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

Biosensors for rapid detection of bacterial pathogens in water, food and environment

Conventional techniques (e.g., culture-based method) for bacterial detection typically require a central laboratory and well-trained technicians, which may take several hours or days. However, recent developments within various disciplines of science and engineering have led to a major paradigm shift in how microorganisms can be detected. The analytical sensors which are widely used for medical applications in the literature are being extended for rapid and on-site monitoring of the bacterial pathogens in food, water and the environment. Especially, within the low-resource settings such as low and middle-income countries, due to the advantages of low cost, rapidness and potential for field-testing, their use is indispensable for sustainable development of the regions. Within this context, this paper discusses analytical methods and biosensors which can be used to ensure food safety, water quality and environmental monitoring. In brief, most of our discussion is focused on various rapid sensors including biosensors and microfluidic chips. The analytical performances such as the sensitivity, specificity and usability of these sensors, as well as a brief comparison with the conventional techniques for bacteria detection, form the core part of the discussion. Furthermore, we provide a holistic viewpoint on how future research should focus on exploring the synergy of different sensing technologies by developing an integrated multiplexed, sensitive and accurate sensors that will enable rapid detection for food safety, water and environmental monitoring.

Comparative Genomics Provides Insights Into Genetic Diversity of Clostridium tyrobutyricum and Potential Implications for Late Blowing Defects in Cheese

Clostridium tyrobutyricum has been recognized as the main cause of late blowing defects (LBD) in cheese leading to considerable economic losses for the dairy industry. Although differences in spoilage ability among strains of this species have been acknowledged, potential links to the genetic diversity and functional traits remain unknown. In the present study, we aimed to investigate and characterize genomic variation, pan-genomic diversity and key traits of C. tyrobutyricum by comparing the genomes of 28 strains. A comparative genomics analysis revealed an “open” pangenome comprising 9,748 genes and a core genome of 1,179 genes shared by all test strains. Among those core genes, the majority of genes encode proteins related to translation, ribosomal structure and biogenesis, energy production and conversion, and amino acid metabolism. A large part of the accessory genome is composed of sets of unique, strain-specific genes ranging from about 5 to more than 980 genes. Furthermore, functional analysis revealed several strain-specific genes related to replication, recombination and repair, cell wall, membrane and envelope biogenesis, and defense mechanisms that might facilitate survival under stressful environmental conditions. Phylogenomic analysis divided strains into two clades: clade I contained human, mud, and silage isolates, whereas clade II comprised cheese and milk isolates. Notably, these two groups of isolates showed differences in certain hypothetical proteins, transcriptional regulators and ABC transporters involved in resistance to oxidative stress. To the best of our knowledge, this is the first study to provide comparative genomics of C. tyrobutyricum strains related to LBD. Importantly, the findings presented in this study highlight the broad genetic diversity of C. tyrobutyricum, which might help us understand the diversity in spoilage potential of C. tyrobutyricum in cheese and provide some clues for further exploring the gene modules responsible for the spoilage ability of this species.

Metagenomics Approaches to Investigate the Neonatal Gut Microbiome

Early infancy is critical for the development of an infant's gut flora. Many factors can influence microbiota development during the pre- and postnatal periods, including maternal factors, antibiotic exposure, mode of delivery, dietary patterns, and feeding type. Therefore, investigating the connection between these variables and host and microbiome interactions in neonatal development would be of great interest. As the "unculturable" era of microbiome research gives way to an intrinsically multidisciplinary field, microbiome research has reaped the advantages of technological advancements in next-generation sequencing, particularly 16S rRNA gene amplicon and shotgun sequencing, which have considerably expanded our knowledge about gut microbiota development during early life. Using omics approaches to explore the neonatal microbiome may help to better understand the link between the microbiome and newborn diseases. Herein, we summarized the metagenomics methods and tools used to advance knowledge on the neonatal microbiome origin and evolution and how the microbiome shapes early and late individuals' lives for health and disease. The way to overcome limitations in neonatal microbiome studies will be discussed.

