Functional assignment of metagenomic data: challenges and applications

Metagenomics studies in aquaculture systems: Big data analysis, bioinformatics, machine learning and quantum computing

The burgeoning field of aquaculture has become a pivotal contributor to global food security and economic growth, presently surpassing capture fisheries in aquatic animal production as evidenced by recent statistics. However, the dense fish populations inherent in aquaculture systems exacerbate abiotic stressors and promote pathogenic spread, posing a risk to sustainability and yield. This study delves into the transformative potential of metagenomics, a method that directly retrieves genetic material from environmental samples, in elucidating microbial dynamics within aquaculture ecosystems. Our findings affirm that metagenomics, bolstered by tools in big data analytics, bioinformatics, and machine learning, can significantly enhance the precision of microbial assessment and pathogen detection. Furthermore, we explore quantum computing's emergent role, which promises unparalleled efficiency in data processing and model construction, poised to address the limitations of conventional computational techniques. Distinct from metabarcoding, metagenomics offers an expansive, unbiased profile of microbial biodiversity, revolutionizing our capacity to monitor, predict, and manage aquaculture systems with high accuracy and adaptability. Despite the challenges of computational demands and variability in data standardization, this study advocates for continued technological integration, thereby fostering resilient and sustainable aquaculture practices in a climate of escalating global food requirements.

Fungal Identifier (FId): An Updated Polymerase Chain Reaction-Restriction Fragment Length Polymorphism Approach to Ease Ascomycetous Yeast Isolates' Identification in Ecological Studies

Culturomics has been temporarily exceeded by the advent of omics approaches such as metabarcoding and metagenomics. However, despite improving our knowledge of microbial population composition, both metabarcoding and metagenomics are not suitable for investigating and experimental testing inferences about microbial ecological roles and evolution. This leads to a recent revival of culturomics approaches, which should be supported by improvements in the available tools for high-throughput microbial identification. This study aimed to update the classical PCR-RFLP approach in light of the currently available knowledge on yeast genomics. We generated and analyzed a database including more than 1400 ascomycetous yeast species, each characterized by PCR-RFLP profiles obtained with 143 different endonucleases. The results allowed for the in silico evaluation of the performance of the tested endonucleases in the yeast species' identification and the generation of FId (Fungal Identifier), an online freely accessible tool for the identification of yeast species according to experimentally obtained PCR-RFLP profiles.

Multi-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning

The human gastrointestinal (gut) microbiome plays a critical role in maintaining host health and has been increasingly recognized as an important factor in precision medicine. High-throughput sequencing technologies have revolutionized -omics data generation, facilitating the characterization of the human gut microbiome with exceptional resolution. The analysis of various -omics data, including metatranscriptomics, metagenomics, glycomics, and metabolomics, holds potential for personalized therapies by revealing information about functional genes, microbial composition, glycans, and metabolites. This multi-omics approach has not only provided insights into the role of the gut microbiome in various diseases but has also facilitated the identification of microbial biomarkers for diagnosis, prognosis, and treatment. Machine learning algorithms have emerged as powerful tools for extracting meaningful insights from complex datasets, and more recently have been applied to metagenomics data via efficiently identifying microbial signatures, predicting disease states, and determining potential therapeutic targets. Despite these rapid advancements, several challenges remain, such as key knowledge gaps, algorithm selection, and bioinformatics software parametrization. In this mini-review, our primary focus is metagenomics, while recognizing that other -omics can enhance our understanding of the functional diversity of organisms and how they interact with the host. We aim to explore the current intersection of multi-omics, precision medicine, and machine learning in advancing our understanding of the gut microbiome. A multidisciplinary approach holds promise for improving patient outcomes in the era of precision medicine, as we unravel the intricate interactions between the microbiome and human health.

A laboratory ice machine as a cold oligotrophic artificial microbial niche for biodiscovery

Microorganisms are ubiquitously distributed in nature and usually appear as biofilms attached to a variety of surfaces. Here, we report the development of a thick biofilm in the drain pipe of several standard laboratory ice machines, and we describe and characterise, through culture-dependent and -independent techniques, the composition of this oligotrophic microbial community. By using culturomics, 25 different microbial strains were isolated and taxonomically identified. The 16S rRNA high-throughput sequencing analysis revealed that Bacteroidota and Proteobacteria were the most abundant bacterial phyla in the sample, followed by Acidobacteriota and Planctomycetota, while ITS high-throughput sequencing uncovered the fungal community was clearly dominated by the presence of a yet-unidentified genus from the Didymellaceae family. Alpha and beta diversity comparisons of the ice machine microbial community against that of other similar cold oligotrophic and/or artificial environments revealed a low similarity between samples, highlighting the ice machine could be considered a cold and oligotrophic niche with a unique selective pressure for colonisation of particular microorganisms. The recovery and analysis of high-quality metagenome-assembled genomes (MAGs) yielded a strikingly high rate of new species. The functional profiling of the metagenome sequences uncovered the presence of proteins involved in extracellular polymeric substance (EPS) and fimbriae biosynthesis and also allowed us to detect the key proteins involved in the cold adaptation mechanisms and oligotrophic metabolic pathways. The metabolic functions in the recovered MAGs confirmed that all MAGs have the genes involved in psychrophilic protein biosynthesis. In addition, the highest number of genes for EPS biosynthesis was presented in MAGs associated with the genus Sphingomonas, which was also recovered by culture-based method. Further, the MAGs with the highest potential gene number for oligotrophic protein production were closely affiliated with the genera Chryseoglobus and Mycobacterium. Our results reveal the surprising potential of a cold oligotrophic microecosystem within a machine as a source of new microbial taxa and provide the scientific community with clues about which microorganisms are able to colonise this ecological niche and what physiological mechanisms they develop. These results pave the way to understand how and why certain microorganisms can colonise similar anthropogenic environments.

Exploring the Influence of Gut Microbiome on Energy Metabolism in Humans

The gut microbiome has a profound influence on host physiology, including energy metabolism, which is the process by which energy from nutrients is transformed into other forms of energy to be used by the body. However, mechanistic evidence for how the microbiome influences energy metabolism is derived from animal models. In this narrative review, we included human studies investigating the relationship between gut microbiome and energy metabolism -i.e., energy expenditure in humans and energy harvest by the gut microbiome. Studies have found no consistent gut microbiome patterns associated with energy metabolism, and most interventions were not effective in modulating the gut microbiome to influence energy metabolism. To date, cause-and-effect relationships and mechanistic evidence on the impact of the gut microbiome on energy expenditure have not been established in humans. Future longitudinal observational studies and randomized controlled trials utilizing robust methodologies and advanced statistical analysis are needed. Such knowledge would potentially inform the design of therapeutic avenues and specific dietary recommendations to improve energy metabolism through gut microbiome modulation.

