Metagenomics - a guide from sampling to data analysis

Metagenomic approaches and opportunities in arid soil research

Arid soils present unique challenges and opportunities for studying microbial diversity and bioactive potential due to the extreme environmental conditions they bear. This review article investigates soil metagenomics as an emerging tool to explore complex microbial dynamics and unexplored bioactive potential in harsh environments. Utilizing advanced metagenomic techniques, diverse microbial populations that grow under extreme conditions such as high temperatures, salinity, high pH levels, and exposure to metals and radiation can be studied. The use of extremophiles to discover novel natural products and biocatalysts emphasizes the role of functional metagenomics in identifying enzymes and secondary metabolites for industrial and pharmaceutical purposes. Metagenomic sequencing uncovers a complex network of microbial diversity, offering significant potential for discovering new bioactive compounds. Functional metagenomics, connecting taxonomic diversity to genetic capabilities, provides a pathway to identify microbes' mechanisms to synthesize valuable secondary metabolites and other bioactive substances. Contrary to the common perception of desert soil as barren land, the metagenomic analysis reveals a rich diversity of life forms adept at extreme survival. It provides valuable findings into their resilience and potential applications in biotechnology. Moreover, the challenges associated with metagenomics in arid soils, such as low microbial biomass, high DNA degradation rates, and DNA extraction inhibitors and strategies to overcome these issues, outline the latest advancements in extraction methods, high-throughput sequencing, and bioinformatics. The importance of metagenomics for investigating diverse environments opens the way for future research to develop sustainable solutions in agriculture, industry, and medicine. Extensive studies are necessary to utilize the full potential of these powerful microbial communities. This research will significantly improve our understanding of microbial ecology and biotechnology in arid environments.

SIP-metagenomics reveals key drivers of rhizospheric Benzo[a]pyrene bioremediation via bioaugmentation with indigenous soil microbes

Rhizoremediation and bioaugmentation have proven effective in promoting benzo[a]pyrene (BaP) degradation in contaminated soils. However, the mechanism underlying bioaugmented rhizospheric BaP degradation with native microbes is poorly understood. In this study, an indigenous BaP degrader (Stenotrophomonas BaP-1) isolated from petroleum-contaminated soil was introduced into ryegrass rhizosphere to investigate the relationship between indigenous degraders and rhizospheric BaP degradation. Stable isotope probing and 16S rRNA gene amplicon sequencing subsequently revealed 15 BaP degraders, 8 of which were directly associated with BaP degradation including Bradyrhizobium and Streptomyces. Bioaugmentation with strain BaP-1 significantly enhanced rhizospheric BaP degradation and shaped the microbial community structure. A correlation of BaP degraders, BaP degradation efficiency, and functional genes identified active degraders and genes encoding polycyclic aromatic hydrocarbon-ring hydroxylating dioxygenase (PAH-RHD) genes as the primary drivers of rhizospheric BaP degradation. Furthermore, strain BaP-1 was shown to not only engage in BaP metabolism but also to increase the abundance of other BaP degraders and PAH-RHD genes, resulting in enhanced rhizospheric BaP degradation. Metagenomic and correlation analyses indicated a significant positive relationship between glyoxylate and dicarboxylate metabolism and BaP degradation, suggesting a role for these pathways in rhizospheric BaP biodegradation. By identifying BaP degraders and characterizing their metabolic characteristics within intricate microbial communities, our study offers valuable insights into the mechanisms of bioaugmented rhizoremediation with indigenous bacteria for high-molecular-weight PAHs in petroleum-contaminated soils.

Unveiling the role of emerging metagenomics for the examination of hypersaline environments

Hypersaline ecosystems are distributed all over the globe. They are subjected to poly-extreme stresses and are inhabited by halophilic microorganisms possessing multiple adaptations. The halophiles have many biotechnological applications such as nutrient supplements, antioxidant synthesis, salt tolerant enzyme production, osmolyte synthesis, biofuel production, electricity generation etc. However, halophiles are still underexplored in terms of complex ecological interactions and functions as compared to other niches. The advent of metagenomics and the recent advancement of next-generation sequencing tools have made it feasible to investigate the microflora of an ecosystem, its interactions and functions. Both target gene and shotgun metagenomic approaches are commonly employed for the taxonomic, phylogenetic, and functional analyses of the hypersaline microbial communities. This review discusses different types of hypersaline niches, their residential microflora, and an overview of the metagenomic approaches used to investigate them. Various applications, hurdles and the recent advancements in metagenomic approaches have also been focused on here for their better understanding and utilization in the study of hypersaline microbiome.

Whole genome sequencing approaches for taxonomic profiling and evaluation of wastewater quality

Tracking metagenomic abundance in wastewater is undoubtedly a powerful tool to detect emerging variants and improve community health. However, there are a few factors that limit environmental water-based genomic monitoring: sampling variability, incomplete coverage, genetic fragmentation, degradation, data analysis and interpretation. The decreasing costs of high-throughput sequencing and high-end supercomputers have increased the use and accuracy of genomic data for microbial detection and monitoring in wastewater samples within any given region. To better understand the microbial dynamics and to determine the target sequencing throughput required to establish taxa that may pose as bio-indicators of an epidemiological outbreak, wastewater samples were collected from distinct locations within the Emirate of Abu Dhabi, United Arab Emirates using appropriate sampling methods. A reference database of ∼27,000 known species was developed and used for further analysis. The results showed that 15 % of data in each sample matched any of ∼27,000 known bacterial, viral, fungal, or protozoan species. Despite the high fraction of unclassified data (85 %), more than 2000 species from >800 genera across >30 phyla were detected in each sample. Both 5 Gb and 10 Gb of sequenced data detected the top ∼2000 species with highest abundance. Doubling the target sequencing throughput (i.e., 10 Gb vs 5 Gb) detected ∼500 additional low-abundance species per sample however it did not affect the overall sample composition or translate into higher per-sample species diversity captured. There was a marginal increase in the number of species detected in each sample beyond 0.20 Gb of classified data. Overall, the results indicate that sequencing to a 3 Gb throughput detects nearly 95 % of all species in the samples.

The Gut Microbiome and Symptom Burden After Kidney Transplantation: An Overview and Research Opportunities

Many kidney transplant recipients continue to experience high symptom burden despite restoration of kidney function. High symptom burden is a significant driver of quality of life. In the post-transplant setting, high symptom burden has been linked to negative outcomes including medication non-adherence, allograft rejection, graft loss, and even mortality. Symbiotic bacteria (microbiota) in the human gastrointestinal tract critically interact with the immune, endocrine, and neurological systems to maintain homeostasis of the host. The gut microbiome has been proposed as an underlying mechanism mediating symptoms in several chronic medical conditions including irritable bowel syndrome, chronic fatigue syndrome, fibromyalgia, and psychoneurological disorders via the gut-brain-microbiota axis, a bidirectional signaling pathway between the enteric and central nervous system. Post-transplant exposure to antibiotics, antivirals, and immunosuppressant medications results in significant alterations in gut microbiota community composition and function, which in turn alter these commensal microorganisms' protective effects. This overview will discuss the current state of the science on the effects of the gut microbiome on symptom burden in kidney transplantation and future directions to guide this field of study.

