Current opportunities and challenges in microbial metagenome analysis--a bioinformatic perspective

MetaBIDx: a new computational approach to bacteria identification in microbiomes

Objectives: This study introduces MetaBIDx, a computational method designed to enhance species prediction in metagenomic environments. The method addresses the challenge of accurate species identification in complex microbiomes, which is due to the large number of generated reads and the ever-expanding number of bacterial genomes. Bacterial identification is essential for disease diagnosis and tracing outbreaks associated with microbial infections. Methods: MetaBIDx utilizes a modified Bloom filter for efficient indexing of reference genomes and incorporates a novel strategy for reducing false positives by clustering species based on their genomic coverages by identified reads. The approach was evaluated and compared with several well-established tools across various datasets. Precision, recall, and F1-score were used to quantify the accuracy of species prediction. Results: MetaBIDx demonstrated superior performance compared to other tools, especially in terms of precision and F1-score. The application of clustering based on approximate coverages significantly improved precision in species identification, effectively minimizing false positives. We further demonstrated that other methods can also benefit from our approach to removing false positives by clustering species based on approximate coverages. Conclusion: With a novel approach to reducing false positives and the effective use of a modified Bloom filter to index species, MetaBIDx represents an advancement in metagenomic analysis. The findings suggest that the proposed approach could also benefit other metagenomic tools, indicating its potential for broader application in the field. The study lays the groundwork for future improvements in computational efficiency and the expansion of microbial databases.

Cyanobacterial Genomes from a Brackish Coastal Lagoon Reveal Potential for Novel Biogeochemical Functions and Their Evolution

Cyanobacteria are recognised for their pivotal roles in aquatic ecosystems, serving as primary producers and major agents in diazotrophic processes. Currently, the primary focus of cyanobacterial research lies in gaining a more detailed understanding of these well-established ecosystem functions. However, their involvement and impact on other crucial biogeochemical cycles remain understudied. This knowledge gap is partially attributed to the challenges associated with culturing cyanobacteria in controlled laboratory conditions and the limited understanding of their specific growth requirements. This can be circumvented partially by the culture-independent methods which can shed light on the genomic potential of cyanobacterial species and answer more profound questions about the evolution of other key biogeochemical functions. In this study, we assembled 83 cyanobacterial genomes from metagenomic data generated from environmental DNA extracted from a brackish water lagoon (Chilika Lake, India). We taxonomically classified these metagenome-assembled genomes (MAGs) and found that about 92.77% of them are novel genomes at the species level. We then annotated these cyanobacterial MAGs for all the encoded functions using KEGG Orthology. Interestingly, we found two previously unreported functions in Cyanobacteria, namely, DNRA (Dissimilatory Nitrate Reduction to Ammonium) and DMSP (Dimethylsulfoniopropionate) synthesis in multiple MAGs using nirBD and dsyB genes as markers. We validated their presence in several publicly available cyanobacterial isolate genomes. Further, we identified incongruities between the evolutionary patterns of species and the marker genes and elucidated the underlying reasons for these discrepancies. This study expands our overall comprehension of the contribution of cyanobacteria to the biogeochemical cycling in coastal brackish ecosystems.

Effects of plants-associated microbiota on cultivation and quality of Chinese herbal medicines

Microbial resource influences the life activities of medicinal plants from several perspectives. Endophytes, rhizosphere microorganisms, and other environmental microorganisms play essential roles in medicinal plant growth and development, plant yield, and clinical efficacy. The microbiota can influence the biosynthesis of active compounds in medicinal plants by stimulating specific metabolic pathways. They induce host plants to improve their resistance to environmental stresses by accumulating secondary metabolites. Microorganisms can interact with their host plants to produce long-term, targeted selection results and improve their ability to adapt to the environment. Due to the interdependence and interaction between microorganisms and medicinal plants, Chinese herbal medicines (CHMs) quality is closely related to the associated microorganisms. This review summarizes the relationship between medicinal plants and their associated microorganisms, including their species, distribution, life activities, and metabolites. Microorganisms can aid in quality control, improve the efficacy of medicinal plants, and provide markers for identifying the origin and storage time of CHMs. Therefore, a comprehensive understanding of the relationship between microorganisms and medicinal plants will help to control the quality of CHMs from different perspectives.

Metagenomics for Pathogen Detection During a Mass Mortality Event in Songbirds

Mass mortality events in wildlife can be indications of an emerging infectious disease. During the spring and summer of 2021, hundreds of dead passerines were reported across the eastern US. Birds exhibited a range of clinical signs including swollen conjunctiva, ocular discharge, ataxia, and nystagmus. As part of the diagnostic investigation, high-throughput metagenomic next-generation sequencing was performed across three molecular laboratories on samples from affected birds. Many potentially pathogenic microbes were detected, with bacteria forming the largest proportion; however, no singular agent was consistently identified, with many of the detected microbes also found in unaffected (control) birds and thus considered to be subclinical infections. Congruent results across laboratories have helped drive further investigation into alternative causes, including environmental contaminants and nutritional deficiencies. This work highlights the utility of metagenomic approaches in investigations of emerging diseases and provides a framework for future wildlife mortality events.

Metagenomic Analysis of Bacterial Community and Isolation of Representative Strains from Vranjska Banja Hot Spring, Serbia

The hot spring Vranjska Banja is the hottest spring on the Balkan Peninsula with a water temperature of 63-95 °C and a pH value of 7.1, in situ. According to the physicochemical analysis, Vranjska Banja hot spring belongs to the bicarbonated and sulfated hyperthermal waters. The structures of microbial community of this geothermal spring are still largely unexplored. In order to determine and monitor the diversity of microbiota of the Vranjska Banja hot spring, a comprehensive culture-independent metagenomic analysis was conducted in parallel with a culture-dependent approach for the first time. Microbial profiling using amplicon sequencing analysis revealed the presence of phylogenetically novel taxa, ranging from species to phyla. Cultivation-based methods resulted in the isolation of 17 strains belonging to the genera Anoxybacillus, Bacillus, Geobacillus, and Hydrogenophillus. Whole-genome sequencing of five representative strains was then performed. The genomic characterization and OrthoANI analysis revealed that the Vranjska Banja hot spring harbors phylogenetically novel species of the genus Anoxybacillus, proving its uniqueness. Moreover, these isolates contain stress response genes that enable them to survive in the harsh conditions of the hot springs. The results of the in silico analysis show that most of the sequenced strains have the potential to produce thermostable enzymes (proteases, lipases, amylases, phytase, chitinase, and glucanase) and various antimicrobial molecules that can be of great importance for industrial, agricultural, and biotechnological applications. Finally, this study provides a basis for further research and understanding of the metabolic potential of these microorganisms.

Environmental Effects on Bee Microbiota

Anthropogenic activities and increased land use, which include industrialization, agriculture and urbanization, directly affect pollinators by changing habitats and floral availability, and indirectly by influencing their microbial composition and diversity. Bees form vital symbioses with their microbiota, relying on microorganisms to perform physiological functions and aid in immunity. As altered environments and climate threaten bees and their microbiota, characterizing the microbiome and its complex relationships with its host offers insights into understanding bee health. This review summarizes the role of sociality in microbiota establishment, as well as examines if such factors result in increased susceptibility to altered microbiota due to environmental changes. We characterize the role of geographic distribution, temperature, precipitation, floral resources, agriculture, and urbanization on bee microbiota. Bee microbiota are affected by altered surroundings regardless of sociality. Solitary bees that predominantly acquire their microbiota through the environment are particularly sensitive to such effects. However, the microbiota of obligately eusocial bees are also impacted by environmental changes despite typically well conserved and socially inherited microbiota. We provide an overview of the role of microbiota in plant-pollinator relationships and how bee microbiota play a larger role in urban ecology, offering microbial connections between animals, humans, and the environment. Understanding bee microbiota presents opportunities for sustainable land use restoration and aiding in wildlife conservation.