South Africa's indigenous microbial diversity for industrial applications: A review of the current status and opportunities

The unique metagenomic, metaviromic libraries and indigenous micro diversity within Southern Africa have the potential for global beneficiation in academia and industry. Microorganisms that flourish at high temperatures, adverse pH conditions, and high salinity are likely to have enzyme systems that function efficiently under those conditions. These attributes afford researchers and industries alternative approaches that could replace existing chemical processes. Thus, a better understanding of African microbial/genetic diversity is crucial for the development of "greener" industries. A concerted drive to exploit the potential locked in biological resources has been previously seen with companies such as Diversa Incorporated and Verenium (Badische Anilin-und SodaFabrik-BASF) both building business models that pioneered the production of high-performance specialty enzymes for a variety of different industrial applications. The market potential and accompanying industry offerings have not been fully exploited in South Africa, nor in Africa at large. Utilization of the continent's indigenous microbial repositories could create long-lasting, sustainable growth in various production sectors, providing economic growth in resource-poor regions. By bolstering local manufacture of high-value bio-based products, scientific and engineering discoveries have the potential to generate new industries which in turn would provide employment avenues for many skilled and unskilled laborers. The positive implications of this could play a role in altering the face of business markets on the continent from costly import-driven markets to income-generating export markets. This review focuses on identifying microbially diverse areas located in South Africa while providing a profile for all associated microbial/genetically derived libraries in this country. A comprehensive list of all the relevant researchers and potential key players is presented, mapping out existing research networks for the facilitation of collaboration. The overall aim of this review is to facilitate a coordinated journey of exploration, one which will hopefully realize the value that South Africa's microbial diversity has to offer.

Scientific novelty beyond the experiment

Practical experiments drive important scientific discoveries in biology, but theory-based research studies also contribute novel-sometimes paradigm-changing-findings. Here, we appraise the roles of theory-based approaches focusing on the experiment-dominated wet-biology research areas of microbial growth and survival, cell physiology, host-pathogen interactions, and competitive or symbiotic interactions. Additional examples relate to analyses of genome-sequence data, climate change and planetary health, habitability, and astrobiology. We assess the importance of thought at each step of the research process; the roles of natural philosophy, and inconsistencies in logic and language, as drivers of scientific progress; the value of thought experiments; the use and limitations of artificial intelligence technologies, including their potential for interdisciplinary and transdisciplinary research; and other instances when theory is the most-direct and most-scientifically robust route to scientific novelty including the development of techniques for practical experimentation or fieldwork. We highlight the intrinsic need for human engagement in scientific innovation, an issue pertinent to the ongoing controversy over papers authored using/authored by artificial intelligence (such as the large language model/chatbot ChatGPT). Other issues discussed are the way in which aspects of language can bias thinking towards the spatial rather than the temporal (and how this biased thinking can lead to skewed scientific terminology); receptivity to research that is non-mainstream; and the importance of theory-based science in education and epistemology. Whereas we briefly highlight classic works (those by Oakes Ames, Francis H.C. Crick and James D. Watson, Charles R. Darwin, Albert Einstein, James E. Lovelock, Lynn Margulis, Gilbert Ryle, Erwin R.J.A. Schrödinger, Alan M. Turing, and others), the focus is on microbiology studies that are more-recent, discussing these in the context of the scientific process and the types of scientific novelty that they represent. These include several studies carried out during the 2020 to 2022 lockdowns of the COVID-19 pandemic when access to research laboratories was disallowed (or limited). We interviewed the authors of some of the featured microbiology-related papers and-although we ourselves are involved in laboratory experiments and practical fieldwork-also drew from our own research experiences showing that such studies can not only produce new scientific findings but can also transcend barriers between disciplines, act counter to scientific reductionism, integrate biological data across different timescales and levels of complexity, and circumvent constraints imposed by practical techniques. In relation to urgent research needs, we believe that climate change and other global challenges may require approaches beyond the experiment.

The potential for plant growth-promoting bacteria to impact crop productivity in future agricultural systems is linked to understanding the principles of microbial ecology

Global climate change poses challenges to land use worldwide, and we need to reconsider agricultural practices. While it is generally accepted that biodiversity can be used as a biomarker for healthy agroecosystems, we must specify what specifically composes a healthy microbiome. Therefore, understanding how holobionts function in native, harsh, and wild habitats and how rhizobacteria mediate plant and ecosystem biodiversity in the systems enables us to identify key factors for plant fitness. A systems approach to engineering microbial communities by connecting host phenotype adaptive traits would help us understand the increased fitness of holobionts supported by genetic diversity. Identification of genetic loci controlling the interaction of beneficial microbiomes will allow the integration of genomic design into crop breeding programs. Bacteria beneficial to plants have traditionally been conceived as "promoting and regulating plant growth". The future perspective for agroecosystems should be that microbiomes, via multiple cascades, define plant phenotypes and provide genetic variability for agroecosystems.

Microbiome and Physicochemical Features Associated with Differential Listeria monocytogenes Growth in Soft, Surface-Ripened Cheeses

Soft-ripened cheeses (SRCs) are at a higher risk for the growth of the foodborne pathogen Listeria monocytogenes due to favorable moisture content and pH compared to other cheeses. L. monocytogenes growth is not consistent across SRCs, however, and may be affected by physicochemical and/or microbiome characteristics of the cheeses. Therefore, the purpose of this study was to investigate how the physicochemical and microbiome profiles of SRCs may affect L. monocytogenes growth. Forty-three SRCs produced from raw (n = 12) or pasteurized (n = 31) milk were inoculated with L. monocytogenes (103 CFU/g), and the pathogen growth was monitored over 12 days at 8°C. In parallel, the pH, water activity (aw), microbial plate counts, and organic acid content of cheeses were measured, and the taxonomic profiles of the cheese microbiomes were measured using 16S rRNA gene targeted amplicon sequencing and shotgun metagenomic sequencing. L. monocytogenes growth differed significantly between cheeses (analysis of variance [ANOVA]; P < 0.001), with increases ranging from 0 to 5.4 log CFU (mean of 2.5 ± 1.2 log CFU), and was negatively correlated with aw. Raw milk cheeses showed significantly lower L. monocytogenes growth than pasteurized-milk cheeses (t test; P = 0.008), possibly due to an increase in microbial competition. L. monocytogenes growth in cheeses was positively correlated with the relative abundance of Streptococcus thermophilus (Spearman correlation; P < 0.0001) and negatively correlated with the relative abundances of Brevibacterium aurantiacum (Spearman correlation; P = 0.0002) and two Lactococcus spp. (Spearman correlation; P < 0.01). These results suggest that the cheese microbiome may influence the food safety in SRCs. IMPORTANCE Previous studies have identified differences in L. monocytogenes growth between SRCs, but no clear mechanism has yet been elucidated. To the best of our knowledge, this is the first study to collect a wide range of SRCs from retail sources and attempt to identify key factors associated with pathogen growth. A key finding in this research was the positive correlation between the relative abundance of S. thermophilus and the growth of L. monocytogenes. The inclusion of S. thermophilus as a starter culture is more common in industrialized SRC production, suggesting that industrial production of SRC may increase the risk of L. monocytogenes growth. Overall, the results of this study further our understanding of the impact of aw and the cheese microbiome on the growth of L. monocytogenes in SRCs, hopefully leading toward the development of SRC starter/ripening cultures that can prevent L. monocytogenes growth.