Methanobrevibacter smithii cell variants in human physiology and pathology: A review

Methanobrevibacter smithii (M. smithii), initially isolated from human feces, has been recognised as a distinct taxon within the Archaea domain following comprehensive phenotypic, genetic, and genomic analyses confirming its uniqueness among methanogens. Its diversity, encompassing 15 genotypes, mirrors that of biotic and host-associated ecosystems in which M. smithii plays a crucial role in detoxifying hydrogen from bacterial fermentations, converting it into mechanically expelled gaseous methane. In microbiota in contact with host epithelial mucosae, M. smithii centres metabolism-driven microbial networks with Bacteroides, Prevotella, Ruminococcus, Veillonella, Enterococcus, Escherichia, Enterobacter, Klebsiella, whereas symbiotic association with the nanoarchaea Candidatus Nanopusillus phoceensis determines small and large cell variants of M. smithii. The former translocate with bacteria to induce detectable inflammatory and serological responses and are co-cultured from blood, urine, and tissular abscesses with bacteria, prototyping M. smithii as a model organism for pathogenicity by association. The sources, mechanisms and dynamics of in utero and lifespan M. smithii acquisition, its diversity, and its susceptibility to molecules of environmental, veterinary, and medical interest still have to be deeply investigated, as only four strains of M. smithii are available in microbial collections, despite the pivotal role this neglected microorganism plays in microbiota physiology and pathologies.

Genetic improvement and genomic resources of important cyprinid species: status and future perspectives for sustainable production

Cyprinid species are the most cultured aquatic species around the world in terms of quantity and total value. They account for 25% of global aquaculture production and significantly contribute to fulfilling the demand for fish food. The aquaculture of these species is facing severe concerns in terms of seed quality, rising feed costs, disease outbreaks, introgression of exotic species, environmental impacts, and anthropogenic activities. Numerous researchers have explored biological issues and potential methods to enhance cyprinid aquaculture. Selective breeding is extensively employed in cyprinid species to enhance specific traits like growth and disease resistance. In this context, we have discussed the efforts made to improve important cyprinid aquaculture practices through genetic and genomic approaches. The recent advances in DNA sequencing technologies and genomic tools have revolutionized the understanding of biological research. The generation of a complete genome and other genomic resources in cyprinid species has significantly strengthened molecular-level investigations into disease resistance, growth, reproduction, and adaptation to changing environments. We conducted a comprehensive review of genomic research in important cyprinid species, encompassing genome, transcriptome, proteome, metagenome, epigenome, etc. This review reveals that considerable data has been generated for cyprinid species. However, the seamless integration of this valuable data into genetic selection programs has yet to be achieved. In the upcoming years, genomic techniques, gene transfer, genome editing tools are expected to bring a paradigm shift in sustainable cyprinid aquaculture production. The comprehensive information presented here will offer insights for the cyprinid aquaculture research community.

A unified web cloud computing platform MiMedSurv for microbiome causal mediation analysis with survival responses

In human microbiome studies, mediation analysis has recently been spotlighted as a practical and powerful analytic tool to survey the causal roles of the microbiome as a mediator to explain the observed relationships between a medical treatment/environmental exposure and a human disease. We also note that, in a clinical research, investigators often trace disease progression sequentially in time; as such, time-to-event (e.g., time-to-disease, time-to-cure) responses, known as survival responses, are prevalent as a surrogate variable for human health or disease. In this paper, we introduce a web cloud computing platform, named as microbiome mediation analysis with survival responses (MiMedSurv), for comprehensive microbiome mediation analysis with survival responses on user-friendly web environments. MiMedSurv is an extension of our prior web cloud computing platform, named as microbiome mediation analysis (MiMed), for survival responses. The two main features that are well-distinguished are as follows. First, MiMedSurv conducts some baseline exploratory non-mediational survival analysis, not involving microbiome, to survey the disparity in survival response between medical treatments/environmental exposures. Then, MiMedSurv identifies the mediating roles of the microbiome in various aspects: (i) as a microbial ecosystem using ecological indices (e.g., alpha and beta diversity indices) and (ii) as individual microbial taxa in various hierarchies (e.g., phyla, classes, orders, families, genera, species). To illustrate its use, we survey the mediating roles of the gut microbiome between antibiotic treatment and time-to-type 1 diabetes. MiMedSurv is freely available on our web server ( http://mimedsurv.micloud.kr ).

Characterization of thermostable carboxypeptidase from high-altitude hot spring metagenome

This study explored the metagenome of the Pir Panjal Hot Spring (PPHS) to identify thermostable hydrolases. The carboxypeptidase (CarP) gene was successfully amplified and cloned into Escherichia coli DH5-α cells, followed by expression in E. coli BL21-DE3 cells. The CarP enzyme was comprehensively characterized in vitro. Sequencing analysis revealed an open reading frame encoding a functional protein of 504 amino acids, with a molecular weight of 58.65 kDa and an isoelectric point of 4.81. The CarP protein was purified using Ni-His affinity chromatography, and the experimental molecular weight matched in silico predictions. The enzyme exhibited significant thermostability and alkaliphilic properties, with optimal activity at 70 °C and pH 10.0. Additionally, the presence of Zn+2 ions at concentrations of 5 and 10 mmol/L enhanced protease activity by 1.4 and 1.5-fold, respectively. This study reports the discovery of a novel, multifunctional, and thermostable CarP from hot-spring metagenomes. The enzyme's stability against high temperatures, metal ions, surfactants, and inhibitors, along with its specific substrate interactions, highlights its potential for various biotechnological applications.

Exploring viral diversity and metagenomics in livestock: insights into disease emergence and spillover risks in cattle

Cattle have a significant impact on human societies in terms of both economics and health. Viral infections pose a relevant problem as they directly or indirectly disrupt the balance within cattle populations. This has negative consequences at the economic level for producers and territories, and also jeopardizes human health through the transmission of zoonotic diseases that can escalate into outbreaks or pandemics. To establish prevention strategies and control measures at various levels (animal, farm, region, or global), it is crucial to identify the viral agents present in animals. Various techniques, including virus isolation, serological tests, and molecular techniques like PCR, are typically employed for this purpose. However, these techniques have two major drawbacks: they are ineffective for non-culturable viruses, and they only detect a small fraction of the viruses present. In contrast, metagenomics offers a promising approach by providing a comprehensive and unbiased analysis for detecting all viruses in a given sample. It has the potential to identify rare or novel infectious agents promptly and establish a baseline of healthy animals. Nevertheless, the routine application of viral metagenomics for epidemiological surveillance and diagnostics faces challenges related to socioeconomic variables, such as resource availability and space dedicated to metagenomics, as well as the lack of standardized protocols and resulting heterogeneity in presenting results. This review aims to provide an overview of the current knowledge and prospects for using viral metagenomics to detect and identify viruses in cattle raised for livestock, while discussing the epidemiological and clinical implications.

Implant Mechanics, Biological Milieu, and Peri-Implantitis: A Narrative Review

Dental implants constitute an important treatment modality for rehabilitating edentulous and partially edentulous arches. With more implant systems in the market, understanding the mechanical aspects of implants is crucial in understanding this indispensable therapy. However, microflora-related factors i.e. biological factors are also crucial. Despite the tremendous success rate of dental implants, it is not averse to failure. Both mechanical and microbial aspects in seclusion or together predispose to implant failure. Newer technological advances have paved the way for advanced techniques to identify the not-so-common flora causing implant failure. This review focuses on detailed mechanical and biological aspects and the sealing agent used to seal the implant-abutment interface. It also focuses on advanced molecular techniques like metagenomics and transcriptomics. A thorough literature search was performed with selected articles from electronic databases. A combination of in-vivo and in-vitro studies were considered to provide comprehensive information on the subject. Both the biomechanical aspects like micro gap, and microleakage, as well as microbial movements play confluent roles in implant failure. The focus should be on the different aspects through which microflora can penetrate the inner parts of the implant. Also, newer culture-independent techniques of detecting previously undetected oral flora should be included in future studies.