Campylobacter Species, Microbiological Source Tracking and Risk Assessment of Bacterial pathogens

Campylobacter species continue to remain critical pathogens of public health interest. They are responsible for approximately 500 million cases of gastroenteritis per year worldwide. Infection occurs through the consumption of contaminated food and water. Microbial risk assessment and source tracking are crucial epidemiological strategies to monitor the outbreak of campylobacteriosis effectively. Various methods have been proposed for microbial source tracking and risk assessment, most of which rely on conventional microbiological techniques such as detecting fecal indicator organisms and other novel microbial source tracking methods, including library-dependent microbial source tracking and library-independent source tracking approaches. However, both the traditional and novel methods have their setbacks. For example, while the conventional techniques are associated with a poor correlation between indicator organism and pathogen presence, on the other hand, it is impractical to interpret qPCR-generated markers to establish the exact human health risks even though it can give information regarding the potential source and relative human risk. Therefore, this article provides up-to-date information on campylobacteriosis, various approaches for source attribution, and risk assessment of bacterial pathogens, including next-generation sequencing approaches such as shotgun metagenomics, which effectively answer the questions of potential pathogens are there and in what quantities.

Altered Ecology of the Respiratory Tract Microbiome and Nosocomial Pneumonia

Nosocomial pneumonia is one of the most frequent infections in critical patients. It is primarily associated with mechanical ventilation leading to severe illness, high mortality, and prolonged hospitalization. The risk of mortality has increased over time due to the rise in multidrug-resistant (MDR) bacterial infections, which represent a global public health threat. Respiratory tract microbiome (RTM) research is growing, and recent studies suggest that a healthy RTM positively stimulates the immune system and, like the gut microbiome, can protect against pathogen infection through colonization resistance (CR). Physiological conditions of critical patients and interventions as antibiotics administration and mechanical ventilation dramatically alter the RTM, leading to dysbiosis. The dysbiosis of the RTM of ICU patients favors the colonization by opportunistic and resistant pathogens that can be part of the microbiota or acquired from the hospital environments (biotic or built ones). Despite recent evidence demonstrating the significance of RTM in nosocomial infections, most of the host-RTM interactions remain unknown. In this context, we present our perspective regarding research in RTM altered ecology in the clinical environment, particularly as a risk for acquisition of nosocomial pneumonia. We also reflect on the gaps in the field and suggest future research directions. Moreover, expected microbiome-based interventions together with the tools to study the RTM highlighting the "omics" approaches are discussed.

A breath of fresh air in microbiome science: shallow shotgun metagenomics for a reliable disentangling of microbial ecosystems

Next-generation sequencing technologies allow accomplishing massive DNA sequencing, uncovering the microbial composition of many different ecological niches. However, the various strategies developed to profile microbiomes make it challenging to retrieve a reliable classification that is able to compare metagenomic data of different studies. Many limitations have been overcome thanks to shotgun sequencing, allowing a reliable taxonomic classification of microbial communities at the species level. Since numerous bioinformatic tools and databases have been implemented, the sequencing methodology is only the first of many choices to make for classifying metagenomic data. Here, we discuss the importance of choosing a reliable methodology to achieve consistent information in uncovering microbiomes.

The Human Gut Microbiome as a Potential Factor in Autism Spectrum Disorder

The high prevalence of gastrointestinal (GI) disorders among autism spectrum disorder (ASD) patients has prompted scientists to look into the gut microbiota as a putative trigger in ASD pathogenesis. Thus, many studies have linked the gut microbial dysbiosis that is frequently observed in ASD patients with the modulation of brain function and social behavior, but little is known about this connection and its contribution to the etiology of ASD. This present review highlights the potential role of the microbiota–gut–brain axis in autism. In particular, it focuses on how gut microbiota dysbiosis may impact gut permeability, immune function, and the microbial metabolites in autistic people. We further discuss recent findings supporting the possible role of the gut microbiome in initiating epigenetic modifications and consider the potential role of this pathway in influencing the severity of ASD. Lastly, we summarize recent updates in microbiota-targeted therapies such as probiotics, prebiotics, dietary supplements, fecal microbiota transplantation, and microbiota transfer therapy. The findings of this paper reveal new insights into possible therapeutic interventions that may be used to reduce and cure ASD-related symptoms. However, well-designed research studies using large sample sizes are still required in this area of study.

