Bioinformatics tools to assess metagenomic data for applied microbiology

Evaluation of the taxonomic classification tools and visualizers for metagenomic analysis using the Oxford nanopore sequence database

Microbial metagenomic identification is generally attributed to the specificity and type of the bioinformatic tools, including classifiers and visualizers. In this study, the performance of two major classifiers, Centrifuge and Kraken2, and two visualizers (Recentrifuge and Krona) has been thoroughly investigated for their efficiency in the identification of the microorganisms using the Whole-Genome Sequence (WGS) database and four targeted databases including NCBI, Silva, Greengenes, and Ribosomal Database Project (RDP). Two standard DNA metagenomic library replicates, Zymo and Zymo-1, were used as quality control. Results showed that Centrifuge gave a higher percentage of Pseudomonas aeruginosa, Escherichia coli, and Salmonella enterica identification than Kraken2. Compared to Recentrifuge, Kraken2 was more accurate in identifying Staphylococcus aureus, Listeria monocytogenes, Bacillus subtilis, and Cryptococcus neoformans. The results of the rest of the detected microorganisms were generally consistent with the two classifiers. Regarding visualizers, both Recentrifuge and Krona provided similar results regarding the abundance of each microbial species regardless of the classifier used. The differences in results between the two mentioned classifiers may be attributed to the specific algorithms each method uses and the sequencing depth. Centrifuge uses a read mapping approach, while Kraken2 uses a k-mer-based system to classify the sequencing reads into taxonomic groups. In conclusion, both Centrifuge and Kraken2 are effective tools for microbial classification. However, the choice of classifier can influence the accuracy of microbial classification and, therefore, should be made carefully, depending on the desired application, even when the same reference database is used.

Microbial communities associated with marine sponges from diverse geographic locations harbor biosynthetic novelty

Marine sponges are a prolific source of biologically active small molecules, many of which originate from sponge-associated bacteria. Identifying the producing bacteria is a key step in developing sustainable routes for the production of these metabolites. To facilitate the required computational analyses, we developed MetaSing, a reproducible singularity-based pipeline for assembly, identification of high-quality metagenome-assembled genomes (MAGs), and analysis of biosynthetic gene clusters (BGCs) from metagenomic short-read data. We applied this pipeline to metagenomic sequencing data from 16 marine sponges collected from New Zealand, Tonga, and the Mediterranean Sea. This analysis yielded 643 MAGs representing 510 species. Of the 2,670 BGCs identified across all samples, 70.8% were linked to a MAG. Comparison of BGCs to those identified from previously sequenced bacteria revealed high biosynthetic novelty in variety of underexplored phyla, including Poribacteria, Acidobacteriota, and Dadabacteria. Alongside the observation that each sample contains unique biosynthetic potential, this holds great promise for natural product discovery and for furthering the understanding of different sponge holobionts.IMPORTANCEDiscovery of new chemical compounds such as natural products is a crucial endeavor to combat the increasing resistance to antibiotics and other drugs. This manuscript demonstrates that microbial communities associated with marine sponges investigated in this work encode the potential to produce novel chemistry. Lesser studied bacterial taxa that are often difficult to cultivate are particularly rich in potential.

Targeting bioinformatics tools to study the dissemination and spread of antibiotic resistant genes in the environment and clinical settings

Antibiotic resistance has expanded as a result of the careless use of antibiotics in the medical field, the food industry, agriculture, and other industries. By means of genetic recombination between commensal and pathogenic bacteria, the microbes obtain antibiotic resistance genes (ARGs). In bacteria, horizontal gene transfer (HGT) is the main mechanism for acquiring ARGs. With the development of high-throughput sequencing, ARG sequence analysis is now feasible and widely available. Preventing the spread of AMR in the environment requires the implementation of ARGs mapping. The metagenomic technique, in particular, has helped in identifying antibiotic resistance within microbial communities. Due to the exponential growth of experimental and clinical data, significant investments in computer capacity, and advancements in algorithmic techniques, the application of machine learning (ML) algorithms to the problem of AMR has attracted increasing attention over the past five years. The review article sheds a light on the application of bioinformatics for the antibiotic resistance monitoring. The most advanced tool currently being employed to catalog the resistome of various habitats are metagenomics and metatranscriptomics. The future lies in the hands of artificial intelligence (AI) and machine learning (ML) methods, to predict and optimize the interaction of antibiotic-resistant compounds with target proteins.

