Computational meta'omics for microbial community studies

Prokaryotic communities associated with marine hydrothermal systems of the Gulf of California

Introduction

Marine hydrothermal systems (MHS) are considered extreme environments due to their unique physicochemical conditions, which are challenging for most organisms. This study investigates the microbial communities in three MHS sites in Baja California Sur, Mexico.

Methods

Sediment samples were collected in two seasons of the year: rainy and dry season. Bacterial DNA was extracted, the V3-V4 regions of the 16S rRNA gene were amplified.

Results and discussion

The analysis of microbial community structure and composition revealed that species richness and diversity were higher at control sites (not influenced by hydrothermal conditions). Samples from the MHS showed temporal variation in richness, as measured by the Chao1 index. Alphaproteobacteria and Gammaproteobacteria were the dominant classes. No significant differences in community structure were found between the seasons or between the control and MHS sites. However, the analysis did reveal differences in community structure among the three hydrothermal locations: Burro, Santispac, and Agua Caliente. The presence of Gammaproteobacteria, Alphaproteobacteria, Deltaproteobacteria, and Betaproteobacteria highlights their key roles in primary production within shallow hydrothermal systems, these microbial communities demonstrate their capacity to colonize diverse substrates. This study enhances the microbiological understanding of hydrothermal environments in Baja California Sur, and molecular analysis of unculturable microbes could provide further insights into their physiology and ecological roles in shallow hydrothermal systems.

The impact of spray-induced gene silencing on cereal phyllosphere microbiota

BACKGROUND

Fusarium head blight (FHB) is a major disease affecting cereal crops including wheat, barley, rye, oats and maize. Its predominant causal agent is the ascomycete fungus Fusarium graminearum, which infects the spikes and thereby reduces grain yield and quality. The frequency and severity of FHB epidemics has increased in recent years, threatening global food security. Spray-induced gene silencing (SIGS) is an alternative technique for tackling this devastating disease through foliar spraying with exogenous double-stranded RNA (dsRNA) to silence specific pathogen genes via RNA interference. This has the advantage of avoiding transgenic approaches, but several aspects of the technology require further development to make it a viable field-level management tool. One such existing knowledge gap is how dsRNA spraying affects the microbiota of the host plants.

RESULTS

We found that the diversity, structure and composition of the bacterial microbiota are subject to changes depending on dsRNA targeted and host studied, while the fungal microbiota in the phyllosphere remained relatively unchanged upon spraying with dsRNA. Analyses of fungal co-occurrence patterns also showed that F. graminearum established itself among the fungal communities through negative interactions with neighbouring fungi. Through these analyses, we have also found bacterial and fungal genera ubiquitous in the phyllosphere, irrespective of dsRNA treatment. These results suggest that although rarer and less abundant microbial species change upon dsRNA spray, the ubiquitous bacterial and fungal components of the phyllosphere in wheat and barley remain unchanged.

CONCLUSION

We show for the first time the effects of exogenous dsRNA spraying on bacterial and fungal communities in the wheat and barley phyllospheres using a high-throughput amplicon sequencing approach. The results obtained further validate the safety and target-specificity of SIGS and emphasize its potential as an environmentally friendly option for managing Fusarium head blight in wheat and barley.

Microbiome and Communication Disorders: A Tutorial for Clinicians

PURPOSE

Emerging research in the field of microbiology has indicated that host-microbiota interactions play a significant role in regulating health and disease. Whereas the gut microbiome has received the most attention, distinct microbiota in other organs (mouth, larynx, and trachea) may undergo microbial shifts that impact disease states. A comprehensive understanding of microbial mechanisms and their role in communication and swallowing deficits may have downstream diagnostic and therapeutic implications.

METHOD

A literature review was completed to provide a broad overview of the microbiome, including differentiation of commensal versus pathogenic bacteria; cellular mechanisms by which bacteria interact with human cells; site-specific microbial compositional shifts in certain organs; and available reports of oral, laryngeal, and tracheal microbial dysbiosis in conditions that are associated with communication and swallowing deficits.

RESULTS/CONCLUSIONS

This review article is a valuable tutorial for clinicians, specifically introducing them to the concept of dysbiosis, with potential contributions to communication and swallowing deficits. Future research should delineate the role of specific pathogenic bacteria in disease pathogenesis to identify therapeutic targets.

Airborne DNA: State of the art - Established methods and missing pieces in the molecular genetic detection of airborne microorganisms, viruses and plant particles

Bioaerosol is composed of different particles, originating from organisms, or their fragments with different origin, shape, and size. Sampling, analysing, identification and describing this airborne diversity has been carried out for over 100 years, and more recently the use of molecular genetic tools has been implemented. However, up to now there are no established protocols or standards for detecting airborne diversity of bacteria, fungi, viruses, pollen, and plant particles. In this review we evaluated commonalities of methods used in molecular genetic based studies in the last 23 years, to give an overview of applicable methods as well as knowledge gaps in diversity assessment. Various sampling techniques show different levels of effectiveness in detecting airborne particles based on their DNA. The storage and processing of samples, as well as DNA processing, influences the outcome of sampling campaigns. Moreover, the decisions on barcode selection, method of analysis, reference database as well as negative and positive controls may severely impact the results obtained. To date, the chain of decisions, methodological biases and error propagation have hindered DNA based molecular sequencing from offering a holistic picture of the airborne biodiversity. Reviewing the available studies, revealed a great diversity in used methodology and many publications didn't state all used methods in detail, making comparisons with other studies difficult or impossible. To overcome these limitations and ensure genuine comparability across studies, it is crucial to standardize protocols. Publications need to include all necessary information to enable comparison among different studies and to evaluate how methodological choices can impacts the results. Besides standardization, implementing of automatic tools and combining of different analytical techniques, such as real-time evaluation combined with sampling and molecular genetic analysis, could assist in achieving the goal of accurately assessing the actual airborne biodiversity.

Exploring interspecific interaction variability in microbiota: A review

Interspecific interactions are an important component and a strong selective force in microbial communities. Over the past few decades, there has been a growing awareness of the variability in microbial interactions, and various studies are already unraveling the inner working dynamics in microbial communities. This has prompted scientists to develop novel techniques for characterizing the varying interspecific interactions among microbes. Here, we review the precise definitions of pairwise and high-order interactions, summarize the key concepts related to interaction variability, and discuss the strengths and weaknesses of emerging characterization techniques. Specifically, we found that most methods can accurately predict or provide direct information about microbial pairwise interactions. However, some of these methods inevitably mask the underlying high-order interactions in the microbial community. Making reasonable assumptions and choosing a characterization method to explore varying microbial interactions should allow us to better understand and engineer dynamic microbial systems.

