Metagenomic approaches in microbial ecology: an update on whole-genome and marker gene sequencing analyses

Expanding the C-S-R framework to incorporate microbial interactions: evidence from methane-consuming communities

Microbial interactions are critical in shaping community assembly and ecosystem functioning, yet classical ecological frameworks such as Grime's Competitor-Stress Tolerator-Ruderal (C-S-R) model primarily emphasize individual traits, overlooking interspecies dependencies. Here, we propose an expansion of the C-S-R framework to incorporate microbial interactions, using methane-consuming communities in methane-fed microcosms as a model system. We present experimental data on both natural and synthetic methane-consuming communities derived from Lake Washington sediments, demonstrating that nitrate availability regulates community dynamics and life strategies. Under nitrogen limitation, the methanotroph Methylomonas adopts stress tolerance via nitrogen fixation but loses its competitive advantage under nitrate-rich conditions. These shifts are linked to the emergence of Methylotenera, a non-methanotrophic methylotroph that relies on cross-fed carbon from methanotrophs (e.g., Methylobacter) and alters competitive outcomes through metabolic coupling. Our findings show that survival strategies are shaped not only by intrinsic traits but also by interaction-based traits that redistribute resources and reshape ecological niches. By integrating these dynamics, we offer a novel perspective on the C-S-R framework that captures both individual and emergent behaviors, providing new insight into microbial community resilience and improving the predictive power of ecological models under environmental change.

Salivary mycobiome alterations in HIV-infected MSM: dominance of Pseudogymnoascus and functional shifts across disease stages

Background

Oral health is increasingly recognized as a crucial determinant of overall health in people living with HIV/AIDS (PLWHA). Specifically, the oral mycobiome may play a pivotal role in HIV-associated oral complications. However, the fungal species involved and their potential as biomarkers for HIV-related oral conditions remain poorly understood. This study investigates salivary fungal profiles in PLWHA who have sex with men (MSM), focusing on diversity, functional shifts, and correlations with disease progression.

Methods

A cross-sectional study included 25 MSM participants divided into five groups: HIV-negative controls (n = 5) and four HIV-positive groups stratified by CD4 count: Stage 0 (HIV RNA-positive/antibody-negative; n = 5), Stage 1 (CD4 ≥500 cells/μL; n = 5), Stage 2 (CD4 200-499 cells/μL; n = 5), and Stage 3 (CD4 <200 cells/μL or opportunistic infections; n = 5). Saliva samples were collected and analyzed using metagenomic sequencing (Illumina NovaSeq platform). Bioinformatic analyses included genome assembly (MEGAHIT), gene clustering (CD-HIT), gene abundance calculation (SOAPaligner), species annotation (BLASTP), and KEGG pathway annotation (KOBAS 2.0). Statistical analyses (Kruskal-Wallis tests, Spearman's correlation) assessed associations between fungal profiles, CD4 count, and viral loads.

Results

A total of 51 fungal genera were identified, with Pseudogymnoascus being the most abundant. Functional analysis revealed 113 shared KEGG pathways, of which 69 were unique to Stage 3, primarily related to metabolic and disease-related processes. Notably, Auricularia exhibited a positive correlation with CD4 count (P ≤ 0.01), while Mucor showed a negative correlation (P = 0.0299).

Conclusions

Salivary mycobiome composition and function shift significantly across HIV stages, reflecting immune decline. Pseudogymnoascus dominance challenges conventional views of oral fungal ecology in immunocompromised hosts. These findings highlight the mycobiome's diagnostic potential for monitoring HIV-related oral health. Longitudinal studies are needed to validate clinical relevance.

A curated bacterial and archaeal 16S rRNA Gene Oral Sequences dataset

In a given species, genomes and 16S rRNA gene sequences, along with their intragenomic copy numbers, can vary greatly across environments. The gene copy numbers are crucial for technologies which estimate microbial abundances based on gene counts, such as polymerase chain reaction and high-throughput sequencing. In these, taxa with fewer genes may be underestimated, while those with more genes might be overestimated. Therefore, it is essential to have accurate gene copy number databases specific to the niche under study. The 16S rRNA Gene Oral Sequences dataset (16SGOSeq) contains the number of 16S rRNA genes and their variants in the complete genomes of the bacterial and archaeal species present in the human oral cavity. It includes 3,192 complete genomes of oral bacteria and 191 complete genomes of oral archaea, from which the 16S rRNA gene sequences were extracted, and the sequence variants were identified. This oral-specific dataset of prokaryotic organisms and the pipeline followed for its construction can be applied by clinical microbiologists, bioinformaticians, or microbial ecologists in future microbiome research.

Advanced computational tools, artificial intelligence and machine-learning approaches in gut microbiota and biomarker identification

The microbiome of the gut is a complex ecosystem that contains a wide variety of microbial species and functional capabilities. The microbiome has a significant impact on health and disease by affecting endocrinology, physiology, and neurology. It can change the progression of certain diseases and enhance treatment responses and tolerance. The gut microbiota plays a pivotal role in human health, influencing a wide range of physiological processes. Recent advances in computational tools and artificial intelligence (AI) have revolutionized the study of gut microbiota, enabling the identification of biomarkers that are critical for diagnosing and treating various diseases. This review hunts through the cutting-edge computational methodologies that integrate multi-omics data-such as metagenomics, metaproteomics, and metabolomics-providing a comprehensive understanding of the gut microbiome's composition and function. Additionally, machine learning (ML) approaches, including deep learning and network-based methods, are explored for their ability to uncover complex patterns within microbiome data, offering unprecedented insights into microbial interactions and their link to host health. By highlighting the synergy between traditional bioinformatics tools and advanced AI techniques, this review underscores the potential of these approaches in enhancing biomarker discovery and developing personalized therapeutic strategies. The convergence of computational advancements and microbiome research marks a significant step forward in precision medicine, paving the way for novel diagnostics and treatments tailored to individual microbiome profiles. Investigators have the ability to discover connections between the composition of microorganisms, the expression of genes, and the profiles of metabolites. Individual reactions to medicines that target gut microbes can be predicted by models driven by artificial intelligence. It is possible to obtain personalized and precision medicine by first gaining an understanding of the impact that the gut microbiota has on the development of disease. The application of machine learning allows for the customization of treatments to the specific microbial environment of an individual.

Spatiotemporal Characteristics of Bacterial Communities in Estuarine Mangrove Sediments in Zhejiang Province, China

Mangrove forests are intertidal ecosystems that harbor diverse microbial communities essential for biogeochemical cycles and energy flow. This study investigated the seasonal and spatial patterns of bacterial communities in the artificially introduced mangrove sediments of the Ao River estuary using 16S rRNA gene amplicon high-throughput sequencing. Alpha diversity analyses indicated that the bacterial community diversity in the mangrove sediments of the Ao River estuary was similar to those of natural-formed mangroves, with the Shannon index ranging from 5.16 to 6.54, which was significantly higher in winter compared to other seasons. The dominant bacterial phyla included Proteobacteria (43.65%), Actinobacteria (11.55%), Desulfobacterota (11.16%), and Bacteroidetes (5.52%), while beta diversity analysis revealed substantial differences in bacterial community structure across different seasons and regions. For instance, the relative abundance of Woeseiaceae and Bacteroidota during the summer was significantly higher than that observed in other seasons. And the relative abundance of Bacillaceae in autumn and winter increased by one order of magnitude compared to spring and summer. Woeseiaceae, Desulfobulbaceae, Thermoanaerobaculaceae, and Sva1033 (family of Desulfobacterota) exhibited significantly higher relative abundance in the unvegetated area, whereas Bacillaceae and S085 (family of Chloroflexi) demonstrated greater abundance in the mangrove area. Seasonal variations in bacterial community structure are primarily attributed to changes in environmental factors, including temperature and salinity. Regional differences in bacterial community structure are primarily associated with environmental stressors, such as wave action, fluctuations in salinity, and organic matter content, which are further complicated by seasonal changes. This study is significant for understanding the microbial diversity and seasonal dynamics of estuarine mangrove wetlands, and it contributes to the assessment of mangrove wetland restoration efforts in Zhejiang Province, providing important guidance for the development of strategies to maintain the health of mangrove ecosystems in the future.