The vaginal microbiota of healthy female cats

The vaginal microbiota of the queen (i.e., female cat) has never been described using culture independent methods. The objectives of the present research were to describe the vaginal microbiota of healthy domestic shorthair queens using both 16S rRNA sequencing and culture, and to assess the effects of age, living environment, and reproductive season on its composition. Thirty queens undergoing elective ovariectomy were included in the study. The vaginal samples were collected just before surgery, from animals under general anaesthesia. Two consecutive mini-swabs were introduced in the queens' vaginal tract. A preliminary study with 10 healthy queens aimed to negate sampling order's effect. Two consecutive samples for sequencing (5 queens, 10 swabs) and culture (5 queens, 10 swabs) were collected, confirming a match (100 % in culture, Bray-Curtis P = 0.96 in sequencing). The experiment included 20 queens that were prospectively grouped based on age (prepubertal N = 10, adult N = 10), living environment (indoor N = 10, outdoor N = 10), and time of the year, whether during the reproductive season (N = 10) or during seasonal anoestrous (N = 10). Bacteria were identified through metataxonomic analysis, amplifying the V1-V2 regions of 16S rRNA gene, and through standard culture followed by MALDI-TOF MS. The feline vaginal microbiota is dominated by Proteobacteria, Firmicutes, Bacteroidota, and Actinobacteria. Escherichia-Shigella, Streptococcus, and Pasteurella were the most abundant genera. Although culture underestimated bacterial richness and diversity compared to sequencing, Escherichia and Streptococcus were the most isolated bacteria. No bacterial growth was observed in 15 % of samples (N = 3/20), whereas growth of one or two bacterial species was observed in 64.7 % (N = 11/17) and 35.3 % (N = 6/17) of cases, respectively. No differences in terms of alpha (Kruskal-Wallis rank sum test P = 0.65) and beta diversity (Bray-Curtis, Unweighted and Weighted UniFrac analyses P > 0.5) were observed. Although a difference in alpha diversity based on phylogenetic tree (P = 0.02) was detected between indoor and outdoor queens. In conclusion, mixed and monoculture of Escherichia coli, Streptococcus canis, Staphylococcus felis, and Enterococcus spp. are normal findings within the cat vagina. Age and reproductive season do not influence the feline vaginal microbiota, whereas further research is needed to elucidate the role of the living environment.

Performance evaluation of a newly developed 2019-nCoV nucleic acid detection kit based on Ion Proton sequencing platform and its comparison with the MGI Tech (DNBSEQ-G99) platform

PURPOSE

To evaluate the performance of a newly developed 2019-nCoV nucleic acid detection kit based on Ion Proton sequencing platform and make comparation with MGI Tech (DNBSEQ-G99) platform.

METHODS

References and clinical samples were used to evaluate the precision, agreement rate, limit of detection (LOD), anti-interference ability and analytical specificity. Twenty-seven clinical specimens were used to make comparison between two platforms.

RESULTS

The kit showed good intra-assay, inter-assay, inter-day precision between different operators and laboratories, fine agreement rate with references, a relatively low LOD of 1 × 103 copies/ml, anti-interference capability of 5 % whole blood and 1mg/ml mucin and no cross reaction with twenty-nine common clinical pathogens. Consistency of variant classification was observed between two platforms. The WGS from Ion Proton tended to have higher coverage and less missing data.

CONCLUSIONS

The newly developed kit has shown satisfactory performances and excellent consistency with DNBSEQ-G99, making it a good alternative choice clinically.

Advances in Culturomics Research on the Human Gut Microbiome: Optimizing Medium Composition and Culture Techniques for Enhanced Microbial Discovery

Despite considerable advancements achieved using next-generation sequencing technologies in exploring microbial diversity, several species of the gut microbiome remain unknown. In this transformative era, culturomics has risen to prominence as a pivotal approach in unveiling realms of microbial diversity that were previously deemed inaccessible. Utilizing innovative strategies to optimize growth and culture medium composition, scientists have successfully cultured hard-to-cultivate microbes. This progress has fostered the discovery and understanding of elusive microbial entities, highlighting their essential role in human health and disease paradigms. In this review, we emphasize the importance of culturomics research on the gut microbiome and provide new theories and insights for expanding microbial diversity via the optimization of cultivation conditions.

Mining thermophiles for biotechnologically relevant enzymes: evaluating the potential of European and Caucasian hot springs

The development of sustainable and environmentally friendly industrial processes is becoming very crucial and demanding for the rapid implementation of innovative bio-based technologies. Natural extreme environments harbor the potential for discovering and utilizing highly specific and efficient biocatalysts that are adapted to harsh conditions. This review focuses on extremophilic microorganisms and their enzymes (extremozymes) from various hot springs, shallow marine vents, and other geothermal habitats in Europe and the Caucasus region. These hot environments have been partially investigated and analyzed for microbial diversity and enzymology. Hotspots like Iceland, Italy, and the Azores harbor unique microorganisms, including bacteria and archaea. The latest results demonstrate a great potential for the discovery of new microbial species and unique enzymes that can be explored for the development of Circular Bioeconomy.Different screening approaches have been used to discover enzymes that are active at extremes of temperature (up 120 °C), pH (0.1 to 11), high salt concentration (up to 30%) as well as activity in the presence of solvents (up to 99%). The majority of published enzymes were revealed from bacterial or archaeal isolates by traditional activity-based screening techniques. However, the latest developments in molecular biology, bioinformatics, and genomics have revolutionized life science technologies. Post-genomic era has contributed to the discovery of millions of sequences coding for a huge number of biocatalysts. Both strategies, activity- and sequence-based screening approaches, are complementary and contribute to the discovery of unique enzymes that have not been extensively utilized so far.