Capturing clinically relevant Campylobacter attributes through direct whole genome sequencing of stool

Campylobacter is the leading bacterial cause of infectious intestinal disease, but the pathogen typically accounts for a very small proportion of the overall stool microbiome in each patient. Diagnosis is even more difficult due to the fastidious nature of Campylobacter in the laboratory setting. This has, in part, driven a change in recent years, from culture-based to rapid PCR-based diagnostic assays which have improved diagnostic detection, whilst creating a knowledge gap in our clinical and epidemiological understanding of Campylobacter genotypes - no isolates to sequence. In this study, direct metagenomic sequencing approaches were used to assess the possibility of replacing genome sequences with metagenome sequences; metagenomic sequencing outputs were used to describe clinically relevant attributes of Campylobacter genotypes. A total of 37 diarrhoeal stool samples with Campylobacter and five samples with an unknown pathogen result were collected and processed with and without filtration, DNA was extracted, and metagenomes were sequenced by short-read sequencing. Culture-based methods were used to validate Campylobacter metagenome-derived genome (MDG) results. Sequence output metrics were assessed for Campylobacter genome quality and accuracy of characterization. Of the 42 samples passing quality checks for analysis, identification of Campylobacter to the genus and species level was dependent on Campylobacter genome read count, coverage and genome completeness. A total of 65% (24/37) of samples were reliably identified to the genus level through Campylobacter MDG, 73% (27/37) by culture and 97% (36/37) by qPCR. The Campylobacter genomes with a genome completeness of over 60% (n=21) were all accurately identified at the species level (100%). Of those, 72% (15/21) were identified to sequence types (STs), and 95% (20/21) accurately identified antimicrobial resistance (AMR) gene determinants. Filtration of stool samples enhanced Campylobacter MDG recovery and genome quality metrics compared to the corresponding unfiltered samples, which improved the identification of STs and AMR profiles. The phylogenetic analysis in this study demonstrated the clustering of the metagenome-derived with culture-derived genomes and revealed the reliability of genomes from direct stool sequencing. Furthermore, Campylobacter genome spiking percentages ranging from 0 to 2% total metagenome abundance in the ONT MinION sequencer, configured to adaptive sequencing, exhibited better assembly quality and accurate identification of STs, particularly in the analysis of metagenomes containing 2 and 1% of Campylobacter jejuni genomes. Direct sequencing of Campylobacter from stool samples provides clinically relevant and epidemiologically important genomic information without the reliance on cultured genomes.

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.

Investigation of acute encephalitis syndrome with implementation of metagenomic next generation sequencing in Nepal

BACKGROUND

The causative agents of Acute Encephalitis Syndrome remain unknown in 68-75% of the cases. In Nepal, the cases are tested only for Japanese encephalitis, which constitutes only about 15% of the cases. However, there could be several organisms, including vaccine-preventable etiologies that cause acute encephalitis, when identified could direct public health efforts for prevention, including addressing gaps in vaccine coverage.

OBJECTIVES

This study employs metagenomic next-generation-sequencing in the investigation of underlying causative etiologies contributing to acute encephalitis syndrome in Nepal.

METHODS

In this study, we investigated 90, Japanese-encephalitis-negative, banked cerebrospinal fluid samples that were collected as part of a national surveillance network in 2016 and 2017. Randomization was done to include three age groups (< 5-years; 5-14-years; >15-years). Only some metadata (age and gender) were available. The investigation was performed in two batches which included total nucleic-acid extraction, followed by individual library preparation (DNA and RNA) and sequencing on Illumina iSeq100. The genomic data were interpreted using Chan Zuckerberg-ID and confirmed with polymerase-chain-reaction.

RESULTS

Human-alphaherpes-virus 2 and Enterovirus-B were seen in two samples. These hits were confirmed by qPCR and semi-nested PCR respectively. Most of the other samples were marred by low abundance of pathogen, possible freeze-thaw cycles, lack of process controls and associated clinical metadata.

CONCLUSION

From this study, two documented causative agents were revealed through metagenomic next-generation-sequencing. Insufficiency of clinical metadata, process controls, low pathogen abundance and absence of standard procedures to collect and store samples in nucleic-acid protectants could have impeded the study and incorporated ambiguity while correlating the identified hits to infection. Therefore, there is need of standardized procedures for sample collection, inclusion of process controls and clinical metadata. Despite challenging conditions, this study highlights the usefulness of mNGS to investigate diseases with unknown etiologies and guide development of adequate clinical-management-algorithms and outbreak investigations in Nepal.

Unlocking the potential of soil microbial communities for bioremediation of emerging organic contaminants: omics-based approaches

The remediation of emerging contaminants presents a pressing environmental challenge, necessitating innovative approaches for effective mitigation. This review article delves into the untapped potential of soil microbial communities in the bioremediation of emerging contaminants. Bioremediation, while a promising method, often proves time-consuming and requires a deep comprehension of microbial intricacies for enhancement. Given the challenges presented by the inability to culture many of these microorganisms, conventional methods are inadequate for achieving this goal. While omics-based methods provide an innovative approach to understanding the fundamental aspects, processes, and connections among microorganisms that are essential for improving bioremediation strategies. By exploring the latest advancements in omics technologies, this review aims to shed light on how these approaches can unlock the hidden capabilities of soil microbial communities, paving the way for more efficient and sustainable remediation solutions.

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.

Metagenomic analysis of endophytic bacteria in seed potato (Solanum tuberosum)

To date, the association of potato tuber microbiota is poorly understood. In this study, the endophytic bacterial flora of seed potato tubers was identified and the diversity of healthy and unhealthy tubers was compared. Metagenomic DNA extracted from healthy and unhealthy samples of seed potato tubers was used for the analysis of microbial communities. Next generation sequencing of the ∼460 bp v3-v4 region of the 16S rRNA gene was carried out using the Illumina Miseq platform. The data were analysed using the Divisive Amplicon Denoising Algorithm 2 pipeline. Sequence analysis of the potato metagenome identified amplicon sequence variants (ASVs) assigned to 745 different taxa belonging to eight Phyla: Firmicutes (46.2%), Proteobacteria (36.9%), Bacteroidetes (1.8%), Actinobacteria (0.1%), Tenericutes (0.005%), Saccharibacteria (0.003%), Verrucomicrobiota (0.003%), and Acidobacteria (0.001%). In healthy seed potato tubers, 55-99% of ASVs belonged to Firmicutes, including Bacillus, Salinibacillus, Staphylococcus, Lysinibacillus, Paenibacillus, and Brevibacillus genera within the taxonomic order Bacillales. However, in the visually unhealthy tubers, only 0.5-3.9% of ASVs belonged to Firmicutes while 84.1-97% of ASVs belonged to Proteobacteria. This study highlights that diverse bacterial communities colonize potato tubers, which contributes to the understanding of plant-microbe interactions and underscores the significance of metagenomic approaches in agricultural research.