Omics approaches to explore the coffee fermentation microecosystem and its effects on cup quality

The cultivation and postharvest processing of coffee constitute the basis of the subsistence and traditional culture for rural family-owned farms, as well as for the economic success of commercial enterprises in many coffee-producing countries worldwide. The quality of the final beverage is determined by a multitude of variables. A key post-harvest factor is the spontaneous fermentation of the coffee beans, conducted directly on the farm, to remove the mucilage that firmly adheres to the beans. The effect of this fermentation step on the aromatic profile of the coffee is not yet sufficiently understood. All of the above have drawn the attention of researchers on the application of various omics approaches to elucidate fermentation processes in more detail. These approaches have been used to study the fermentation of Arabica (Coffea arabica) beans, as this species is economically most important worldwide. It is known that Arabica mild coffee is obtained through the wet method, which involves fermenting depulped coffee beans using various strategies and then washing the fermented coffee with clean water. In contrast, the fermentation of Canephora coffee beans has been much less studied using omics technologies. This review highlights the trends and future research in coffee fermentation based on a scientometric analysis, supplemented by a traditional systematic literature review. It highlights the composition of the coffee fermentation microbiome, as elucidated by metagenomics applications, in light of several factors that can influence its structure. Additionally, it considers the metabolites associated with microbial metabolism that can influence the chemical composition of coffee beans and, consequently, the cup quality. In this way, this review evidences the promising path in understanding microbial functions in coffee fermentation and in particular in the development of microbial inocula and in the refinement of fermentation processes to improve coffee quality.

Leveraging innovative diagnostics as a tool to contain superbugs

The evolutionary adaptation of pathogens to biological materials has led to an upsurge in drug-resistant superbugs that significantly threaten public health. Treating most infections is an uphill task, especially those associated with multi-drug-resistant pathogens, biofilm formation, persister cells, and pathogens that have acquired robust colonization and immune evasion mechanisms. Innovative diagnostic solutions are crucial for identifying and understanding these pathogens, initiating efficient treatment regimens, and curtailing their spread. While next-generation sequencing has proven invaluable in diagnosis over the years, the most glaring drawbacks must be addressed quickly. Many promising pathogen-associated and host biomarkers hold promise, but their sensitivity and specificity remain questionable. The integration of CRISPR-Cas9 enrichment with nanopore sequencing shows promise in rapid bacterial diagnosis from blood samples. Moreover, machine learning and artificial intelligence are proving indispensable in diagnosing pathogens. However, despite renewed efforts from all quarters to improve diagnosis, accelerated bacterial diagnosis, especially in Africa, remains a mystery to this day. In this review, we discuss current and emerging diagnostic approaches, pinpointing the limitations and challenges associated with each technique and their potential to help address drug-resistant bacterial threats. We further critically delve into the need for accelerated diagnosis in low- and middle-income countries, which harbor more infectious disease threats. Overall, this review provides an up-to-date overview of the diagnostic approaches needed for a prompt response to imminent or possible bacterial infectious disease outbreaks.

Advancing Gut Microbiome Research: The Shift from Metagenomics to Multi-Omics and Future Perspectives

The gut microbiome, a dynamic and integral component of human health, has co-evolved with its host, playing essential roles in metabolism, immunity, and disease prevention. Traditional microbiome studies, primarily focused on microbial composition, have provided limited insights into the functional and mechanistic interactions between microbiota and their host. The advent of multi-omics technologies has transformed microbiome research by integrating genomics, transcriptomics, proteomics, and metabolomics, offering a comprehensive, systems-level understanding of microbial ecology and host-microbiome interactions. These advances have propelled innovations in personalized medicine, enabling more precise diagnostics and targeted therapeutic strategies. This review highlights recent breakthroughs in microbiome research, demonstrating how these approaches have elucidated microbial functions and their implications for health and disease. Additionally, it underscores the necessity of standardizing multi-omics methodologies, conducting large-scale cohort studies, and developing novel platforms for mechanistic studies, which are critical steps toward translating microbiome research into clinical applications and advancing precision medicine.

A review of neural networks for metagenomic binning

One of the main goals of metagenomic studies is to describe the taxonomic diversity of microbial communities. A crucial step in metagenomic analysis is metagenomic binning, which involves the (supervised) classification or (unsupervised) clustering of metagenomic sequences. Various machine learning models have been applied to address this task. In this review, the contributions of artificial neural networks (ANN) in the context of metagenomic binning are detailed, addressing both supervised, unsupervised, and semi-supervised approaches. 34 ANN-based binning tools are systematically compared, detailing their architectures, input features, datasets, advantages, disadvantages, and other relevant aspects. The findings reveal that deep learning approaches, such as convolutional neural networks and autoencoders, achieve higher accuracy and scalability than traditional methods. Gaps in benchmarking practices are highlighted, and future directions are proposed, including standardized datasets and optimization of architectures, for third-generation sequencing. This review provides support to researchers in identifying trends and selecting suitable tools for the metagenomic binning problem.

Integrating microbial profiling and machine learning for inference of drowning sites: a forensic investigation in the Northwest River

Drowning incidents present significant challenges for forensic investigators in determining the exact site of occurrence. Traditional forensic methods often rely on physical evidence and circumstantial clues, but the emerging field of forensic microbiology offers a promising avenue for enhancing precision and reliability in site inference. Our study investigates the application of microbiome analysis in inferring drowning sites, focusing on microbial diversity in water samples and lung tissues of drowned animals from different sites in the Northwest River. We utilized 16S rDNA sequencing to analyze microbial diversity in water samples and lung tissues, revealing distinct microbial signatures associated with drowning sites. Our findings highlight variations in species richness and diversity across different sampling points, indicating the influence of environmental factors on microbial community structure. Machine learning models trained on microbial data from lung tissues demonstrated high accuracy in predicting drowning sites, with cross-validation accuracy ranging from 83.53% ± 3.99% to 95.07% ± 3.17%. Notably, the Gradient Boosting Machine (GBM) method achieved a classification accuracy of 95.07% ± 3.17% for different sampling points at a submersion time of 1 day. Moreover, our cross-species site inference results revealed that utilizing data from drowned mice to predict the drowning sites of rabbits in location W5 achieved an accuracy of 72.22%. In conclusion, our study underscores the potential of microbiome analysis in forensic investigations of drowning incidents. By integrating microbial data with traditional forensic techniques, there is significant potential to enhance the reliability of scene inferences, thereby making substantial contributions to case investigations and judicial trials.IMPORTANCEBy employing advanced techniques like microbial profiling and machine learning, the study aims to enhance the accuracy of determining drowning sites, which is crucial for both legal proceedings. By analyzing microbial diversity in water samples and drowned animal lung tissues, the study sheds light on how environmental factors and victim-related variables influence microbial communities. The findings not only advance our understanding of forensic microbiology but also offer practical implications for improving investigative techniques in cases of drowning.

Therapeutic Modulation of the Microbiome in Oncology: Current Trends and Future Directions

Cancer is a predominant cause of mortality worldwide, necessitating the development of innovative therapeutic techniques. The human microbiome, particularly the gut microbiota, has become a significant element in cancer research owing to its essential role in sustaining health and influencing disease progression. This review examines the microbiome's makeup and essential functions, including immunological modulation and metabolic regulation, which may be evaluated using sophisticated methodologies such as metagenomics and 16S rRNA sequencing. The microbiome influences cancer development by promoting inflammation, modulating the immune system, and producing carcinogenic compounds. Dysbiosis, or microbial imbalance, can undermine the epithelial barrier and facilitate cancer. The microbiome influences chemotherapy and radiation results by modifying drug metabolism, either enhancing or reducing therapeutic efficacy and contributing to side effects and toxicity. Comprehending these intricate relationships emphasises the microbiome's significance in oncology and accentuates the possibility for microbiome-targeted therapeutics. Contemporary therapeutic approaches encompass the utilisation of probiotics and dietary components to regulate the microbiome, enhance treatment efficacy, and minimise unwanted effects. Advancements in research indicate that personalised microbiome-based interventions, have the potential to transform cancer therapy, by providing more effective and customised treatment alternatives. This study aims to provide a comprehensive analysis of the microbiome's influence on the onset and treatment of cancer, while emphasising current trends and future possibilities for therapeutic intervention.