Bioinformatic approaches for studying the microbiome of fermented food

High-throughput DNA sequencing-based approaches continue to revolutionise our understanding of microbial ecosystems, including those associated with fermented foods. Metagenomic and metatranscriptomic approaches are state-of-the-art biological profiling methods and are employed to investigate a wide variety of characteristics of microbial communities, such as taxonomic membership, gene content and the range and level at which these genes are expressed. Individual groups and consortia of researchers are utilising these approaches to produce increasingly large and complex datasets, representing vast populations of microorganisms. There is a corresponding requirement for the development and application of appropriate bioinformatic tools and pipelines to interpret this data. This review critically analyses the tools and pipelines that have been used or that could be applied to the analysis of metagenomic and metatranscriptomic data from fermented foods. In addition, we critically analyse a number of studies of fermented foods in which these tools have previously been applied, to highlight the insights that these approaches can provide.

Bacterial diversity in the aquatic system in India based on metagenome analysis-a critical review

Microbial analysis has become one of the most critical areas in aquatic ecology and a crucial component for assessing the contribution of microbes in food web dynamics and biogeochemical processes. Initial research was focused on estimating the abundance and distribution of the microbes using microscopy and culture-based analysis, which are undoubtedly complex tasks. Over the past few decades, microbiologists have endeavored to apply and extend molecular techniques to address pertinent questions related to the function and metabolism of microbes in aquatic ecology. Metagenomics analysis has revolutionized aquatic ecology studies involving the investigation of the genome of a mixed community of organisms in an ecosystem to identify microorganisms, their functionality, and the discovery of novel proteins. This review discusses the metagenomics analysis of bacterial diversity in and around different aquatic systems in India.

Decoding the microbial universe with metagenomics: a brief insight

A major part of any biological system on earth involves microorganisms, of which the majority are yet to be cultured. The conventional methods of culturing microbes have given fruitful outcomes yet have limitations. The curiosity for better understanding has led to the development of culture-independent molecular methods that help push aside the roadblocks of earlier methods. Metagenomics unifies the scientific community in search of a better understanding of the functioning of the ecosystem and its component organisms. This approach has opened a new paradigm in advanced research. It has brought to light the vast diversity and novelty among microbial communities and their genomes. This review focuses on the development of this field over time, the techniques and analysis of data generated through sequencing platforms, and its prominent interpretation and representation.

Mammals' sperm microbiome: current knowledge, challenges, and perspectives on metagenomics of seminal samples

Bacterial growth is highly detrimental to sperm quality and functionality. However, during the last few years, using sequencing techniques with a metagenomic approach, it has been possible to deepen the study of bacteria-sperm relationships and describe non-culturable species and synergistic and antagonistic relationships between the different species in mammalian animals. We compile the recent metagenomics studies performed on mammalian semen samples and provide updated evidence to understand the importance of the microbial communities in the results of sperm quality and sperm functionality of males, looking for future perspectives on how these technologies can collaborate in the development of andrological knowledge.

New Insights into the Effect of Fipronil on the Soil Bacterial Community

Fipronil is a broad-spectrum insecticide with remarkable efficacy that is widely used to control insect pests around the world. However, its extensive use has led to increasing soil and water contamination. This fact is of concern and makes it necessary to evaluate the risk of undesirable effects on non-target microorganisms, such as the microbial community in water and/or soil. Studies using the metagenomic approach to assess the effects of fipronil on soil microbial communities are scarce. In this context, the present study was conducted to identify microorganisms that can biodegrade fipronil and that could be of great environmental interest. For this purpose, the targeted metabarcoding approach was performed in soil microcosms under two environmental conditions: fipronil exposure and control (without fipronil). After a 35-day soil microcosm period, the 16S ribosomal RNA (rRNA) gene of all samples was sequenced using the ion torrent personal genome machine (PGM) platform. Our study showed the presence of Proteobacteria, Actinobacteria, and Firmicutes in all of the samples; however, the presence of fipronil in the soil samples resulted in a significant increase in the concentration of bacteria from these phyla. The statistical results indicate that some bacterial genera benefited from soil exposure to fipronil, as in the case of bacteria from the genus Thalassobacillus, while others were affected, as in the case of bacteria from the genus Streptomyces. Overall, the results of this study provide a potential contribution of fipronil-degrading bacteria.