Metagenome-assembled genomes: concepts, analogies, and challenges

Metagenome-assembled genomes (MAGs) are microbial genomes reconstructed from metagenome data. In the last few years, many thousands of MAGs have been reported in the literature, for a variety of environments and host-associated microbiota, including humans. MAGs have helped us better understand microbial populations and their interactions with the environment where they live; moreover most MAGs belong to novel species, therefore helping to decrease the so-called microbial dark matter. However, questions about the biological reality of MAGs have not, in general, been properly addressed. In this review, I define the notions of hypothetical MAGs and conserved hypothetical MAGs. These notions should help with the understanding of the biological reality of MAGs, their worldwide occurrence, and the efforts to improve MAG recovery processes.

Identification of isolated or mixed strains from long reads: a challenge met on Streptococcus thermophilus using a MinION sequencer

This study aimed to provide efficient recognition of bacterial strains on personal computers from MinION (Nanopore) long read data. Thanks to the fall in sequencing costs, the identification of bacteria can now proceed by whole genome sequencing. MinION is a fast, but highly error-prone sequencing device and it is a challenge to successfully identify the strain content of unknown simple or complex microbial samples. It is heavily constrained by memory management and fast access to the read and genome fragments. Our strategy involves three steps: indexing of known genomic sequences for a given or several bacterial species; a request process to assign a read to a strain by matching it to the closest reference genomes; and a final step looking for a minimum set of strains that best explains the observed reads. We have applied our method, called ORI, on 77 strains of Streptococcus thermophilus. We worked on several genomic distances and obtained a detailed classification of the strains, together with a criterion that allows merging of what we termed 'sibling' strains, only separated by a few mutations. Overall, isolated strains can be safely recognized from MinION data. For mixtures of several non-sibling strains, results depend on strain abundance.

Novel methods of microbiome analysis in the food industry

The study of the food microbiome has gained considerable interest in recent years, mainly due to the wide range of applications that can be derived from the analysis of metagenomes. Among these applications, it is worth mentioning the possibility of using metagenomic analyses to determine food authenticity, to assess the microbiological safety of foods thanks to the detection and tracking of pathogens, antibiotic resistance genes and other undesirable traits, as well to identify the microorganisms responsible for food processing defects. Metataxonomics and metagenomics are currently the gold standard methodologies to explore the full potential of metagenomes in the food industry. However, there are still a number of challenges that must be solved in order to implement these methods routinely in food chain monitoring, and for the regulatory agencies to take them into account in their opinions. These challenges include the difficulties of analysing foods and food-related environments with a low microbial load, the lack of validated bioinformatics pipelines adapted to food microbiomes and the difficulty of assessing the viability of the detected microorganisms. This review summarizes the methods of microbiome analysis that have been used, so far, in foods and food-related environments, with a specific focus on those involving Next-Generation Sequencing technologies.

The oral microbiome: Role of key organisms and complex networks in oral health and disease