A Comprehensive Metagenome Study Identifies Distinct Biological Pathways in Asthma Patients: An In-Silico Approach

Asthma is a multifactorial disease with phenotypes and several clinical and pathophysiological characteristics. Besides innate and adaptive immune responses, the gut microbiome generates Treg cells, mediating the allergic response to environmental factors and exposure to allergens. Because of the complexity of asthma, microbiome analysis and other precision medicine methods are now widely regarded as essential elements of efficient disease therapy. An in-silico pipeline enables the comparative taxonomic profiling of 16S rRNA metagenomic profiles of 20 asthmatic patients and 15 healthy controls utilizing QIIME2. Further, PICRUSt supports downstream gene enrichment and pathway analysis, inferring the enriched pathways in a diseased state. A significant abundance of the phylum Proteobacteria, Sutterella, and Megamonas is identified in asthma patients and a diminished genus Akkermansia. Nasal samples reveal a high relative abundance of Mycoplasma in the nasal samples. Further, differential functional profiling identifies the metabolic pathways related to cofactors and amino acids, secondary metabolism, and signaling pathways. These findings support that a combination of bacterial communities is involved in mediating the responses involved in chronic respiratory conditions like asthma by exerting their influence on various metabolic pathways.

Evaluation of DNA extraction kits for long-read shotgun metagenomics using Oxford Nanopore sequencing for rapid taxonomic and antimicrobial resistance detection

During a bacterial infection or colonization, the detection of antimicrobial resistance (AMR) is critical, but slow due to culture-based approaches for clinical and screening samples. Culture-based phenotypic AMR detection and confirmation require up to 72 hours (h) or even weeks for slow-growing bacteria. Direct shotgun metagenomics by long-read sequencing using Oxford Nanopore Technologies (ONT) may reduce the time for bacterial species and AMR gene identification. However, screening swabs for metagenomics is complex due to the range of Gram-negative and -positive bacteria, diverse AMR genes, and host DNA present in the samples. Therefore, DNA extraction is a critical initial step. We aimed to compare the performance of different DNA extraction protocols for ONT applications to reliably identify species and AMR genes using a shotgun long-read metagenomic approach. We included three different sample types: ZymoBIOMICS Microbial Community Standard, an in-house mock community of ESKAPE pathogens including Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Escherichia coli (ESKAPE Mock), and anonymized clinical swab samples. We processed all sample types with four different DNA extraction kits utilizing different lysis (enzymatic vs. mechanical) and purification (spin-column vs. magnetic beads) methods. We used kits from Qiagen (QIAamp DNA Mini and QIAamp PowerFecal Pro DNA) and Promega (Maxwell RSC Cultured Cells and Maxwell RSC Buccal Swab DNA). After extraction, samples were subject to the Rapid Barcoding Kit (RBK004) for library preparation followed by sequencing on the GridION with R9.4.1 flow cells. The fast5 files were base called to fastq files using Guppy in High Accuracy (HAC) mode with the inbuilt MinKNOW software. Raw read quality was assessed using NanoPlot and human reads were removed using Minimap2 alignment against the Hg38 genome. Taxonomy identification was performed on the raw reads using Kraken2 and on assembled contigs using Minimap2. The AMR genes were identified using Minimap2 with alignment against the CARD database on both the raw reads and assembled contigs. We identified all bacterial species present in the Zymo Mock Community (8/8) and ESKAPE Mock (6/6) with Qiagen PowerFecal Pro DNA kit (chemical and mechanical lysis) at read and assembly levels. Enzymatic lysis retrieved fewer aligned bases for the Gram-positive species (Staphylococcus aureus and Enterococcus faecium) from the ESKAPE Mock on the assembly level compared to the mechanical lysis. We detected the AMR genes from Gram-negative and -positive species in the ESKAPE Mock with the QIAamp PowerFecal Pro DNA kit on reads level with a maximum median time of 1.9 h of sequencing. Long-read metagenomics with ONT may reduce the turnaround time in screening for AMR genes. Currently, the QIAamp PowerFecal Pro DNA kit (chemical and mechanical lysis) for DNA extraction along with the Rapid Barcoding Kit for the ONT sequencing captured the best taxonomy and AMR identification for our specific use case.

Machine Learning Approaches for Microorganism Identification, Virulence Assessment, and Antimicrobial Susceptibility Evaluation Using DNA Sequencing Methods: A Systematic Review

Microbial infections pose a substantial global health challenge, particularly impacting immunocompromised individuals and exacerbating the issue of antimicrobial resistance (AMR). High virulence of pathogens can lead to severe infections and prolonged antimicrobial treatment, increasing the risk of developing resistant strains. Integrating machine-learning (ML) with DNA sequencing technologies offers potential solutions by enhancing microbial identification, virulence assessment, and antimicrobial susceptibility evaluation. This review explores recent advancements in these integrated approaches, addressing current limitations and identifying gaps in the literature. A comprehensive literature search was conducted across databases including PubMed, Scopus, Web of Science, and IEEE Xplore, covering publications from January 2014 to June 2024. Using a detailed Boolean search string, relevant studies focusing on ML applications in microorganism identification, antimicrobial susceptibility testing, and microbial virulence were included. The screening process involved a two-stage review of titles, abstracts, and full texts, with data extraction and critical appraisal performed using the QIAO tool. Data were analyzed through narrative synthesis to identify common themes and innovations. Out of 1,650 initially identified records, 19 studies met the inclusion criteria. These studies primarily focused on AMR, with additional research on microbial virulence and identification. Machine learning algorithms such as Random Forest, Support Vector Machines, and Convolutional Neural Networks, combined with DNA sequencing techniques like Whole Genome Sequencing and Metagenomic Sequencing, demonstrated significant advancements in predictive accuracy and efficiency. High-quality studies achieved impressive performance metrics, including F1-scores up to 0.88 and AUC scores up to 0.96. The integration of ML and DNA sequencing technologies has significantly enhanced microbial analysis, improving the identification of pathogens, assessment of virulence, and evaluation of antimicrobial susceptibility. Despite advancements, challenges such as data quality, high costs, and model interpretability persist. This review highlights the need for continued innovation and provides recommendations for future research to address these limitations and improve disease management and public health strategies. The systematic review is registered with PROSPERO (CRD42024571347).

Culturomics- and metagenomics-based insights into the soil microbiome preservation and application for sustainable agriculture

Soil health is crucial for global food production in the context of an ever-growing global population. Microbiomes, a combination of microorganisms and their activities, play a pivotal role by biodegrading contaminants, maintaining soil structure, controlling nutrients' cycles, and regulating the plant responses to biotic and abiotic stresses. Microbiome-based solutions along the soil-plant continuum, and their scaling up from laboratory experiments to field applications, hold promise for enhancing agricultural sustainability by harnessing the power of microbial consortia. Synthetic microbial communities, i.e., selected microbial consortia, are designed to perform specific functions. In contrast, natural communities leverage indigenous microbial populations that are adapted to local soil conditions, promoting ecosystem resilience, and reducing reliance on external inputs. The identification of microbial indicators requires a holistic approach. It is fundamental for current understanding the soil health status and for providing a comprehensive assessment of sustainable land management practices and conservation efforts. Recent advancements in molecular technologies, such as high-throughput sequencing, revealed the incredible diversity of soil microbiomes. On one hand, metagenomic sequencing allows the characterization of the entire genetic composition of soil microbiomes, and the examination of their functional potential and ecological roles; on the other hand, culturomics-based approaches and metabolic fingerprinting offer complementary information by providing snapshots of microbial diversity and metabolic activities both in and ex-situ. Long-term storage and cryopreservation of mixed culture and whole microbiome are crucial to maintain the originality of the sample in microbiome biobanking and for the development and application of microbiome-based innovation. This review aims to elucidate the available approaches to characterize diversity, function, and resilience of soil microbial communities and to develop microbiome-based solutions that can pave the way for harnessing nature's untapped resources to cultivate crops in healthy soils, to enhance plant resilience to abiotic and biotic stresses, and to shape thriving ecosystems unlocking the potential of soil microbiomes is key to sustainable agriculture. Improving management practices by incorporating beneficial microbial consortia, and promoting resilience to climate change by facilitating adaptive strategies with respect to environmental conditions are the global challenges of the future to address the issues of climate change, land degradation and food security.