Recent Advances in Metagenomic Approaches, Applications, and Challenge

Advances in metagenomics analysis with the advent of next-generation sequencing have extended our knowledge of microbial communities as compared to conventional techniques providing advanced approach to identify novel and uncultivable microorganisms based on their genetic information derived from a particular environment. Shotgun metagenomics involves investigating the DNA of the entire community without the requirement of PCR amplification. It provides access to study all genes present in the sample. On the other hand, amplicon sequencing targets taxonomically important marker genes, the analysis of which is restricted to previously known DNA sequences. While sequence-based metagenomics is used to analyze DNA sequences directly from the environment without the requirement of library construction and with limited identification of novel genes and products that can be complemented by functional genomics, function-based metagenomics requires fragmentation and cloning of extracted metagenome DNA in a suitable host with subsequent functional screening and sequencing clone for detection of a novel gene. Although advances were made in metagenomics, different challenges arise. This review provides insight into advances in the metagenomic approaches combined with next-generation sequencing, their recent applications highlighting the emerging ones, such as in astrobiology, forensic sciences, and SARS-CoV-2 infection diagnosis, and the challenges associated. This review further discusses the different types of metagenomics and outlines advancements in bioinformatics tools and their significance in the analysis of metagenomic datasets.

AirDNA sampler: An efficient and simple device enabling high-yield, high-quality airborne environment DNA for metagenomic applications

Analyzing temporal and spatial distributions of airborne particles of biological origins is vital for the assessment and monitoring of air quality, especially with regard to public health, environmental ecology, and atmospheric chemistry. However, the analysis is frequently impeded by the low levels of biomass in the air, especially with metagenomic DNA analysis to explore diversity and composition of living organisms and their components in the air. To obtain sufficient amounts of metagenomic DNA from bioaerosols, researchers usually need a long sampling time with an expensive high-volume air sampler. This work shows the utilization of an air sampling device containing an economical, high-volume portable ventilation fan in combination with customized multi-sheet filter holders to effectively obtain high yields of genomic DNA in a relatively short time. The device, named 'AirDNA' sampler, performed better than other commercial air samplers, including MD8 Airport and Coriolis compact air samplers. Using the AirDNA sampler, an average DNA yield of 40.49 ng (12.47-23.24 ng at 95% CI) was obtained in only 1 hour of air sampling with a 0.85 probability of obtaining ≥10 ng of genomic DNA. The genomic DNA obtained by the AirDNA system is of suitable quantity and quality to be further used for amplicon metabarcoding sequencing of 16S, 18S, and cytochrome c oxidase I (COI) regions, indicating that it can be used to detect various prokaryotes and eukaryotes. Our results showed the effectiveness of our AirDNA sampling apparatus with a simple setup and affordable devices to obtain metagenomic DNA for short-term or long-term spatiotemporal analysis. The technique is well suited for monitoring air in built environments, especially monitoring bioaerosols for health purposes and for fine-scale spatiotemporal environmental studies.

To assemble or not to assemble: metagenomic profiling of microbially mediated biogeochemical pathways in complex communities

High-throughput profiling of microbial functional traits involved in various biogeochemical cycling pathways using shotgun metagenomic sequencing has been routinely applied in microbial ecology and environmental science. Multiple bioinformatics data processing approaches are available, including assembly-based (single-sample assembly and multi-sample assembly) and read-based (merged reads and raw data). However, it remains not clear how these different approaches may differ in data analyses and affect result interpretation. In this study, using two typical shotgun metagenome datasets recovered from geographically distant coastal sediments, the performance of different data processing approaches was comparatively investigated from both technical and biological/ecological perspectives. Microbially mediated biogeochemical cycling pathways, including nitrogen cycling, sulfur cycling and B12 biosynthesis, were analyzed. As a result, multi-sample assembly provided the most amount of usable information for targeted functional traits, at a high cost of computational resources and running time. Single-sample assembly and read-based analysis were comparable in obtaining usable information, but the former was much more time- and resource-consuming. Critically, different approaches introduced much stronger variations in microbial profiles than biological differences. However, community-level differences between the two sampling sites could be consistently observed despite the approaches being used. In choosing an appropriate approach, researchers shall balance the trade-offs between multiple factors, including the scientific question, the amount of usable information, computational resources and time cost. This study is expected to provide valuable technical insights and guidelines for the various approaches used for metagenomic data analysis.

LMAS: evaluating metagenomic short de novo assembly methods through defined communities

BACKGROUND

The de novo assembly of raw sequence data is key in metagenomic analysis. It allows recovering draft genomes from a pool of mixed raw reads, yielding longer sequences that offer contextual information and provide a more complete picture of the microbial community.

FINDINGS

To better compare de novo assemblers for metagenomic analysis, LMAS (Last Metagenomic Assembler Standing) was developed as a flexible platform allowing users to evaluate assembler performance given known standard communities. Overall, in our test datasets, k-mer De Bruijn graph assemblers outperformed the alternative approaches but came with a greater computational cost. Furthermore, assemblers branded as metagenomic specific did not consistently outperform other genomic assemblers in metagenomic samples. Some assemblers still in use, such as ABySS, MetaHipmer2, minia, and VelvetOptimiser, perform relatively poorly and should be used with caution when assembling complex samples. Meaningful strain resolution at the single-nucleotide polymorphism level was not achieved, even by the best assemblers tested.

CONCLUSIONS

The choice of a de novo assembler depends on the computational resources available, the replicon of interest, and the major goals of the analysis. No single assembler appeared an ideal choice for short-read metagenomic prokaryote replicon assembly, each showing specific strengths. The choice of metagenomic assembler should be guided by user requirements and characteristics of the sample of interest, and LMAS provides an interactive evaluation platform for this purpose. LMAS is open source, and the workflow and its documentation are available at https://github.com/B-UMMI/LMAS and https://lmas.readthedocs.io/, respectively.

Representing bacteria with unique genomic signatures

Classifying or identifying bacteria in metagenomic samples is an important problem in the analysis of metagenomic data. This task can be computationally expensive since microbial communities usually consist of hundreds to thousands of environmental microbial species. We proposed a new method for representing bacteria in a microbial community using genomic signatures of those bacteria. With respect to the microbial community, the genomic signatures of each bacterium are unique to that bacterium; they do not exist in other bacteria in the community. Further, since the genomic signatures of a bacterium are much smaller than its genome size, the approach allows for a compressed representation of the microbial community. This approach uses a modified Bloom filter to store short k-mers with hash values that are unique to each bacterium. We show that most bacteria in many microbiomes can be represented uniquely using the proposed genomic signatures. This approach paves the way toward new methods for classifying bacteria in metagenomic samples.