Metagenomic Analysis of Garden Soil-Derived Microbial Consortia and Unveiling Their Metabolic Potential in Mitigating Toxic Hexavalent Chromium

Soil microbial communities connect to the functional environment and play an important role in the biogeochemical cycle and waste degradation. The current study evaluated the distribution of the core microbial population of garden soil in the Varanasi region of Uttar Pradesh, India and their metabolic potential for mitigating toxic hexavalent chromium from wastewater. Metagenomes contain 0.2 million reads and 56.5% GC content. The metagenomic analysis provided insight into the relative abundance of soil microbial communities and revealed the domination of around 200 bacterial species belonging to different phyla and four archaeal phyla. The top 10 abundant genera in garden soil were Gemmata, Planctomyces, Steroidobacter, Pirellula, Pedomicrobium, Rhodoplanes, Nitrospira Mycobacterium, Pseudonocardia, and Acinetobacter. In this study, Gemmata was dominating bacterial genera. Euryarchaeota, Parvarchaeota, and Crenarchaeota archaeal species were present with low abundance in soil samples. X-ray photoelectric spectroscopy (XPS) analysis indicates the presence of carbon, nitrogen-oxygen, calcium, phosphorous, and silica in the soil. Soil-derived bacterial consortia showed high hexavalent chromium [Cr (VI)] removal efficiency (99.37%). The bacterial consortia isolated from garden soil had an important role in the hexavalent chromium bioremediation, and thus, this study could be beneficial for the design of a heavy-metal treatment system.

Diversity and functional traits of indigenous soil microbial flora associated with salinity and heavy metal concentrations in agricultural fields within the Indus Basin region, Pakistan

Soil salinization and heavy metal (HM) contamination are major challenges facing agricultural systems worldwide. Determining how soil microbial communities respond to these stress factors and identifying individual phylotypes with potential to tolerate these conditions while promoting plant growth could help prevent negative impacts on crop productivity. This study used amplicon sequencing and several bioinformatic programs to characterize differences in the composition and potential functional capabilities of soil bacterial, fungal, and archaeal communities in five agricultural fields that varied in salinity and HM concentrations within the Indus basin region of Pakistan. The composition of bacteria with the potential to fix atmospheric nitrogen (N) and produce the enzyme 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase were also determined. Microbial communities were dominated by: Euryarchaeota (archaea), Actinobacteria, Proteobacteria, Planctomycetota, Firimicutes, Patescibacteria and Acidobacteria (bacteria), and Ascomycota (fungi), and all soils contained phylotypes capable of N-fixation and ACC-deaminase production. Salinity influenced bacterial, but not archaeal or fungal communities. Both salinity and HM altered the relative abundance of many phylotypes that could potentially promote or harm plant growth. These stress factors also appeared to influence the potential functional capabilities of the microbial communities, especially in their capacity to cycle phosphorous, produce siderophores, and act as symbiotrophs or pathotrophs. Results of this study confirm that farms in this region are at risk due to salinization and excessive levels of some toxic heavy metals, which could negatively impact crop and human health. Changes in soil microbial communities and their potential functional capabilities are also likely to affect several critical agroecosystem services related to nutrient cycling, pathogen suppression, and plant stress tolerance. Many potentially beneficial phylotypes were identified that appear to be salt and HM tolerant and could possibly be exploited to promote these services within this agroecosystem. Future efforts to isolate these phylotypes and determine whether they can indeed promote plant growth and/or carry out other important soil processes are recommended. At the same time, identifying ways to promote the abundance of these unique phylotypes either through modifying soil and crop management practices, or developing and applying them as inoculants, would be helpful for improving crop productivity in this region.

Fungal isolates influence the quality of Peucedanum praeruptorum Dunn

The symbiotic relationship between beneficial microorganisms and plants plays a vital role in natural and agricultural ecosystems. Although Peucedanum praeruptorum Dunn is widely distributed, its development is greatly limited by early bolting. The reason for early bolting in P. praeruptorum remains poorly characterized. We focus on the plant related microorganisms, including endophytes and rhizosphere microorganisms, by combining the traditional isolation and culture method with metagenomic sequencing technology. We found that the OTUs of endophytes and rhizosphere microorganisms showed a positive correlation in the whole growth stage of P. praeruptorum. Meanwhile, the community diversity of endophytic and rhizosphere fungi showed an opposite change trend, and bacteria showed a similar change trend. Besides, the microbial communities differed during the pre- and post-bolting stages of P. praeruptorum. Beneficial bacterial taxa, such as Pseudomonas and Burkholderia, and fungal taxa, such as Didymella and Fusarium, were abundant in the roots in the pre-bolting stage. Further, a strain belonging to Didymella was obtained by traditional culture and was found to contain praeruptorin A, praeruptorin B, praeruptorin E. In addition, we showed that the fungus could affect its effective components when it was inoculated into P. praeruptorum. This work provided a research reference for the similar biological characteristics of perennial one-time flowering plants, such as Saposhnikovia divaricate, Angelica sinensis and Angelica dahurica.