GCNSA: DNA storage encoding with a graph convolutional network and self-attention

DNA Encoding, as a key step in DNA storage, plays an important role in reading and writing accuracy and the storage error rate. However, currently, the encoding efficiency is not high enough and the encoding speed is not fast enough, which limits the performance of DNA storage systems. In this work, a DNA storage encoding system with a graph convolutional network and self-attention (GCNSA) is proposed. The experimental results show that DNA storage code constructed by GCNSA increases by 14.4% on average under the basic constraints, and by 5%-40% under other constraints. The increase of DNA storage codes effectively improves the storage density of 0.7-2.2% in the DNA storage system. The GCNSA predicted more DNA storage codes in less time while ensuring the quality of codes, which lays a foundation for higher read and write efficiency in DNA storage.

Deep embedding and alignment of protein sequences

Protein sequence alignment is a key component of most bioinformatics pipelines to study the structures and functions of proteins. Aligning highly divergent sequences remains, however, a difficult task that current algorithms often fail to perform accurately, leaving many proteins or open reading frames poorly annotated. Here we leverage recent advances in deep learning for language modeling and differentiable programming to propose DEDAL (deep embedding and differentiable alignment), a flexible model to align protein sequences and detect homologs. DEDAL is a machine learning-based model that learns to align sequences by observing large datasets of raw protein sequences and of correct alignments. Once trained, we show that DEDAL improves by up to two- or threefold the alignment correctness over existing methods on remote homologs and better discriminates remote homologs from evolutionarily unrelated sequences, paving the way to improvements on many downstream tasks relying on sequence alignment in structural and functional genomics.

Classification of bacterial plasmid and chromosome derived sequences using machine learning

Plasmids are important genetic elements that facilitate horizonal gene transfer between bacteria and contribute to the spread of virulence and antimicrobial resistance. Most bacterial genome sequences in the public archives exist in draft form with many contigs, making it difficult to determine if a contig is of chromosomal or plasmid origin. Using a training set of contigs comprising 10,584 chromosomes and 10,654 plasmids from the PATRIC database, we evaluated several machine learning models including random forest, logistic regression, XGBoost, and a neural network for their ability to classify chromosomal and plasmid sequences using nucleotide k-mers as features. Based on the methods tested, a neural network model that used nucleotide 6-mers as features that was trained on randomly selected chromosomal and plasmid subsequences 5kb in length achieved the best performance, outperforming existing out-of-the-box methods, with an average accuracy of 89.38% ± 2.16% over a 10-fold cross validation. The model accuracy can be improved to 92.08% by using a voting strategy when classifying holdout sequences. In both plasmids and chromosomes, subsequences encoding functions involved in horizontal gene transfer-including hypothetical proteins, transporters, phage, mobile elements, and CRISPR elements-were most likely to be misclassified by the model. This study provides a straightforward approach for identifying plasmid-encoding sequences in short read assemblies without the need for sequence alignment-based tools.

Marine Bioprospecting, Biocatalysis and Process Development

Oceans possess tremendous diversity in microbial life. The enzymatic machinery that marine bacteria present is the result of extensive evolution to assist cell survival under the harsh and continuously changing conditions found in the marine environment. Several bacterial cells and enzymes are already used at an industrial scale, but novel biocatalysts are still needed for sustainable industrial applications, with benefits for both public health and the environment. Metagenomic techniques have enabled the discovery of novel biocatalysts, biosynthetic pathways, and microbial identification without their cultivation. However, a key stage for application of novel biocatalysts is the need for rapid evaluation of the feasibility of the bioprocess. Cultivation of not-yet-cultured bacteria is challenging and requires new methodologies to enable growth of the bacteria present in collected environmental samples, but, once a bacterium is isolated, its enzyme activities are easily measured. High-throughput screening techniques have also been used successfully, and innovative in vitro screening platforms to rapidly identify relevant enzymatic activities continue to improve. Small-scale approaches and process integration could improve the study and development of new bioprocesses to produce commercially interesting products. In this work, the latest studies related to (i) the growth of marine bacteria under laboratorial conditions, (ii) screening techniques for bioprospecting, and (iii) bioprocess development using microreactors and miniaturized systems are reviewed and discussed.

Functional characterization of prokaryotic dark matter: the road so far and what lies ahead

Eight-hundred thousand to one trillion prokaryotic species may inhabit our planet. Yet, fewer than two-hundred thousand prokaryotic species have been described. This uncharted fraction of microbial diversity, and its undisclosed coding potential, is known as the "microbial dark matter" (MDM). Next-generation sequencing has allowed to collect a massive amount of genome sequence data, leading to unprecedented advances in the field of genomics. Still, harnessing new functional information from the genomes of uncultured prokaryotes is often limited by standard classification methods. These methods often rely on sequence similarity searches against reference genomes from cultured species. This hinders the discovery of unique genetic elements that are missing from the cultivated realm. It also contributes to the accumulation of prokaryotic gene products of unknown function among public sequence data repositories, highlighting the need for new approaches for sequencing data analysis and classification. Increasing evidence indicates that these proteins of unknown function might be a treasure trove of biotechnological potential. Here, we outline the challenges, opportunities, and the potential hidden within the functional dark matter (FDM) of prokaryotes. We also discuss the pitfalls surrounding molecular and computational approaches currently used to probe these uncharted waters, and discuss future opportunities for research and applications.

Omics approaches in bioremediation of environmental contaminants: An integrated approach for environmental safety and sustainability

Non-degradable pollutants have emerged as a result of industrialization, population growth, and lifestyle changes, endangering human health and the environment. Bioremediation is the process of clearing hazardous contaminants with the help of microorganisms, and cost-effective approach. The low-cost and environmentally acceptable approach to removing environmental pollutants from ecosystems is microbial bioremediation. However, to execute these different bioremediation approaches successfully, this is imperative to have a complete understanding of the variables impacting the development, metabolism, dynamics, and native microbial communities' activity in polluted areas. The emergence of new technologies like next-generation sequencing, protein and metabolic profiling, and advanced bioinformatic tools have provided critical insights into microbial communities and underlying mechanisms in environmental contaminant bioremediation. These omics approaches are meta-genomics, meta-transcriptomics, meta-proteomics, and metabolomics. Moreover, the advancements in these technologies have greatly aided in determining the effectiveness and implementing microbiological bioremediation approaches. At Environmental Protection Agency (EPA)-The government placed special emphasis on exploring how molecular and "omic" technologies may be used to determine the nature, behavior, and functions of the intrinsic microbial communities present at pollution containment systems. Several omics techniques are unquestionably more informative and valuable in elucidating the mechanism of the process and identifying the essential player's involved enzymes and their regulatory elements. This review provides an overview and description of the omics platforms that have been described in recent reports on omics approaches in bioremediation and that demonstrate the effectiveness of integrated omics approaches and their novel future use.