Research advancements on the diversity and host interaction of gut microbiota in chickens

The maintenance of host health and immune function is heavily dependent on the gut microbiota. However, the precise contribution of individual microbial taxa to regulating the overall functionality of the gut microbiome remains inadequately investigated. Chickens are commonly used as models for studying poultry gut microbiota, with high-throughput 16S rRNA sequencing has emerged as a valuable tool for assessing both its composition and functionality. The interactions between the gut's microbial community and its host significantly influence health outcomes, disease susceptibility, and various mechanisms affecting gastrointestinal function. Despite substantial research efforts, the dynamic nature of this microbial ecosystem has led to inconsistencies in findings related to chicken gut microbiota, which is largely attributed to variations in rearing conditions. Consequently, the interaction between the chickens' gut microflora and its host remains inadequately explored. This review highlights recent advances in understanding these relationships, with a specific focus on microbial composition, diversity, functional mechanisms, and their potential implications for improving poultry production.

Comparative analysis of gut microbiota of Chinese Kunming dog, German Shepherd dog, and Belgian Malinois dog

IMPORTANCE

The composition of the gut microbiota is essential for a dog's health and its adaptation to the environment. Different bacteria can produce the same essential metabolites beneficial to health owing to bacterial functional redundancy in microbial communities.

OBJECTIVE

This study examined the gut bacterial communities of dogs from different breeds, all kept under identical domestication conditions.

METHODS

Noninvasive sampling and 16S rRNA high-throughput sequencing were used to compare the composition and function of the gut microbiota of three dog breeds: the Chinese Kunming dog (CKD), German Shepherd dog (GSD), and Belgian Malinois dog (BMD).

RESULTS

The gut microbiota of the three dog breeds consisted of 257 species across 146 genera, 60 families, 35 orders, 15 classes, and 10 phyla. The dominant bacterial phyla across the three breeds were Firmicutes (57.44%), Fusobacteriota (28.86%), and Bacteroidota (7.63%), while the dominant bacterial genera across the three breeds were Peptostreptococcus (21.08%), Fusobacterium (18.50%), Lactobacillus (12.37%), and Cetobacter (10.29%). Further analysis revealed significant differences in the intestinal flora of the three breeds at the phylum and genus levels. The intestinal flora of BMD was significantly richer than that of CKD and GSD. The functional prediction and Kyoto Encyclopedia of Genes and Genomes analysis showed that the primary functions of the gut microbiota in these breeds were similar, with significant enrichment in various metabolic pathways, including carbohydrate and amino acid metabolism, secondary metabolite biosynthesis, and microbial metabolism in different environments. The intestinal flora of these breeds also played a crucial role in genetic information processing, including transcription, translation, replication, and material transport.

CONCLUSIONS AND RELEVANCE

These results provide novel insights into the intestinal flora of intervention dogs and suggest novel methods to improve their health status, which help increase microbial diversity and normalize metabolite production in diseased dogs.

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.

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

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

Gut microbiota as a sensor of autoimmune response and treatment for rheumatoid arthritis

Rheumatoid arthritis (RA) is considered a multifactorial condition where interaction between the genetic and environmental factors lead to immune dysregulation causing autoreactivity. While among the various genetic factors, HLA-DR4 and DQ8, have been reported to be the strongest risk factors, the role of various environmental factors has been unclear. Though events initiating autoreactivity remain unknown, a mucosal origin of RA has gained attention based on the recent observations with the gut dysbiosis in patients. However, causality of gut dysbiosis has been difficult to prove in humans. Mouse models, especially mice expressing RA-susceptible and -resistant HLA class II genes have helped unravel the complex interactions between genetic factors and gut microbiome. This review describes the interactions between HLA genes and gut dysbiosis in sex-biased preclinical autoreactivity and discusses the potential use of endogenous commensals as indicators of treatment efficacy as well as therapeutic tool to suppress pro-inflammatory response in rheumatoid arthritis.

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.

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.

Microbial diagnostics in periodontal diseases

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

Statistical normalization methods in microbiome data with application to microbiome cancer research

Mounting evidence has shown that gut microbiome is associated with various cancers, including gastrointestinal (GI) tract and non-GI tract cancers. But microbiome data have unique characteristics and pose major challenges when using standard statistical methods causing results to be invalid or misleading. Thus, to analyze microbiome data, it not only needs appropriate statistical methods, but also requires microbiome data to be normalized prior to statistical analysis. Here, we first describe the unique characteristics of microbiome data and the challenges in analyzing them (Section 2). Then, we provide an overall review on the available normalization methods of 16S rRNA and shotgun metagenomic data along with examples of their applications in microbiome cancer research (Section 3). In Section 4, we comprehensively investigate how the normalization methods of 16S rRNA and shotgun metagenomic data are evaluated. Finally, we summarize and conclude with remarks on statistical normalization methods (Section 5). Altogether, this review aims to provide a broad and comprehensive view and remarks on the promises and challenges of the statistical normalization methods in microbiome data with microbiome cancer research examples.

Automated high-content image-based characterization of microorganism behavioral diversity and distribution

Microorganisms have evolved complex systems to respond to environmental signals. Gradients of particular molecules and elemental ions alter the behavior of microbes and their distribution within their environment. Microdevices coupled with automated image-based methods are now employed to analyze the instantaneous distribution and motion behaviors of microbial species in controlled environments at small temporal scales, mimicking, to some extent, macro conditions. Such technologies have so far been adopted for investigations mainly on individual species. Similar versatile approaches must now be developed for the characterization of multiple and complex interactions between a microbial community and its environment. Here, we provide a comprehensive step-by-step method for the characterization of species-specific behavior in a synthetic mixed microbial suspension in response to an environmental driver. By coupling accessible microfluidic devices with automated image analysis approaches, we evaluated the behavioral response of three morphologically different telluric species (Phytophthora parasitica, Vorticella microstoma, Enterobacter aerogenes) to a potassium gradient driver. Using the TrackMate plug-in algorithm, we performed morphometric and then motion analyses to characterize the response of each microbial species to the driver. Such an approach enabled to confirm the different morphological features of the three species and simultaneously characterize their specific motion in reaction to the driver and their co-interaction dynamics. By increasing the complexity of suspensions, this approach could be integrated in a framework for phenotypic analysis in microbial ecology research, helping to characterize how key drivers influence microbiota assembly at microbiota host-environment interfaces.