Metagenomics versus total RNA sequencing: most accurate data-processing tools, microbial identification accuracy and perspectives for ecological assessments

Metagenomics and total RNA sequencing (total RNA-Seq) have the potential to improve the taxonomic identification of diverse microbial communities, which could allow for the incorporation of microbes into routine ecological assessments. However, these target-PCR-free techniques require more testing and optimization. In this study, we processed metagenomics and total RNA-Seq data from a commercially available microbial mock community using 672 data-processing workflows, identified the most accurate data-processing tools, and compared their microbial identification accuracy at equal and increasing sequencing depths. The accuracy of data-processing tools substantially varied among replicates. Total RNA-Seq was more accurate than metagenomics at equal sequencing depths and even at sequencing depths almost one order of magnitude lower than those of metagenomics. We show that while data-processing tools require further exploration, total RNA-Seq might be a favorable alternative to metagenomics for target-PCR-free taxonomic identifications of microbial communities and might enable a substantial reduction in sequencing costs while maintaining accuracy. This could be particularly an advantage for routine ecological assessments, which require cost-effective yet accurate methods, and might allow for the incorporation of microbes into ecological assessments.

Transporter Engineering in Microbial Cell Factory Boosts Biomanufacturing Capacity

Microbial cell factories (MCFs) are typical and widely used platforms in biomanufacturing for designing and constructing synthesis pathways of target compounds in microorganisms. In MCFs, transporter engineering is especially significant for improving the biomanufacturing efficiency and capacity through enhancing substrate absorption, promoting intracellular mass transfer of intermediate metabolites, and improving transmembrane export of target products. This review discusses the current methods and strategies of mining and characterizing suitable transporters and presents the cases of transporter engineering in the production of various chemicals in MCFs.

Effects of heavy metals stress on chicken manures composting via the perspective of microbial community feedback

Heavy metal pollution was the main risk during livestock manures composting, in which microorganisms played a vital role. However, response strategies of microbial community to heavy metals stress (HMS) remained largely unclear. Therefore, the objective of this study was to reveal the ecological adaptation and counter-effect of bacterial community under HMS during chicken manures composting, and evaluating environmental implications of HMS on composting. The degradation of organic matters (more than 6.4%) and carbohydrate (more than 19.8%) were enhanced under intense HMS, suggesting that microorganisms could quickly adapt to the HMS to ensure smooth composting. Meanwhile, HMS increased keystone nodes and strengthened significant positive correlation relationships between genera (p < 0.05), indicating that bacteria resisted HMS through cooperating during composting. In addition, different bacterial groups performed various functions to cope with HMS. Specific bacterial groups responded to HMS, and certain groups regulated bacterial networks. Therefore, bacterial community had the extraordinary potential to deal with HMS and guarantee chicken manures composting even in the presence of high concentrations of heavy metals.

A roadmap for metagenomic enzyme discovery

Covering: up to 2021Metagenomics has yielded massive amounts of sequencing data offering a glimpse into the biosynthetic potential of the uncultivated microbial majority. While genome-resolved information about microbial communities from nearly every environment on earth is now available, the ability to accurately predict biocatalytic functions directly from sequencing data remains challenging. Compared to primary metabolic pathways, enzymes involved in secondary metabolism often catalyze specialized reactions with diverse substrates, making these pathways rich resources for the discovery of new enzymology. To date, functional insights gained from studies on environmental DNA (eDNA) have largely relied on PCR- or activity-based screening of eDNA fragments cloned in fosmid or cosmid libraries. As an alternative, shotgun metagenomics holds underexplored potential for the discovery of new enzymes directly from eDNA by avoiding common biases introduced through PCR- or activity-guided functional metagenomics workflows. However, inferring new enzyme functions directly from eDNA is similar to searching for a 'needle in a haystack' without direct links between genotype and phenotype. The goal of this review is to provide a roadmap to navigate shotgun metagenomic sequencing data and identify new candidate biosynthetic enzymes. We cover both computational and experimental strategies to mine metagenomes and explore protein sequence space with a spotlight on natural product biosynthesis. Specifically, we compare in silico methods for enzyme discovery including phylogenetics, sequence similarity networks, genomic context, 3D structure-based approaches, and machine learning techniques. We also discuss various experimental strategies to test computational predictions including heterologous expression and screening. Finally, we provide an outlook for future directions in the field with an emphasis on meta-omics, single-cell genomics, cell-free expression systems, and sequence-independent methods.