Impact of Stagnation on the Diversity of Cyanobacteria in Drinking Water Treatment Plant Sludge

Health-related concerns about cyanobacteria-laden sludge of drinking water treatment plants (DWTPs) have been raised in the past few years. Microscopic taxonomy, shotgun metagenomic sequencing, and microcystin (MC) measurement were applied to study the fate of cyanobacteria and cyanotoxins after controlled sludge storage (stagnation) in the dark in a full-scale drinking water treatment plant within 7 to 38 days. For four out of eight dates, cyanobacterial cell growth was observed by total taxonomic cell counts during sludge stagnation. The highest observed cell growth was 96% after 16 days of stagnation. Cell growth was dominated by potential MC producers such as Microcystis, Aphanocapsa, Chroococcus, and Dolichospermum. Shotgun metagenomic sequencing unveiled that stagnation stress shifts the cyanobacterial communities from the stress-sensitive Nostocales (e.g., Dolichospermum) order towards less compromised orders and potential MC producers such as Chroococcales (e.g., Microcystis) and Synechococcales (e.g., Synechococcus). The relative increase of cyanotoxin producers presents a health challenge when the supernatant of the stored sludge is recycled to the head of the DWTP or discharged into the source. These findings emphasize the importance of a strategy to manage cyanobacteria-laden sludge and suggest practical approaches should be adopted to control health/environmental impacts of cyanobacteria and cyanotoxins in sludge.

Drastic reduction of false positive species in samples of insects by intersecting the default output of two popular metagenomic classifiers

The use of high-throughput sequencing to recover short DNA reads of many species has been widely applied on biodiversity studies, either as amplicon metabarcoding or shotgun metagenomics. These reads are assigned to taxa using classifiers. However, for different reasons, the results often contain many false positives. Here we focus on the reduction of false positive species attributable to the classifiers. We benchmarked two popular classifiers, BLASTn followed by MEGAN6 (BM) and Kraken2 (K2), to analyse shotgun sequenced artificial single-species samples of insects. To reduce the number of misclassified reads, we combined the output of the two classifiers in two different ways: (1) by keeping only the reads that were attributed to the same species by both classifiers (intersection approach); and (2) by keeping the reads assigned to some species by any classifier (union approach). In addition, we applied an analytical detection limit to further reduce the number of false positives species. As expected, both metagenomic classifiers used with default parameters generated an unacceptably high number of misidentified species (tens with BM, hundreds with K2). The false positive species were not necessarily phylogenetically close, as some of them belonged to different orders of insects. The union approach failed to reduce the number of false positives, but the intersection approach got rid of most of them. The addition of an analytic detection limit of 0.001 further reduced the number to ca. 0.5 false positive species per sample. The misidentification of species by most classifiers hampers the confidence of the DNA-based methods for assessing the biodiversity of biological samples. Our approach to alleviate the problem is straightforward and significantly reduced the number of reported false positive species.

Insufficiency in functional genomics studies, data, and applications: A case study of bio-prospecting research in ruminant microbiome

Over the last two decades, biotechnology has advanced at a rapid pace, propelled by the incorporation of bio-products into various aspects of pharmaceuticals, industry, and the environment. These developments have sparked interest in the bioprospecting of microorganisms and their products in a variety of niche environments. Furthermore, the use of omics technologies has greatly aided our analyses of environmental samples by elucidating the microbial ecological framework, biochemical pathways, and bio-products. However, the more often overemphasis on taxonomic identification in most research publications, as well as the data associated with such studies, is detrimental to immediate industrial and commercial applications. This review identifies several factors that contribute to the complexity of sequence data analysis as potential barriers to the pragmatic application of functional genomics, utilizing recent research on ruminants to demonstrate these limitations in the hopes of broadening our horizons and drawing attention to this gap in bioprospecting studies for other niche environments as well. The review also aims to emphasize the importance of routinely incorporating functional genomics into environmental metagenomics analyses in order to improve solutions that drive rapid industrial biocatalysis developments from derived outputs with the aim of achieving potential benefits in energy-use reduction and environmental considerations for current and future applications.

Combating biotic stresses in plants by synthetic microbial communities: Principles, applications, and challenges

Presently, agriculture worldwide is facing the major challenge of feeding the increasing population sustainably. The conventional practices have not only failed to meet the projected needs, but also led to tremendous environmental consequences. Hence, to ensure a food-secure and environmentally sound future, the major thrust is on sustainable alternatives. Due to challenges associated with conventional means of application of biocontrol agents in the management of biotic stresses in agro-ecosystems, significant transformations in this context is needed. The crucial role played by soil microbiomes in efficiently and sustainably managing the agricultural production has unfolded a newer approach of rhizospheric engineering that shows immense promise in mitigating biotic stresses in an eco-friendly manner. The strategy of generating synthetic microbial communities (SynCom), by integrating omics approaches with traditional techniques of enumeration and in-depth analysis of plant-microbe interactions, is encouraging. The review discusses the significance of the rhizospheric microbiome in plant's fitness, and its manipulation for enhancing plant attributes. The focus of the review is to critically analyze the potential tools for the design and utilization of SynCom as a sustainable approach for rhizospheric engineering to ameliorate biotic stresses in plants. Further, based on the synthesis of reports in the area, we have put forth possible solutions to some of the critical issues that impair the large-scale application of SynComs in agriculture.

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.

Microbiota Communities of Healthy and Bacterial Pustule Diseased Soybean

Soybean is an important source of protein and for a wide range of agricultural, food, and industrial applications. Soybean is being affected by Xanthomonas citri pv. glycines, a causal pathogen of bacterial pustule disease, result in a reduction in yield and quality. Diverse microbial communities of plants are involved in various plant stresses is known. Therefore, we designed to investigate the microbial community differentiation depending on the infection of X. citri pv. glycines. The microbial community's abundance, diversity, and similarity showed a difference between infected and non-infected soybean. Microbiota community analysis, excluding X. citri pv. glycines, revealed that Pseudomonas spp. would increase the population of the infected soybean. Results of DESeq analyses suggested that energy metabolism, secondary metabolite, and TCA cycle metabolism were actively diverse in the non-infected soybeans. Additionally, Streptomyces bacillaris S8, an endophyte microbiota member, was nominated as a key microbe in the healthy soybeans. Genome analysis of S. bacillaris S8 presented that salinomycin may be the critical antibacterial metabolite. Our findings on the composition of soybean microbiota communities and the key strain information will contribute to developing biological control strategies against X. citri pv. glycines.

Sponge–Microbial Symbiosis and Marine Extremozymes: Current Issues and Prospects

Marine microorganisms have great potential for producing extremozymes. They enter useful relationships like many other organisms in the marine habitat. Sponge–microbial symbiosis enables both sponges and microorganisms to mutually benefit each other while performing their activities within the ecosystem. Sponges, because of their nature as marine cosmopolitan benthic epifaunas and filter feeders, serve as a host for many extremophilic marine microorganisms. Potential extremozymes from microbial symbionts are largely dependent on their successful relationship. Extremozymes have found relevance in food processing, bioremediation, detergent, and drug production. Species diversity approach, industrial-scale bioremediation, integrative bioremediation software, government and industrial support are considered. The high cost of sampling, limited research outcomes, low species growth in synthetic media, laborious nature of metagenomics projects, difficulty in the development of synthetic medium, limited number of available experts, and technological knowhow are current challenges. The unique properties of marine extremozymes underpin their application in industry and biotechnological processes. There is therefore an urgent need for the development of cost-effective methods with government and industry support.