Deep embeddings to comprehend and visualize microbiome protein space

Understanding the function of microbial proteins is essential to reveal the clinical potential of the microbiome. The application of high-throughput sequencing technologies allows for fast and increasingly cheaper acquisition of data from microbial communities. However, many of the inferred protein sequences are novel and not catalogued, hence the possibility of predicting their function through conventional homology-based approaches is limited, which indicates the need for further research on alignment-free methods. Here, we leverage a deep-learning-based representation of proteins to assess its utility in alignment-free analysis of microbial proteins. We trained a language model on the Unified Human Gastrointestinal Protein catalogue and validated the resulting protein representation on the bacterial part of the SwissProt database. Finally, we present a use case on proteins involved in SCFA metabolism. Results indicate that the deep learning model manages to accurately represent features related to protein structure and function, allowing for alignment-free protein analyses. Technologies that contextualize metagenomic data are a promising direction to deeply understand the microbiome.

Pathway Tools Management of Pathway/Genome Data for Microbial Communities

The Pathway Tools (PTools) software provides a suite of capabilities for storing and analyzing integrated collections of genomic and metabolic information in the form of organism-specific Pathway/Genome Databases (PGDBs). A microbial community is represented in PTools by generating a PGDB from each metagenome-assembled genome (MAG). PTools computes a metabolic reconstruction for each organism, and predicts its operons. The properties of individual MAGs can be investigated using the many search and visualization operations within PTools. PTools also enables the user to investigate the properties of the microbial community by issuing searches across the full community, and by performing comparative operations across genome and pathway information. The software can generate a metabolic network diagram for the community, and it can overlay community omics datasets on that network diagram. PTools also provides a tool for searching for metabolic transformation routes across an organism community.

Multiple profile models extract features from protein sequence data and resolve functional diversity of very different protein families

Functional classification of proteins from sequences alone has become a critical bottleneck in understanding the myriad of protein sequences that accumulate in our databases. The great diversity of homologous sequences hides, in many cases, a variety of functional activities that cannot be anticipated. Their identification appears critical for a fundamental understanding of the evolution of living organisms and for biotechnological applications. ProfileView is a sequence-based computational method, designed to functionally classify sets of homologous sequences. It relies on two main ideas: the use of multiple profile models whose construction explores evolutionary information in available databases, and a novel definition of a representation space in which to analyse sequences with multiple profile models combined together. ProfileView classifies protein families by enriching known functional groups with new sequences and discovering new groups and subgroups. We validate ProfileView on seven classes of widespread proteins involved in the interaction with nucleic acids, amino acids and small molecules, and in a large variety of functions and enzymatic reactions. Profile-View agrees with the large set of functional data collected for these proteins from the literature regarding the organisation into functional subgroups and residues that characterise the functions. In addition, ProfileView resolves undefined functional classifications and extracts the molecular determinants underlying protein functional diversity, showing its potential to select sequences towards accurate experimental design and discovery of novel biological functions. On protein families with complex domain architecture, ProfileView functional classification reconciles domain combinations, unlike phylogenetic reconstruction. ProfileView proves to outperform the functional classification approach PANTHER, the two k-mer based methods CUPP and eCAMI and a neural network approach based on Restricted Boltzmann Machines. It overcomes time complexity limitations of the latter.

Shotgun metagenomics of Cheonggukjang, a fermented soybean food of Korea: Community structure, predictive functionalities and amino acids profile

Cheonggukjang is a naturally fermented soybean food of Korea. The present study was aimed to reveal the whole microbial community structure of naturally fermented cheonggukjang along with the prediction of microbial functional profiles by shotgun metagenomic sequence analysis. Metataxonomic profile of cheonggukjang samples showed different domains viz. bacteria (95.83%), virus (2.26%), unclassified (1.84%), eukaryotes (0.05%) and archaea (0.005%). Overall, 44 phyla, 286 families, 722 genera and 1437 species were identified. Firmicutes was the most abundant phylum (98.04%) followed by Proteobacteria (1.49%), Deinococcus-Thermus (0.14%). Bacillus thermoamylovorans was the most abundant species in cheonggukjang followed by Bacillus licheniformis, Bacillus glycinifermentans, Bacillus subtilis, Bacillus paralicheniformis, Bacillus amyloliquifaciens, Brevibacillus borstelensis, Brevibacillus sonorensis Brevibacillus, Acinetobacter, Carnobacterium, Paenibacillus, Cronobacter Enterococcus, Enterobacter, Terriglobus, Psychrobacter and Virgibacillus. A colossal diversity of the genus Bacillus was detected with 150 species. Functional analysis of cheonggukjang metagenome revealed the genes for the synthesis and metabolism of wide range of bioactive compounds including, various essential amino acids, conjugated amino acids, different vitamins, flavonoids, and enzymes. Amino acid profiles obtained from KEGG annotation in cheonggukjang were validated with experimental result of amino acid profiles.

Considerations for Constructing a Protein Sequence Database for Metaproteomics

Mass spectrometry-based metaproteomics has emerged as a prominent technique for interrogating the functions of specific organisms in microbial communities, in addition to total community function. Identifying proteins by mass spectrometry requires matching mass spectra of fragmented peptide ions to a database of protein sequences corresponding to the proteins in the sample. This sequence database determines which protein sequences can be identified from the measurement, and as such the taxonomic and functional information that can be inferred from a metaproteomics measurement. Thus, the construction of the protein sequence database directly impacts the outcome of any metaproteomics study. Several factors, such as source of sequence information and database curation, need to be considered during database construction to maximize accurate protein identifications traceable to the species of origin. In this review, we provide an overview of existing strategies for database construction and the relevant studies that have sought to test and validate these strategies. Based on this review of the literature and our experience we provide a decision tree and best practices for choosing and implementing database construction strategies.

The neonatal microbiome in utero and beyond: perinatal influences and long-term impacts

The neonatal microbiome offers a valuable model for studying the origins of human health and disease. As the field of metagenomics expands, we also increase our understanding of early life influences on its development. In this review we will describe common techniques used to define and measure the microbiome. We will review in utero influences, normal perinatal development, and known risk factors for abnormal neonatal microbiome development. Finally, we will summarize current evidence that links early life microbial impacts on the development of chronic inflammatory diseases, obesity, and atopy.

A Comparative Taxonomic Profile of Microbial Polyethylene and Hydrocarbon-Degrading Communities in Diverse Environments

Background

Polyethylene (PE) is one of the most abundant plastic wastes which accumulates in marine and terrestrial environments. As microbial degradation has been a promising approach for the bioremediation of polluted environments, identification of the microbial community profile where these pollutants accumulate, has recently been in focus.

Objective

We have investigated the taxonomic and functional characteristics of polyethylene- degrading microorganisms in a plastic waste recycling site in Tehran, Iran.