PGPB and/or AM Fungi Consortia Affect Tomato Native Rhizosphere Microbiota

Tomatoes are one of the most important crops worldwide and also play a central role in the human diet. Microbial consortia are microorganism associations, often employed as bioinoculants, that can interact with the native rhizosphere microbiota. The aim of this study was to evaluate the impact of a bacterial-based biostimulant (Pseudomonas fluorescens and Bacillus amyloliquefaciens) (PSBA) in combination, or not, with a commercial inoculum Micomix (Rhizoglomus irregulare, Funnelliformis mosseae, Funnelliformis caledonium, Bacillus licheniformis, Bacillus mucilaginosus) (MYC) on the native rhizosphere communities and on tomato production. The trial was carried out using Solanum lycopersicum in an open field as follows: control full NPK (CFD), control reduced NPK (CRD), MYC, PSBA, PSBA + MYC. Bacterial population in the different samples were characterized using a next generation sequencing approach. The bioinocula effect on the native rhizosphere microbiota resulted in significant variation both in alpha and beta diversity and in a specific signature associated with the presence of biostimulants, especially in the presence of co-inoculation (PSBA + MYC). In particular, the high initial biodiversity shifts in the community composition occurred and consisted in the increase in the abundance of genera correlated to the soil acidification and in an enhanced density of nitrogen-fixing microbes. The results also highlighted the well-known rhizosphere effect.

The Impact of a Commercial Biostimulant on the Grape Mycobiota of Vitis vinifera cv. Barbera

Reducing the use of fungicides, insecticides, and herbicides in order to limit environmental pollution and health risks for agricultural operators and consumers is one of the goals of European regulations. In fact, the European Commission developed a package of measures (the European Green Deal) to promote the sustainable use of natural resources and strengthen the resilience of European agri-food systems. As a consequence, new plant protection products, such as biostimulants, have been proposed as alternatives to agrochemicals. Their application in agroecosystems could potentially open new scenarios regarding the microbiota. In particular, the vineyard microbiota and the microbiota on the grape surface can be affected by biostimulants and lead to different wine features. The aim of this work was to assess the occurrence of a possible variation in the mycobiota due to the biostimulant application. Therefore, our attention has been focused on the yeast community of grape bunches from vines subjected to the phytostimulant BION®50WG treatment. This work was carried out in the CREA-VE experimental vineyard of Vitis vinifera cv. Barbera in Asti (Piedmont, Italy). The composition of fungal communities on grapes from three experimental conditions such as IPM (integrated pest management), IPM+BION®50WG, and IPM+water foliar nebulization was compared by a metabarcoding approach. Our results revealed the magnitude of alpha and beta diversity, and the microbial biodiversity index and specific fungal signatures were highlighted by comparing the abundance of yeast and filamentous fungi in IPM and BION®50WG treatments. No significant differences in the mycobiota of grapevines subjected to the three treatments were detected.

Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine

Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.

Integrative meta-omics in Galaxy and beyond

BACKGROUND

'Omics methods have empowered scientists to tackle the complexity of microbial communities on a scale not attainable before. Individually, omics analyses can provide great insight; while combined as "meta-omics", they enhance the understanding of which organisms occupy specific metabolic niches, how they interact, and how they utilize environmental nutrients. Here we present three integrative meta-omics workflows, developed in Galaxy, for enhanced analysis and integration of metagenomics, metatranscriptomics, and metaproteomics, combined with our newly developed web-application, ViMO (Visualizer for Meta-Omics) to analyse metabolisms in complex microbial communities.

RESULTS

In this study, we applied the workflows on a highly efficient cellulose-degrading minimal consortium enriched from a biogas reactor to analyse the key roles of uncultured microorganisms in complex biomass degradation processes. Metagenomic analysis recovered metagenome-assembled genomes (MAGs) for several constituent populations including Hungateiclostridium thermocellum, Thermoclostridium stercorarium and multiple heterogenic strains affiliated to Coprothermobacter proteolyticus. The metagenomics workflow was developed as two modules, one standard, and one optimized for improving the MAG quality in complex samples by implementing a combination of single- and co-assembly, and dereplication after binning. The exploration of the active pathways within the recovered MAGs can be visualized in ViMO, which also provides an overview of the MAG taxonomy and quality (contamination and completeness), and information about carbohydrate-active enzymes (CAZymes), as well as KEGG annotations and pathways, with counts and abundances at both mRNA and protein level. To achieve this, the metatranscriptomic reads and metaproteomic mass-spectrometry spectra are mapped onto predicted genes from the metagenome to analyse the functional potential of MAGs, as well as the actual expressed proteins and functions of the microbiome, all visualized in ViMO.

CONCLUSION

Our three workflows for integrative meta-omics in combination with ViMO presents a progression in the analysis of 'omics data, particularly within Galaxy, but also beyond. The optimized metagenomics workflow allows for detailed reconstruction of microbial community consisting of MAGs with high quality, and thus improves analyses of the metabolism of the microbiome, using the metatranscriptomics and metaproteomics workflows.

Complex alpha and beta mannan foraging by the human gut bacteria

The human gut microbiota (HGM), a community of trillions of microbes, underscores its contribution by impacting many facets of host health and disease. In the HGM, Bacteroidota and Bacillota represent dominant bacterial phyla, which mainly rely on the glycans recalcitrant to host digestion to meet their energy requirements. Accordingly, the impact of dietary and host-derived glycans in the assembly and operation of these dominant microbial communities continues to be an area of active research. Among various glycans, mannans represent an integral component of the human diet. Apart from their health effects, the diverse and complex mannan structures bears molecular signatures that alter the expression of specific gene clusters in selected Bacteroidota and Bacillota species. Both the phyla possess variable and sophisticated loci of mannan recognition proteins, hydrolytic enzymes, transporters, and other metabolic proteins to sense, capture and utilize mannans as an energy source. The current review summarizes mannan structural diversity, and strategies adopted by select species of the HGM bacteria to forage mannans by focusing primarily on glycoside hydrolases and their effects on host health and metabolism.

Integrative biohydrogen- and biomethane-producing bioprocesses for comprehensive production of biohythane

The production of biohythane, a combination of energy-dense hydrogen and methane, from the anaerobic digestion of low-cost organic wastes has attracted attention as a potential candidate for the transition to a sustainable circular economy. Substantial research has been initiated to upscale the process engineering to establish a hythane-based economy by addressing major challenges associated with the process and product upgrading. This review provides an overview of the feasibility of biohythane production in various anaerobic digestion systems (single-stage, dual-stage) and possible technologies to upgrade biohythane to hydrogen-enriched renewable natural gas. The main goal of this review is to promote research in biohythane production technology by outlining critical needs, including meta-omics and metabolic engineering approaches for the advancements in biohythane production technology.

Decoding the microbiome and metabolome of the Panchagavya-An indigenous fermented bio-formulation

For the first time, updated molecular techniques were used to validate and elucidate the effect of the Panchagavya. Metagenomics was used to decipher the bacterial microbiome structure, which showed promising results for their existence and abundance in the Panchagavya.

