight the pivotal role of seasonal changes in primary productivity in shaping the bacterial communities, their functional profiles and driving the biogeochemical cycling in the EAS.

Protocol for the construction and functional profiling of metagenome-assembled genomes for microbiome analyses

Constructing metagenome-assembled genomes (MAGs) from complex metagenomic samples involves a series of bioinformatics operations, each requiring deep bioinformatics knowledge. Here, we present a protocol for constructing MAGs and conducting functional profiling to address biological questions. We describe steps for system configuration, data downloads, read processing, removal of human DNA contamination, metagenomic assembly, and statistical quality assessment of the final assembly. Additionally, we detail procedures for the construction and refinement of MAGs, as well as the functional profiling of MAGs.

Enhancing antimicrobial resistance detection with MetaGeneMiner: Targeted gene extraction from metagenomes

BACKGROUND

Accurately and efficiently extracting microbial genomic sequences from complex metagenomic data is crucial for advancing our understanding in fields such as clinical diagnostics, environmental microbiology, and biodiversity. As sequencing technologies evolve, this task becomes increasingly challenging due to the intricate nature of microbial communities and the vast amount of data generated. Especially in intensive care units (ICUs), infections caused by antibiotic-resistant bacteria are increasingly prevalent among critically ill patients, significantly impacting the effectiveness of treatments and patient prognoses. Therefore, obtaining timely and accurate information about infectious pathogens is of paramount importance for the treatment of patients with severe infections, which enables precisely targeted anti-infection therapies, and a tool that can extract microbial genomic sequences from metagenomic dataset would be of help.

METHODS

We developed MetaGeneMiner to help with retrieving specific microbial genomic sequences from metagenomes using a k-mer-based approach. It facilitates the rapid and accurate identification and analysis of pathogens. The tool is designed to be user-friendly and efficient on standard personal computers, allowing its use across a wide variety of settings. We validated MetaGeneMiner using eight metagenomic samples from ICU patients, which demonstrated its efficiency and accuracy.

RESULTS

The software extensively retrieved coding sequences of pathogens Acinetobacter baumannii and herpes simplex virus type 1 and detected a variety of resistance genes. All documentation and source codes for MetaGeneMiner are freely available at https://gitee.com/sculab/MetaGeneMiner.

CONCLUSIONS

It is foreseeable that MetaGeneMiner possesses the potential for applications across multiple domains, including clinical diagnostics, environmental microbiology, gut microbiome research, as well as biodiversity and conservation biology. Particularly in ICU settings, MetaGeneMiner introduces a novel, rapid, and precise method for diagnosing and treating infections in critically ill patients. This tool is capable of efficiently identifying infectious pathogens, guiding personalized and precise treatment strategies, and monitoring the development of antibiotic resistance, significantly impacting the diagnosis and treatment of severe infections.

Molecular Diagnostics for Invasive Fungal Diseases: Current and Future Approaches

Invasive fungal diseases (IFDs) comprise a growing healthcare burden, especially given the expanding population of immunocompromised hosts. Early diagnosis of IFDs is required to optimise therapy with antifungals, especially in the setting of rising rates of antifungal resistance. Molecular techniques including nucleic acid amplification tests and whole genome sequencing have potential to offer utility in overcoming limitations with traditional phenotypic testing. However, standardisation of methodology and interpretations of these assays is an ongoing undertaking. The utility of targeted Aspergillus detection has been well-defined, with progress in investigations into the role of targeted assays for Candida, Pneumocystis, Cryptococcus, the Mucorales and endemic mycoses. Likewise, whilst broad-range polymerase chain reaction assays have been in use for some time, pathology stewardship and optimising diagnostic yield is a continuing exercise. As costs decrease, there is also now increased access and experience with whole genome sequencing, including metagenomic sequencing, which offers unparalleled resolution especially in the investigations of potential outbreaks. However, their role in routine diagnostic use remains uncommon and standardisation of techniques and workflow are required for wider implementation.