Recent Advances of Marine Sponge-Associated Microorganisms as a Source of Commercially Viable Natural Products

Many industrially significant compounds have been derived from natural products in the environment. Research efforts so far have contributed to the discovery of beneficial natural products that have improved the quality of life on Earth. As one of the sources of natural products, marine sponges have been progressively recognised as microbial hotspots with reports of the sponges harbouring diverse microbial assemblages, genetic material, and metabolites with multiple industrial applications. Therefore, this paper aims at reviewing the recent literature (primarily published between 2016 and 2022) on the types and functions of natural products synthesised by sponge-associated microorganisms, thereby helping to bridge the gap between research and industrial applications. The metabolites that have been derived from sponge-associated microorganisms, mostly bacteria, fungi, and algae, have shown application prospects especially in medicine, cosmeceutical, environmental protection, and manufacturing industries. Sponge bacteria-derived natural products with medical properties harboured anticancer, antibacterial, antifungal, and antiviral functions. Efforts in re-identifying the origin of known and future sponge-sourced natural products would further clarify the roles and significance of microbes within marine sponges.

A Bacterial Genome and Culture Collection of Gut Microbial in Weanling Piglet

The microbiota hosted in the pig gastrointestinal tract are important to health of this biomedical model. However, the individual species and functional repertoires that make up the pig gut microbiome remain largely undefined. Here we comprehensively investigated the genomes and functions of the piglet gut microbiome using culture-based and metagenomics approaches. A collection included 266 cultured genomes and 482 metagenome-assembled genomes (MAGs) that were clustered to 428 species across 10 phyla was established. Among these clustered species, 333 genomes represent potential new species. Less matches between cultured genomes and MAGs revealed a substantial bias for the acquisition of reference genomes by the two strategies. Glycoside hydrolases was the dominant category of carbohydrate-active enzymes. Four-hundred forty-five secondary metabolite biosynthetic genes were predicted from 292 genomes with bacteriocin being the most. Pan genome analysis of Limosilactobacillus reuteri uncover the biosynthesis of reuterin was strain-specific and the production was experimentally determined. This study provides a comprehensive view of the microbiome composition and the function landscape of the gut of weanling piglets and a valuable bacterial resource for further experimentations. IMPORTANCE The microorganism communities resided in mammalian gastrointestinal tract impacted the health and disease of the host. Our study complements metagenomic analysis with culture-based approach to establish a bacteria and genome collection and comprehensively investigate the microbiome composition and function of the gut of weanling piglets. We provide a valuable resource for further study of gut microbiota of weanling piglet and development of probiotics for prevention of disease.

Water and soil contaminated by arsenic: the use of microorganisms and plants in bioremediation

Owing to their roles in the arsenic (As) biogeochemical cycle, microorganisms and plants offer significant potential for developing innovative biotechnological applications able to remediate As pollutions. This possible use in bioremediation processes and phytomanagement is based on their ability to catalyse various biotransformation reactions leading to, e.g. the precipitation, dissolution, and sequestration of As, stabilisation in the root zone and shoot As removal. On the one hand, genomic studies of microorganisms and their communities are useful in understanding their metabolic activities and their interaction with As. On the other hand, our knowledge of molecular mechanisms and fate of As in plants has been improved by laboratory and field experiments. Such studies pave new avenues for developing environmentally friendly bioprocessing options targeting As, which worldwide represents a major risk to many ecosystems and human health.

Long-read metagenomic sequencing reveals shifts in associations of antibiotic resistance genes with mobile genetic elements from sewage to activated sludge

BACKGROUND

There is concern that the microbially rich activated sludge environment of wastewater treatment plants (WWTPs) may contribute to the dissemination of antibiotic resistance genes (ARGs). We applied long-read (nanopore) sequencing to profile ARGs and their neighboring genes to illuminate their fate in the activated sludge treatment by comparing their abundance, genetic locations, mobility potential, and bacterial hosts within activated sludge relative to those in influent sewage across five WWTPs from three continents.

RESULTS

The abundances (gene copies per Gb of reads, aka gc/Gb) of all ARGs and those carried by putative pathogens decreased 75-90% from influent sewage (192-605 gc/Gb) to activated sludge (31-62 gc/Gb) at all five WWTPs. Long reads enabled quantification of the percent abundance of ARGs with mobility potential (i.e., located on plasmids or co-located with other mobile genetic elements (MGEs)). The abundance of plasmid-associated ARGs decreased at four of five WWTPs (from 40-73 to 31-68%), and ARGs co-located with transposable, integrative, and conjugative element hallmark genes showed similar trends. Most ARG-associated elements decreased 0.35-13.52% while integrative and transposable elements displayed slight increases at two WWTPs (1.4-2.4%). While resistome and taxonomic compositions both shifted significantly, host phyla for chromosomal ARG classes remained relatively consistent, indicating vertical gene transfer via active biomass growth in activated sludge as the key pathway of chromosomal ARG dissemination.

CONCLUSIONS

Overall, our results suggest that the activated sludge process acted as a barrier against the proliferation of most ARGs, while those that persisted or increased warrant further attention. Video abstract.

Reconstruction of Archaeal Genomes from Short-Read Metagenomes

As the majority of biological diversity remains unexplored and uncultured, investigating it requires culture-independent approaches. Archaea in particular suffer from a multitude of issues that make their culturing problematic, from them being frequently members of the rare biosphere, to low growth rates, to them thriving under very specific and often extreme environmental and community conditions that are difficult to replicate. OMICs techniques are state of the art approaches that allow direct high-throughput investigations of environmental samples at all levels from nucleic acids to proteins, lipids, and secondary metabolites. Metagenomics, as the foundation for other OMICs techniques, facilitates the identification and functional characterization of the microbial community members and can be combined with other methods to provide insights into the microbial activities, both on the RNA and protein levels. In this chapter, we provide a step-by-step workflow for the recovery of archaeal genomes from metagenomes, starting from raw short-read sequences. This workflow can be applied to recover bacterial genomes as well.

Applications of Metagenomics for Unrevealing the Extended Horizons of Microbiota Prevalence from Soil to Human Health

Phylogenetic analysis of different ecosystems has shown that the number of microbial communities in a single sample exceeds their cultured counterparts. Microbes have been found throughout nature and can thrive in adverse conditions. Besides inhabiting diverse environments, they also play a key role in the maintenance of the ecosystem. Most of these microbes are either unculturable or difficult to culture with conventional culturing methods. Metagenomics is an emerging field of science that has been in the light for a decade and offers a potential way to assess microbial diversity. The development of metagenomics opens new ways to study genetic material directly from the environmental samples. DNA sequencing and synthesis technologies are making it possible to read and write entire genomes. The huge amount of data obtained from genome sequencing inevitably requires bioinformatics tools to handle and further process them for analysis. Advances in DNA sequencing and high-performance computing have brought about exemplar improvement in metagenomics, allowing in-depth study of the largely unexplored frontier of microbial life. This culture-independent method provides extensive information regarding the structure, composition, and function of the diverse assemblages of the environmental microbes. The current review presents an overview of the technical aspects of metagenomics along with its diverse applications.