Materials and Methods

We have analyzed and compared a 16S rRNA dataset from this study with 15 datasets from 4 diverse plastic and oil polluted habitats to identify and evaluate bacterial communities involved in bioremediation.

Results

Our findings reveal that Proteobacteria, Actinobacteria, Acidobacteria and Cloroflexi were the dominant phyla and Actinobacteria, Alphaproteobacteria, Gammaproteobacteria and Acidimicrobia were dominant classes in these samples. The most dominant Kegg Orthology associated with PE bioremediation in these samples are related to peroxidases, alcohol dehydrogenases, monooxygenases and dioxygenases.

Conclusions

Long-term presence of contaminants in soil could lead to changes in bacterial phyla abundance, resulting in metabolic adaptations to optimize biological activity and waste management in a diverse group of bacteria.

The Relationship between the Gut Microbiome and Metformin as a Key for Treating Type 2 Diabetes Mellitus

Metformin is the first-line pharmacotherapy for treating type 2 diabetes mellitus (T2DM); however, its mechanism of modulating glucose metabolism is elusive. Recent advances have identified the gut as a potential target of metformin. As patients with metabolic disorders exhibit dysbiosis, the gut microbiome has garnered interest as a potential target for metabolic disease. Henceforth, studies have focused on unraveling the relationship of metabolic disorders with the human gut microbiome. According to various metagenome studies, gut dysbiosis is evident in T2DM patients. Besides this, alterations in the gut microbiome were also observed in the metformin-treated T2DM patients compared to the non-treated T2DM patients. Thus, several studies on rodents have suggested potential mechanisms interacting with the gut microbiome, including regulation of glucose metabolism, an increase in short-chain fatty acids, strengthening intestinal permeability against lipopolysaccharides, modulating the immune response, and interaction with bile acids. Furthermore, human studies have demonstrated evidence substantiating the hypotheses based on rodent studies. This review discusses the current knowledge of how metformin modulates T2DM with respect to the gut microbiome and discusses the prospect of harnessing this mechanism in treating T2DM.

An Introduction to Whole-Metagenome Shotgun Sequencing Studies

Microbial communities are found across diverse environments, including within and across the human body. As many microbes are unculturable in the lab, much of what is known about a microbiome-a collection of bacteria, fungi, archaea, and viruses inhabiting an environment--is from the sequencing of DNA from within the constituent community. Here, we provide an introduction to whole-metagenome shotgun sequencing studies, a ubiquitous approach for characterizing microbial communities, by reviewing three major research areas in metagenomics: assembly, community profiling, and functional profiling. Though not exhaustive, these areas encompass a large component of the metagenomics literature. We discuss each area in depth, the challenges posed by whole-metagenome shotgun sequencing, and approaches fundamental to the solutions of each. We conclude by discussing promising areas for future research. Though our emphasis is on the human microbiome, the methods discussed are broadly applicable across study systems.

Dental Calculus as a Tool to Study the Evolution of the Mammalian Oral Microbiome

Dental calculus, the calcified form of the mammalian oral microbial plaque biofilm, is a rich source of oral microbiome, host, and dietary biomolecules and is well preserved in museum and archaeological specimens. Despite its wide presence in mammals, to date, dental calculus has primarily been used to study primate microbiome evolution. We establish dental calculus as a valuable tool for the study of nonhuman host microbiome evolution, by using shotgun metagenomics to characterize the taxonomic and functional composition of the oral microbiome in species as diverse as gorillas, bears, and reindeer. We detect oral pathogens in individuals with evidence of oral disease, assemble near-complete bacterial genomes from historical specimens, characterize antibiotic resistance genes, reconstruct components of the host diet, and recover host genetic profiles. Our work demonstrates that metagenomic analyses of dental calculus can be performed on a diverse range of mammalian species, which will allow the study of oral microbiome and pathogen evolution from a comparative perspective. As dental calculus is readily preserved through time, it can also facilitate the quantification of the impact of anthropogenic changes on wildlife and the environment.

Metabolomics as an Emerging Tool in the Search for Astrobiologically Relevant Biomarkers

It is now routinely possible to sequence and recover microbial genomes from environmental samples. To the degree it is feasible to assign transcriptional and translational functions to these genomes, it should be possible, in principle, to largely understand the complete molecular inputs and outputs of a microbial community. However, gene-based tools alone are presently insufficient to describe the full suite of chemical reactions and small molecules that compose a living cell. Metabolomic tools have developed quickly and now enable rapid detection and identification of small molecules within biological and environmental samples. The convergence of these technologies will soon facilitate the detection of novel enzymatic activities, novel organisms, and potentially extraterrestrial life-forms on solar system bodies. This review explores the methodological problems and scientific opportunities facing researchers who hope to apply metabolomic methods in astrobiology-related fields, and how present challenges might be overcome.

Gut-Brain Axis and Neurodegeneration: State-of-the-Art of Meta-Omics Sciences for Microbiota Characterization

Recent advances in the field of meta-omics sciences and related bioinformatics tools have allowed a comprehensive investigation of human-associated microbiota and its contribution to achieving and maintaining the homeostatic balance. Bioactive compounds from the microbial community harboring the human gut are involved in a finely tuned network of interconnections with the host, orchestrating a wide variety of physiological processes. These includes the bi-directional crosstalk between the central nervous system, the enteric nervous system, and the gastrointestinal tract (i.e., gut-brain axis). The increasing accumulation of evidence suggest a pivotal role of the composition and activity of the gut microbiota in neurodegeneration. In the present review we aim to provide an overview of the state-of-the-art of meta-omics sciences including metagenomics for the study of microbial genomes and taxa strains, metatranscriptomics for gene expression, metaproteomics and metabolomics to identify and/or quantify microbial proteins and metabolites, respectively. The potential and limitations of each discipline were highlighted, as well as the advantages of an integrated approach (multi-omics) to predict microbial functions and molecular mechanisms related to human diseases. Particular emphasis is given to the latest results obtained with these approaches in an attempt to elucidate the link between the gut microbiota and the most common neurodegenerative diseases, such as multiple sclerosis (MS), Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS).

The Neonatal Microbiome and Metagenomics: What Do We Know and What Is the Future?

The human microbiota includes the trillions of microorganisms living in the human body whereas the human microbiome includes the genes and gene products of this microbiota. Bacteria were historically largely considered to be pathogens that inevitably led to human disease. However, because of advances in both cultivation-based methods and the advent of metagenomics, bacteria are now recognized to be largely beneficial commensal organisms and thus, key to normal and healthy human development. This relatively new area of medical research has elucidated insights into diseases such as inflammatory bowel disease and obesity, as well as metabolic and atopic disorders. However, much remains unknown about the complexity of microbe-microbe and microbe-host interactions. Future efforts aimed at answering key questions pertaining to the early establishment of the microbiome, alongside what defines its dysbiosis, will likely lead to long-term health and mitigation of disease. Here, we review the relevant literature pertaining to modulations in the perinatal and neonatal microbiome, the impact of environmental and maternal factors in shaping the neonatal microbiome, and future questions and directions in the exciting emerging arena of metagenomic medicine.