Assessment of Skimmed Milk Flocculation for Bacterial Enrichment from Water Samples, and Benchmarking of DNA Extraction and 16S rRNA Databases for Metagenomics

Water samples for bacterial microbiome studies undergo biomass concentration, DNA extraction, and taxonomic identification steps. Through benchmarking, we studied the applicability of skimmed milk flocculation (SMF) for bacterial enrichment, an adapted in-house DNA extraction protocol, and six 16S rRNA databases (16S-DBs). Surface water samples from two rivers were treated with SMF and vacuum filtration (VF) and subjected to amplicon or shotgun metagenomics. A microbial community standard underwent five DNA extraction protocols, taxonomical identification with six different 16S-DBs, and evaluation by the Measurement Integrity Quotient (MIQ) score. In SMF samples, the skimmed milk was metabolized by members of lactic acid bacteria or genera such as Polaromonas, Macrococcus, and Agitococcus, resulting in increased relative abundance (p < 0.5) up to 5.0 log fold change compared to VF, rendering SMF inapplicable for bacterial microbiome studies. The best-performing DNA extraction protocols were FastSpin Soil, the in-house method, and EurX. All 16S-DBs yielded comparable MIQ scores within each DNA extraction kit, ranging from 61-66 (ZymoBIOMICs) up to 80-82 (FastSpin). DNA extraction kits exert more bias toward the composition than 16S-DBs. This benchmarking study provided valuable information to inform future water metagenomic study designs.

A Proteogenomic Approach to Unveiling the Complex Biology of the Microbiome

The complex biology of the microbiome was elucidated once the genomics era began. The proteogenomic approach analyzes and integrates genetic makeup (genomics) and microbial communities' expressed proteins (proteomics). Therefore, researchers gained insights into gene expression, protein functions, and metabolic pathways, understanding microbial dynamics and behavior, interactions with host cells, and responses to environmental stimuli. In this context, our work aims to bring together data regarding the application of genomics, proteomics, and bioinformatics in microbiome research and to provide new perspectives for applying microbiota modulation in clinical practice with maximum efficiency. This review also synthesizes data from the literature, shedding light on the potential biomarkers and therapeutic targets for various diseases influenced by the microbiome.

Seasonal dynamics in leaf litter decomposing microbial communities in temperate forests: a whole-genome- sequencing-based study

Leaf litter decomposition, a crucial component of the global carbon cycle, relies on the pivotal role played by microorganisms. However, despite their ecological importance, leaf-litter-decomposing microorganism taxonomic and functional diversity needs additional study. This study explores the taxonomic composition, dynamics, and functional role of microbial communities that decompose leaf litter of forest-forming tree species in two ecologically unique regions of Europe. Twenty-nine microbial metagenomes isolated from the leaf litter of eight forest-forming species of woody plants were investigated by Illumina technology using read- and assembly-based approaches of sequences analysis. The taxonomic structure of the microbial community varies depending on the stage of litter decomposition; however, the community's core is formed by Pseudomonas, Sphingomonas, Stenotrophomonas, and Pedobacter genera of Bacteria and by Aureobasidium, Penicillium, Venturia genera of Fungi. A comparative analysis of the taxonomic structure and composition of the microbial communities revealed that in both regions, seasonal changes in structure take place; however, there is no clear pattern in its dynamics. Functional gene analysis of MAGs revealed numerous metabolic profiles associated with leaf litter degradation. This highlights the diverse metabolic capabilities of microbial communities and their implications for ecosystem processes, including the production of volatile organic compounds (VOCs) during organic matter decomposition. This study provides important advances in understanding of ecosystem processes and the carbon cycle, underscoring the need to unravel the intricacies of microbial communities within these contexts.

Taxonomic and functional metagenomic assessment of a Dolichospermum bloom in a large and deep lake south of the Alps

Untargeted genetic approaches can be used to explore the high metabolic versatility of cyanobacteria. In this context, a comprehensive metagenomic shotgun analysis was performed on a population of Dolichospermum lemmermannii collected during a surface bloom in Lake Garda in the summer of 2020. Using a phylogenomic approach, the almost complete metagenome-assembled genome obtained from the analysis allowed to clarify the taxonomic position of the species within the genus Dolichospermum and contributed to frame the taxonomy of this genus within the ADA group (Anabaena/Dolichospermum/Aphanizomenon). In addition to common functional traits represented in the central metabolism of photosynthetic cyanobacteria, the genome annotation uncovered some distinctive and adaptive traits that helped define the factors that promote and maintain bloom-forming heterocytous nitrogen-fixing Nostocales in oligotrophic lakes. In addition, genetic clusters were identified that potentially encode several secondary metabolites that were previously unknown in the populations evolving in the southern Alpine Lake district. These included geosmin, anabaenopetins, and other bioactive compounds. The results expanded the knowledge of the distinctive competitive traits that drive algal blooms and provided guidance for more targeted analyses of cyanobacterial metabolites with implications for human health and water resource use.

New insights into biologic interpretation of bioinformatic pipelines for fish eDNA metabarcoding: A case study in Pearl River estuary

Environmental DNA (eDNA) metabarcoding is an emerging tool for monitoring biological communities in aquatic ecosystems. The selection of bioinformatic pipelines significantly impacts the results of biodiversity assessments. However, there is currently no consensus on the appropriate bioinformatic pipelines for fish community analysis in eDNA metabarcoding. In this study, we compared three bioinformatic pipelines (Uparse, DADA2, and UNOISE3) using real and mock (constructed with 15/30 known fish) communities to investigate the differences in biological interpretation during the data analysis process in eDNA metabarcoding. Performance evaluation and diversity analyses revealed that the choice of bioinformatic pipeline could impact the biological results of metabarcoding experiments. Among the three pipelines, the operational taxonomic units (OTU)-based pipeline (Uparse) showed the best performance (sensitivity: 0.6250 ± 0.0166; compositional similarity: 0.4000 ± 0.0571), the highest richness (25-102) and minimal inter-group differences in alpha diversity. It suggested the OTU-based pipeline possessed superior capability in fish diversity monitoring compared to ASV/ZOTU-based pipeline. Additionally, the Bray-Curtis distance matrix achieved the highest discriminative effect in the PCoA (43.3%-53.89%) and inter-group analysis (P < 0.01), indicating it was better at distinguishing compositional differences or specific genera of fish community at different sampling sites than other distance matrices. These findings provide new insights into fish community monitoring through eDNA metabarcoding in estuarine environments.

Exploring microbial worlds: a review of whole genome sequencing and its application in characterizing the microbial communities

The classical microbiology techniques have inherent limitations in unraveling the complexity of microbial communities, necessitating the pivotal role of sequencing in studying the diversity of microbial communities. Whole genome sequencing (WGS) enables researchers to uncover the metabolic capabilities of the microbial community, providing valuable insights into the microbiome. Herein, we present an overview of the rapid advancements achieved thus far in the use of WGS in microbiome research. There was an upsurge in publications, particularly in 2021 and 2022 with the United States, China, and India leading the metagenomics research landscape. The Illumina platform has emerged as the widely adopted sequencing technology, whereas a significant focus of metagenomics has been on understanding the relationship between the gut microbiome and human health where distinct bacterial species have been linked to various diseases. Additionally, studies have explored the impact of human activities on microbial communities, including the potential spread of pathogenic bacteria and antimicrobial resistance genes in different ecosystems. Furthermore, WGS is used in investigating the microbiome of various animal species and plant tissues such as the rhizosphere microbiome. Overall, this review reflects the importance of WGS in metagenomics studies and underscores its remarkable power in illuminating the variety and intricacy of the microbiome in different environments.