Diverse patterns of correspondence between protist metabarcodes and protist metagenome-assembled genomes

Two common approaches to study the composition of environmental protist communities are metabarcoding and metagenomics. Raw metabarcoding data are usually processed into Operational Taxonomic Units (OTUs) or amplicon sequence variants (ASVs) through clustering or denoising approaches, respectively. Analogous approaches are used to assemble metagenomic reads into metagenome-assembled genomes (MAGs). Understanding the correspondence between the data produced by these two approaches can help to integrate information between the datasets and to explain how metabarcoding OTUs and MAGs are related with the underlying biological entities they are hypothesised to represent. MAGs do not contain the commonly used barcoding loci, therefore sequence homology approaches cannot be used to match OTUs and MAGs. We made an attempt to match V9 metabarcoding OTUs from the 18S rRNA gene (V9 OTUs) and MAGs from the Tara Oceans expedition based on the correspondence of their relative abundances across the same set of samples. We evaluated several metrics for detecting correspondence between features in these two datasets and developed controls to filter artefacts of data structure and processing. After selecting the best-performing metrics, ranking the V9 OTU/MAG matches by their proportionality/correlation coefficients and applying a set of selection criteria, we identified candidate matches between V9 OTUs and MAGs. In some cases, V9 OTUs and MAGs could be matched with a one-to-one correspondence, implying that they likely represent the same underlying biological entity. More generally, matches we observed could be classified into 4 scenarios: one V9 OTU matches many MAGs; many V9 OTUs match many MAGs; many V9 OTUs match one MAG; one V9 OTU matches one MAG. Notably, we found some instances in which different OTU-MAG matches from the same taxonomic group were not classified in the same scenario, with all four scenarios possible even within the same taxonomic group, illustrating that factors beyond taxonomic lineage influence the relationship between OTUs and MAGs. Overall, each scenario produces a different interpretation of V9 OTUs, MAGs and how they compare in terms of the genomic and ecological diversity they represent.

Epiphytic Bacterial Community Analysis of Ulva prolifera in Garorim and Muan Bays, Republic of Korea

The bacterial communities related to seaweed can vary considerably across different locations, and these variations influence the seaweed's nutrition, growth, and development. To study this further, we evaluated the bacteria found on the green marine seaweed Ulva prolifera from Garorim Bay and Muan Bay, two key locations on Republic of Korea's west coast. Our analysis found notable differences in the bacterial communities between the two locations. Garorim Bay hosted a more diverse bacterial population, with the highest number of ASVs (871) compared to Muan Bay's 156 ASVs. In Muan Bay, more than 50% of the bacterial community was dominated by Pseudomonadota. On the other hand, Garorim Bay had a more balanced distribution between Bacteroidota and Pseudomonadota (37% and 35.5%, respectively). Additionally, Cyanobacteria, particularly Cyanothece aeruginosa, were found in significant numbers in Garorim Bay, making up 8% of the community. Mineral analysis indicated that Garorim Bay had higher levels of S, Na, Mg, Ca, and Fe. Function-wise, both locations exhibited bacterial enrichment in amino acid production, nucleosides, and nucleotide pathways. In conclusion, this study broadens our understanding of the bacterial communities associated with Ulva prolifera in Korean waters and provides a foundation for future research on the relationships between U. prolifera and its bacteria.

Temporal composition of the cervicovaginal microbiome associates with hrHPV infection outcomes in a longitudinal study

BACKGROUND

Persistent infections with high-risk human papillomavirus (hrHPV) can cause cervical squamous intraepithelial lesions (SIL) that may progress to cancer. The cervicovaginal microbiome (CVM) correlates with SIL, but the temporal composition of the CVM after hrHPV infections has not been fully clarified.

METHODS

To determine the association between the CVM composition and infection outcome, we applied high-resolution microbiome profiling using the circular probe-based RNA sequencing technology on a longitudinal cohort of cervical smears obtained from 141 hrHPV DNA-positive women with normal cytology at first visit, of whom 51 were diagnosed by cytology with SIL six months later.