Trends in Free-access Genomic Data Accelerate Advances in Cyanobacteria Taxonomy

Free access databases of DNA sequences containing microbial genetic information have changed the way scientists look at the microbial world. Currently, the NCBI database includes about 516 distinct search results for Cyanobacterial genomes distributed in a taxonomy based on a polyphasic approach. While their classification and taxonomic relationships are widely used as is, recent proposals to alter their grouping include further exploring the relationship between Cyanobacteria and Melainabacteria. Nowadays, most cyanobacteria still are named under the Botanical Code; however, there is a proposal made by the Genome Taxonomy Database (GTDB) to harmonize cyanobacteria nomenclature with the other bacteria, an initiative to standardize microbial taxonomy based on genome phylogeny, in order to contribute to an overall better phylogenetic resolution of microbiota. Furthermore, the assembly level of the genomes and their geographical origin demonstrates some trends of cyanobacteria genomics on the scientific community, such as low availability of complete genomes and underexplored sampling locations. By describing how available cyanobacterial genomes from free-access databases fit within different taxonomic classifications, this mini-review provides a holistic view of the current knowledge of cyanobacteria and indicates some steps towards improving our efforts to create a more cohesive and inclusive classifying system, which can be greatly improved by using large-scale sequencing and metagenomic techniques.

Seasonal and annual changes in the microbial communities of Ofunato Bay, Japan, based on metagenomics

Five years of datasets from 2015 to 2019 of whole genome shotgun sequencing for cells trapped on 0.2-µm filters of seawater collected monthly from Ofunato Bay, an enclosed bay in Japan, were analysed, which included the 2015 data that we had reported previously. Nucleotide sequences were determined for extracted DNA from three locations for both the upper (1 m) and deeper (8 or 10 m) depths. The biotic communities analysed at the domain level comprised bacteria, eukaryotes, archaea and viruses. The relative abundance of bacteria was over 60% in most months for the five years. The relative abundance of the SAR86 cluster was highest in the bacterial group, followed by Candidatus Pelagibacter and Planktomarina. The relative abundance of Ca. Pelagibacter showed no relationship with environmental factors, and those of SAR86 and Planktomarina showed positive correlations with salinity and dissolved oxygen, respectively. The bacterial community diversity showed seasonal changes, with high diversity around September and low diversity around January for all five years. Nonmetric multidimensional scaling analysis also revealed that the bacterial communities in the bay were grouped in a season-dependent manner and linked with environmental variables such as seawater temperature, salinity and dissolved oxygen.

Microbial Community Dysbiosis and Functional Gene Content Changes in Apple Flowers due to Fire Blight

Despite the plant microbiota plays an important role in plant health, little is known about the potential interactions of the flower microbiota with pathogens. In this study, we investigated the microbial community of apple blossoms when infected with Erwinia amylovora. The long-read sequencing technology, which significantly increased the genome sequence resolution, thus enabling the characterization of fire blight-induced changes in the flower microbial community. Each sample showed a unique microbial community at the species level. Pantoea agglomerans and P. allii were the most predominant bacteria in healthy flowers, whereas E. amylovora comprised more than 90% of the microbial population in diseased flowers. Furthermore, gene function analysis revealed that glucose and xylose metabolism were enriched in diseased flowers. Overall, our results showed that the microbiome of apple blossoms is rich in specific bacteria, and the nutritional composition of flowers is important for the incidence and spread of bacterial disease.

Metagenomic-based Approach for the Analysis of Yeast Diversity Associated with Amylase Production in Lai (Durio kutejensis)

This study reported the application of a next generation sequencing (NGS) analysis of yeast diversity in native Indonesian fruit, Durio kutejensis, collected from Borneo, Central Kalimantan. The analysis was designed to observe the microbial consortium associated with solid state fermentation (SSF) for amylase production. Together with the additional data from culture-dependent analysis, we observed the morphological features, molecular characteristics, and amylase concentration produced by each isolate. We performed Solid State Fermentation (SSF) for amylase production and the enzyme activity was then determined using UV-Vis spectrophotometer at 540 nm. Result obtained from metagenomic approach consist of 4 group that fungal species included in the Ascomycota identified as Botryosphaeria dothidea (1.35%), Lasiodiplodia crassispora (17.62%), Aureobasidium pullulans (55.02%), Paraphoma chrysanthemicola (11.38%), Preussia funiculate (1.90%), Sporormiella intermedia (0.82%), Myrothecium gramineum (1.35%), Fusarium oxysporum (6.24%), Fusarium proliferatum (3.25%) and Phialemoniopsis curvata (1.08%). The results of isolation using culturable medium in the form of YMA obtained 40 yeast isolates. A total of 40 representative isolates from durian fruit were screened, two positive amylase isolates based on clear zones formed were DU 4.2 (Candida sorboxylosa) and DU4.22 (Cyberlindnera fabianii) isolates with amylolytic index of DU 4.2 isolates at 0.24 and DU 4.22 at 0.72 with an incubation time of 48 h. The highest amylase enzyme activity was found in isolate DU 4.2 of 31.21 U / mL.

Expanded catalog of microbial genes and metagenome-assembled genomes from the pig gut microbiome

Gut microbiota plays an important role in pig health and production. Still, availability of sequenced genomes and functional information for most pig gut microbes remains limited. Here we perform a landscape survey of the swine gut microbiome, spanning extensive sample sources by deep metagenomic sequencing resulting in an expanded gene catalog named pig integrated gene catalog (PIGC), containing 17,237,052 complete genes clustered at 90% protein identity from 787 gut metagenomes, of which 28% are unknown proteins. Using binning analysis, 6339 metagenome-assembled genomes (MAGs) were obtained, which were clustered to 2673 species-level genome bins (SGBs), among which 86% (2309) SGBs are unknown based on current databases. Using the present gene catalog and MAGs, we identified several strain-level differences between the gut microbiome of wild boars and commercial Duroc pigs. PIGC and MAGs provide expanded resources for swine gut microbiome-related research.

Evolution of Microbial Genomics: Conceptual Shifts over a Quarter Century

Prokaryote genomics started in earnest in 1995, with the complete sequences of two small bacterial genomes, those of Haemophilus influenzae and Mycoplasma genitalium. During the next quarter century, the prokaryote genome database has been growing exponentially, with no saturation in sight. For most of these 25 years, genome sequencing remained limited to cultivable microbes. Together with next-generation sequencing methods, advances in metagenomics and single-cell genomics have lifted this limitation, providing for an increasingly unbiased characterization of the global prokaryote diversity. Advances in computational genomics followed the progress of genome sequencing, even if occasionally lagging behind. Several major new branches of bacteria and archaea were discovered, including Asgard archaea, the apparent closest relatives of eukaryotes and expansive groups of bacteria and archaea with small genomes thought to be symbionts of other prokaryotes. Comparative analysis of numerous prokaryote genomes spanning a wide range of evolutionary distances changed the conceptual foundations of microbiology, supplanting the notion of species genomes with fixed gene sets with that of dynamic pangenomes and the notion of a single Tree of Life (ToL) with a statistical tree-like trend among individual gene trees. Strides were also made towards a theory and quantitative laws of prokaryote genome evolution.

Can Cyanobacterial Diversity in the Source Predict the Diversity in Sludge and the Risk of Toxin Release in a Drinking Water Treatment Plant?