Melodious tuning of microbial dynamics in biofloc, cage, and pond culturing system: a study on Pangasius pangasius fish gut microbiome

Aquaculture, a rapidly expanding sector, meets the global surging demand for aquatic food. Pangasius, a highly valued freshwater species, has seen a significant increase in demand due to its adaptability and potential for high yields, making it a promising candidate for aquaculture in India. This study investigates the gut microbiome composition of Pangasius pangasius fish cultured in three different systems (biofloc, cage, and pond). Metagenomic DNA extraction and 16S rRNA gene-targeted sequencing were performed. Outcomes revealed distinct microbial compositions across culture types, with significant differences in species richness and diversity, specifically in the biofloc system, compared to cages and ponds. Taxonomic analysis identified prevalent phyla such as Firmicutes and Fusobacteriota, with varying abundances among culture systems. The genus-level analysis highlighted dominant genera such as Cetobacterium and WWE3. Functional profiling indicated differences in enzymatic activity and metabolic pathways, emphasizing each culture sample type's unique microbial community structures. Notably, the microbiota from BF samples exhibited significant differences and unique metabolic pathways compared to the microbiota from C and P samples, which showed greater similarity and shared several common metabolic pathways. These findings highlight substantial differences in microbial diversity across the culturing systems, reflecting the microbiota's ability to adapt to specific environments and their potential role in promoting fish growth within those environments. Overall, this study provides insights into the gut microbiome diversity and functionality in Pangasius pangasius across different aquaculture environments, contributing to a better understanding of host-microbe interactions and aquaculture management strategies.

Microbial oases in the ice: A state-of-the-art review on cryoconite holes as diversity hotspots and their scientific connotations

Cryoconite holes, small meltwater pools on the surface of glaciers and ice sheets, represent extremely cold ecosystems teeming with diverse microbial life. Cryoconite holes exhibit greater susceptibility to the impacts of climate change, underlining the imperative nature of investigating microbial communities as an essential module of polar and alpine ecosystem monitoring efforts. Microbes in cryoconite holes play a critical role in nutrient cycling and can produce bioactive compounds, holding promise for industrial and pharmaceutical innovation. Understanding microbial diversity in these delicate ecosystems is essential for effective conservation strategies. Therefore, this review discusses the microbial diversity in these extreme environments, aiming to unveil the complexity of their microbial communities. The current study envisages that cryoconite holes as distinctive ecosystems encompass a multitude of taxonomically diverse and functionally adaptable microorganisms that exhibit a rich microbial diversity and possess intricate ecological functions. By investigating microbial diversity and ecological functions of cryoconite holes, this study aims to contribute valuable insights into the broader field of environmental microbiology and enhance further understanding of these ecosystems. This review seeks to provide a holistic overview regarding the formation, evolution, characterization, and molecular adaptations of cryoconite holes. Furthermore, future research directions and challenges underlining the need for long-term monitoring, and ethical considerations in preserving these pristine environments are also provided. Addressing these challenges and resolutely pursuing future research directions promises to enrich our comprehension of microbial diversity within cryoconite holes, revealing the broader ecological and biogeochemical implications. The inferences derived from the present study will provide researchers, ecologists, and policymakers with a profound understanding of the significance and utility of cryoconite holes in unveiling the microbial diversity and its potential applications.

Microbiome modeling: a beginner's guide

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

Omics approaches open new horizons in major depressive disorder: from biomarkers to precision medicine

Major depressive disorder (MDD) is a recurrent episodic mood disorder that represents the third leading cause of disability worldwide. In MDD, several factors can simultaneously contribute to its development, which complicates its diagnosis. According to practical guidelines, antidepressants are the first-line treatment for moderate to severe major depressive episodes. Traditional treatment strategies often follow a one-size-fits-all approach, resulting in suboptimal outcomes for many patients who fail to experience a response or recovery and develop the so-called "therapy-resistant depression". The high biological and clinical inter-variability within patients and the lack of robust biomarkers hinder the finding of specific therapeutic targets, contributing to the high treatment failure rates. In this frame, precision medicine, a paradigm that tailors medical interventions to individual characteristics, would help allocate the most adequate and effective treatment for each patient while minimizing its side effects. In particular, multi-omic studies may unveil the intricate interplays between genetic predispositions and exposure to environmental factors through the study of epigenomics, transcriptomics, proteomics, metabolomics, gut microbiomics, and immunomics. The integration of the flow of multi-omic information into molecular pathways may produce better outcomes than the current psychopharmacological approach, which targets singular molecular factors mainly related to the monoamine systems, disregarding the complex network of our organism. The concept of system biomedicine involves the integration and analysis of enormous datasets generated with different technologies, creating a "patient fingerprint", which defines the underlying biological mechanisms of every patient. This review, centered on precision medicine, explores the integration of multi-omic approaches as clinical tools for prediction in MDD at a single-patient level. It investigates how combining the existing technologies used for diagnostic, stratification, prognostic, and treatment-response biomarkers discovery with artificial intelligence can improve the assessment and treatment of MDD.

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.

Long-term conservation tillage increase cotton rhizosphere sequestration of soil organic carbon by changing specific microbial CO2 fixation pathways in coastal saline soil

Coastal saline soil is an important reserve resource for arable land globally. Data from 10 years of continuous stubble return and subsoiling experiments have revealed that these two conservation tillage measures significantly improve cotton rhizosphere soil organic carbon sequestration in coastal saline soil. However, the contribution of microbial fixation of atmospheric carbon dioxide (CO2) has remained unclear. Here, metagenomics and metabolomics analyses were used to deeply explore the microbial CO2 fixation process in rhizosphere soil of coastal saline cotton fields under long-term stubble return and subsoiling. Metagenomics analysis showed that stubble return and subsoiling mainly optimized CO2 fixing microorganism (CFM) communities by increasing the abundance of Acidobacteria, Gemmatimonadetes, and Chloroflexi, and improving composition diversity. Conjoint metagenomics and metabolomics analyses investigated the effects of stubble return and subsoiling on the reverse tricarboxylic acid (rTCA) cycle. The conversion of citrate to oxaloacetate was inhibited in the citrate cleavage reaction of the rTCA cycle. More citrate was converted to acetyl-CoA, which enhanced the subsequent CO2 fixation process of acetyl-CoA conversion to pyruvate. In the rTCA cycle reductive carboxylation reaction from 2-oxoglutarate to isocitrate, synthesis of the oxalosuccinate intermediate product was inhibited, with strengthened CO2 fixation involving the direct conversion of 2-oxoglutarate to isocitrate. The collective results demonstrate that stubble return and subsoiling optimizes rhizosphere CFM communities by increasing microbial diversity, in turn increasing CO2 fixation by enhancing the utilization of rTCA and 3-hydroxypropionate/4-hydroxybutyrate cycles by CFMs. These events increase the microbial CO2 fixation in the cotton rhizosphere, thereby promoting the accumulation of microbial biomass, and ultimately improving rhizosphere soil organic carbon. This study clarifies the impact of conservation tillage measures on microbial CO2 fixation in cotton rhizosphere of coastal saline soil, and provides fundamental data for the improvement of carbon sequestration in saline soil in agricultural ecosystems.

Investigating antimicrobial resistance genes in Kenya, Uganda and Tanzania cattle using metagenomics

Antimicrobial resistance (AMR) is a growing problem in African cattle production systems, posing a threat to human and animal health and the associated economic value chain. However, there is a poor understanding of the resistomes in small-holder cattle breeds in East African countries. This study aims to examine the distribution of antimicrobial resistance genes (ARGs) in Kenya, Tanzania, and Uganda cattle using a metagenomics approach. We used the SqueezeMeta-Abricate (assembly-based) pipeline to detect ARGs and benchmarked this approach using the Centifuge-AMRplusplus (read-based) pipeline to evaluate its efficiency. Our findings reveal a significant number of ARGs of critical medical and economic importance in all three countries, including resistance to drugs of last resort such as carbapenems, suggesting the presence of highly virulent and antibiotic-resistant bacterial pathogens (ESKAPE) circulating in East Africa. Shared ARGs such as aph(6)-id (aminoglycoside phosphotransferase), tet (tetracycline resistance gene), sul2 (sulfonamide resistance gene) and cfxA_gen (betalactamase gene) were detected. Assembly-based methods revealed fewer ARGs compared to read-based methods, indicating the sensitivity and specificity of read-based methods in resistome characterization. Our findings call for further surveillance to estimate the intensity of the antibiotic resistance problem and wider resistome classification. Effective management of livestock and antibiotic consumption is crucial in minimizing antimicrobial resistance and maximizing productivity, making these findings relevant to stakeholders, agriculturists, and veterinarians in East Africa and Africa at large.