Datasets of fungal diversity and pseudo-chromosomal genomes of mangrove rhizosphere soil in China

With climate change and anthropic influence on the coastal ecosystems, mangrove ecosystems are disappearing at an alarming rate. Accordingly, it becomes important to track, study, record and store the mangrove microbial community considering their ecological importance and potential for biotechnological applications. Here, we provide information on mangrove fungal community composition and diversity in mangrove ecosystems with different plant species and from various locations differing in relation to anthropic influences. We describe twelve newly assembled genomes, including four chromosomal-level genomes of fungal isolates from the mangrove ecosystems coupled with functional annotations. We envisage that these data will be of value for future studies including comparative genome analysis and large-scale temporal and/or spatial research to elucidate the potential mechanisms by which mangrove fungal communities assemble and evolve. We further anticipate that the genomes represent valuable resources for bioprospecting related to industrial or clinical uses.

Impact of microorganisms on key processes of organic acid metabolism during the occurrence and disappearance of paocai pellicle

In vegetable fermentation, pellicle is a common quality deterioration phenomenon. This study investigates the characteristics of glucose, organic acids, amino acids, and biogenic amines during the pellicle occurrence and disappearance of paocai. The results revealed a slight increase in pH of the fermentation system after pellicle occurred, and glucose was the main carbohydrate that microbial activity primary relied on. The microorganisms responsible for pellicle formation consumed organic acids in brine, but the lactic acid in paocai gradually increased and exceeded 25 mg/g. The appearance of pellicle caused a decrease in total free amino acids from 200.390 mg/100 g to 172.079 when pellicle occurred, whereas its impact on biogenic amines was not apparent. Through Kyoto Encyclopedia of Genes and Genomes pathway enrichment of metagenomics sequencing data, screening, and sorting of the key enzymes involved in organic acid metabolism, it was observed that the composition and species of the key microorganisms capable of metabolizing organic acids were more abundant before the appearance of pellicle. When pellicle occurred, lactic acid may be metabolized by Lactobacillus plantarum; in contrast, Lactobacillus and Pichia were associated with citric acid metabolism, and Lactobacillus, Pichia, Saccharomycodes, and Kazachstania were linked to malic acid metabolism. Moreover, Prevotella, Kazachstania, Lactobacillus, Vibrio, and Siphonobacter were implicated in succinic acid metabolism. Additionally, the production of tartaric acid and oxalic acid in paocai and brine resulted from abiotic effects. This knowledge offers a theoretical basis for precise control of paocai fermentation process. PRACTICAL APPLICATION: Our study revealed the specific situation of the metabolites produced by the microorganisms during the pollution and recovery process of pellicle in paocai fermentation, especially the effect of pellicle on the key process of organic acid metabolism. These research results provided theoretical basis for precise control of paocai fermentation.

TLE4 downregulation identified by WGCNA and machine learning algorithm promotes papillary thyroid carcinoma progression via activating JAK/STAT pathway

Background: Papillary Thyroid Carcinoma (PTC), a common type of thyroid cancer, has a pathogenesis that is not fully understood. This study utilizes a range of public databases, sophisticated bioinformatics tools, and empirical approaches to explore the key genetic components and pathways implicated in PTC, particularly concentrating on the Transducin-Like Enhancer of Split 4 (TLE4) gene. Methods: Public databases such as TCGA and GEO were utilized to conduct differential gene expression analysis in PTC. Hub genes were identified using Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning techniques, including Random Forest, LASSO regression, and SVM-RFE, were employed for biomarker identification. The clinical impact of the TLE4 gene was assessed in terms of diagnostic accuracy, prognostic value, and its functional enrichment analysis in PTC. Additionally, the study focused on understanding the role of TLE4 in the dynamics of immune cell infiltration, gene function enhancement, and behaviors of PTC cells like growth, migration, and invasion. To complement these analyses, in vivo studies were performed using a xenograft mouse model. Results: 244 genes with significant differential expression across various databases were identified. WGCNA indicated a strong link between specific gene modules and PTC. Machine learning analysis brought the TLE4 gene into focus as a key biomarker. Bioinformatics studies verified that TLE4 expression is lower in PTC, linking it to immune cell infiltration and the JAK-STAT signaling pathways. Experimental data revealed that decreased TLE4 expression in PTC cell lines leads to enhanced cell growth, migration, invasion, and activates the JAK/STAT pathway. In contrast, TLE4 overexpression in these cells inhibited tumor growth and metastasis. Conclusions: This study sheds light on TLE4's crucial role in PTC pathogenesis, positioning it as a potential biomarker and target for therapy. The integration of multi-omics data and advanced analytical methods provides a robust framework for understanding PTC at a molecular level, potentially guiding personalized treatment strategies.

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.

OMD Curation Toolkit: a workflow for in-house curation of public omics datasets

BACKGROUND

Major advances in sequencing technologies and the sharing of data and metadata in science have resulted in a wealth of publicly available datasets. However, working with and especially curating public omics datasets remains challenging despite these efforts. While a growing number of initiatives aim to re-use previous results, these present limitations that often lead to the need for further in-house curation and processing.

RESULTS

Here, we present the Omics Dataset Curation Toolkit (OMD Curation Toolkit), a python3 package designed to accompany and guide the researcher during the curation process of metadata and fastq files of public omics datasets. This workflow provides a standardized framework with multiple capabilities (collection, control check, treatment and integration) to facilitate the arduous task of curating public sequencing data projects. While centered on the European Nucleotide Archive (ENA), the majority of the provided tools are generic and can be used to curate datasets from different sources.

CONCLUSIONS

Thus, it offers valuable tools for the in-house curation previously needed to re-use public omics data. Due to its workflow structure and capabilities, it can be easily used and benefit investigators in developing novel omics meta-analyses based on sequencing data.

Current Trends and Challenges of Microbiome Research in Prostate Cancer

PURPOSE OF REVIEW

The role of the gut microbiome in prostate cancer is an emerging area of research interest. However, no single causative organism has yet been identified. The goal of this paper is to examine the role of the microbiome in prostate cancer and summarize the challenges relating to methodology in specimen collection, sequencing technology, and interpretation of results.

RECENT FINDINGS

Significant heterogeneity still exists in methodology for stool sampling/storage, preservative options, DNA extraction, and sequencing database selection/in silico processing. Debate persists over primer choice in amplicon sequencing as well as optimal methods for data normalization. Statistical methods for longitudinal microbiome analysis continue to undergo refinement. While standardization of methodology may help yield more consistent results for organism identification in prostate cancer, this is a difficult task due to considerable procedural variation at each step in the process. Further reproducibility and methodology research is required.

The role of insect gut microbiota in host fitness, detoxification and nutrient supplementation

Insects are incredibly diverse, ubiquitous and have successfully flourished out of the dynamic and often unpredictable nature of evolutionary processes. The resident microbiome has accompanied the physical and biological adaptations that enable their continued survival and proliferation in a wide array of environments. The host insect and microbiome's bidirectional relationship exhibits their capability to influence each other's physiology, behavior and characteristics. Insects are reported to rely directly on the microbial community to break down complex food, adapt to nutrient-deficit environments, protect themselves from natural adversaries and control the expression of social behavior. High-throughput metagenomic approaches have enhanced the potential for determining the abundance, composition, diversity and functional activities of microbial fauna associated with insect hosts, enabling in-depth investigation into insect-microbe interactions. We undertook a review of some of the major advances in the field of metagenomics, focusing on insect-microbe interaction, diversity and composition of resident microbiota, the functional capability of endosymbionts and discussions on different symbiotic relationships. The review aims to be a valuable resource on insect gut symbiotic microbiota by providing a comprehensive understanding of how insect gut symbionts systematically perform a range of functions, viz., insecticide degradation, nutritional support and immune fitness. A thorough understanding of manipulating specific gut symbionts may aid in developing advanced insect-associated research to attain health and design strategies for pest management.

Current progresses and challenges for microbiome research in human health: a perspective

It is becoming increasingly clear that the human microbiota, also known as "the hidden organ", possesses a pivotal role in numerous processes involved in maintaining the physiological functions of the host, such as nutrient extraction, biosynthesis of bioactive molecules, interplay with the immune, endocrine, and nervous systems, as well as resistance to the colonization of potential invading pathogens. In the last decade, the development of metagenomic approaches based on the sequencing of the bacterial 16s rRNA gene via Next Generation Sequencing, followed by whole genome sequencing via third generation sequencing technologies, has been one of the great advances in molecular biology, allowing a better profiling of the human microbiota composition and, hence, a deeper understanding of the importance of microbiota in the etiopathogenesis of different pathologies. In this scenario, it is of the utmost importance to comprehensively characterize the human microbiota in relation to disease pathogenesis, in order to develop novel potential treatment or preventive strategies by manipulating the microbiota. Therefore, this perspective will focus on the progress, challenges, and promises of the current and future technological approaches for microbiome profiling and analysis.