Synergistic effects of exercise and catalase overexpression on gut microbiome

Exercise influences metabolic parameters in part by modulating redox stress and as recently suggested, by affecting the gut microbiome. However, whether excess endogenous antioxidant potentiates or interferes with the beneficial effects of exercise on the gut microbiome is not known. A comparison of the gut microbiome of C57Bl6 (C57/WT) mice to the 'stress-less' catalase overexpressing mice models ([Tg(CAT)± ] and Bob-Cat), that were either exercised or remained sedentary, showed differences in both alpha and beta diversity. The significant variation was explained by genotypes along with exercise, suggesting a synergistic relationship between exercise and genotypic traits. Linear discriminant analysis effect size (LEfSe) analysis also revealed differential taxa within the exercised/genotype cohorts in contrast to those within sedentary/genotype cohorts. Functional pathway predictions from PICRUSt2 showed enrichment for the metabolism of short-chain fatty acids, butanoate and propanoate pathways in exercised groups. Spearman correlations between enriched taxa and metabolic parameters showed correlations with body or fat weight in some of the cohorts. However, there were significant correlations of differential taxa among all cohorts against parameters that predict energy metabolism, such as respiratory exchange ratio and energy expenditure. Overall, our study showed that there was a synergistic beneficial influence of antioxidant overexpression and exercise on the gut microbiome.

A systematic review on omics data (metagenomics, metatranscriptomics, and metabolomics) in the role of microbiome in gallbladder disease

Microbiotas are the range of microorganisms (mainly bacteria and fungi) colonizing multicellular, macroscopic organisms. They are crucial for several metabolic functions affecting the health of the host. However, difficulties hamper the investigation of microbiota composition in cultivating microorganisms in standard growth media. For this reason, our knowledge of microbiota can benefit from the analysis of microbial macromolecules (DNA, transcripts, proteins, or by-products) present in various samples collected from the host. Various omics technologies are used to obtain different data. Metagenomics provides a taxonomical profile of the sample. It can also be used to obtain potential functional information. At the same time, metatranscriptomics can characterize members of a microbiome responsible for specific functions and elucidate genes that drive the microbiotas relationship with its host. Thus, while microbiota refers to microorganisms living in a determined environment (taxonomy of microorganisms identified), microbiome refers to the microorganisms and their genes living in a determined environment and, of course, metagenomics focuses on the genes and collective functions of identified microorganisms. Metabolomics completes this framework by determining the metabolite fluxes and the products released into the environment. The gallbladder is a sac localized under the liver in the human body and is difficult to access for bile and tissue sampling. It concentrates the bile produced in the hepatocytes, which drains into bile canaliculi. Bile promotes fat digestion and is released from the gallbladder into the upper small intestine in response to food. Considered sterile originally, recent data indicate that bile microbiota is associated with the biliary tract's inflammation and carcinogenesis. The sample size is relevant for omic studies of rare diseases, such as gallbladder carcinoma. Although in its infancy, the study of the biliary microbiota has begun taking advantage of several omics strategies, mainly based on metagenomics, metabolomics, and mouse models. Here, we show that omics analyses from the literature may provide a more comprehensive image of the biliary microbiota. We review studies performed in this environmental niche and focus on network-based approaches for integrative studies.

Ecosystem-specific microbiota and microbiome databases in the era of big data

The rapid development of sequencing methods over the past decades has accelerated both the potential scope and depth of microbiota and microbiome studies. Recent developments in the field have been marked by an expansion away from purely categorical studies towards a greater investigation of community functionality. As in-depth genomic and environmental coverage is often distributed unequally across major taxa and ecosystems, it can be difficult to identify or substantiate relationships within microbial communities. Generic databases containing datasets from diverse ecosystems have opened a new era of data accessibility despite costs in terms of data quality and heterogeneity. This challenge is readily embodied in the integration of meta-omics data alongside habitat-specific standards which help contextualise datasets both in terms of sample processing and background within the ecosystem. A special case of large genomic repositories, ecosystem-specific databases (ES-DB's), have emerged to consolidate and better standardise sample processing and analysis protocols around individual ecosystems under study, allowing independent studies to produce comparable datasets. Here, we provide a comprehensive review of this emerging tool for microbial community analysis in relation to current trends in the field. We focus on the factors leading to the formation of ES-DB's, their comparison to traditional microbial databases, the potential for ES-DB integration with meta-omics platforms, as well as inherent limitations in the applicability of ES-DB's.

Molecular Evolution of the Activating Transcription Factors Shapes the Adaptive Cellular Responses to Oxidative Stress

Reactive oxygen species (ROS) play an essential part in physiology of individual cell. ROS can cause damage to various biomolecules, including DNA. The systems that have developed to harness the impacts of ROS are antique evolutionary adaptations that are intricately linked to almost every aspect of cellular function. This research reveals the idea that during evolution, rather than being largely conserved, the molecular pathways reacting to oxidative stress have intrinsic flexibility. The coding sequences of the ATF2, ATF3, ATF4, and ATF6 genes were aligned to examine selection pressure on the genes, which were shown to be very highly conserved among vertebrate species. A total of 33 branches were explicitly evaluated for their capacity to diversify selection. After accounting for multiple testing, significance was determined using the likelihood ratio test with a threshold of p ≤ 0.05. Positive selection signs in these genes were detected across vertebrate lineages. In the selected test branches of our phylogeny, the synonymous rate variation revealed evidence (LRT, p value = 0.011 ≤ 0.05) of gene-wide episodic diversifying selection. As a result, there is evidence that diversifying selection occurred at least once on at least one test branch. These findings indicate that the activities of ROS-responsive systems are also theoretically flexible and may be altered by environmental selection pressure. By determining where the genes encoding these processes are "targeted" during evolution, we may better understand the mechanism of adaptation to oxidative stress during evolution.

Metaomics in Clinical Laboratory: Potential Driving Force for Innovative Disease Diagnosis

Currently, more and more studies suggested that reductionism was lack of holistic and integrative view of biological processes, leading to limited understanding of complex systems like microbiota and the associated diseases. In fact, microbes are rarely present in individuals but normally live in complex multispecies communities. With the recent development of a variety of metaomics techniques, microbes could be dissected dynamically in both temporal and spatial scales. Therefore, in-depth understanding of human microbiome from different aspects such as genomes, transcriptomes, proteomes, and metabolomes could provide novel insights into their functional roles, which also holds the potential in making them diagnostic biomarkers in many human diseases, though there is still a huge gap to fill for the purpose. In this mini-review, we went through the frontlines of the metaomics techniques and explored their potential applications in clinical diagnoses of human diseases, e.g., infectious diseases, through which we concluded that novel diagnostic methods based on human microbiomes shall be achieved in the near future, while the limitations of these techniques such as standard procedures and computational challenges for rapid and accurate analysis of metaomics data in clinical settings were also examined.