High-throughput DNA extraction and cost-effective miniaturized metagenome and amplicon library preparation of soil samples for DNA sequencing

Reductions in sequencing costs have enabled widespread use of shotgun metagenomics and amplicon sequencing, which have drastically improved our understanding of the microbial world. However, large sequencing projects are now hampered by the cost of library preparation and low sample throughput, comparatively to the actual sequencing costs. Here, we benchmarked three high-throughput DNA extraction methods: ZymoBIOMICS™ 96 MagBead DNA Kit, MP BiomedicalsTM FastDNATM-96 Soil Microbe DNA Kit, and DNeasy® 96 PowerSoil® Pro QIAcube® HT Kit. The DNA extractions were evaluated based on length, quality, quantity, and the observed microbial community across five diverse soil types. DNA extraction of all soil types was successful for all kits, however DNeasy® 96 PowerSoil® Pro QIAcube® HT Kit excelled across all performance parameters. We further used the nanoliter dispensing system I.DOT One to miniaturize Illumina amplicon and metagenomic library preparation volumes by a factor of 5 and 10, respectively, with no significant impact on the observed microbial communities. With these protocols, DNA extraction, metagenomic, or amplicon library preparation for one 96-well plate are approx. 3, 5, and 6 hours, respectively. Furthermore, the miniaturization of amplicon and metagenome library preparation reduces the chemical and plastic costs from 5.0 to 3.6 and 59 to 7.3 USD pr. sample. This enhanced efficiency and cost-effectiveness will enable researchers to undertake studies with greater sample sizes and diversity, thereby providing a richer, more detailed view of microbial communities and their dynamics.

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.

A Review of Web-Based Metagenomics Platforms for Analysing Next-Generation Sequence Data

Metagenomics has now evolved as a promising technology for understanding the microbial population in the environment. By metagenomics, a number of extreme and complex environment has been explored for their microbial population. Using this technology, researchers have brought out novel genes and their potential characteristics, which have robust applications in food, pharmaceutical, scientific research, and other biotechnological fields. A sequencing platform can provide a sequence of microbial populations in any given environment. The sequence needs to be analysed computationally to derive meaningful information. It is presumed that only bioinformaticians with extensive computational skills can process the sequencing data till the downstream end. However, numerous open-source software and online servers are available to analyse the metagenomic data developed for a biologist with less computational skills. This review is focused on bioinformatics tools such as Galaxy, CSI-NGS portal, ANASTASIA and SHAMAN, EBI- metagenomics, IDseq, and MG-RAST for analysing metagenomic data.

Molecular docking and metagenomics assisted mitigation of microplastic pollution

Microplastics, tiny, flimsy, and direct progenitors of principal and subsidiary plastics, cause environmental degradation in aquatic and terrestrial entities. Contamination concerns include irrevocable impacts, potential cytotoxicity, and negative health effects on mortals. The detection, recovery, and degradation strategies of these pollutants in various biota and ecosystems, as well as their impact on plants, animals, and humans, have been a topic of significant interest. But the natural environment is infested with several types of plastics, all having different chemical makeup, structure, shape, and origin. Plastic trash acts as a substrate for microbial growth, creating biofilms on the plastisphere surface. This colonizing microbial diversity can be glimpsed with meta-genomics, a culture-independent approach. Owing to its comprehensive description of microbial communities, genealogical evidence on unconventional biocatalysts or enzymes, genomic correlations, evolutionary profile, and function, it is being touted as one of the promising tools in identifying novel enzymes for the degradation of polymers. Additionally, computational tools such as molecular docking can predict the binding of these novel enzymes to the polymer substrate, which can be validated through in vitro conditions for its environmentally feasible applications. This review mainly deals with the exploration of metagenomics along with computational tools to provide a clearer perspective into the microbial potential in the biodegradation of microplastics. The computational tools due to their polymathic nature will be quintessential in identifying the enzyme structure, binding affinities of the prospective enzymes to the substrates, and foretelling of degradation pathways involved which can be quite instrumental in the furtherance of the plastic degradation studies.

Utility of metagenomics for bioremediation: a comprehensive review on bioremediation mechanisms and microbial dynamics of river ecosystem

Global industrialization has contributed substantial amounts of chemical pollutants in rivers, resulting in an uninhabitable state and impacting different life forms. Moreover, water macrophytes, such as water hyacinths, are abundantly present in polluted rivers, significantly affecting the overall water biogeochemistry. Bioremediation involves utilizing microbial metabolic machinery and is one of the most viable approaches for removing toxic pollutants. Conventional techniques generate limited information on the indigenous microbial population and their xenobiotic metabolism, failing the bioremediation process. Metagenomics can overcome these limitations by providing in-depth details of microbial taxa and functionality-related information required for successful biostimulation and augmentation. An in-depth summary of the findings related to pollutant metabolizing genes and enzymes in rivers still needs to be collated. The present study details bioremediation genes and enzymes functionally mined from polluted river ecosystems worldwide using a metagenomic approach. Several studies reported a wide variety of pollutant-degrading enzymes involved in the metabolism of dyes, plastics, persistent organic pollutants, and aromatic hydrocarbons. Additionally, few studies also noted a shift in the microbiome of the rivers upon exposure to contaminants, crucially affecting the ecological determinant processes. Furthermore, minimal studies have focused on the role of water-hyacinth-associated microbes in the bioremediation potentials, suggesting the need for the bioprospecting of these lesser-studied microbes. Overall, our study summarizes the prospects and utilities of the metagenomic approach and proposes the need to employ it for efficient bioremediation.

Omics approaches in asthma research: Challenges and opportunities

Asthma, a chronic respiratory disease with a global prevalence of approximately 300 million individuals, presents a significant societal and economic burden. This multifaceted syndrome exhibits diverse clinical phenotypes and pathogenic endotypes influenced by various factors. The advent of omics technologies has revolutionized asthma research by delving into the molecular foundation of the disease to unravel its underlying mechanisms. Omics technologies are employed to systematically screen for potential biomarkers, encompassing genes, transcripts, methylation sites, proteins, and even the microbiome components. This review provides an insightful overview of omics applications in asthma research, with a special emphasis on genetics, transcriptomics, epigenomics, and the microbiome. We explore the cutting-edge methods, discoveries, challenges, and potential future directions in the realm of asthma omics research. By integrating multi-omics and non-omics data through advanced statistical techniques, we aspire to advance precision medicine in asthma, guiding diagnosis, risk assessment, and personalized treatment strategies for this heterogeneous condition.