Deep learning methods in metagenomics: a review

The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analysing metagenomic data remains challenging due to several factors, including reference catalogues, sparsity and compositionality. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification and disease prediction. Beyond generating predictive models, a key aspect of these methods is also their interpretability. This article reviews DL approaches in metagenomics, including convolutional networks, autoencoders and attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the microbiome's key role in our health.

Exploring Cereal Metagenomics: Unravelling Microbial Communities for Improved Food Security

Food security is an urgent global challenge, with cereals playing a crucial role in meeting the nutritional requirements of populations worldwide. In recent years, the field of metagenomics has emerged as a powerful tool for studying the microbial communities associated with cereal crops and their impact on plant health and growth. This chapter aims to provide a comprehensive overview of cereal metagenomics and its role in enhancing food security through the exploration of beneficial and pathogenic microbial interactions. Furthermore, we will examine how the integration of metagenomics with other tools can effectively address the adverse effects on food security. For this purpose, we discuss the integration of metagenomic data and machine learning in providing novel insights into the dynamic interactions shaping plant-microbe relationships. We also shed light on the potential applications of leveraging microbial diversity and epigenetic modifications in improving crop resilience and yield sustainability. Ultimately, cereal metagenomics has revolutionized the field of food security by harnessing the potential of beneficial interactions between cereals and their microbiota, paving the way for sustainable agricultural practices.

Inferring the ecological and evolutionary determinants of community genetic diversity

Understanding the relative contributions of ecological and evolutionary processes to the structuring of ecological communities is needed to improve our ability to predict how communities may respond to future changes in an increasingly human-modified world. Metabarcoding methods make it possible to gather population genetic data for all species within a community, unlocking a new axis of data to potentially unveil the origins and maintenance of biodiversity at local scales. Here, we present a new eco-evolutionary simulation model for investigating community assembly dynamics using metabarcoding data. The model makes joint predictions of species abundance, genetic variation, trait distributions and phylogenetic relationships under a wide range of parameter settings (e.g. high speciation/low dispersal or vice versa) and across a range of community states, from pristine and unmodified to heavily disturbed. We first demonstrate that parameters governing metacommunity and local community processes leave detectable signatures in simulated biodiversity data axes. Next, using a simulation-based machine learning approach we show that neutral and non-neutral models are distinguishable and that reasonable estimates of several model parameters within the local community can be obtained using only community-scale genetic data, while phylogenetic information is required to estimate those describing metacommunity dynamics. Finally, we apply the model to soil microarthropod metabarcoding data from the Troodos mountains of Cyprus, where we find that communities in widespread forest habitats are structured by neutral processes, while high-elevation and isolated habitats act as an abiotic filter generating non-neutral community structure. We implement our model within the ibiogen R package, a package dedicated to the investigation of island, and more generally community-scale, biodiversity using community-scale genetic data.

Metagenomics and metatranscriptomics as potential driving forces for the exploration of diversity and functions of micro-eukaryotes in soil

Micro-eukaryotes are ubiquitous and play vital roles in diverse ecological systems, yet their diversity and functions are scarcely known. This may be due to the limitations of formerly used conventional culture-based methods. Metagenomics and metatranscriptomics are enabling to unravel the genomic, metabolic, and phylogenetic diversity of micro-eukaryotes inhabiting in different ecosystems in a more comprehensive manner. The in-depth study of structural and functional characteristics of micro-eukaryote community residing in soil is crucial for the complete understanding of this major ecosystem. This review provides a deep insight into the methodologies employed under these approaches to study soil micro-eukaryotic organisms. Furthermore, the review describes available computational tools, pipelines, and database sources and their manipulation for the analysis of sequence data of micro-eukaryotic origin. The challenges and limitations of these approaches are also discussed in detail. In addition, this review summarizes the key findings of metagenomic and metatranscriptomic studies on soil micro-eukaryotes. It also highlights the exploitation of these methods to study the structural as well as functional profiles of soil micro-eukaryotic community and to screen functional eukaryotic protein coding genes for biotechnological applications along with the future perspectives in the field.

Simultaneous analysis of seven 16S rRNA hypervariable gene regions increases efficiency in marine bacterial diversity detection

Environmental DNA sequencing is the gold standard to reveal microbial community structures. In most applications, a one-fragment PCR approach is applied to amplify a taxonomic marker gene, usually a hypervariable region of the 16S rRNA gene. We used a new reverse complement (RC)-PCR-based assay that amplifies seven out of the nine hypervariable regions of the 16S rRNA gene, to interrogate bacterial communities in sediment samples collected from different coastal marine sites with an impact gradient. In parallel, we employed a traditional one-fragment analysis of the hypervariable V3-V4 region to investigate whether the RC-PCR reveals more of the 'unseen' diversity obtained by the one-fragment approach. As a benchmark for the full deck of diversity, we subjected the samples to PCR-free metagenomic sequencing. None of the two PCR-based approaches recorded the full taxonomic repertoire obtained from the metagenomics datasets. However, the RC-PCR approach detected 2.8 times more bacterial genera compared to the near-saturation sequenced V3-V4 samples. RC-PCR is an ideal compromise between the standard one-fragment approach and metagenomics sequencing and may guide future environmental sequencing studies, in which bacterial diversity is a central subject.

Phylogeny and functional diversity of halophilic microbial communities from a thalasso environment

The El-Rawda solar saltern, located in North Sinai, Egypt, is formed through the process of water evaporation from the Bradawil lagoon. This evaporation leads to the precipitation of gypsum, halite minerals, and salt flats, which subsequently cover the southern and eastern areas of the lagoon. This study employed the shotgun metagenomic approach, the illumine platform, and bioinformatic tools to investigate the taxonomic composition and functional diversity of halophilic microbial communities in solar saltern. The metagenomic reads obtained from the brine sample exhibited a greater count compared to those from the sediment sample. Notably, the brine sample was primarily characterized by an abundance of archaea, while the sediment sample displayed a dominant abundance of bacteria. Both samples exhibited a relatively low abundance of eukaryotes, while viruses were only found in the brine sample. Furthermore, the comparative analysis of functional pathways showed many important processes related to central metabolism and protein processing in brine and sediment samples. In brief, this research makes a valuable contribution to the understanding of very halophilic ecosystems in Egypt, providing insights into their microbial biodiversity and functional processes.

Molecular systems biology approaches to investigate mechanisms of gut-brain communication in neurological diseases

BACKGROUND

Whilst the incidence of neurological diseases is increasing worldwide, treatment remains mostly limited to symptom management. The gut-brain axis, which encompasses the communication routes between microbiota, gut and brain, has emerged as a crucial area of investigation for identifying new preventive and therapeutic targets in neurological disease.

METHODS

Due to the inter-organ, systemic nature of the gut-brain axis, together with the multitude of biomolecules and microbial species involved, molecular systems biology approaches are required to accurately investigate the mechanisms of gut-brain communication. High-throughput omics profiling, together with computational methodologies such as dimensionality reduction or clustering, machine learning, network inference and genome-scale metabolic models, allows novel biomarkers to be discovered and elucidates mechanistic insights.

RESULTS

In this review, the general concepts of experimental and computational methodologies for gut-brain axis research are introduced and their applications are discussed, mainly in human cohorts. Important aspects are further highlighted concerning rational study design, sampling procedures and data modalities relevant for gut-brain communication, strengths and limitations of methodological approaches and some future perspectives.

CONCLUSION

Multi-omics analyses, together with advanced data mining, are essential to functionally characterize the gut-brain axis and put forward novel preventive or therapeutic strategies in neurological disease.