Current State and Challenges of the Global Outcomes of Dental Caries Research in the Meta-Omics Era

Despite significant healthcare advances in the 21st century, the exact etiology of dental caries remains unsolved. The past two decades have witnessed a tremendous growth in our understanding of dental caries amid the advent of revolutionary omics technologies. Accordingly, a consensus has been reached that dental caries is a community-scale metabolic disorder, and its etiology is beyond a single causative organism. This conclusion was based on a variety of microbiome studies following the flow of information along the central dogma of biology from genomic data to the end products of metabolism. These studies were facilitated by the unprecedented growth of the next- generation sequencing tools and omics techniques, such as metagenomics and metatranscriptomics, to estimate the community composition of oral microbiome and its functional potential. Furthermore, the rapidly evolving proteomics and metabolomics platforms, including nuclear magnetic resonance spectroscopy and/or mass spectrometry coupled with chromatography, have enabled precise quantification of the translational outcomes. Although the majority supports 'conserved functional changes' as indicators of dysbiosis, it remains unclear how caries dynamics impact the microbiota functions and vice versa, over the course of disease onset and progression. What compounds the situation is the host-microbiota crosstalk. Genome-wide association studies have been undertaken to elucidate the interaction of host genetic variation with the microbiome. However, these studies are challenged by the complex interaction of host genetics and environmental factors. All these complementary approaches need to be orchestrated to capture the key players in this multifactorial disease. Herein, we critically review the milestones in caries research focusing on the state-of-art singular and integrative omics studies, supplemented with a bibliographic network analysis to address the oral microbiome, the host factors, and their interactions. Additionally, we highlight gaps in the dental literature and shed light on critical future research questions and study designs that could unravel the complexities of dental caries, the most globally widespread disease.

Structural and Evolutionary Adaptations of Nei-Like DNA Glycosylases Proteins Involved in Base Excision Repair of Oxidative DNA Damage in Vertebrates

Oxidative stress is a type of stress that damages DNA and can occur from both endogenous and exogenous sources. Damage to DNA caused by oxidative stress can result in base modifications that promote replication errors and the formation of sites of base loss, which pose unique challenges to the preservation of genomic integrity. However, the adaptive evolution of the DNA repair mechanism is poorly understood in vertebrates. This research aimed to explore the evolutionary relationships, physicochemical characteristics, and comparative genomic analysis of the Nei-like glycosylase gene family involved in DNA base repair in the vertebrates. The genomic sequences of NEIL1, NEIL2, and NEIL3 genes were aligned to observe selection constraints in the genes, which were relatively low conserved across vertebrate species. The positive selection signals were identified in these genes across the vertebrate lineages. We identified that only about 2.7% of codons in these genes were subjected to positive selection. We also revealed that positive selection pressure was increased in the Fapy-DNA-glyco and H2TH domain, which are involved in the base excision repair of DNA that has been damaged by oxidative stress. Gene structure, motif, and conserved domain analysis indicated that the Nei-like glycosylase genes in mammals and avians are evolutionarily low conserved compared to other glycosylase genes in other "vertebrates" species. This study revealed that adaptive selection played a critical role in the evolution of Nei-like glycosylase in vertebrate species. Systematic comparative genome analyses will give key insights to elucidate the links between DNA repair and the development of lifespan in various organisms as more diverse vertebrate genome sequences become accessible.

Biliary Diseases from the Microbiome Perspective: How Microorganisms Could Change the Approach to Benign and Malignant Diseases

Recent evidence regarding microbiota is modifying the cornerstones on pathogenesis and the approaches to several gastrointestinal diseases, including biliary diseases. The burden of biliary diseases, indeed, is progressively increasing, considering that gallstone disease affects up to 20% of the European population. At the same time, neoplasms of the biliary system have an increasing incidence and poor prognosis. Framing the specific state of biliary eubiosis or dysbiosis is made difficult by the use of heterogeneous techniques and the sometimes unwarranted invasive sampling in healthy subjects. The influence of the microbial balance on the health status of the biliary tract could also account for some of the complications surrounding the post-liver-transplant phase. The aim of this extensive narrative review is to summarize the current evidence on this topic, to highlight gaps in the available evidence in order to guide further clinical research in these settings, and, eventually, to provide new tools to treat biliary lithiasis, biliopancreatic cancers, and even cholestatic disease.

Inferring microbiota functions from taxonomic genes: a review

Deciphering microbiota functions is crucial to predict ecosystem sustainability in response to global change. High-throughput sequencing at the individual or community level has revolutionized our understanding of microbial ecology, leading to the big data era and improving our ability to link microbial diversity with microbial functions. Recent advances in bioinformatics have been key for developing functional prediction tools based on DNA metabarcoding data and using taxonomic gene information. This cheaper approach in every aspect serves as an alternative to shotgun sequencing. Although these tools are increasingly used by ecologists, an objective evaluation of their modularity, portability, and robustness is lacking. Here, we reviewed 100 scientific papers on functional inference and ecological trait assignment to rank the advantages, specificities, and drawbacks of these tools, using a scientific benchmarking. To date, inference tools have been mainly devoted to bacterial functions, and ecological trait assignment tools, to fungal functions. A major limitation is the lack of reference genomes-compared with the human microbiota-especially for complex ecosystems such as soils. Finally, we explore applied research prospects. These tools are promising and already provide relevant information on ecosystem functioning, but standardized indicators and corresponding repositories are still lacking that would enable them to be used for operational diagnosis.

Using integrated meta-omics to appreciate the role of the gut microbiota in epilepsy

The way the human microbiota may modulate neurological pathologies is a fascinating matter of research. Epilepsy is a common neurological disorder, which has been largely investigated in correlation with microbiota health and function. However, the mechanisms that regulate this apparent connection are scarcely defined, and extensive effort has been conducted to understand the role of microbiota in preventing and reducing epileptic seizures. Intestinal bacteria seem to modulate the seizure frequency mainly by releasing neurotransmitters and inflammatory mediators. In order to elucidate the complex microbial contribution to epilepsy pathophysiology, integrated meta-omics could be pivotal. In fact, the combination of two or more meta-omics approaches allows a multifactorial study of microbial activity within the frame of disease or drug treatments. In this review, we provide information depicting and supporting the use of multi-omics to study the microbiota-epilepsy connection. We described different meta-omics analyses (metagenomics, metatranscriptomics, metaproteomics and metabolomics), focusing on current technical challenges in stool collection procedures, sample extraction methods and data processing. We further discussed the current advantages and limitations of using the integrative approach of multi-omics in epilepsy investigations.

Systematic characterization of human gut microbiome-secreted molecules by integrated multi-omics

The human gut microbiome produces a complex mixture of biomolecules that interact with human physiology and play essential roles in health and disease. Crosstalk between micro-organisms and host cells is enabled by different direct contacts, but also by the export of molecules through secretion systems and extracellular vesicles. The resulting molecular network, comprised of various biomolecular moieties, has so far eluded systematic study. Here we present a methodological framework, optimized for the extraction of the microbiome-derived, extracellular biomolecular complement, including nucleic acids, (poly)peptides, and metabolites, from flash-frozen stool samples of healthy human individuals. Our method allows simultaneous isolation of individual biomolecular fractions from the same original stool sample, followed by specialized omic analyses. The resulting multi-omics data enable coherent data integration for the systematic characterization of this molecular complex. Our results demonstrate the distinctiveness of the different extracellular biomolecular fractions, both in terms of their taxonomic and functional composition. This highlights the challenge of inferring the extracellular biomolecular complement of the gut microbiome based on single-omic data. The developed methodological framework provides the foundation for systematically investigating mechanistic links between microbiome-secreted molecules, including those that are typically vesicle-associated, and their impact on host physiology in health and disease.