States of oral health and disease reflect the compositional and functional capacities of, as well as the interspecies interactions within, the oral microbiota. The oral cavity exists as a highly dynamic microbial environment that harbors many distinct substrata and microenvironments that house diverse microbial communities. Specific to the oral cavity, the nonshedding dental surfaces facilitate the development of highly complex polymicrobial biofilm communities, characterized not only by the distinct microbes comprising them, but cumulatively by their activities. Adding to this complexity, the oral cavity faces near-constant environmental challenges, including those from host diet, salivary flow, masticatory forces, and introduction of exogenous microbes. The composition of the oral microbiome is shaped throughout life by factors including host genetics, maternal transmission, as well as environmental factors, such as dietary habits, oral hygiene practice, medications, and systemic factors. This dynamic ecosystem presents opportunities for oral microbial dysbiosis and the development of dental and periodontal diseases. The application of both in vitro and culture-independent approaches has broadened the mechanistic understandings of complex polymicrobial communities within the oral cavity, as well as the environmental, local, and systemic underpinnings that influence the dynamics of the oral microbiome. Here, we review the present knowledge and current understanding of microbial communities within the oral cavity and the influences and challenges upon this system that encourage homeostasis or provoke microbiome perturbation, and thus contribute to states of oral health or disease.

Metagenomic Analysis of Fecal Archaea, Bacteria, Eukaryota, and Virus in Przewalski's Horses Following Anthelmintic Treatment

Intestinal microbiota is involved in immune response and metabolism of the host. The frequent use of anthelmintic compounds for parasite expulsion causes disturbance to the equine intestinal microbiota. However, most studies were on the effects of such treatment on the intestinal bacterial microbes; none is on the entire microbial community including archaea and eukaryotic and viral community in equine animals. This study is the first to explore the differences of the microbial community composition and structure in Przewalski's horses prior to and following anthelmintic treatment, and to determine the corresponding changes of their functional attributes based on metagenomic sequencing. Results showed that in archaea, the methanogen of Euryarchaeota was the dominant phylum. Under this phylum, anthelmintic treatment increased the Methanobrevibacter genus and decreased the Methanocorpusculum genus and two other dominant archaea species, Methanocorpusculum labreanum and Methanocorpusculum bavaricum. In bacteria, Firmicutes and Bacteroidetes were the dominant phyla. Anthelmintic treatment increased the genera of Clostridium and Eubacterium and decreased those of Bacteroides and Prevotella and dominant bacteria species. These altered genera were associated with immunity and digestion. In eukaryota, anthelmintic treatment also changed the genera related to digestion and substantially decreased the relative abundances of identified species. In virus, anthelmintic treatment increased the genus of unclassified_d__Viruses and decreased those of unclassified_f__Siphoviridae and unclassified_f__Myoviridae. Most of the identified viral species were classified into phage, which were more sensitive to anthelmintic treatment than other viruses. Furthermore, anthelmintic treatment was found to increase the number of pathogens related to some clinical diseases in horses. The COG and KEGG function analysis showed that the intestinal microbiota of Przewalski's horse mainly participated in the carbohydrate and amino acid metabolism. The anthelmintic treatment did not change their overall function; however, it displaced the population of the functional microbes involved in each function or pathway. These results provide a complete view on the changes caused by anthelmintic treatment in the intestinal microbiota of the Przewalski's horses.

Nanopore Sequencing Provides Rapid and Reliable Insight Into Microbial Profiles of Intensive Care Units

Fast and accurate identification of pathogens is an essential task in healthcare settings. Second-generation sequencing platforms such as Illumina have greatly expanded the capacity with which different organisms can be detected in hospital samples, and third-generation nanopore-driven sequencing devices such as Oxford Nanopore's minION have recently emerged as ideal sequencing platforms for routine healthcare surveillance due to their long-read capacity and high portability. Despite its great potential, protocols and analysis pipelines for nanopore sequencing are still being extensively validated. In this work, we assess the ability of nanopore sequencing to provide reliable community profiles based on 16S rRNA sequencing in comparison to traditional Illumina platforms using samples collected from Intensive Care Units of a hospital in Brazil. While our results demonstrate that lower throughputs may be a shortcoming of the method in more complex samples, we show that the use of single-use Flongle flowcells in nanopore sequencing runs can provide insightful information on the community composition in healthcare settings.