RESULTS

Here we show that women with a microbial community characterized by low diversity and high Lactobacillus crispatus abundance at both visits exhibit low risk to SIL development, while women with a microbial community characterized by high diversity and Lactobacillus depletion at first visit have a higher risk of developing SIL. At the level of individual species, we observed that a high abundance for Gardnerella vaginalis and Atopobium vaginae at both visits associate with SIL outcomes. These species together with Dialister micraerophilus showed a moderate discriminatory power for hrHPV infection progression.

CONCLUSIONS

Our results suggest that the CVM can potentially be used as a biomarker for cervical disease and SIL development after hrHPV infection diagnosis with implications on cervical cancer prevention strategies and treatment of SIL.

Prebiotics, probiotics, synbiotics and postbiotics to adolescents in metabolic syndrome

The prevalence of childhood and adolescent obesity has globally reached alarming dimensions and many adolescents affected by obesity already present one or more obesity-related comorbidities. In recent years, emerging evidence supporting the role of gut microbiota in the pathophysiology of metabolic diseases has been reported and the use of prebiotics, probiotics, synbiotics and postbiotics as a strategy to manipulate gut microbiota has become popular. The aim of this review is to explore the relationship between gut microbiota and metabolic syndrome in adolescents and to discuss the potential use of prebiotics, probiotics, synbiotics and postbiotics for the prevention and treatment of this clinical picture in adolescence. According to the most recent literature, prebiotics, probiotics and synbiotics have no clear effect on MetS, but a possible modulation of anthropometric parameters has been observed after synbiotic supplementation. Only one study has examined the role of postbiotics in alleviating metabolic complications in children with obesity but not in adolescents. More extensive research is needed to support the conclusions drawn so far and to develop effective microbiome-based interventions that may help improving the quality of life of children and adolescents exposed to the increasing prevalence of MetS.

Benchmarking short-, long- and hybrid-read assemblers for metagenome sequencing of complex microbial communities

Metagenome community analyses, driven by the continued development in sequencing technology, is rapidly providing insights in many aspects of microbiology and becoming a cornerstone tool. Illumina, Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio) are the leading technologies, each with their own advantages and drawbacks. Illumina provides accurate reads at a low cost, but their length is too short to close bacterial genomes. Long reads overcome this limitation, but these technologies produce reads with lower accuracy (ONT) or with lower throughput (PacBio high-fidelity reads). In a critical first analysis step, reads are assembled to reconstruct genomes or individual genes within the community. However, to date, the performance of existing assemblers has never been challenged with a complex mock metagenome. Here, we evaluate the performance of current assemblers that use short, long or both read types on a complex mock metagenome consisting of 227 bacterial strains with varying degrees of relatedness. We show that many of the current assemblers are not suited to handle such a complex metagenome. In addition, hybrid assemblies do not fulfil their potential. We conclude that ONT reads assembled with CANU and Illumina reads assembled with SPAdes offer the best value for reconstructing genomes and individual genes of complex metagenomes, respectively.

Limited variation in microbial communities across populations of Macrosteles leafhoppers (Hemiptera: Cicadellidae)