Conventional processes (coagulation, flocculation, sedimentation, and filtration) are widely used in drinking water treatment plants and are considered a good treatment strategy to eliminate cyanobacterial cells and cell-bound cyanotoxins. The diversity of cyanobacteria was investigated using taxonomic cell counts and shotgun metagenomics over two seasons in a drinking water treatment plant before, during, and after the bloom. Changes in the community structure over time at the phylum, genus, and species levels were monitored in samples retrieved from raw water (RW), sludge in the holding tank (ST), and sludge supernatant (SST). Aphanothece clathrata brevis, Microcystis aeruginosa, Dolichospermum spiroides , and Chroococcus minimus were predominant species detected in RW by taxonomic cell counts. Shotgun metagenomics revealed that Proteobacteria was the predominant phylum in RW before and after the cyanobacterial bloom. Taxonomic cell counts and shotgun metagenomic showed that the Dolichospermum bloom occurred inside the plant. Cyanobacteria and Bacteroidetes were the major bacterial phyla during the bloom. Shotgun metagenomics also showed that Synechococcus, Microcystis , and Dolichospermum were the predominant detected cyanobacterial genera in the samples. Conventional treatment removed more than 92% of cyanobacterial cells but led to cell accumulation in the sludge up to 31 times more than in the RW influx. Coagulation/sedimentation selectively removed more than 96% of Microcystis and Dolichospermum. Cyanobacterial community in the sludge varied from raw water to sludge during sludge storage (1-13 days). This variation was due to the selective removal of coagulation/sedimentation as well as the accumulation of captured cells over the period of storage time. However, the prediction of the cyanobacterial community composition in the SST remained a challenge. Among nutrient parameters, orthophosphate availability was related to community profile in RW samples, whereas communities in ST were influenced by total nitrogen, Kjeldahl nitrogen (N- Kjeldahl), total and particulate phosphorous, and total organic carbon (TOC). No trend was observed on the impact of nutrients on SST communities. This study profiled new health-related, environmental, and technical challenges for the production of drinking water due to the complex fate of cyanobacteria in cyanobacteria-laden sludge and supernatant.

Detection of Pathogenic Microbe Composition Using Next-Generation Sequencing Data

Next-generation sequencing (NGS) technologies have provided great opportunities to analyze pathogenic microbes with high-resolution data. The main goal is to accurately detect microbial composition and abundances in a sample. However, high similarity among sequences from different species and the existence of sequencing errors pose various challenges. Numerous methods have been developed for quantifying microbial composition and abundance, but they are not versatile enough for the analysis of samples with mixtures of noise. In this paper, we propose a new computational method, PGMicroD, for the detection of pathogenic microbial composition in a sample using NGS data. The method first filters the potentially mistakenly mapped reads and extracts multiple species-related features from the sequencing reads of 16S rRNA. Then it trains an Support Vector Machine classifier to predict the microbial composition. Finally, it groups all multiple-mapped sequencing reads into the references of the predicted species to estimate the abundance for each kind of species. The performance of PGMicroD is evaluated based on both simulation and real sequencing data and is compared with several existing methods. The results demonstrate that our proposed method achieves superior performance. The software package of PGMicroD is available at https://github.com/BDanalysis/PGMicroD.

Metagenomic Association Analysis of Gut Symbiont Limosilactobacillus reuteri Without Host-Specific Genome Isolation

Limosilactobacillus reuteri is a model symbiont that colonizes the guts of vertebrates in studies on host adaptation of the gut symbiont. Previous studies have investigated host-specific phylogenetic and functional properties by isolating the genomic sequence. This dependency on genome isolation is a significant bottleneck. Here, we propose a method to study the association between L. reuteri and its hosts directly from metagenomic reads without strain isolation using pan-genomes. We characterized the host-specificity of L. reuteri in metagenomic samples, not only in previously studied organisms (mice and pigs) but also in dogs. For each sample, two types of profiles were generated: (1) genome-based strain type abundance profiles and (2) gene composition profiles. Our profiles showed host-association of L. reuteri in both phylogenetic and functional aspects without depending on host-specific genome isolation. We observed not only the presence of host-specific lineages, but also the dominant lineages associated with the different hosts. Furthermore, we showed that metagenome-assembled genomes provide detailed insights into the host-specificity of L. reuteri. We inferred evolutionary trajectories of host-associative L. reuteri strains in the metagenomic samples by placing the metagenome-assembled genomes into a phylogenetic tree and identified novel host-specific genes that were unannotated in existing pan-genome databases. Our pan-genomic approach reduces the need for time-consuming and expensive host-specific genome isolation, while producing consistent results with previous host-association findings in mice and pigs. Additionally, we predicted associations that have not yet been studied in dogs.

Exploring the Lignin Catabolism Potential of Soil-Derived Lignocellulolytic Microbial Consortia by a Gene-Centric Metagenomic Approach

An exploration of the ligninolytic potential of lignocellulolytic microbial consortia can improve our understanding of the eco-enzymology of lignin conversion in nature. In this study, we aimed to detect enriched lignin-transforming enzymes on metagenomes from three soil-derived microbial consortia that were cultivated on "pre-digested" plant biomass (wheat straw, WS1-M; switchgrass, SG-M; and corn stover, CS-M). Of 60 selected enzyme-encoding genes putatively involved in lignin catabolism, 20 genes were significantly abundant in WS1-M, CS-M, and/or SG-M consortia compared with the initial forest soil inoculum metagenome (FS1). These genes could be involved in lignin oxidation (e.g., superoxide dismutases), oxidative stress responses (e.g., catalase/peroxidases), generation of protocatechuate (e.g., vanAB genes), catabolism of gentisate, catechol and 3-phenylpropionic acid (e.g., gentisate 1,2-dioxygenases, muconate cycloisomerases, and hcaAB genes), the beta-ketoadipate pathway (e.g., pcaIJ genes), and tolerance to lignocellulose-derived inhibitors (e.g., thymidylate synthases). The taxonomic affiliation of 22 selected lignin-transforming enzymes from WS1-M and CS-M consortia metagenomes revealed that Pseudomonadaceae, Alcaligenaceae, Sphingomonadaceae, Caulobacteraceae, Comamonadaceae, and Xanthomonadaceae are the key bacterial families in the catabolism of lignin. A predictive "model" was sketched out, where each microbial population has the potential to metabolize an array of aromatic compounds through different pathways, suggesting that lignin catabolism can follow a "task division" strategy. Here, we have established an association between functions and taxonomy, allowing a better understanding of lignin transformations in soil-derived lignocellulolytic microbial consortia, and pinpointing some bacterial taxa and catabolic genes as ligninolytic trait-markers.

Computational Methods for Strain-Level Microbial Detection in Colony and Metagenome Sequencing Data

Metagenomic sequencing is a powerful tool for examining the diversity and complexity of microbial communities. Most widely used tools for taxonomic profiling of metagenomic sequence data allow for a species-level overview of the composition. However, individual strains within a species can differ greatly in key genotypic and phenotypic characteristics, such as drug resistance, virulence and growth rate. Therefore, the ability to resolve microbial communities down to the level of individual strains within a species is critical to interpreting metagenomic data for clinical and environmental applications, where identifying a particular strain, or tracking a particular strain across a set of samples, can help aid in clinical diagnosis and treatment, or in characterizing yet unstudied strains across novel environmental locations. Recently published approaches have begun to tackle the problem of resolving strains within a particular species in metagenomic samples. In this review, we present an overview of these new algorithms and their uses, including methods based on assembly reconstruction and methods operating with or without a reference database. While existing metagenomic analysis methods show reasonable performance at the species and higher taxonomic levels, identifying closely related strains within a species presents a bigger challenge, due to the diversity of databases, genetic relatedness, and goals when conducting these analyses. Selection of which metagenomic tool to employ for a specific application should be performed on a case-by case basis as these tools have strengths and weaknesses that affect their performance on specific tasks. A comprehensive benchmark across different use case scenarios is vital to validate performance of these tools on microbial samples. Because strain-level metagenomic analysis is still in its infancy, development of more fine-grained, high-resolution algorithms will continue to be in demand for the future.