Using machine learning approaches for multi-omics data analysis: A review

With the development of modern high-throughput omic measurement platforms, it has become essential for biomedical studies to undertake an integrative (combined) approach to fully utilise these data to gain insights into biological systems. Data from various omics sources such as genetics, proteomics, and metabolomics can be integrated to unravel the intricate working of systems biology using machine learning-based predictive algorithms. Machine learning methods offer novel techniques to integrate and analyse the various omics data enabling the discovery of new biomarkers. These biomarkers have the potential to help in accurate disease prediction, patient stratification and delivery of precision medicine. This review paper explores different integrative machine learning methods which have been used to provide an in-depth understanding of biological systems during normal physiological functioning and in the presence of a disease. It provides insight and recommendations for interdisciplinary professionals who envisage employing machine learning skills in multi-omics studies.

Metaproteomics to Decipher CF Host-Microbiota Interactions: Overview, Challenges and Future Perspectives

Cystic fibrosis (CF) is a hereditary disease caused by mutations in the CF transmembrane conductance regulator (CFTR) gene, triggering dysfunction of the anion channel in several organs including the lung and gut. The main cause of morbidity and mortality is chronic infection. The microbiota is now included among the additional factors that could contribute to the exacerbation of patient symptoms, to treatment outcome, and more generally to the phenotypic variability observed in CF patients. In recent years, various omics tools have started to shed new light on microbial communities associated with CF and host-microbiota interactions. In this context, proteomics targets the key effectors of the responses from organisms, and thus their phenotypes. Recent advances are promising in terms of gaining insights into the CF microbiota and its relation with the host. This review provides an overview of the contributions made by proteomics and metaproteomics to our knowledge of the complex host-microbiota partnership in CF. Considering the strengths and weaknesses of proteomics-based approaches in profiling the microbiota in the context of other diseases, we illustrate their potential and discuss possible strategies to overcome their limitations in monitoring both the respiratory and intestinal microbiota in sample from patients with CF.

Interkingdom Gut Microbiome and Resistome of the Cockroach Blattella germanica

Cockroaches are intriguing animals with two coexisting symbiotic systems, an endosymbiont in the fat body, involved in nitrogen metabolism, and a gut microbiome whose diversity, complexity, role, and developmental dynamics have not been fully elucidated. In this work, we present a metagenomic approach to study Blattella germanica populations not treated, treated with kanamycin, and recovered after treatment, both naturally and by adding feces to the diet, with the aim of better understanding the structure and function of its gut microbiome along the development as well as the characterization of its resistome.IMPORTANCE For the first time, we analyze the interkingdom hindgut microbiome of this species, including bacteria, fungi, archaea, and viruses. Network analysis reveals putative cooperation between core bacteria that could be key for ecosystem equilibrium. We also show how antibiotic treatments alter microbiota diversity and function, while both features are restored after one untreated generation. Combining data from B. germanica treated with three antibiotics, we have characterized this species' resistome. It includes genes involved in resistance to several broad-spectrum antibiotics frequently used in the clinic. The presence of genetic elements involved in DNA mobilization indicates that they can be transferred among microbiota partners. Therefore, cockroaches can be considered reservoirs of antibiotic resistance genes (ARGs) and potential transmission vectors.

The National Ecological Observatory Network's soil metagenomes: assembly and basic analysis

The National Ecological Observatory Network (NEON) annually performs shotgun metagenomic sequencing to sample genes within soils at 47 sites across the United States. NEON serves as a valuable educational resource, thanks to its open data policies and programming tutorials, but there is currently no introductory tutorial for performing analyses with the soil shotgun metagenomic dataset. Here, we describe a workflow for processing raw soil metagenome sequencing reads using the Sunbeam bioinformatics pipeline. The workflow includes cleaning and processing raw reads, taxonomic classification, assembly into contigs, annotation of predicted genes using custom protein databases, and exporting assemblies to the KBase platform for downstream analysis. This workflow is designed to be robust to annual data releases from NEON, and the underlying Snakemake framework can manage complex software dependencies. The workflow presented here aims to increase the accessibility of NEON's shotgun metagenome data, which can provide important clues about soil microbial communities and their ecological roles.