Genome-resolved metagenomics of Venice Lagoon surface sediment bacteria reveals high biosynthetic potential and metabolic plasticity as successful strategies in an impacted environment

Bacteria living in sediments play essential roles in marine ecosystems and deeper insights into the ecology and biogeochemistry of these largely unexplored organisms can be obtained from 'omics' approaches. Here, we characterized metagenome-assembled-genomes (MAGs) from the surface sediment microbes of the Venice Lagoon (northern Adriatic Sea) in distinct sub-basins exposed to various natural and anthropogenic pressures. MAGs were explored for biodiversity, major marine metabolic processes, anthropogenic activity-related functions, adaptations at the microscale, and biosynthetic gene clusters. Starting from 126 MAGs, a non-redundant dataset of 58 was compiled, the majority of which (35) belonged to (Alpha- and Gamma-) Proteobacteria. Within the broad microbial metabolic repertoire (including C, N, and S metabolisms) the potential to live without oxygen emerged as one of the most important features. Mixotrophy was also found as a successful lifestyle. Cluster analysis showed that different MAGs encoded the same metabolic patterns (e.g., C fixation, sulfate oxidation) thus suggesting metabolic redundancy. Antibiotic and toxic compounds resistance genes were coupled, a condition that could promote the spreading of these genetic traits. MAGs showed a high biosynthetic potential related to antimicrobial and biotechnological classes and to organism defense and interactions as well as adaptive strategies for micronutrient uptake and cellular detoxification. Our results highlighted that bacteria living in an impacted environment, such as the surface sediments of the Venice Lagoon, may benefit from metabolic plasticity as well as from the synthesis of a wide array of secondary metabolites, promoting ecosystem resilience and stability toward environmental pressures.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42995-023-00192-z.

Application of omics technologies in cariology research: A critical review with bibliometric analysis

OBJECTIVES

To review the application of omics technologies in the field of cariology research and provide critical insights into the emerging opportunities and challenges.

DATA & SOURCES

Publications on the application of omics technologies in cariology research up to December 2022 were sourced from online databases, including PubMed, Web of Science and Scopus. Two independent reviewers assessed the relevance of the publications to the objective of this review.

STUDY SELECTION

Studies that employed omics technologies to investigate dental caries were selected from the initial pool of identified publications. A total of 922 publications with one or more omics technologies adopted were included for comprehensive bibliographic analysis. (Meta)genomics (676/922, 73 %) is the predominant omics technology applied for cariology research in the included studies. Other applied omics technologies are metabolomics (108/922, 12 %), proteomics (105/922, 11 %), and transcriptomics (76/922, 8 %).

CONCLUSION

This study identified an emerging trend in the application of multiple omics technologies in cariology research. Omics technologies possess significant potential in developing strategies for the detection, staging evaluation, risk assessment, prevention, and management of dental caries. Despite the numerous challenges that lie ahead, the integration of multi-omics data obtained from individual biological samples, in conjunction with artificial intelligence technology, may offer potential avenues for further exploration in caries research.

CLINICAL SIGNIFICANCE

This review presented a comprehensive overview of the application of omics technologies in cariology research and discussed the advantages and challenges of using these methods to detect, assess, predict, prevent, and treat dental caries. It contributes to steering research for improved understanding of dental caries and advancing clinical translation of cariology research outcomes.

Harnessing root exudates for plant microbiome engineering and stress resistance in plants

A wide range of abiotic and biotic stresses adversely affect plant's growth and production. Under stress, one of the main responses of plants is the modulation of exudates excreted in the rhizosphere, which consequently leads to alterations in the resident microbiota. Thus, the exudates discharged into the rhizospheric environment play a preponderant role in the association and formation of plant-microbe interactions. In this review, we aimed to provide a synthesis of the latest and most pertinent literature on the diverse biochemical and structural compositions of plant root exudates. Also, this work investigates into their multifaceted role in microbial nutrition and intricate signaling processes within the rhizosphere, which includes quorum-sensing molecules. Specifically, it explores the contributions of low molecular weight compounds, such as carbohydrates, phenolics, organic acids, amino acids, and secondary metabolites, as well as the significance of high molecular weight compounds, including proteins and polysaccharides. It also discusses the state-of-the-art omics strategies that unveil the vital role of root exudates in plant-microbiome interactions, including defense against pathogens like nematodes and fungi. We propose multiple challenges and perspectives, including exploiting plant root exudates for host-mediated microbiome engineering. In this discourse, root exudates and their derived interactions with the rhizospheric microbiota should receive greater attention due to their positive influence on plant health and stress mitigation.

Unraveling metagenomics through long-read sequencing: a comprehensive review

The study of microbial communities has undergone significant advancements, starting from the initial use of 16S rRNA sequencing to the adoption of shotgun metagenomics. However, a new era has emerged with the advent of long-read sequencing (LRS), which offers substantial improvements over its predecessor, short-read sequencing (SRS). LRS produces reads that are several kilobases long, enabling researchers to obtain more complete and contiguous genomic information, characterize structural variations, and study epigenetic modifications. The current leaders in LRS technologies are Pacific Biotechnologies (PacBio) and Oxford Nanopore Technologies (ONT), each offering a distinct set of advantages. This review covers the workflow of long-read metagenomics sequencing, including sample preparation (sample collection, sample extraction, and library preparation), sequencing, processing (quality control, assembly, and binning), and analysis (taxonomic annotation and functional annotation). Each section provides a concise outline of the key concept of the methodology, presenting the original concept as well as how it is challenged or modified in the context of LRS. Additionally, the section introduces a range of tools that are compatible with LRS and can be utilized to execute the LRS process. This review aims to present the workflow of metagenomics, highlight the transformative impact of LRS, and provide researchers with a selection of tools suitable for this task.

Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities

Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.

Effect of wastewater collection and concentration methods on assessment of viral diversity

Monitoring of potentially pathogenic human viruses in wastewater is of crucial importance to understand disease trends in communities, predict potential outbreaks, and boost preparedness and response by public health departments. High throughput metagenomic sequencing opens an opportunity to expand the capabilities of wastewater surveillance. However, there are major bottlenecks in the metagenomic enabled wastewater surveillance, including the complexities in selecting appropriate sampling and concentration/virus enrichment methods as well as in bioinformatic analysis of complex samples with low human virus concentrations. To evaluate the abilities of two commonly used sampling and concentration methods in virus identification, virus communities concentrated with Virus Adsorption-Elution (VIRADEL) and PolyEthylene Glycol (PEG) precipitation were compared for three interceptor sites. Results indicated that more viral reads were obtained by the VIRADEL concentration method, with 2.84 ± 0.57 % viral reads in the sample. For samples concentrated with PEG, the average proportion of viral reads in the sample was 0.63 ± 0.19 %. In all wastewater samples, bacteriophage affiliated with the families Siphoviridae, Myoviridae and Podoviridae were found to be the abundant populations. Comparison against a custom Swiss-Prot human virus database indicated that the relatively abundant human viruses (average proportions in human virus community greater than 1.00 %) in samples concentrated with the VIRADEL method were Orthopoxvirus, Rhadinovirus, Parapoxvirus, Varicellovirus, Hepatovirus, Simplexvirus, Molluscipoxvirus, Parechovirus, Lymphocryptovirus, and Spumavirus. In samples concentrated with the PEG method, fewer human viruses were found to be relatively abundant. These were Orthopoxvirus, Rhadinovirus, Varicellovirus, Simplexvirus, Molluscipoxvirus, Lymphocryptovirus, and Betacoronavirus. Contigs of Betacoronavirus, which contains severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), were identified in VIRADEL and PEG samples. Our study demonstrates the feasibility of using metagenomics in wastewater surveillance as a first screening tool and the need for selecting the appropriate virus concentration methods and optimizing bioinformatic approaches in analyzing metagenomic data of wastewater samples.