Pre- and post-sequencing recommendations for functional annotation of human fecal metagenomes

Background

Shotgun metagenomes are often assembled prior to annotation of genes which biases the functional capacity of a community towards its most abundant members. For an unbiased assessment of community function, short reads need to be mapped directly to a gene or protein database. The ability to detect genes in short read sequences is dependent on pre- and post-sequencing decisions. The objective of the current study was to determine how library size selection, read length and format, protein database, e-value threshold, and sequencing depth impact gene-centric analysis of human fecal microbiomes when using DIAMOND, an alignment tool that is up to 20,000 times faster than BLASTX.

Results

Using metagenomes simulated from a database of experimentally verified protein sequences, we find that read length, e-value threshold, and the choice of protein database dramatically impact detection of a known target, with best performance achieved with longer reads, stricter e-value thresholds, and a custom database. Using publicly available metagenomes, we evaluated library size selection, paired end read strategy, and sequencing depth. Longer read lengths were acheivable by merging paired ends when the sequencing library was size-selected to enable overlaps. When paired ends could not be merged, a congruent strategy in which both ends are independently mapped was acceptable. Sequencing depths of 5 million merged reads minimized the error of abundance estimates of specific target genes, including an antimicrobial resistance gene.

Conclusions

Shotgun metagenomes of DNA extracted from human fecal samples sequenced using the Illumina platform should be size-selected to enable merging of paired end reads and should be sequenced in the PE150 format with a minimum sequencing depth of 5 million merge-able reads to enable detection of specific target genes. Expecting the merged reads to be 180-250 bp in length, the appropriate e-value threshold for DIAMOND would then need to be more strict than the default. Accurate and interpretable results for specific hypotheses will be best obtained using small databases customized for the research question.

Current Trends in Diagnostics of Viral Infections of Unknown Etiology

Viruses are evolving at an alarming rate, spreading and inconspicuously adapting to cutting-edge therapies. Therefore, the search for rapid, informative and reliable diagnostic methods is becoming urgent as ever. Conventional clinical tests (PCR, serology, etc.) are being continually optimized, yet provide very limited data. Could high throughput sequencing (HTS) become the future gold standard in molecular diagnostics of viral infections? Compared to conventional clinical tests, HTS is universal and more precise at profiling pathogens. Nevertheless, it has not yet been widely accepted as a diagnostic tool, owing primarily to its high cost and the complexity of sample preparation and data analysis. Those obstacles must be tackled to integrate HTS into daily clinical practice. For this, three objectives are to be achieved: (1) designing and assessing universal protocols for library preparation, (2) assembling purpose-specific pipelines, and (3) building computational infrastructure to suit the needs and financial abilities of modern healthcare centers. Data harvested with HTS could not only augment diagnostics and help to choose the correct therapy, but also facilitate research in epidemiology, genetics and virology. This information, in turn, could significantly aid clinicians in battling viral infections.

Why interindividual variation in response to consumption of plant food bioactives matters for future personalised nutrition

Food phytochemicals are increasingly considered to play a key role in the cardiometabolic health effects of plant foods. However, the heterogeneity in responsiveness to their intake frequently observed in clinical trials can hinder the beneficial effects of these compounds in specific subpopulations. A range of factors, including genetic background, gut microbiota, age, sex and health status, could be involved in these interindividual variations; however, the current knowledge is limited and fragmented. The European network, European Cooperation in Science and Technology (COST)-POSITIVe, has analysed, in a systematic way, existing knowledge with the aim to better understand the factors responsible for the interindividual variation in response to the consumption of the major families of plant food bioactives, regarding their bioavailability and bioefficacy. If differences in bioavailability, likely reflecting differences in human subjects' genetics or in gut microbiota composition and functionality, are believed to underpin much of the interindividual variability, the key molecular determinants or microbial species remain to be identified. The systematic analysis of published studies conducted to assess the interindividual variation in biomarkers of cardiometabolic risk suggested some factors (such as adiposity and health status) as involved in between-subject variation. However, the contribution of these factors is not demonstrated consistently across the different compounds and biological outcomes and would deserve further investigations. The findings of the network clearly highlight that the human subjects' intervention studies published so far are not adequate to investigate the relevant determinants of the absorption/metabolism and biological responsiveness. They also emphasise the need for a new generation of intervention studies designed to capture this interindividual variation.

Elucidation of Codon Usage Signatures across the Domains of Life

Because of the degeneracy of the genetic code, multiple codons are translated into the same amino acid. Despite being “synonymous,” these codons are not equally used. Selective pressures are thought to drive the choice among synonymous codons within a genome, while GC content, which is typically attributed to mutational drift, is the major determinant of variation across species. Here, we find that in addition to GC content, interspecies codon usage signatures can also be detected. More specifically, we show that a single amino acid, arginine, is the major contributor to codon usage bias differences across domains of life. We then exploit this finding and show that domain-specific codon bias signatures can be used to classify a given sequence into its corresponding domain of life with high accuracy. We then wondered whether the inclusion of codon usage codon autocorrelation patterns, which reflects the nonrandom distribution of codon occurrences throughout a transcript, might improve the classification performance of our algorithm. However, we find that autocorrelation patterns are not domain-specific, and surprisingly, are unrelated to tRNA reusage, in contrast to previous reports. Instead, our results suggest that codon autocorrelation patterns are a by-product of codon optimality throughout a sequence, where highly expressed genes display autocorrelated “optimal” codons, whereas lowly expressed genes display autocorrelated “nonoptimal” codons.

Milk microbiota: Characterization methods and role in cheese production

Milk is a complex body fluid aimed at addressing the nutritional and defensive needs of the mammal's newborns. Harbored microbiota plays a pivotal role throughout the cheesemaking process and contributes to the development of flavor and texture typical of different type of cheeses. Understanding the dairy microbiota dynamics is of paramount importance for controlling the qualitative, sensorial and biosafety features of the dairy products. Although many studies investigated the contribution of single or few microorganisms, still there is some information lacking about microbial communities. The widespread of the omics platforms and bioinformatic tools enable the investigation of the cheese-associated microbial community in both phylogenetical and functional terms, highlighting the effects of the diverse cheesemaking variables. In this review, the most relevant literature is revised to provide an introduction of the milk- and cheese-associated microbiota, along with their structural and functional dynamics in relation to the diverse cheesemaking technologies and influencing variables. Also, we focus our attention on the latest omics technologies adopted in dairy microbiota investigation. Discussion on the key-steps and major drawbacks of each omics discipline is provided along with a collection of results from the latest research studies performed to unravel the fascinating world of the dairy-associated microbiota. SIGNIFICANCE: Understanding the milk- and cheese- associated microbial community is nowadays considered a key factor in the dairy industry, since it allows a comprehensive knowledge on how all phases of the cheesemaking process impact the harbored microflora; thus, predict the consequences in the finished products in terms of texture, organoleptic characteristics, palatability and biosafety. This review, collect the pioneering and milestones works so far performed in the field of dairy microbiota, and provide the basic guidance to whom approaching the cheese microbiota investigation by means of the latest omics technologies. Also, the review emphasizes the benefits and drawbacks of the omics disciplines, and underline how the integration of diverse omics sciences enhance a comprehensive depiction of the cheese microbiota. In turn, a better consciousness of the dairy microbiota might results in the application of improved starter cultures, cheesemaking practices and technologies; supporting a bio-safe and standardized production of cheese, with a strong economic benefit for both large-scale industries and local traditional dairy farms.