DNA barcoding of macrofauna act as a tool for assessing marine ecosystem

Nowadays, marine ecosystem monitoring and assessment are increasingly depending on variety of molecular tools. With these background, DNA barcoding play a key role in species identification with increasing speed and accuracy, and although the suitability for developing genetic tools like genomic AMBI (gAMBI). Presently we have submitted 13 benthic polychaete species using mtCOI to GenBank. Of these, nine species were newly submitted, and hence they act as a benchmark and reference organism for identifying respective polychaete species worldwide in the near future. Based on that, our study results tend to be helpful for motivating among the researcher in order to implementing the genomic AMBI (gAMBI).

Optimization of Wastewater Phosphorus Removal in Winter Temperatures Using an Anaerobic–Critical Aerobic Strategy in a Pilot-Scale Sequencing Batch Reactor

Biological phosphorus removal using an anaerobic–aerobic sequencing batch reactor (SBR) in a low temperature can be difficult to remove, and aeration always accounts for nearly half of the total electricity costs at many wastewater treatment plants. In this study, a pilot-scale anaerobic–critical aerobic SBR (A–CA SBR) was developed for synthetic domestic wastewater. More importantly, the phase, whose concentration of diffused oxygen was controlled at 1.0–1.5 mg/L, was defined as a critical aerobic phase, which reduced expenses during the operation. To be specific, half of the ammonia was removed within 10 days and no NO3−–N was accumulated during the process. From the SEM and metagenome analysis, Rhodocyclus, Zooglea, Dechloromonas, and Simplicispira had the ability to remove phosphorus and NO3−–N simultaneously, which proved the existence of a potential double-layer sludge structure under an A–CA operational condition. All of the results disclose that the pilot-scale A–CA SBR is a reliable manipulation strategy for phosphorus removal under low temperatures, which can hopefully apply to practical wastewater remediation.

Signature of Gut Microbiome by Conventional and Advanced Analysis Techniques: Advantages and Disadvantages

Gut microbiota is considered as a human organ with its own specific functions and complexity. Development of novel techniques such as shut gun sequencing, metagenomics, and next-generation sequencing (NGS) has enabled bypassing the traditional culturedependent bias and has significantly expanded our understanding of the composition, diversity, and roles of the gut microbiota in human health and diseases. Although amplicon sequencing characterizes the taxonomic composition of the gut microbiome, it is impossible to cover the direct evidence of the microbial biological functions related to the gut microbial community. Hence, the critical next step for gut microbiome studies is shifting from gene/ genome-centric analysis to mechanism-centric techniques by integrating omics data with experimental results. Realizing gut microbial diversity and their bioactive metabolites function will provide insight into the clinical application of gut microbiota in diagnoses and treatments of several diseases. In this review, we focused on explaining the conventional and advanced microbiome analysis techniques regarding gut microbiota investigation with considering the advantages and disadvantages of the platforms.

De novo diploid genome assembly for genome-wide structural variant detection

Detection of structural variants (SVs) on the basis of read alignment to a reference genome remains a difficult problem. De novo assembly, traditionally used to generate reference genomes, offers an alternative for SV detection. However, it has not been applied broadly to human genomes because of fundamental limitations of short-fragment approaches and high cost of long-read technologies. We here show that 10× linked-read sequencing supports accurate SV detection. We examined variants in six de novo 10× assemblies with diverse experimental parameters from two commonly used human cell lines: NA12878 and NA24385. The assemblies are effective for detecting mid-size SVs, which were discovered by simple pairwise alignment of the assemblies’ contigs to the reference (hg38). Our study also shows that the base-pair level SV breakpoint accuracy is high, with a majority of SVs having precisely correct sizes and breakpoints. Setting the ancestral state of SV loci by comparing to ape orthologs allows inference of the actual molecular mechanism (insertion or deletion) causing the mutation. In about half of cases, the mechanism is the opposite of the reference-based call. We uncover 214 SVs that may have been maintained as polymorphisms in the human lineage since before our divergence from chimp. Overall, we show that de novo assembly of 10× linked-read data can achieve cost-effective SV detection for personal genomes.

Bioinformatics for Marine Products: An Overview of Resources, Bottlenecks, and Perspectives

The sea represents a major source of biodiversity. It exhibits many different ecosystems in a huge variety of environmental conditions where marine organisms have evolved with extensive diversification of structures and functions, making the marine environment a treasure trove of molecules with potential for biotechnological applications and innovation in many different areas. Rapid progress of the omics sciences has revealed novel opportunities to advance the knowledge of biological systems, paving the way for an unprecedented revolution in the field and expanding marine research from model organisms to an increasing number of marine species. Multi-level approaches based on molecular investigations at genomic, metagenomic, transcriptomic, metatranscriptomic, proteomic, and metabolomic levels are essential to discover marine resources and further explore key molecular processes involved in their production and action. As a consequence, omics approaches, accompanied by the associated bioinformatic resources and computational tools for molecular analyses and modeling, are boosting the rapid advancement of biotechnologies. In this review, we provide an overview of the most relevant bioinformatic resources and major approaches, highlighting perspectives and bottlenecks for an appropriate exploitation of these opportunities for biotechnology applications from marine resources.

Identification of discriminatory antibiotic resistance genes among environmental resistomes using extremely randomized tree algorithm

BACKGROUND

The interconnectivities of built and natural environments can serve as conduits for the proliferation and dissemination of antibiotic resistance genes (ARGs). Several studies have compared the broad spectrum of ARGs (i.e., "resistomes") in various environmental compartments, but there is a need to identify unique ARG occurrence patterns (i.e., "discriminatory ARGs"), characteristic of each environment. Such an approach will help to identify factors influencing ARG proliferation, facilitate development of relative comparisons of the ARGs distinguishing various environments, and help pave the way towards ranking environments based on their likelihood of contributing to the spread of clinically relevant antibiotic resistance. Here we formulate and demonstrate an approach using an extremely randomized tree (ERT) algorithm combined with a Bayesian optimization technique to capture ARG variability in environmental samples and identify the discriminatory ARGs. The potential of ERT for identifying discriminatory ARGs was first evaluated using in silico metagenomic datasets (simulated metagenomic Illumina sequencing data) with known variability. The application of ERT was then demonstrated through analyses using publicly available and in-house metagenomic datasets associated with (1) different aquatic habitats (e.g., river, wastewater influent, hospital effluent, and dairy farm effluent) to compare resistomes between distinct environments and (2) different river samples (i.e., Amazon, Kalamas, and Cam Rivers) to compare resistome characteristics of similar environments.

RESULTS

The approach was found to readily identify discriminatory ARGs in the in silico datasets. Also, it was not found to be biased towards ARGs with high relative abundance, which is a common limitation of feature projection methods, and instead only captured those ARGs that elicited significant profiles. Analyses of publicly available metagenomic datasets further demonstrated that the ERT approach can effectively differentiate real-world environmental samples and identify discriminatory ARGs based on pre-defined categorizing schemes.

CONCLUSIONS

Here a new methodology was formulated to characterize and compare variances in ARG profiles between metagenomic data sets derived from similar/dissimilar environments. Specifically, identification of discriminatory ARGs among samples representing various environments can be identified based on factors of interest. The methodology could prove to be a particularly useful tool for ARG surveillance and the assessment of the effectiveness of strategies for mitigating the spread of antibiotic resistance. The python package is hosted in the Git repository: https://github.com/gaarangoa/ExtrARG.