Microbial symbionts play crucial roles in insect biology, yet their diversity, distribution, and temporal dynamics across host populations remain poorly understood. In this study, we investigated the spatio-temporal distribution of bacterial symbionts within the widely distributed and economically significant leafhopper genus Macrosteles, with a focus on Macrosteles laevis. Using host and symbiont marker gene amplicon sequencing, we explored the intricate relationships between these insects and their microbial partners. Our analysis of the cytochrome oxidase subunit I (COI) gene data revealed several intriguing findings. First, there was no strong genetic differentiation across M. laevis populations, suggesting gene flow among them. Second, we observed significant levels of heteroplasmy, indicating the presence of multiple mitochondrial haplotypes within individuals. Third, parasitoid infections were prevalent, highlighting the complex ecological interactions involving leafhoppers. The 16S rRNA data confirmed the universal presence of ancient nutritional endosymbionts-Sulcia and Nasuia-in M. laevis. Additionally, we found a high prevalence of Arsenophonus, another common symbiont. Interestingly, unlike most previously studied species, M. laevis exhibited only occasional cases of infection with known facultative endosymbionts and other bacteria. Notably, there was no significant variation in symbiont prevalence across different populations or among sampling years within the same population. Comparatively, facultative endosymbionts such as Rickettsia, Wolbachia, Cardinium and Lariskella were more common in other Macrosteles species. These findings underscore the importance of considering both host and symbiont dynamics when studying microbial associations. By simultaneously characterizing host and symbiont marker gene amplicons in large insect collections, we gain valuable insights into the intricate interplay between insects and their microbial partners. Understanding these dynamics contributes to our broader comprehension of host-microbe interactions in natural ecosystems.

The microbiome in recurrent pregnancy loss - A scoping review

Recurrent pregnancy loss (RPL) is a troubling condition that affects couples worldwide. Despite extensive research efforts, many RPL cases remain unexplained, highlighting the need for novel approaches to unravel its underlying mechanisms. Recent advances in microbiome research have shed light on the potential role of the microbiome in reproductive health and outcomes. Based on a systematic literature research, this review aims to comprehensively explore the current understanding of the microbiome's involvement in RPL, focusing on the vaginal, endometrial, and gut microbiomes. Evidence from the available studies is examined to explain the relationship between the microbiome and RPL. Furthermore, we discuss the diagnostic potential of the microbiome, therapeutic interventions, and future directions in microbiome research for RPL. Understanding the complex interactions between the microbiome and reproductive health holds promise for developing targeted interventions to help patients today diagnosed as unexplained.

Microbial diagnostics in periodontal diseases

Microbial analytical methods have been instrumental in elucidating the complex microbial etiology of periodontal diseases, by shaping our understanding of subgingival community dynamics. Certain pathobionts can orchestrate the establishment of dysbiotic communities that can subvert the host immune system, triggering inflammation and tissue destruction. Yet, diagnosis and management of periodontal conditions still rely on clinical and radiographic examinations, overlooking the well-established microbial etiology. This review summarizes the chronological emergence of periodontal etiological models and the co-evolution with technological advances in microbial detection. We additionally review the microbial analytical approaches currently accessible to clinicians, highlighting their value in broadening the periodontal assessment. The epidemiological importance of obtaining culture-based antimicrobial susceptibility profiles of periodontal taxa for antibiotic resistance surveillance is also underscored, together with clinically relevant analytical approaches to guide antibiotherapy choices, when necessary. Furthermore, the importance of 16S-based community and shotgun metagenomic profiling is discussed in outlining dysbiotic microbial signatures. Because dysbiosis precedes periodontal damage, biomarker identification offers early diagnostic possibilities to forestall disease relapses during maintenance. Altogether, this review highlights the underutilized potential of clinical microbiology in periodontology, spotlighting the clinical areas most conductive to its diagnostic implementation for enhancing prevention, treatment predictability, and addressing global antibiotic resistance.

Post-stroke cognitive impairment: exploring molecular mechanisms and omics biomarkers for early identification and intervention