Metagenomic Analysis of Viromes of Aedes Mosquitoes across India

Metagenomic analysis of Aedes aegypti and Ae. albopictus mosquitoes from diverse geographical regions of India revealed the presence of several insect viruses of human interest. Most abundant reads found in Ae. aegypti mosquitoes were of Phasi Charoen-like virus (PCLV), Choristoneura fumiferana granulovirus (CfGV), Cell fusing agent virus (CFAV), and Wenzhou sobemo-like virus 4 (WSLV4), whereas WSLV4 and CfGV constituted the highest percentage of reads in Ae. albopictus viromes. Other reads that were of low percentage included Hubei mosquito virus 2 (HMV2), Porcine astrovirus 4 (PAstV4), and Wild Boar astrovirus (WBAstV). PCLV and CFAV, which were found to be abundant in Ae. aegypti viromes were absent in Ae. albopictus viromes. Among the viromes analyzed, Ae. aegypti sampled from Pune showed the highest percentage (79.82%) of viral reads, while Ae. aegypti mosquitoes sampled from Dibrugarh showed the lowest percentage (3.47%). Shamonda orthobunyavirus (SHAV), African swine fever virus (ASFV), Aroa virus (AROAV), and Ilheus virus (ILHV), having the potential to infect vertebrates, including humans, were also detected in both mosquito species, albeit with low read numbers. Reads of gemykibivirus, avian retrovirus, bacteriophages, herpesviruses, and viruses infecting protozoans, algae, etc., were also detected in the mosquitoes. A high percentage of reads in the Ae. albopictus mosquito samples belonged to unclassified viruses and warrant further investigation. The data generated in the present work may not only lead to studies to explain the influence of these viruses on the replication and transmission of viruses of clinical importance but also to find applications as biocontrol agents against pathogenic viruses.

Precision enzyme discovery through targeted mining of metagenomic data

Metagenomics has opened new avenues for exploring the genetic potential of uncultured microorganisms, which may serve as promising sources of enzymes and natural products for industrial applications. Identifying enzymes with improved catalytic properties from the vast amount of available metagenomic data poses a significant challenge that demands the development of novel computational and functional screening tools. The catalytic properties of all enzymes are primarily dictated by their structures, which are predominantly determined by their amino acid sequences. However, this aspect has not been fully considered in the enzyme bioprospecting processes. With the accumulating number of available enzyme sequences and the increasing demand for discovering novel biocatalysts, structural and functional modeling can be employed to identify potential enzymes with novel catalytic properties. Recent efforts to discover new polysaccharide-degrading enzymes from rumen metagenome data using homology-based searches and machine learning-based models have shown significant promise. Here, we will explore various computational approaches that can be employed to screen and shortlist metagenome-derived enzymes as potential biocatalyst candidates, in conjunction with the wet lab analytical methods traditionally used for enzyme characterization.

MiMultiCat: A Unified Cloud Platform for the Analysis of Microbiome Data with Multi-Categorical Responses

The field of the human microbiome is rapidly growing due to the recent advances in high-throughput sequencing technologies. Meanwhile, there have also been many new analytic pipelines, methods and/or tools developed for microbiome data preprocessing and analytics. They are usually focused on microbiome data with continuous (e.g., body mass index) or binary responses (e.g., diseased vs. healthy), yet multi-categorical responses that have more than two categories are also common in reality. In this paper, we introduce a new unified cloud platform, named MiMultiCat, for the analysis of microbiome data with multi-categorical responses. The two main distinguishing features of MiMultiCat are as follows: First, MiMultiCat streamlines a long sequence of microbiome data preprocessing and analytic procedures on user-friendly web interfaces; as such, it is easy to use for many people in various disciplines (e.g., biology, medicine, public health). Second, MiMultiCat performs both association testing and prediction modeling extensively. For association testing, MiMultiCat handles both ecological (e.g., alpha and beta diversity) and taxonomical (e.g., phylum, class, order, family, genus, species) contexts through covariate-adjusted or unadjusted analysis. For prediction modeling, MiMultiCat employs the random forest and gradient boosting algorithms that are well suited to microbiome data while providing nice visual interpretations. We demonstrate its use through the reanalysis of gut microbiome data on obesity with body mass index categories. MiMultiCat is freely available on our web server.

The Evolving Microbiome of Dental Caries

Dental caries is a significant oral and public health problem worldwide, especially in low-income populations. The risk of dental caries increases with frequent intake of dietary carbohydrates, including sugars, leading to increased acidity and disruption of the symbiotic diverse and complex microbial community of health. Excess acid production leads to a dysbiotic shift in the bacterial biofilm composition, demineralization of tooth structure, and cavities. Highly acidic and acid-tolerant species associated with caries include Streptococcus mutans, Lactobacillus, Actinomyces, Bifidobacterium, and Scardovia species. The differences in microbiotas depend on tooth site, extent of carious lesions, and rate of disease progression. Metagenomics and metatranscriptomics not only reveal the structure and genetic potential of the caries-associated microbiome, but, more importantly, capture the genetic makeup of the metabolically active microbiome in lesion sites. Due to its multifactorial nature, caries has been difficult to prevent. The use of topical fluoride has had a significant impact on reducing caries in clinical settings, but the approach is costly; the results are less sustainable for high-caries-risk individuals, especially children. Developing treatment regimens that specifically target S. mutans and other acidogenic bacteria, such as using nanoparticles, show promise in altering the cariogenic microbiome, thereby combatting the disease.

Skin Microbial Composition and Genetic Mutation Analysis in Precision Medicine for Epidermolysis Bullosa

Epidermolysis bullosa (EB) is an inherited skin disease representing a spectrum of rare genetic disorders. These conditions share the common trait that causes fragile skin, resulting in the development of blisters and erosions. The inheritance follows an autosomal pattern, and the array of clinical presentations leads to significant physical suffering, considerable morbidity, and mortality. Despite EB having no cure, effectively managing EB remains an exceptional challenge due to its rarity and complexity, occasionally casting a profound impact on the lives of affected individuals. Considering that EB management requires a multidisciplinary approach, this sometimes worsens the condition of patients with EB due to inappropriate handling. Thus, more appropriate and precise treatment management of EB is essentially needed. Advanced technology in medicine and health comes into the bioinformatics era. Including treatment for skin diseases, omics-based approaches aim to evaluate and handle better disease management and treatment. In this work, we review several approaches regarding the implementation of omics-based technology, including genetics, pathogenic mutation, skin microbiomics, and metagenomics analysis for EB. In addition, we highlight recent updates on the potential of metagenomics analysis in precision medicine for EB.

Microglia and Microbiome-Gut-Brain Axis

The mammalian gut contains a community of microorganisms called gut microbiome. The gut microbiome is integrated into mammalian physiology, contributing to metabolism, production of metabolites, and promoting immunomodulatory actions. Microglia, the brain's resident innate immune cells, play an essential role in homeostatic neurogenesis, synaptic remodeling, and glial maturation. Microglial dysfunction has been implicated in the pathogenesis of several neuropsychiatric disorders. Recent findings indicate that microglia are influenced by the gut microbiome and their derived metabolites throughout life. The pathways by which microbiota regulate microglia have only started to be understood, but this discovery has the potential to provide valuable insights into the pathogenesis of brain disorders associated with an altered microbiome. Here, we discuss the recent literature on the role of the gut microbiome in modulating microglia during development and adulthood and summarize the key findings on this bidirectional crosstalk in selected examples of neuropsychiatric and neurodegenerative disorders. We also highlight some current caveats and perspectives for the field.

Metagenomics in the fight against zoonotic viral infections: A focus on SARS-CoV-2 analogues

Zoonotic viral infections continue to pose significant threats to global public health, as highlighted by the COVID-19 pandemic caused by the SARS-CoV-2 virus. The emergence of SARS-CoV-2 served as a stark reminder of the potential for zoonotic transmission of viruses from animals to humans. Understanding the origins and dynamics of zoonotic viruses is critical for early detection, prevention, and effective management of future outbreaks. Metagenomics has emerged as a powerful tool for investigating the virome of diverse ecosystems, shedding light on the diversity of viral populations, their hosts, and potential zoonotic spillover events. We provide an in-depth examination of metagenomic approaches, including, NGS metagenomics, shotgun metagenomics, viral metagenomics, and single-virus metagenomics, highlighting their strengths and limitations in identifying and characterizing zoonotic viral pathogens. This review underscores the pivotal role of metagenomics in enhancing our ability to detect, monitor, and mitigate zoonotic viral infections, using SARS-CoV-2 analogues as a case study. We emphasize the need for continued interdisciplinary collaboration among virologists, ecologists, and bioinformaticians to harness the full potential of metagenomic approaches in safeguarding public health against emerging zoonotic threats.