Lack of long-term acclimation in Antarctic encrusting species suggests vulnerability to warming

Marine encrusting communities play vital roles in benthic ecosystems and have major economic implications with regards to biofouling. However, their ability to persist under projected warming scenarios remains poorly understood and is difficult to study under realistic conditions. Here, using heated settlement panel technologies, we show that after 18 months Antarctic encrusting communities do not acclimate to either +1 °C or +2 °C above ambient temperatures. There is significant up-regulation of the cellular stress response in warmed animals, their upper lethal temperatures decline with increasing ambient temperature and population genetic analyses show little evidence of differential survival of genotypes with treatment. By contrast, biofilm bacterial communities show no significant differences in community structure with temperature. Thus, metazoan and bacterial responses differ dramatically, suggesting that ecosystem responses to future climate change are likely to be far more complex than previously anticipated.

Genetic adaptation and physiological acclimation can potentially buffer species against climate change. Here, the authors perform a long-term warming experiment of Antarctic encrusting communities and show that focal animal species failed to acclimate and lacked genetic variation in tolerance to warming.

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.

CO2 conversion to methane and biomass in obligate methylotrophic methanogens in marine sediments

Methyl substrates are important compounds for methanogenesis in marine sediments but diversity and carbon utilization by methylotrophic methanogenic archaea have not been clarified. Here, we demonstrate that RNA-stable isotope probing (SIP) requires ¹³C-labeled bicarbonate as co-substrate for identification of methylotrophic methanogens in sediment samples of the Helgoland mud area, North Sea. Using lipid-SIP, we found that methylotrophic methanogens incorporate 60–86% of dissolved inorganic carbon (DIC) into lipids, and thus considerably more than what can be predicted from known metabolic pathways (~40% contribution). In slurry experiments amended with the marine methylotroph Methanococcoides methylutens, up to 12% of methane was produced from CO₂, indicating that CO₂-dependent methanogenesis is an alternative methanogenic pathway and suggesting that obligate methylotrophic methanogens grow in fact mixotrophically on methyl compounds and DIC. Although methane formation from methanol is the primary pathway of methanogenesis, the observed high DIC incorporation into lipids is likely linked to CO₂-dependent methanogenesis, which was triggered when methane production rates were low. Since methylotrophic methanogenesis rates are much lower in marine sediments than under optimal conditions in pure culture, CO₂ conversion to methane is an important but previously overlooked methanogenic process in sediments for methylotrophic methanogens.

MGS-Fast: Metagenomic shotgun data fast annotation using microbial gene catalogs

BACKGROUND

Current methods used for annotating metagenomics shotgun sequencing (MGS) data rely on a computationally intensive and low-stringency approach of mapping each read to a generic database of proteins or reference microbial genomes.

RESULTS

We developed MGS-Fast, an analysis approach for shotgun whole-genome metagenomic data utilizing Bowtie2 DNA-DNA alignment of reads that is an alternative to using the integrated catalog of reference genes database of well-annotated genes compiled from human microbiome data. This method is rapid and provides high-stringency matches (>90% DNA sequence identity) of the metagenomics reads to genes with annotated functions. We demonstrate the use of this method with data from a study of liver disease and synthetic reads, and Human Microbiome Project shotgun data, to detect differentially abundant Kyoto Encyclopedia of Genes and Genomes gene functions in these experiments. This rapid annotation method is freely available as a Galaxy workflow within a Docker image.

CONCLUSIONS

MGS-Fast can confidently transfer functional annotations from gene databases to metagenomic reads, with speed and accuracy.

Multiple Data Analyses and Statistical Approaches for Analyzing Data from Metagenomic Studies and Clinical Trials

Metagenomics, also known as environmental genomics, is the study of the genomic content of a sample of organisms (microbes) obtained from a common habitat. Metagenomics and other "omics" disciplines have captured the attention of researchers for several decades. The effect of microbes in our body is a relevant concern for health studies. There are plenty of studies using metagenomics which examine microorganisms that inhabit niches in the human body, sometimes causing disease, and are often correlated with multiple treatment conditions. No matter from which environment it comes, the analyses are often aimed at determining either the presence or absence of specific species of interest in a given metagenome or comparing the biological diversity and the functional activity of a wider range of microorganisms within their communities. The importance increases for comparison within different environments such as multiple patients with different conditions, multiple drugs, and multiple time points of same treatment or same patient. Thus, no matter how many hypotheses we have, we need a good understanding of genomics, bioinformatics, and statistics to work together to analyze and interpret these datasets in a meaningful way. This chapter provides an overview of different data analyses and statistical approaches (with example scenarios) to analyze metagenomics samples from different medical projects or clinical trials.

Understanding the microbial basis of body odor in pre-pubescent children and teenagers

BACKGROUND

Even though human sweat is odorless, bacterial growth and decomposition of specific odor precursors in it is believed to give rise to body odor in humans. While mechanisms of odor generation have been widely studied in adults, little is known for teenagers and pre-pubescent children who have distinct sweat composition from immature apocrine and sebaceous glands, but are arguably more susceptible to the social and psychological impact of malodor.

RESULTS

We integrated information from whole microbiome analysis of multiple skin sites (underarm, neck, and head) and multiple time points (1 h and 8 h after bath), analyzing 180 samples in total to perform the largest metagenome-wide association study to date on malodor. Significant positive correlations were observed between odor intensity and the relative abundance of Staphylococcus hominis, Staphylococcus epidermidis, and Cutibacterium avidum, as well as negative correlation with Acinetobacter schindleri and Cutibacterium species. Metabolic pathway analysis highlighted the association of isovaleric and acetic acid production (sour odor) from enriched S. epidermidis (teen underarm) and S. hominis (child neck) enzymes and sulfur production from Staphylococcus species (teen underarm) with odor intensity, in good agreement with observed odor characteristics in pre-pubescent children and teenagers. Experiments with cultures on human and artificial sweat confirmed the ability of S. hominis and S. epidermidis to independently produce malodor with distinct odor characteristics.

CONCLUSIONS

These results showcase the power of skin metagenomics to study host-microbial co-metabolic interactions, identifying distinct pathways for odor generation from sweat in pre-pubescent children and teenagers and highlighting key enzymatic targets for intervention.

An assessment of the microbial community in an urban fringing tidal marsh with an emphasis on petroleum hydrocarbon degradative genes

Small fringing marshes are ecologically important habitats often impacted by petroleum. We characterized the phylogenetic structure (16S rRNA) and petroleum hydrocarbon degrading alkane hydroxylase genes (alkB and CYP 153A1) in a sediment microbial community from a New Hampshire fringing marsh, using alkane-exposed dilution cultures to enrich for petroleum degrading bacteria. 16S rRNA and alkB analysis demonstrated that the initial sediment community was dominated by Betaproteobacteria (mainly Comamonadaceae) and Gammaproteobacteria (mainly Pseudomonas), while CYP 153A1 sequences predominantly matched Rhizobiales. 24 h of exposure to n-hexane, gasoline, dodecane, or dilution culture alone reduced functional and phylogenetic diversity, enriching for Gammaproteobacteria, especially Pseudomonas. Gammaproteobacteria continued to dominate for 10 days in the n-hexane and no alkane exposed samples, while dodecane and gasoline exposure selected for gram-positive bacteria. The data demonstrate that small fringing marshes in New England harbor petroleum-degrading bacteria, suggesting that petroleum degradation may be an important fringing marsh ecosystem function.