In silico characterization, docking, and simulations to understand host-pathogen interactions in an effort to enhance crop production in date palms

Food safety remains a significant challenge despite the growth and development in agricultural research and the advent of modern biotechnological and agricultural tools. Though the agriculturist struggles to aid the growing population's needs, many pathogen-based plant diseases by their direct impact on cell division and tissue development have led to the loss of tons of food crops every year. Though there are many conventional and traditional methods to overcome this issue, the amount and time spend are huge. Scientists have developed systems biology tools to study the root cause of the problem and rectify it. Host-pathogen protein interactions (HPIs) have a promising role in identifying the pathogens' strategy to conquer the host organism. In this paper, the interactions between the host Rhynchophorus ferrugineus (an invasive wood-boring pest that destroys palm) and the pathogens Proteus mirabilis, Serratia marcescens, and Klebsiella pneumoniae are comprehensively studied using protein-protein interactions, molecular docking, and followed by 200 ns molecular dynamic simulations. This study elucidates the structural and functional basis of these proteins leading towards better plant health, production, and reliability.

Microbial river-to-sea continuum: gradients in benthic and planktonic diversity, osmoregulation and nutrient cycling

BACKGROUND

Coastal aquatic ecosystems include chemically distinct, but highly interconnected environments. Across a freshwater-to-marine transect, aquatic communities are exposed to large variations in salinity and nutrient availability as tidal cycles create periodic fluctuations in local conditions. These factors are predicted to strongly influence the resident microbial community structure and functioning, and alter the structure of aquatic food webs and biogeochemical cycles. Nevertheless, little is known about the spatial distribution of metabolic properties across salinity gradients, and no study has simultaneously surveyed the sediment and water environments. Here, we determined patterns and drivers of benthic and planktonic prokaryotic and microeukaryotic community assembly across a river and tidal lagoon system by collecting sediments and planktonic biomass at nine shallow subtidal sites in the summer. Genomic and transcriptomic analyses, alongside a suite of complementary geochemical data, were used to determine patterns in the distribution of taxa, mechanisms of salt tolerance, and nutrient cycling.

RESULTS

Taxonomic and metabolic profiles related to salt tolerance and nutrient cycling of the aquatic microbiome were found to decrease in similarity with increasing salinity, and distinct trends in diversity were observed between the water column and sediment. Non-saline and saline communities adopted divergent strategies for osmoregulation, with an increase in osmoregulation-related transcript expression as salinity increased in the water column due to lineage-specific adaptations to salt tolerance. Results indicated a transition from phosphate limitation in freshwater habitats to nutrient-rich conditions in the brackish zone, where distinct carbon, nitrogen and sulfur cycling processes dominated. Phosphorus acquisition-related activity was highest in the freshwater zone, along with dissimilatory nitrate reduction to ammonium in freshwater sediment. Activity associated with denitrification, sulfur metabolism and photosynthesis were instead highest in the brackish zone, where photosynthesis was dominated by distinct microeukaryotes in water (Cryptophyta) and sediment (diatoms). Despite microeukaryotes and archaea being rare relative to bacteria, results indicate that they contributed more to photosynthesis and ammonia oxidation, respectively.

CONCLUSIONS

Our study demonstrates clear freshwater-saline and sediment-water ecosystem boundaries in an interconnected coastal aquatic system and provides a framework for understanding the relative importance of salinity, planktonic-versus-benthic habitats and nutrient availability in shaping aquatic microbial metabolic processes, particularly in tidal lagoon systems. Video abstract.

NetCom: A Network-Based Tool for Predicting Metabolic Activities of Microbial Communities Based on Interpretation of Metagenomics Data

The study of microbial activity can be viewed as a triangle with three sides: environment (dominant resources in a specific habitat), community (species dictating a repertoire of metabolic conversions) and function (production and/or utilization of resources and compounds). Advances in metagenomics enable a high-resolution description of complex microbial communities in their natural environments and support a systematic study of environment-community-function associations. NetCom is a web-tool for predicting metabolic activities of microbial communities based on network-based interpretation of assembled and annotated metagenomics data. The algorithm takes as an input, lists of differentially abundant enzymatic reactions and generates the following outputs: (i) pathway associations of differently abundant enzymes; (ii) prediction of environmental resources that are unique to each treatment, and their pathway associations; (iii) prediction of compounds that are produced by the microbial community, and pathway association of compounds that are treatment-specific; (iv) network visualization of enzymes, environmental resources and produced compounds, that are treatment specific (2 and 3D). The tool is demonstrated on metagenomic data from rhizosphere and bulk soil samples. By predicting root-specific activities, we illustrate the relevance of our framework for forecasting the impact of soil amendments on the corresponding microbial communities. NetCom is available online.

Identifying fungal-host associations in an amphibian host system

Host-associated microbes can interact with macro-organisms in a number of ways that affect host health. Few studies of host-associated microbiomes, however, focus on fungi. In addition, it is difficult to discern whether a fungal organism found in or on an ectotherm host is associating with it in a durable, symbiotic interaction versus a transient one, and to what extent the habitat and host share microbes. We seek to identify these host-microbe interactions on an amphibian, the Colorado boreal toad (Anaxyrus boreas boreas). We sequenced the ITS1 region of the fungal community on the skin of wild toads (n = 124) from four sites in the Colorado Rocky Mountains, across its physiologically dynamic developmental life stages. We also sampled the common habitats used by boreal toads: water from their natal wetland and aquatic pond sediment. We then examined diversity patterns within different life stages, between host and habitat, and identified fungal taxa that could be putatively host-associated with toads by using an indicator species analysis on toad versus environmental samples. Host and habitat were strikingly similar, with the exception of toad eggs. Post-hatching toad life stages were distinct in their various fungal diversity measures. We identified eight fungal taxa that were significantly associated with eggs, but no other fungal taxa were associated with other toad life stages compared with their environmental habitat. This suggests that although pre- and post-metamorphic toad life stages differ from each other, the habitat and host fungal communities are so similar that identifying obligate host symbionts is difficult with the techniques used here. This approach does, however, leverage sequence data from host and habitat samples to predict which microbial taxa are host-associated versus transient microbes, thereby condensing a large set of sequence data into a smaller list of potential targets for further consideration.