Metagenome-Assembled Genomes Contribute to Unraveling of the Microbiome of Cocoa Fermentation

Metagenomic studies about cocoa fermentation have mainly reported on the analysis of short reads for determination of operational taxonomic units. However, it is also important to determine metagenome-assembled genomes (MAGs), which are genomes deriving from the assembly of metagenomics. For this research, all the cocoa metagenomes from public databases were downloaded, resulting in five data sets: one from Ghana and four from Brazil. In addition, in silico approaches were used to describe putative phenotypes and the metabolic potential of MAGs. A total of 17 high-quality MAGs were recovered from these microbiomes, as follows: (i) for fungi, Yamadazyma tenuis (n = 1); (ii) lactic acid bacteria, Limosilactobacillus fermentum (n = 5), Liquorilactobacillus cacaonum (n = 1), Liquorilactobacillus nagelli (n = 1), Leuconostoc pseudomesenteroides (n = 1), and Lactiplantibacillus plantarum subsp. plantarum (n = 1); (iii) acetic acid bacteria, Acetobacter senegalensis (n = 2) and Kozakia baliensis (n = 1); and (iv) Bacillus subtilis (n = 1), Brevundimonas sp. (n = 2), and Pseudomonas sp. (n = 1). Medium-quality MAGs were also recovered from cocoa microbiomes, including some that, to our knowledge, were not previously detected in this environment (Liquorilactobacillus vini, Komagataeibacter saccharivorans, and Komagataeibacter maltaceti) and others previously described (Fructobacillus pseudoficulneus and Acetobacter pasteurianus). Taken together, the MAGs were useful for providing an additional description of the microbiome of cocoa fermentation, revealing previously overlooked microorganisms, with prediction of key phenotypes and biochemical pathways. IMPORTANCE The production of chocolate starts with the harvesting of cocoa fruits and the spontaneous fermentation of the seeds in a microbial succession that depends on yeasts, lactic acid bacteria, and acetic acid bacteria in order to eliminate bitter and astringent compounds present in the raw material, which will be further roasted and grinded to originate the cocoa powder that will enter the food processing industry. The microbiota of cocoa fermentation is not completely known, and yet it advanced from culture-based studies to the advent of next-generation DNA sequencing, with the generation of a myriad of data that need bioinformatic approaches to be properly analyzed. Although the majority of metagenomic studies have been based on short reads (operational taxonomic units), it is also important to analyze entire genomes to determine more precisely possible ecological roles of different species. Metagenome-assembled genomes (MAGs) are very useful for this purpose; here, MAGs from cocoa fermentation microbiomes are described, and the possible implications of their phenotypic and metabolic potentials are discussed.

A review on application of next-generation sequencing methods for profiling of protozoan parasites in water: Current methodologies, challenges, and perspectives

The advancement in metagenomic techniques has provided novel tools for profiling human parasites in environmental matrices, such as water and wastewater. However, application of metagenomic techniques for the profiling of protozoan parasites in environmental matrices is not commonly reported in the literature. The key factors leading to the less common use of metagenomics are the complexity and large eukaryotic genome, the prevalence of small parasite populations in environmental samples compared to bacteria, difficulties in extracting DNA from (oo)cysts, and limited reference databases for parasites. This calls for further research to develop optimized methods specifically looking at protozoan parasites in the environment. This study reviews the current workflow, methods and provide recommendations for the standardization of techniques. The article identifies and summarizes the key methods, advantages, and limitations associated with metagenomic analysis, like sample pre-processing, DNA extraction, sequencing approaches, and analysis methods. The study enhances the understanding and application of standardized protocols for profiling of protozoan parasite community from highly complexe samples and further creates a resourceful comparison among datasets without any biases.

Measuring reproducibility of virus metagenomics analyses using bootstrap samples from FASTQ-files

MOTIVATION

High-throughput sequencing data can be affected by different technical errors, e.g. from probe preparation or false base calling. As a consequence, reproducibility of experiments can be weakened. In virus metagenomics, technical errors can result in falsely identified viruses in samples from infected hosts. We present a new resampling approach based on bootstrap sampling of sequencing reads from FASTQ-files in order to generate artificial replicates of sequencing runs which can help to judge the robustness of an analysis. In addition, we evaluate a mixture model on the distribution of read counts per virus to identify potentially false positive findings.

RESULTS

The evaluation of our approach on an artificially generated dataset with known viral sequence content shows in general a high reproducibility of uncovering viruses in sequencing data, i.e. the correlation between original and mean bootstrap read count was highly correlated. However, the bootstrap read counts can also indicate reduced or increased evidence for the presence of a virus in the biological sample. We also found that the mixture-model fits well to the read counts, and furthermore, it provides a higher accuracy on the original or on the bootstrap read counts than on the difference between both. The usefulness of our methods is further demonstrated on two freely available real-world datasets from harbor seals.

AVAILABILITY AND IMPLEMENTATION

We provide a Phyton tool, called RESEQ, available from https://github.com/babaksaremi/RESEQ that allows efficient generation of bootstrap reads from an original FASTQ-file.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Shotgun metagenomic analysis of kombucha mutualistic community exposed to Mars-like environment outside the International Space Station

Kombucha is a multispecies microbial ecosystem mainly composed of acetic acid bacteria and osmophilic acid-tolerant yeasts, which is used to produce a probiotic drink. Furthermore, Kombucha Mutualistic Community (KMC) has been recently proposed to be used during long space missions as both a living functional fermented product to improve astronauts' health and an efficient source of bacterial nanocellulose. In this study, we compared KMC structure and functions before and after samples were exposed to the space/Mars-like environment outside the International Space Station in order to investigate the changes related to their re-adaptation to Earth-like conditions by shotgun metagenomics, using both diversity and functional analyses of Community Ecology and Complex Networks approach. Our study revealed that the long-term exposure to space/Mars-like conditions on low Earth orbit may disorganize the KMC to such extent that it will not restore the initial community structure; however, KMC core microorganisms of the community were maintained. Nonetheless, there were no significant differences in the community functions, meaning that the KMC communities are ecologically resilient. Therefore, despite the extremely harsh conditions, key KMC species revived and provided the community with the genetic background needed to survive long periods of time under extraterrestrial conditions.