Post-stroke cognitive impairment (PSCI) is a major stroke consequence that has a severe impact on patients' quality of life and survival rate. For this reason, it is especially crucial to identify and intervene early in high-risk groups during the acute phase of stroke. Currently, there are no reliable and efficient techniques for the early diagnosis, appropriate evaluation, or prognostication of PSCI. Instead, plenty of biomarkers in stroke patients have progressively been linked to cognitive impairment in recent years. High-throughput omics techniques that generate large amounts of data and process it to a high quality have been used to screen and identify biomarkers of PSCI in order to investigate the molecular mechanisms of the disease. These techniques include metabolomics, which explores dynamic changes in the organism, gut microbiomics, which studies host-microbe interactions, genomics, which elucidates deeper disease mechanisms, transcriptomics and proteomics, which describe gene expression and regulation. We looked through electronic databases like PubMed, the Cochrane Library, Embase, Web of Science, and common databases for each omics to find biomarkers that might be connected to the pathophysiology of PSCI. As all, we found 34 studies: 14 in the field of metabolomics, 5 in the field of gut microbiomics, 5 in the field of genomics, 4 in the field of transcriptomics, and 7 in the field of proteomics. We discovered that neuroinflammation, oxidative stress, and atherosclerosis may be the primary causes of PSCI development, and that metabolomics may play a role in the molecular mechanisms of PSCI. In this study, we summarized the existing issues across omics technologies and discuss the latest discoveries of PSCI biomarkers in the context of omics, with the goal of investigating the molecular causes of post-stroke cognitive impairment. We also discuss the potential therapeutic utility of omics platforms for PSCI mechanisms, diagnosis, and intervention in order to promote the area's advancement towards precision PSCI treatment.

Integrating metagenomic and isolation strategies revealed high contamination of pathogenies and resistome in market shrimps

This study employs a comprehensive approach combining metagenomic analysis and bacterial isolation to elucidate the microbial composition, antibiotic resistance genes (ARGs), and virulence factors (VFGs) present in shrimps from market and supermarket. Metagenomic analysis of shrimps revealed a dominance of Proteobacteria and Bacteroidetes with Firmicutes notably enriched in some samples. On the other hand, the dominant bacteria isolated included Citrobacter portucalensis, Escherichia coli, Salmonella enterica, Vibrio species and Klebsiella pneumonaie. Metagenomic analysis unveiled a diverse spectrum of 23 main types and 380 subtypes of ARGs in shrimp samples including many clinical significant ARGs such as blaKPC, blaNDM, mcr, tet(X4) etc. Genomic analysis of isolated bacterial strains identified 14 ARG types with 109 subtype genes, which complemented the metagenomic data. Genomic analysis also allowed us to identify a rich amount of MDR plasmids, which provided further insights into the dissemination of resistance genes in different species of bacteria in the same samples. Examination of VFGs and mobile genetic elements (MGEs) in both metagenomic and bacterial genomes revealed a complex landscape of factors contributing to bacterial virulence and genetic mobility. Potential co-occurrence patterns of ARGs and VFGs within human pathogenic bacteria underlined the intricate interplay between antibiotic resistance and virulence. In conclusion, this integrated analysis for the first time provides a comprehensive view and sheds new light on the potential hazards associated with shrimp products in the markets. The findings underscore the necessity of ongoing surveillance and intervention strategies to mitigate risks posed by antibiotic-resistant bacteria in the food supply chain using the novel comprehensive approaches.

Clinical applications of metagenomics next-generation sequencing in infectious diseases

Infectious diseases are a great threat to human health. Rapid and accurate detection of pathogens is important in the diagnosis and treatment of infectious diseases. Metagenomics next-generation sequencing (mNGS) is an unbiased and comprehensive approach for detecting all RNA and DNA in a sample. With the development of sequencing and bioinformatics technologies, mNGS is moving from research to clinical application, which opens a new avenue for pathogen detection. Numerous studies have revealed good potential for the clinical application of mNGS in infectious diseases, especially in difficult-to-detect, rare, and novel pathogens. However, there are several hurdles in the clinical application of mNGS, such as: (1) lack of universal workflow validation and quality assurance; (2) insensitivity to high-host background and low-biomass samples; and (3) lack of standardized instructions for mass data analysis and report interpretation. Therefore, a complete understanding of this new technology will help promote the clinical application of mNGS to infectious diseases. This review briefly introduces the history of next-generation sequencing, mainstream sequencing platforms, and mNGS workflow, and discusses the clinical applications of mNGS to infectious diseases and its advantages and disadvantages.