Decoding the microbiome for the development of translational applications: Overview, challenges and pitfalls

Recent studies have highlighted the potential of 'translational' microbiome research in addressing real-world challenges pertaining to human health, nutrition and disease. Additionally, outcomes of microbiome research have also positively impacted various aspects pertaining to agricultural productivity, fuel or energy requirements, and stability/preservation of various ecological habitats. Microbiome data is multi-dimensional with various types of data comprising nucleic and protein sequences, metabolites as well as various metadata related to host and or environment. This poses a major challenge for computational analysis and interpretation of data to reach meaningful, reproducible (and replicable) biological conclusions. In this review, we first describe various aspects of microbiomes that make them an attractive tool/target for developing various translational applications. The challenge of deciphering signatures from an information-rich resource like the microbiome is also discussed. Subsequently, we present three case-studies that exemplify the potential of microbiome- based solutions in solving real-world problems. The final part of the review attempts to familiarize readers with the importance of a robust study design and the diligence required during every stage of analysis for achieving solutions with potential translational value.

Hierarchical Hidden Markov models enable accurate and diverse detection of antimicrobial resistance sequences

The characterization of antimicrobial resistance genes from high-throughput sequencing data has become foundational in public health research and regulation. This requires mapping sequence reads to databases of known antimicrobial resistance genes to determine the genes present in the sample. Mapping sequence reads to known genes is traditionally accomplished using alignment. Alignment methods have high specificity but are limited in their ability to detect sequences that are divergent from the reference database, which can result in a substantial false negative rate. We address this shortcoming through the creation of Meta-MARC, which enables detection of diverse resistance sequences using hierarchical, DNA-based Hidden Markov Models. We first describe Meta-MARC and then demonstrate its efficacy on simulated and functional metagenomic datasets. Meta-MARC has higher sensitivity relative to competing methods. This sensitivity allows for detection of sequences that are divergent from known antimicrobial resistance genes. This functionality is imperative to expanding existing antimicrobial gene databases.

Bioinformatics tools for quantitative and functional metagenome and metatranscriptome data analysis in microbes

Metagenomic and metatranscriptomic sequencing approaches are more frequently being used to link microbiota to important diseases and ecological changes. Many analyses have been used to compare the taxonomic and functional profiles of microbiota across habitats or individuals. While a large portion of metagenomic analyses focus on species-level profiling, some studies use strain-level metagenomic analyses to investigate the relationship between specific strains and certain circumstances. Metatranscriptomic analysis provides another important insight into activities of genes by examining gene expression levels of microbiota. Hence, combining metagenomic and metatranscriptomic analyses will help understand the activity or enrichment of a given gene set, such as drug-resistant genes among microbiome samples. Here, we summarize existing bioinformatics tools of metagenomic and metatranscriptomic data analysis, the purpose of which is to assist researchers in deciding the appropriate tools for their microbiome studies. Additionally, we propose an Integrated Meta-Function mapping pipeline to incorporate various reference databases and accelerate functional gene mapping procedures for both metagenomic and metatranscriptomic analyses.

Metagenomic insights into diazotrophic communities across Arctic glacier forefields

Microbial nitrogen fixation is crucial for building labile nitrogen stocks and facilitating higher plant colonisation in oligotrophic glacier forefield soils. Here, the diazotrophic bacterial community structure across four Arctic glacier forefields was investigated using metagenomic analysis. In total, 70 soil metagenomes were used for taxonomic interpretation based on 185 nitrogenase (nif) sequences, extracted from assembled contigs. The low number of recovered genes highlights the need for deeper sequencing in some diverse samples, to uncover the complete microbial populations. A key group of forefield diazotrophs, found throughout the forefields, was identified using a nifH phylogeny, associated with nifH Cluster I and III. Sequences related most closely to groups including Alphaproteobacteria, Betaproteobacteria, Cyanobacteria and Firmicutes. Using multiple nif genes in a Last Common Ancestor analysis revealed a diverse range of diazotrophs across the forefields. Key organisms identified across the forefields included Nostoc, Geobacter, Polaromonas and Frankia. Nitrogen fixers that are symbiotic with plants were also identified, through the presence of root associated diazotrophs, which fix nitrogen in return for reduced carbon. Additional nitrogen fixers identified in forefield soils were metabolically diverse, including fermentative and sulphur cycling bacteria, halophiles and anaerobes.

NanoARG: a web service for detecting and contextualizing antimicrobial resistance genes from nanopore-derived metagenomes

BACKGROUND

Direct and indirect selection pressures imposed by antibiotics and co-selective agents and horizontal gene transfer are fundamental drivers of the evolution and spread of antibiotic resistance. Therefore, effective environmental monitoring tools should ideally capture not only antibiotic resistance genes (ARGs), but also mobile genetic elements (MGEs) and indicators of co-selective forces, such as metal resistance genes (MRGs). A major challenge towards characterizing the potential human health risk of antibiotic resistance is the ability to identify ARG-carrying microorganisms, of which human pathogens are arguably of greatest risk. Historically, short reads produced by next-generation sequencing technologies have hampered confidence in assemblies for achieving these purposes.

RESULTS

Here, we introduce NanoARG, an online computational resource that takes advantage of the long reads produced by nanopore sequencing technology. Specifically, long nanopore reads enable identification of ARGs in the context of relevant neighboring genes, thus providing valuable insight into mobility, co-selection, and pathogenicity. NanoARG was applied to study a variety of nanopore sequencing data to demonstrate its functionality. NanoARG was further validated through characterizing its ability to correctly identify ARGs in sequences of varying lengths and a range of sequencing error rates.

CONCLUSIONS

NanoARG allows users to upload sequence data online and provides various means to analyze and visualize the data, including quantitative and simultaneous profiling of ARGs, MRGs, MGEs, and putative pathogens. A user-friendly interface allows users the analysis of long DNA sequences (including assembled contigs), facilitating data processing, analysis, and visualization. NanoARG is publicly available and freely accessible at https://bench.cs.vt.edu/nanoarg .