Tuberculosis Diagnosis by Metagenomic Next-generation Sequencing on Bronchoalveolar Lavage Fluid: a cross-sectional analysis

BACKGROUND

Metagenomic next-generation sequencing (mNGS) is an effective diagnostic method for infectious diseases, however, its clinical utility for tuberculosis (TB) diagnosis remains to be demonstrated.

METHODS

A total of 322 bronchoalveolar lavage fluid (BALF) samples were collected from 311 suspected and confirmed pulmonary TB patients and tested by mNGS, acid-fast bacillus (AFB) smear by microscopy, Xpert® MTB/RIF (Xpert), mycobacterium culture and bacterial/fungal culture. Diagnostic performance of mNGS was compared with conventional methods for detection of Mycobacterium tuberculosis complex (MTBC) and other pathogens in BALF. Underlying factors associated with positive detection in pulmonary TB patients were investigated.

RESULTS

mNGS, Xpert and culture presented a high proportion of complete matching for MTBC detection (244/322, 75.8%). In pulmonary TB patients pre-treatment the sensitivity of MTBC detection by mNGS, Xpert, culture and smear was 59.9% (85/142), 69.0% (98/142), 59.9% (85/142) and 24.6% (35/142), respectively, and 79.6% overall; MTBC was detected by mNGS in 33.2% (5/34) Xpert and culture negative samples. Positive MTBC detection by mNGS was affected by Vitamin D, erythrocyte sedimentation rate, TB initial treatment/retreatment, and cavity in chest imaging (χ² = 37.42, P < 0.001), but not by prior anti-TB therapy within 3 months. mNGS was able to detect new potential pathogens in 8.7% (28/322) of samples.

CONCLUSIONS

Combining mNGS with conventional detection methods could increase the detection rate for MTBC. Additionally, mNGS could identify pathogens in a non-targeted approach for better diagnosis of coinfection.

The Lung Microbiome of Three Young Brazilian Patients With Cystic Fibrosis Colonized by Fungi

Microbial communities infiltrate the respiratory tract of cystic fibrosis patients, where chronic colonization and infection lead to clinical decline. This report aims to provide an overview of the diversity of bacterial and fungal species from the airway secretion of three young CF patients with severe pulmonary disease. The bacterial and fungal microbiomes were investigated by culture isolation, metataxonomics, and metagenomics shotgun. Virulence factors and antibiotic resistance genes were also explored. A. fumigatus was isolated from cultures and identified in high incidence from patient sputum samples. Candida albicans, Penicillium sp., Hanseniaspora sp., Torulaspora delbrueckii, and Talaromyces amestolkiae were isolated sporadically. Metataxonomics and metagenomics detected fungal reads (Saccharomyces cerevisiae, A. fumigatus, and Schizophyllum sp.) in one sputum sample. The main pathogenic bacteria identified were Staphylococcus aureus, Pseudomonas aeruginosa, Burkholderia cepacia complex, and Achromobacter xylosoxidans. The canonical core CF microbiome is composed of species from the genera Streptococcus, Neisseria, Rothia, Prevotella, and Haemophilus. Thus, the airways of the three young CF patients presented dominant bacterial genera and interindividual variability in microbial community composition and diversity. Additionally, a wide diversity of virulence factors and antibiotic resistance genes were identified in the CF lung microbiomes, which may be linked to the clinical condition of the CF patients. Understanding the microbial community is crucial to improve therapy because it may have the opposite effect, restructuring the pathogenic microbiota. Future studies focusing on the influence of fungi on bacterial diversity and microbial interactions in CF microbiomes will be welcome to fulfill this huge gap of fungal influence on CF physiopathology.

Quantitative Microbial Risk Assessment and Molecular Biology: Paths to Integration

Quantitative microbial risk assessment (QMRA) has now been in use for over 35 years and has formed the basis for developing criteria for ensuring public health related to water, food, and remediation, to name a few areas. The initial data for QMRA (both in exposure assessment and in dose response assessment) came from measurements using assays for viability, such as plate counts, plaque assays, or animal infectivity. With the increasing use of molecular methods for the measurement of microorganisms in the environment, it has become important to assess how to use such data to estimate infectious disease risks. The limitations to the use of such data and needs to resolve the limitations will be addressed.