Critical review of 16S rRNA gene sequencing workflow in microbiome studies: From primer selection to advanced data analysis

The multi-batch reanalysis approach of jointly reevaluating gene/genome sequences from different works has gained particular relevance in the literature in recent years. The large amount of 16S ribosomal ribonucleic acid (rRNA) gene sequence data stored in public repositories and information in taxonomic databases of the same gene far exceeds that related to complete genomes. This review is intended to guide researchers new to studying microbiota, particularly the oral microbiota, using 16S rRNA gene sequencing and those who want to expand and update their knowledge to optimise their decision-making and improve their research results. First, we describe the advantages and disadvantages of using the 16S rRNA gene as a phylogenetic marker and the latest findings on the impact of primer pair selection on diversity and taxonomic assignment outcomes in oral microbiome studies. Strategies for primer selection based on these results are introduced. Second, we identified the key factors to consider in selecting the sequencing technology and platform. The process and particularities of the main steps for processing 16S rRNA gene-derived data are described in detail to enable researchers to choose the most appropriate bioinformatics pipeline and analysis methods based on the available evidence. We then produce an overview of the different types of advanced analyses, both the most widely used in the literature and the most recent approaches. Several indices, metrics and software for studying microbial communities are included, highlighting their advantages and disadvantages. Considering the principles of clinical metagenomics, we conclude that future research should focus on rigorous analytical approaches, such as developing predictive models to identify microbiome-based biomarkers to classify health and disease states. Finally, we address the batch effect concept and the microbiome-specific methods for accounting for or correcting them.

Ecology of the respiratory tract microbiome

A thriving multi-kingdom microbial ecosystem inhabits the respiratory tract: the respiratory tract microbiome (RTM). In recent years, the contribution of the RTM to human health has become a crucial research aspect. However, research into the key ecological processes, such as robustness, resilience, and microbial interaction networks, has only recently started. This review leans on an ecological framework to interpret the human RTM and determine how the ecosystem functions and assembles. Specifically, the review illustrates the ecological RTM models and discusses microbiome establishment, community structure, diversity stability, and critical microbial interactions. Lastly, the review outlines the RTM responses to ecological disturbances, as well as the promising approaches for restoring ecological balance.

Amplicon-Based Microbiome Profiling: From Second- to Third-Generation Sequencing for Higher Taxonomic Resolution

The 16S rRNA amplicon-based sequencing approach represents the most common and cost-effective strategy with great potential for microbiome profiling. The use of second-generation sequencing (NGS) technologies has led to protocols based on the amplification of one or a few hypervariable regions, impacting the outcome of the analysis. Nowadays, comparative studies are necessary to assess different amplicon-based approaches, including the full-locus sequencing currently feasible thanks to third-generation sequencing (TGS) technologies. This study compared three different methods to achieve the deepest microbiome taxonomic characterization: (a) the single-region approach, (b) the multiplex approach, covering several regions of the target gene/region, both based on NGS short reads, and (c) the full-length approach, which analyzes the whole length of the target gene thanks to TGS long reads. Analyses carried out on benchmark microbiome samples, with a known taxonomic composition, highlighted a different classification performance, strongly associated with the type of hypervariable regions and the coverage of the target gene. Indeed, the full-length approach showed the greatest discriminating power, up to species level, also on complex real samples. This study supports the transition from NGS to TGS for the study of the microbiome, even if experimental and bioinformatic improvements are still necessary.

Recovery of metagenomic data from the Aedes aegypti microbiome using a reproducible snakemake pipeline: MINUUR

Background: Ongoing research of the mosquito microbiome aims to uncover novel strategies to reduce pathogen transmission. Sequencing costs, especially for metagenomics, are however still significant. A resource that is increasingly used to gain insights into host-associated microbiomes is the large amount of publicly available genomic data based on whole organisms like mosquitoes, which includes sequencing reads of the host-associated microbes and provides the opportunity to gain additional value from these initially host-focused sequencing projects. Methods: To analyse non-host reads from existing genomic data, we developed a snakemake workflow called MINUUR (Microbial INsights Using Unmapped Reads). Within MINUUR, reads derived from the host-associated microbiome were extracted and characterised using taxonomic classifications and metagenome assembly followed by binning and quality assessment. We applied this pipeline to five publicly available Aedes aegypti genomic datasets, consisting of 62 samples with a broad range of sequencing depths. Results: We demonstrate that MINUUR recovers previously identified phyla and genera and is able to extract bacterial metagenome assembled genomes (MAGs) associated to the microbiome. Of these MAGS, 42 are high-quality representatives with >90% completeness and <5% contamination. These MAGs improve the genomic representation of the mosquito microbiome and can be used to facilitate genomic investigation of key genes of interest. Furthermore, we show that samples with a high number of KRAKEN2 assigned reads produce more MAGs. Conclusions: Our metagenomics workflow, MINUUR, was applied to a range of Aedes aegypti genomic samples to characterise microbiome-associated reads. We confirm the presence of key mosquito-associated symbionts that have previously been identified in other studies and recovered high-quality bacterial MAGs. In addition, MINUUR and its associated documentation are freely available on GitHub and provide researchers with a convenient workflow to investigate microbiome data included in the sequencing data for any applicable host genome of interest.

In silico evaluation and selection of the best 16S rRNA gene primers for use in next-generation sequencing to detect oral bacteria and archaea

BACKGROUND

Sequencing has been widely used to study the composition of the oral microbiome present in various health conditions. The extent of the coverage of the 16S rRNA gene primers employed for this purpose has not, however, been evaluated in silico using oral-specific databases. This paper analyses these primers using two databases containing 16S rRNA sequences from bacteria and archaea found in the human mouth and describes some of the best primers for each domain.

RESULTS

A total of 369 distinct individual primers were identified from sequencing studies of the oral microbiome and other ecosystems. These were evaluated against a database reported in the literature of 16S rRNA sequences obtained from oral bacteria, which was modified by our group, and a self-created oral archaea database. Both databases contained the genomic variants detected for each included species. Primers were evaluated at the variant and species levels, and those with a species coverage (SC) ≥75.00% were selected for the pair analyses. All possible combinations of the forward and reverse primers were identified, with the resulting 4638 primer pairs also evaluated using the two databases. The best bacteria-specific pairs targeted the 3-4, 4-7, and 3-7 16S rRNA gene regions, with SC levels of 98.83-97.14%; meanwhile, the optimum archaea-specific primer pairs amplified regions 5-6, 3-6, and 3-6, with SC estimates of 95.88%. Finally, the best pairs for detecting both domains targeted regions 4-5, 3-5, and 5-9, and produced SC values of 95.71-94.54% and 99.48-96.91% for bacteria and archaea, respectively.

CONCLUSIONS

Given the three amplicon length categories (100-300, 301-600, and >600 base pairs), the primer pairs with the best coverage values for detecting oral bacteria were as follows: KP_F048-OP_R043 (region 3-4; primer pair position for Escherichia coli J01859.1: 342-529), KP_F051-OP_R030 (4-7; 514-1079), and KP_F048-OP_R030 (3-7; 342-1079). For detecting oral archaea, these were as follows: OP_F066-KP_R013 (5-6; 784-undefined), KP_F020-KP_R013 (3-6; 518-undefined), and OP_F114-KP_R013 (3-6; 340-undefined). Lastly, for detecting both domains jointly they were KP_F020-KP_R032 (4-5; 518-801), OP_F114-KP_R031 (3-5; 340-801), and OP_F066-OP_R121 (5-9; 784-1405). The primer pairs with the best coverage identified herein are not among those described most widely in the oral microbiome literature. Video Abstract.