Climatic Zone and Soil Properties Determine the Biodiversity of the Soil Bacterial Communities Associated to Native Plants from Desert Areas of North-Central Algeria

Algeria is the largest country in Africa characterized by semi-arid and arid sites, located in the North, and hypersaline zones in the center and South of the country. Several autochthonous plants are well known as medicinal plants, having in common tolerance to aridity, drought and salinity. In their natural environment, they live with a great amount of microbial species that altogether are indicated as plant microbiota, while the plants are now viewed as a “holobiont”. In this work, the microbiota of the soil associated to the roots of fourteen economically relevant autochthonous plants from Algeria have been characterized by an innovative metagenomic approach with a dual purpose: (i) to deepen the knowledge of the arid and semi-arid environment and (ii) to characterize the composition of bacterial communities associated with indigenous plants with a strong economic/commercial interest, in order to make possible the improvement of their cultivation. The results presented in this work highlighted specific signatures which are mainly determined by climatic zone and soil properties more than by the plant species.

Mantis: flexible and consensus-driven genome annotation

BACKGROUND

The rapid development of the (meta-)omics fields has produced an unprecedented amount of high-resolution and high-fidelity data. Through the use of these datasets we can infer the role of previously functionally unannotated proteins from single organisms and consortia. In this context, protein function annotation can be described as the identification of regions of interest (i.e., domains) in protein sequences and the assignment of biological functions. Despite the existence of numerous tools, challenges remain in terms of speed, flexibility, and reproducibility. In the big data era, it is also increasingly important to cease limiting our findings to a single reference, coalescing knowledge from different data sources, and thus overcoming some limitations in overly relying on computationally generated data from single sources.

RESULTS

We implemented a protein annotation tool, Mantis, which uses database identifiers intersection and text mining to integrate knowledge from multiple reference data sources into a single consensus-driven output. Mantis is flexible, allowing for the customization of reference data and execution parameters, and is reproducible across different research goals and user environments. We implemented a depth-first search algorithm for domain-specific annotation, which significantly improved annotation performance compared to sequence-wide annotation. The parallelized implementation of Mantis results in short runtimes while also outputting high coverage and high-quality protein function annotations.

CONCLUSIONS

Mantis is a protein function annotation tool that produces high-quality consensus-driven protein annotations. It is easy to set up, customize, and use, scaling from single genomes to large metagenomes. Mantis is available under the MIT license at https://github.com/PedroMTQ/mantis.

Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3

Culture-independent analyses of microbial communities have progressed dramatically in the last decade, particularly due to advances in methods for biological profiling via shotgun metagenomics. Opportunities for improvement continue to accelerate, with greater access to multi-omics, microbial reference genomes, and strain-level diversity. To leverage these, we present bioBakery 3, a set of integrated, improved methods for taxonomic, strain-level, functional, and phylogenetic profiling of metagenomes newly developed to build on the largest set of reference sequences now available. Compared to current alternatives, MetaPhlAn 3 increases the accuracy of taxonomic profiling, and HUMAnN 3 improves that of functional potential and activity. These methods detected novel disease-microbiome links in applications to CRC (1262 metagenomes) and IBD (1635 metagenomes and 817 metatranscriptomes). Strain-level profiling of an additional 4077 metagenomes with StrainPhlAn 3 and PanPhlAn 3 unraveled the phylogenetic and functional structure of the common gut microbe Ruminococcus bromii, previously described by only 15 isolate genomes. With open-source implementations and cloud-deployable reproducible workflows, the bioBakery 3 platform can help researchers deepen the resolution, scale, and accuracy of multi-omic profiling for microbial community studies.

Microbial fuel cells: a comprehensive review for beginners

Microbial fuel cells (MFCs) have shown immense potential as a one-stop solution for three major sustainability issues confronting the world today-energy security, global warming and wastewater management. MFCs represent a cross-disciplinary platform for research at the confluence of the natural and engineering sciences. The diversity of variables influencing performance of MFCs has garnered research interest across varied scientific disciplines since the beginning of this century. The increasing number of research publications has made it necessary to keep track of work being carried out by research groups across the globe and consolidate significant findings on a regular basis. Review articles are often the nodal points for beginners who are required to undertake an exploratory survey of literature to identify a suitable research problem. This 'review of reviews' is a ready-reckoner that directs readers to relevant reviews and research articles reporting significant developments in MFC research in the last two decades. The article also highlights the areas needing research attention which when addressed could take this technology a few more steps closer to practical implementation.

Multi-omics data integration considerations and study design for biological systems and disease

With the advancement of next-generation sequencing and mass spectrometry, there is a growing need for the ability to merge biological features in order to study a system as a whole. Features such as the transcriptome, methylome, proteome, histone post-translational modifications and the microbiome all influence the host response to various diseases and cancers. Each of these platforms have technological limitations due to sample preparation steps, amount of material needed for sequencing, and sequencing depth requirements. These features provide a snapshot of one level of regulation in a system. The obvious next step is to integrate this information and learn how genes, proteins, and/or epigenetic factors influence the phenotype of a disease in context of the system. In recent years, there has been a push for the development of data integration methods. Each method specifically integrates a subset of omics data using approaches such as conceptual integration, statistical integration, model-based integration, networks, and pathway data integration. In this review, we discuss considerations of the study design for each data feature, the limitations in gene and protein abundance and their rate of expression, the current data integration methods, and microbiome influences on gene and protein expression. The considerations discussed in this review should be regarded when developing new algorithms for integrating multi-omics data.

Recent Advanced Technologies for the Characterization of Xenobiotic-Degrading Microorganisms and Microbial Communities

Global environmental contamination with a complex mixture of xenobiotics has become a major environmental issue worldwide. Many xenobiotic compounds severely impact the environment due to their high toxicity, prolonged persistence, and limited biodegradability. Microbial-assisted degradation of xenobiotic compounds is considered to be the most effective and beneficial approach. Microorganisms have remarkable catabolic potential, with genes, enzymes, and degradation pathways implicated in the process of biodegradation. A number of microbes, including Alcaligenes, Cellulosimicrobium, Microbacterium, Micrococcus, Methanospirillum, Aeromonas, Sphingobium, Flavobacterium, Rhodococcus, Aspergillus, Penecillium, Trichoderma, Streptomyces, Rhodotorula, Candida, and Aureobasidium, have been isolated and characterized, and have shown exceptional biodegradation potential for a variety of xenobiotic contaminants from soil/water environments. Microorganisms potentially utilize xenobiotic contaminants as carbon or nitrogen sources to sustain their growth and metabolic activities. Diverse microbial populations survive in harsh contaminated environments, exhibiting a significant biodegradation potential to degrade and transform pollutants. However, the study of such microbial populations requires a more advanced and multifaceted approach. Currently, multiple advanced approaches, including metagenomics, proteomics, transcriptomics, and metabolomics, are successfully employed for the characterization of pollutant-degrading microorganisms, their metabolic machinery, novel proteins, and catabolic genes involved in the degradation process. These technologies are highly sophisticated, and efficient for obtaining information about the genetic diversity and community structures of microorganisms. Advanced molecular technologies used for the characterization of complex microbial communities give an in-depth understanding of their structural and functional aspects, and help to resolve issues related to the biodegradation potential of microorganisms. This review article discusses the biodegradation potential of microorganisms and provides insights into recent advances and omics approaches employed for the specific characterization of xenobiotic-degrading microorganisms from contaminated environments.