Twnbiome: a public database of the healthy Taiwanese gut microbiome

With new advances in next generation sequencing (NGS) technology at reduced costs, research on bacterial genomes in the environment has become affordable. Compared to traditional methods, NGS provides high-throughput sequencing reads and the ability to identify many species in the microbiome that were previously unknown. Numerous bioinformatics tools and algorithms have been developed to conduct such analyses. However, in order to obtain biologically meaningful results, the researcher must select the proper tools and combine them to construct an efficient pipeline. This complex procedure may include tens of tools, each of which require correct parameter settings. Furthermore, an NGS data analysis involves multiple series of command-line tools and requires extensive computational resources, which imposes a high barrier for biologists and clinicians to conduct NGS analysis and even interpret their own data. Therefore, we established a public gut microbiome database, which we call Twnbiome, created using healthy subjects from Taiwan, with the goal of enabling microbiota research for the Taiwanese population. Twnbiome provides users with a baseline gut microbiome panel from a healthy Taiwanese cohort, which can be utilized as a reference for conducting case-control studies for a variety of diseases. It is an interactive, informative, and user-friendly database. Twnbiome additionally offers an analysis pipeline, where users can upload their data and download analyzed results. Twnbiome offers an online database which non-bioinformatics users such as clinicians and doctors can not only utilize to access a control set of data, but also analyze raw data with a few easy clicks. All results are customizable with ready-made plots and easily downloadable tables. Database URL: http://twnbiome.cgm.ntu.edu.tw/ .

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.

A comprehensive review of microbial contamination in the indoor environment: sources, sampling, health risks, and mitigation strategies

The quality of the indoor environment significantly impacts human health and productivity, especially given the amount of time individuals spend indoors globally. While chemical pollutants have been a focus of indoor air quality research, microbial contaminants also have a significant bearing on indoor air quality. This review provides a comprehensive overview of microbial contamination in built environments, covering sources, sampling strategies, and analysis methods. Microbial contamination has various origins, including human occupants, pets, and the outdoor environment. Sampling strategies for indoor microbial contamination include air, surface, and dust sampling, and various analysis methods are used to assess microbial diversity and complexity in indoor environments. The review also discusses the health risks associated with microbial contaminants, including bacteria, fungi, and viruses, and their products in indoor air, highlighting the need for evidence-based studies that can relate to specific health conditions. The importance of indoor air quality is emphasized from the perspective of the COVID-19 pandemic. A section of the review highlights the knowledge gap related to microbiological burden in indoor environments in developing countries, using India as a representative example. Finally, potential mitigation strategies to improve microbiological indoor air quality are briefly reviewed.

MiTree: A Unified Web Cloud Analytic Platform for User-Friendly and Interpretable Microbiome Data Mining Using Tree-Based Methods

The advent of next-generation sequencing has greatly accelerated the field of human microbiome studies. Currently, investigators are seeking, struggling and competing to find new ways to diagnose, treat and prevent human diseases through the human microbiome. Machine learning is a promising approach to help such an effort, especially due to the high complexity of microbiome data. However, many of the current machine learning algorithms are in a "black box", i.e., they are difficult to understand and interpret. In addition, clinicians, public health practitioners and biologists are not usually skilled at computer programming, and they do not always have high-end computing devices. Thus, in this study, we introduce a unified web cloud analytic platform, named MiTree, for user-friendly and interpretable microbiome data mining. MiTree employs tree-based learning methods, including decision tree, random forest and gradient boosting, that are well understood and suited to human microbiome studies. We also stress that MiTree can address both classification and regression problems through covariate-adjusted or unadjusted analysis. MiTree should serve as an easy-to-use and interpretable data mining tool for microbiome-based disease prediction modeling, and should provide new insights into microbiome-based diagnostics, treatment and prevention. MiTree is an open-source software that is available on our web server.

Unveiling the Gut Microbiome: How Junk Food Impacts the Gut

The human gut microbiome, a complex community of microorganisms, profoundly influences human health and disease. Bacteroidetes and Firmicutes make up the majority of the normal human gut microbiota. These microorganisms wield considerable influence over our physiological functions, impacting both our well-being and our susceptibility to disease. The surge of interest in the gut microbiome over the past decade has been remarkable. Once overlooked, the gastrointestinal tract's microbiota has gained recognition for its significance in maintaining optimal health. The food industry has capitalized on this, flooding the market with "probiotic" and "fermented" products. This article aims to provide a critical review of the current literature on the gut microbiome and its significance in human health, with a particular focus on the impact of dietary choices, especially junk food, on the composition and function of the gut microbiota. Microbes possess the remarkable ability to unlock nutrients from otherwise indigestible substances. The gut microbiome of individuals who consume healthy foods and those who prefer junk food varies significantly. Healthy diets promote a diverse and beneficial gut microbiome, while junk food consumption often leads to a less diverse microbiome with negative consequences for health.

MIST: A microbial identification and source tracking system for next-generation sequencing data

The Professional Committee of Microbiology of the National Pharmacopoeia Commission organized the drafting of the Technical Guidelines for Microbial Whole Genome Sequencing (WGS), aiming to standardize the method process and technical indicators of microbial WGS and ensure the accuracy of sequencing and identification. On the basis of the Guidelines, we developed an integrated microbial identification and source tracking (MIST) system, which could meet the needs of microbial identification and contamination investigation in food and drug quality control. MIST integrates three analysis pipelines: 16S/18S/internal transcribed spacer amplicon-based microbial identification, WGS-based microbial identification, and single-nucleotide polymorphism-based microbial source tracking. MIST can analyze sequence data in a variety of formats, such as Fasta, base call file, and FASTQ. It can be connected to a high-throughput sequencing instrument to acquire sequencing data directly. We also developed a publicly accessible web server for MIST (http://syj.i-sanger.cn).

Visualization of the individual blood microbiome to study the etiology of sarcoidosis

Introduction

Single microbial pathogens or host-microbiome dysbiosis are the causes of lung diseases with suspected infectious etiology. Metagenome sequencing provides an overview of the microbiome content. Due to the rarity of most granulomatous lung diseases collecting large systematic datasets is challenging. Thus, single-patient data often can only be summarized visually.

Objective

To increase the information gain from a single-case metagenome analysis we suggest a quantitative and qualitative approach.

Results

The 16S metagenomic results of 7 patients with pulmonary sarcoidosis were compared with those of 22 healthy individuals. From lysed blood, total microbial DNA was extracted and sequenced. Cleaned data reads were identified taxonomically using Kraken 2 software. Individual metagenomic data were visualized with a Sankey diagram, Krona chart, and a heat-map. We identified five genera that were exclusively present or significantly enhanced in patients with sarcoidosis - Veillonella, Prevotella, Cutibacterium, Corynebacterium, and Streptococcus.

Conclusions

Our approach can characterize the blood microbiome composition and diversity in rare diseases at an individual level. Investigation of the blood microbiome in patients with granulomatous lung diseases of unknown etiology, such as sarcoidosis could enhance our comprehension of their origin and pathogenesis and potentially uncover novel personalized therapeutics.