Exploring the emerging role of the microbiome in cancer immunotherapy

The activity of the commensal microbiota significantly impacts human health and has been linked to the development of many diseases, including cancer. Gnotobiotic animal models have shown that the microbiota has many effects on host physiology, including on the development and regulation of immune responses. More recently, evidence has indicated that the microbiota can more specifically influence the outcome of cancer immunotherapy. Therapeutic interventions to optimize microbiota composition to improve immunotherapy outcomes have shown promise in mouse studies. Ongoing endeavors are translating these pre-clinical findings to early stage clinical testing. In this review we summarize 1) basic methodologies and considerations for studies of host-microbiota interactions; 2) experimental evidence towards a causal link between gut microbiota composition and immunotherapeutic efficacy; 3) possible mechanisms governing the microbiota-mediated impact on immunotherapy efficacy. Moving forward, there is need for a deeper understanding of the underlying biological mechanisms that link specific bacterial strains to host immunity. Integrating microbiome effects with other tumor and host factors regulating immunotherapy responsiveness versus resistance could facilitate optimization of therapeutic outcomes.

Shotgun Metagenomics of a Water Kefir Fermentation Ecosystem Reveals a Novel Oenococcus Species

Water kefir is a fruity, sour, slightly alcoholic and carbonated beverage, which is made by fermentation of an aqueous sucrose solution in the presence of dried figs and water kefir grains. These polysaccharide grains contain lactic acid bacteria (LAB), yeasts, and sometimes bifidobacteria and/or acetic acid bacteria, which consume sucrose to produce exopolysaccharides, lactic acid, acetic acid, ethanol, and carbon dioxide. Shotgun metagenomic sequencing was used to examine the microbial species diversity present at two time points during water kefir fermentation in detail, both in the water kefir liquor and on the water kefir grains, hence representing four samples. Lactobacillus harbinensis, Lactobacillus hilgardii, Lactobacillus nagelii, Lactobacillus paracasei, and a Lactobacillus species similar to Lactobacillus hordei/mali were present in the water kefir examined, along with Bifidobacterium aquikefiri and two yeast species, namely Saccharomyces cerevisiae and Dekkera bruxellensis. In addition, evidence for a novel Oenococcus species related to Oenococcus oeni and Oenococcus kitaharae was found. Its genome was derived from the metagenome and made available under the name of Candidatus Oenococcus aquikefiri. Through functional analysis of the four metagenomic data sets, it was possible to link the production of lactic acid, acetic acid, ethanol, and carbon dioxide to subgroups of the microbial species found. In particular, the production of mannitol from fructose was linked to L. hilgardii, Candidatus O. aquikefiri, and B. aquikefiri, whereas glycerol production was associated with S. cerevisiae. Also, there were indications of cross-feeding, for instance in the case of amino acid supply. Few bacterial species could synthesize a limited number of cofactors, making them reliant on the figs or S. cerevisiae. The LAB species in turn were found to be capable of contributing to water kefir grain growth, as dextransucrase-encoding genes were attributed to L. hilgardii, L. hordei/mali, and Candidatus O. aquikefiri.

Recovery and functional validation of hidden soil enzymes in metagenomic libraries

The vast microbial diversity on the planet represents an invaluable source for identifying novel activities with potential industrial and therapeutic application. In this regard, metagenomics has emerged as a group of strategies that have significantly facilitated the analysis of DNA from multiple environments and has expanded the limits of known microbial diversity. However, the functional characterization of enzymes, metabolites, and products encoded by diverse microbial genomes is limited by the inefficient heterologous expression of foreign genes. We have implemented a pipeline that combines NGS and Sanger sequencing as a way to identify fosmids within metagenomic libraries. This strategy facilitated the identification of putative proteins, subcloning of targeted genes and preliminary characterization of selected proteins. Overall, the in silico approach followed by the experimental validation allowed us to efficiently recover the activity of previously hidden enzymes derived from agricultural soil samples. Therefore, the methodology workflow described herein can be applied to recover activities encoded by environmental DNA from multiple sources.

Metabolic Modeling of Human Gut Microbiota on a Genome Scale: An Overview

There is growing interest in the metabolic interplay between the gut microbiome and host metabolism. Taxonomic and functional profiling of the gut microbiome by next-generation sequencing (NGS) has unveiled substantial richness and diversity. However, the mechanisms underlying interactions between diet, gut microbiome and host metabolism are still poorly understood. Genome-scale metabolic modeling (GSMM) is an emerging approach that has been increasingly applied to infer diet⁻microbiome, microbe⁻microbe and host⁻microbe interactions under physiological conditions. GSMM can, for example, be applied to estimate the metabolic capabilities of microbes in the gut. Here, we discuss how meta-omics datasets such as shotgun metagenomics, can be processed and integrated to develop large-scale, condition-specific, personalized microbiota models in healthy and disease states. Furthermore, we summarize various tools and resources available for metagenomic data processing and GSMM, highlighting the experimental approaches needed to validate the model predictions.

Critical issues in application of molecular methods to environmental virology

Waterborne diseases have significant public health and socioeconomic implications worldwide. Many viral pathogens are commonly associated with water-related diseases, namely enteric viruses. Also, novel recently discovered human-associated viruses have been shown to be a causative agent of gastroenteritis or other clinical symptoms. A wide range of analytical methods is available for virus detection in environmental water samples. Viral isolation is historically carried out via propagation on permissive cell lines; however, some enteric viruses are difficult or not able to propagate on existing cell lines. Real-time polymerase chain reaction (qPCR) screening of viral nucleic acid is routinely used to investigate virus contamination in water due to the high sensitivity and specificity. Additionally, the introduction of metagenomic approaches into environmental virology has facilitated the discovery of viruses that cannot be grown in cell culture. This review (i) highlights the applications of molecular techniques in environmental virology such as PCR and its modifications to overcome the critical issues associated with the inability to discriminate between infectious viruses and nonviable viruses, (ii) outlines the strengths and weaknesses of Nucleic Acid Sequence Based Amplification (NASBA) and microarray, (iii) discusses the role of digital PCR as an emerging water quality monitoring assay and its advantages over qPCR, (iv) addresses the viral metagenomics in terms of detecting emerging viral pathogens and diversity in aquatic environment. Indeed, there are many challenges for selecting methods to detect classic and emerging viruses in environmental samples. While the existing techniques have revealed the importance and diversity of viruses in the water environment, further developments are necessary to enable more rapid and accurate methodologies for viral water quality monitoring and regulation.

The Methodology Behind Network Thinking: Graphs to Analyze Microbial Complexity and Evolution

In the post genomic era, large and complex molecular datasets from genome and metagenome sequencing projects expand the limits of what is possible for bioinformatic analyses. Network-based methods are increasingly used to complement phylogenetic analysis in studies in molecular evolution, including comparative genomics, classification, and ecological studies. Using network methods, the vertical and horizontal relationships between all genes or genomes, whether they are from cellular chromosomes or mobile genetic elements, can be explored in a single expandable graph. In recent years, development of new methods for the construction and analysis of networks has helped to broaden the availability of these approaches from programmers to a diversity of users. This chapter introduces the different kinds of networks based on sequence similarity that are already available to tackle a wide range of biological questions, including sequence similarity networks, gene-sharing networks and bipartite graphs, and a guide for their construction and analyses.