Tumor-Associated Microbiome: Where Do We Stand?

The study of the human microbiome in oncology is a growing and rapidly evolving field. In the past few years, there has been an exponential increase in the number of studies investigating associations of microbiome and cancer, from oncogenesis and cancer progression to resistance or sensitivity to specific anticancer therapies. The gut microbiome is now known to play a significant role in antitumor immune responses and in predicting the efficacy of immune-checkpoint inhibitors in cancer patients. Beyond the gut, the tumor-associated microbiome-microbe communities located either in the tumor or within its body compartment-seems to interact with the local microenvironment and the tumor immune contexture, ultimately impacting cancer progression and treatment outcome. However, pre-clinical research focusing on causality and mechanistic pathways as well as proof-of-concept studies are still needed to fully understand the potential clinical utility of microbiome in cancer patients. Moreover, there is a need for the standardization of methodology and the implementation of quality control across microbiome studies to allow for a better interpretation and greater comparability of the results reported between them. This review summarizes the accumulating evidence in the field and discusses the current and upcoming challenges of microbiome studies.

Are recently deglaciated areas at both poles colonised by the same bacteria?

Polar glacier forefields offer an unprecedented framework for studying community assembly processes in regions that are geographically and climatically isolated. Through amplicon sequence variant (ASV) inference, we compared the composition and structure of soil bacterial communities from glacier forefields in Iceland and Antarctica to assess overlap between communities and the impact of established cryptogamic covers on the uniqueness of their taxa. These pioneer microbial communities were found to share only 8% of ASVs and each taxonomic group's contribution to the shared ASV data subset was heterogeneous and independent of their relative abundance. Although the presence of ASVs specific to one glacier forefield and/or different cryptogam cover values confirms the existence of habitat specialist bacteria, our data show that the influence of cryptogams on the edaphic bacterial community structure also varied also depending on the taxonomic group. Hence, the establishment of distinct cryptogamic covers is probably not the only factor driving the uniqueness of bacterial communities at both poles. The structure of bacterial communities colonising deglaciated areas seems also conditioned by lineage-specific limitations in their dispersal capacity and/or their establishment and persistence in these isolated and hostile regions.

From sequence to information

Today massive amounts of sequenced metagenomic and metatranscriptomic data from different ecological niches and environmental locations are available. Scientific progress depends critically on methods that allow extracting useful information from the various types of sequence data. Here, we will first discuss types of information contained in the various flavours of biological sequence data, and how this information can be interpreted to increase our scientific knowledge and understanding. We argue that a mechanistic understanding of biological systems analysed from different perspectives is required to consistently interpret experimental observations, and that this understanding is greatly facilitated by the generation and analysis of dynamic mathematical models. We conclude that, in order to construct mathematical models and to test mechanistic hypotheses, time-series data are of critical importance. We review diverse techniques to analyse time-series data and discuss various approaches by which time-series of biological sequence data have been successfully used to derive and test mechanistic hypotheses. Analysing the bottlenecks of current strategies in the extraction of knowledge and understanding from data, we conclude that combined experimental and theoretical efforts should be implemented as early as possible during the planning phase of individual experiments and scientific research projects.

This article is part of the theme issue ‘Integrative research perspectives on marine conservation’.

Impact of host genetics on gut microbiome: Take-home lessons from human and mouse studies

The intestinal microbiome has emerged as an important component involved in various diseases. Therefore, the interest in understanding the factors shaping its composition is growing. The gut microbiome, often defined as a complex trait, contains diverse components and its properties are determined by a combination of external and internal effects. Although much effort has been invested so far, it is still difficult to evaluate the extent to which human genetics shape the composition of the gut microbiota. However, in mouse studies, where the environmental factors are better controlled, the effect of the genetic background was significant. The purpose of this paper is to provide a current assessment of the role of human host genetics in shaping the gut microbiome composition. Despite the inconsistency of the reported results, it can be estimated that the genetic factor affects a portion of the microbiome. However, this effect is currently lower than the initial estimates, and it is difficult to separate the genetic influence from the environmental effect. Additionally, despite the differences between the microbial composition of humans and mice, results from mouse models can strengthen our knowledge of host genetics underlying the human gut microbial variation.

Microbiome Engineering: Synthetic Biology of Plant-Associated Microbiomes in Sustainable Agriculture

To support an ever-increasing population, modern agriculture faces numerous challenges that pose major threats to global food and energy security. Plant-associated microbes, with their many plant growth-promoting (PGP) traits, have enormous potential in helping to solve these challenges. However, the results of their use in agriculture have been variable, probably because of poor colonization. Phytomicrobiome engineering is an emerging field of synthetic biology that may offer ways to alleviate this limitation. This review highlights recent advances in both bottom-up and top-down approaches to engineering non-model bacteria and microbiomes to promote beneficial plant-microbe interactions, as well as advances in strategies to evaluate these interactions. Biosafety, biosecurity, and biocontainment strategies to address the environmental concerns associated with field use of synthetic microbes are also discussed.

Integrated network modeling approach defines key metabolic responses of soil microbiomes to perturbations

The soil environment is constantly changing due to shifts in soil moisture, nutrient availability and other conditions. To contend with these changes, soil microorganisms have evolved a variety of ways to adapt to environmental perturbations, including regulation of gene expression. However, it is challenging to untangle the complex phenotypic response of the soil to environmental change, partly due to the absence of predictive modeling frameworks that can mechanistically link molecular-level changes in soil microorganisms to a community’s functional phenotypes (or metaphenome). Towards filling this gap, we performed a combined analysis of metabolic and gene co-expression networks to explore how the soil microbiome responded to changes in soil moisture and nutrient conditions and to determine which genes were expressed under a given condition. Our integrated modeling approach revealed previously unknown, but critically important aspects of the soil microbiomes’ response to environmental perturbations. Incorporation of metabolomic and transcriptomic data into metabolic reaction networks identified condition-specific signature genes that are uniquely associated with dry, wet, and glycine-amended conditions. A subsequent gene co-expression network analysis revealed that drought-associated genes occupied more central positions in a network model of the soil community, compared to the genes associated with wet, and glycine-amended conditions. These results indicate the occurrence of system-wide metabolic coordination when soil microbiomes cope with moisture or nutrient perturbations. Importantly, the approach that we demonstrate here to analyze large-scale multi-omics data from a natural soil environment is applicable to other microbiome systems for which multi-omics data are available.