Microbial community structure and exploration of bioremediation enzymes: functional metagenomics insight into Arabian Sea sediments

Deep-sea sediments provide important information on oceanic biogeochemical processes mediated by the microbiome and their functional roles which could be unravelled using genomic tools. The present study aimed to delineate microbial taxonomic and functional profiles from Arabian Sea sediment samples through whole metagenome sequencing using Nanopore technology. Arabian Sea is considered as a major microbial reservoir with significant bio-prospecting potential which needs to be explored extensively using recent advances in genomics. Assembly, co-assembly, and binning methods were used to predict Metagenome Assembled Genomes (MAGs) which were further characterized by their completeness and heterogeneity. Nanopore sequencing of Arabian Sea sediment samples generated around 1.73 tera basepairs of data. Proteobacteria (78.32%) was found to be the most dominant phylum followed by Bacteroidetes (9.55%) and Actinobacteria (2.14%) in the sediment metagenome. Further, 35 MAGs from assembled and 38 MAGs of co-assembled reads were generated from long-read sequence dataset with major representations from the genera Marinobacter, Kangiella, and Porticoccus. RemeDB analysis revealed a high representation of pollutant-degrading enzymes involved in hydrocarbon, plastic and dye degradation. Validation of enzymes with long nanopore reads using BlastX resulted in better characterization of complete gene signatures involved in hydrocarbon (6-monooxygenase and 4-hydroxyacetophenone monooxygenase) and dye degradation (Arylsulfatase). Enhancing the cultivability of deep-sea microbes predicted from the uncultured WGS approaches by I-tip method resulted in isolation of facultative extremophiles. This study presents a comprehensive insight into the taxonomic and functional profiles of Arabian Sea sediments, indicating a potential hotspot for bioprospection.

Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters

Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.

Composition, structure, and functional shifts of prokaryotic communities in response to co-composting of various nitrogenous green feedstocks

BACKGROUND

Thermophilic composting is a promising method of sanitizing pathogens in manure and a source of agriculturally important thermostable enzymes and microorganisms from organic wastes. Despite the extensive studies on compost prokaryotes, shifts in microbial profiles under the influence of various green materials and composting days are still not well understood, considering the complexity of the green material sources. Here, the effect of regimens of green composting material on the diversity, abundance, and metabolic capacity of prokaryotic communities in a thermophilic compost environment was examined.

METHODS

Total community 16S rRNA was recovered from triplicate compost samples of Lantana-based, Tithonia-based, Grass-based, and mixed (Lantana + Tithonia + Grass)- based at 21, 42, 63, and 84 days of composting. The 16S rRNA was sequenced using the Illumina Miseq platform. Bioinformatics analysis was done using Divisive Amplicon Denoising Algorithm version 2 (DADA2) R version 4.1 and Phylogenetic Investigation of Communities by Reconstruction of Unobserved States version 2 (PICRUSt2) pipelines for community structure and metabolic profiles, respectively. In DADA2, prokaryotic classification was done using the Refseq-ribosomal database project (RDP) and SILVA version 138 databases.

RESULTS

Our results showed apparent differences in prokaryotic community structure for total diversity and abundance within the four compost regimens and composting days. The study showed that the most prevalent phyla during composting included Acidobacteriota, Actinobacteriota, Bacteroidota, Chloroflexi, and Proteobacteria. Additionally, there were differences in the overall diversity of metabolic pathways but no significant differences among the various compost treatments on major metabolic pathways like carbohydrate biosynthesis, carbohydrate degradation, and nitrogen biosynthesis.

CONCLUSION

Various sources of green material affect the succession of compost nutrients and prokaryotic communities. The similarity of amounts of nutrients, such as total Nitrogen, at the end of the composting process, despite differences in feedstock material, indicates a significant influence of composting days on the stability of nutrients during composting.

Role of microbiota and microbiota-derived short-chain fatty acids in PDAC

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive lethal diseases among other cancer types. Gut microbiome and its metabolic regulation play a crucial role in PDAC. Metabolic regulation in the gut is a complex process that involves microbiome and microbiome-derived short-chain fatty acids (SCFAs). SCFAs regulate inflammation, as well as lipid and glucose metabolism, through different pathways. This review aims to summarize recent developments in PDAC in the context of gut and oral microbiota and their associations with short-chain fatty acid (SCFA). In addition to this, we discuss possible therapeutic applications using microbiota in PDAC.

Decoding the microbial universe with metagenomics: a brief insight

A major part of any biological system on earth involves microorganisms, of which the majority are yet to be cultured. The conventional methods of culturing microbes have given fruitful outcomes yet have limitations. The curiosity for better understanding has led to the development of culture-independent molecular methods that help push aside the roadblocks of earlier methods. Metagenomics unifies the scientific community in search of a better understanding of the functioning of the ecosystem and its component organisms. This approach has opened a new paradigm in advanced research. It has brought to light the vast diversity and novelty among microbial communities and their genomes. This review focuses on the development of this field over time, the techniques and analysis of data generated through sequencing platforms, and its prominent interpretation and representation.

The role of microbial ecology in improving the performance of anaerobic digestion of sewage sludge

The use of next-generation diagnostic tools to optimise the anaerobic digestion of municipal sewage sludge has the potential to increase renewable natural gas recovery, improve the reuse of biosolid fertilisers and help operators expand circular economies globally. This review aims to provide perspectives on the role of microbial ecology in improving digester performance in wastewater treatment plants, highlighting that a systems biology approach is fundamental for monitoring mesophilic anaerobic sewage sludge in continuously stirred reactor tanks. We further highlight the potential applications arising from investigations into sludge ecology. The principal limitation for improvements in methane recoveries or in process stability of anaerobic digestion, especially after pre-treatment or during co-digestion, are ecological knowledge gaps related to the front-end metabolism (hydrolysis and fermentation). Operational problems such as stable biological foaming are a key problem, for which ecological markers are a suitable approach. However, no biomarkers exist yet to assist in monitoring and management of clade-specific foaming potentials along with other risks, such as pollutants and pathogens. Fundamental ecological principles apply to anaerobic digestion, which presents opportunities to predict and manipulate reactor functions. The path ahead for mapping ecological markers on process endpoints and risk factors of anaerobic digestion will involve numerical ecology, an expanding field that employs metrics derived from alpha, beta, phylogenetic, taxonomic, and functional diversity, as well as from phenotypes or life strategies derived from genetic potentials. In contrast to addressing operational issues (as noted above), which are effectively addressed by whole population or individual biomarkers, broad improvement and optimisation of function will require enhancement of hydrolysis and acidogenic processes. This will require a discovery-based approach, which will involve integrative research involving the proteome and metabolome. This will utilise, but overcome current limitations of DNA-centric approaches, and likely have broad application outside the specific field of anaerobic digestion.

A novel approach for combining the metagenome, metaresistome, metareplicome and causal inference to determine the microbes and their antibiotic resistance gene repertoire that contribute to dysbiosis

The use of whole metagenomic data to infer the relative abundance of all its microbes is well established. The same data can be used to determine the replication rate of all eubacterial taxa with circular chromosomes. Despite their availability, the replication rate profiles (metareplicome) have not been fully exploited in microbiome analyses. Another relatively new approach is the application of causal inferencing to analyse microbiome data that goes beyond correlational studies. A novel scalable pipeline called MeRRCI (Metagenome, metaResistome, and metaReplicome for Causal Inferencing) was developed. MeRRCI combines efficient computation of the metagenome (bacterial relative abundance), metaresistome (antimicrobial gene abundance) and metareplicome (replication rates), and integrates environmental variables (metadata) for causality analysis using Bayesian networks. MeRRCI was applied to an infant gut microbiome data set to investigate the microbial community's response to antibiotics. Our analysis suggests that the current treatment stratagem contributes to preterm infant gut dysbiosis, allowing a proliferation of pathobionts. The study highlights the specific antibacterial resistance genes that may contribute to exponential cell division in the presence of antibiotics for various pathogens, namely Klebsiella pneumoniae, Citrobacter freundii, Staphylococcus epidermidis, Veilonella parvula and Clostridium perfringens. These organisms often contribute to the harmful long-term sequelae seen in these young infants.

Combination of Whole Genome Sequencing and Metagenomics for Microbiological Diagnostics

Whole genome sequencing (WGS) provides the highest resolution for genome-based species identification and can provide insight into the antimicrobial resistance and virulence potential of a single microbiological isolate during the diagnostic process. In contrast, metagenomic sequencing allows the analysis of DNA segments from multiple microorganisms within a community, either using an amplicon- or shotgun-based approach. However, WGS and shotgun metagenomic data are rarely combined, although such an approach may generate additive or synergistic information, critical for, e.g., patient management, infection control, and pathogen surveillance. To produce a combined workflow with actionable outputs, we need to understand the pre-to-post analytical process of both technologies. This will require specific databases storing interlinked sequencing and metadata, and also involves customized bioinformatic analytical pipelines. This review article will provide an overview of the critical steps and potential clinical application of combining WGS and metagenomics together for microbiological diagnosis.