The National Ecological Observatory Network's soil metagenomes: assembly and basic analysis

The largest dataset of soil metagenomes has recently been released by the National Ecological Observatory Network (NEON), which performs annual shotgun sequencing of soils at 47 sites across the United States. NEON serves as a valuable educational resource, thanks to its open data and programming tutorials, but there is currently no introductory tutorial for accessing and analyzing the soil shotgun metagenomic dataset. Here, we describe methods for processing raw soil metagenome sequencing reads using a bioinformatics pipeline tailored to the high complexity and diversity of the soil microbiome. We describe the rationale, necessary resources, and implementation of steps such as cleaning raw reads, taxonomic classification, assembly into contigs or genomes, annotation of predicted genes using custom protein databases, and exporting data for downstream analysis. The workflow presented here aims to increase the accessibility of NEON's shotgun metagenome data, which can provide important clues about soil microbial communities and their ecological roles.

ResistoXplorer: a web-based tool for visual, statistical and exploratory data analysis of resistome data

The study of resistomes using whole metagenomic sequencing enables high-throughput identification of resistance genes in complex microbial communities, such as the human microbiome. Over recent years, sophisticated and diverse pipelines have been established to facilitate raw data processing and annotation. Despite the progress, there are no easy-to-use tools for comprehensive visual, statistical and functional analysis of resistome data. Thus, exploration of the resulting large complex datasets remains a key bottleneck requiring robust computational resources and technical expertise, which creates a significant hurdle for advancements in the field. Here, we introduce ResistoXplorer, a user-friendly tool that integrates recent advancements in statistics and visualization, coupled with extensive functional annotations and phenotype collection, to enable high-throughput analysis of common outputs generated from metagenomic resistome studies. ResistoXplorer contains three modules—the ‘Antimicrobial Resistance Gene Table’ module offers various options for composition profiling, functional profiling and comparative analysis of resistome data; the ‘Integration’ module supports integrative exploratory analysis of resistome and microbiome abundance profiles derived from metagenomic samples; finally, the ‘Antimicrobial Resistance Gene List’ module enables users to intuitively explore the associations between antimicrobial resistance genes and the microbial hosts using network visual analytics to gain biological insights. ResistoXplorer is publicly available at http://www.resistoxplorer.no.

Current State and Future Directions of Genetics and Genomics of Endophytic Fungi for Bioprospecting Efforts

The bioprospecting of secondary metabolites from endophytic fungi received great attention in the 1990s and 2000s, when the controversy around taxol production from Taxus spp. endophytes was at its height. Since then, hundreds of reports have described the isolation and characterization of putative secondary metabolites from endophytic fungi. However, only very few studies also report the genetic basis for these phenotypic observations. With low sequencing cost and fast sample turnaround, genetics- and genomics-based approaches have risen to become comprehensive approaches to study natural products from a wide-range of organisms, especially to elucidate underlying biosynthetic pathways. However, in the field of fungal endophyte biology, elucidation of biosynthetic pathways is still a major challenge. As a relatively poorly investigated group of microorganisms, even in the light of recent efforts to sequence more fungal genomes, such as the 1000 Fungal Genomes Project at the Joint Genome Institute (JGI), the basis for bioprospecting of enzymes and pathways from endophytic fungi is still rather slim. In this review we want to discuss the current approaches and tools used to associate phenotype and genotype to elucidate biosynthetic pathways of secondary metabolites in endophytic fungi through the lens of bioprospecting. This review will point out the reported successes and shortcomings, and discuss future directions in sampling, and genetics and genomics of endophytic fungi. Identifying responsible biosynthetic genes for the numerous secondary metabolites isolated from endophytic fungi opens the opportunity to explore the genetic potential of producer strains to discover novel secondary metabolites and enhance secondary metabolite production by metabolic engineering resulting in novel and more affordable medicines and food additives.