Metagenomic analysis of potential pathogens from blood donors in Guangzhou, China

OBJECTIVES

This study aimed to identify the emerging/reemerging pathogens in blood donation samples.

BACKGROUND

A metagenomic analysis has previously been used to look for pathogens but in this study, the relationship with aminotransferase (ALT) is described.

METHODS/MATERIALS

Excluding samples reactive to hepatitis B virus, hepatitis C virus, human immunodeficiency syndrome virus or syphilis and plasma samples were stratified into three groups of ALT levels (IU/L): A ≤ 50, B 51 to 69 and C ≥ 70, respectively. Each group was mixed in a pool of 100 samples, from which DNA and cDNA libraries were established for next generation sequencing and analysis. Pathogens of interest were identified by immunoassays, nested-polymerase chain reaction, phylogenetic analysis and pathogen detection in follow-up donors.

RESULTS

Several new or reemerging transfusion-transmitted pathogens were identified; Streptococcus suis, Babesia species and Toxoplasma gondii were found in the three ALT groups, Epstein-Barr virus (EBV) only in group C. Ten S. suis nucleic acid positive samples were detected, all closely phylogenetically related to reference strains. A donor in group A carried both S. suis genome and specific IgM in follow-up samples. This strain was identified as nontoxic S. suis. Five samples contained a short fragment of Babesia species SpeI-AvaI gene, while T. gondii was identified in 20 samples as a short fragment of 18S rDNA gene. In group C, two samples contained EBV genome.

CONCLUSIONS

Blood donations that contained S. suis, Babesia species and T. gondii sequences might represent potential transfusion risks. EBV, a potential cause of elevated ALT, was detected. Metagenomic analysis might be a useful technology for monitoring blood safety.

Finding a common core microbiota in two Brazilian dairies through culture and DNA metabarcoding studies

Dairy foods are complex ecosystems composed of microorganisms from different origins that can affect flavor and safety of final products. The objective of this paper is to assess the in-house microbiota of two Brazilian dairies and to discuss the possible implications of the taxa determined for food protection. In total, 27 samples from dairies were cultured in selective (Baird Parker, de Man, Rogosa and Sharpe) and non-selective (Brain Heart Infusion) media, and the isolates were identified by Sanger sequencing. Moreover, metagenomic DNA was directly extracted from samples and the structure of the bacterial community was determined by massive DNA sequencing followed by bioinformatics analyses. The results showed the majority of isolates belonged to the group of lactic acid bacteria, but Enterobacteriaceae, Staphylococcacceae, Bacillaceae, Pseudomonadaceae and Moraxellaceae were also detected. From the reads obtained in metataxonomics analyses, a heatmap was constructed and the top 20 OTUs (operational taxonomic units) were determined. Besides, 12 most prevalent bacterial taxa were assigned to the core microbiota of the dairies evaluated, which included Thiomonas thermosulfata, Alkalibacillus salilacus, Pseudomonas clemancea, Erythrobacter aquimans, Tetragenococcus doogicus, Macrococcus brunensis, Pseudomonas ludensis, Streptococcus dentinousetti, Serratia entomophila, Vagococcus teuberi, Lactococcus fujiensis and Tolumonas auensis. In conclusion, the results reveal the presence of bacteria that may be related to spoilage and also foodborne diseases, in microbial niches that also present rare taxa, highlighting the importance to consider culture-independent results to evaluate and improve food safety.

Dextran Aldehyde in Biocatalysis: More Than a Mere Immobilization System

Dextran aldehyde (dexOx), resulting from the periodate oxidative cleavage of 1,2-diol moiety inside dextran, is a polymer that is very useful in many areas, including as a macromolecular carrier for drug delivery and other biomedical applications. In particular, it has been widely used for chemical engineering of enzymes, with the aim of designing better biocatalysts that possess improved catalytic properties, making them more stable and/or active for different catalytic reactions. This polymer possesses a very flexible hydrophilic structure, which becomes inert after chemical reduction; therefore, dexOx comes to be highly versatile in a biocatalyst design. This paper presents an overview of the multiple applications of dexOx in applied biocatalysis, e.g., to modulate the adsorption of biomolecules on carrier surfaces in affinity chromatography and biosensors design, to serve as a spacer arm between a ligand and the support in biomacromolecule immobilization procedures or to generate artificial microenvironments around the enzyme molecules or to stabilize multimeric enzymes by intersubunit crosslinking, among many other applications.

Three-Stage Single-Chambered Microbial Fuel Cell Biosensor Inoculated with Exiguobacterium aestuarii YC211 for Continuous Chromium (VI) Measurement

Chromium (VI) [Cr(VI)] compounds display high toxic, mutagenic, and carcinogenic potential. Biological analysis techniques (e.g., such as enzyme-based or cell-based sensors) have been developed to measure Cr(VI); however, these biological elements are sensitive to the environment, limited to measuring trace Cr(VI), and require deployment offsite. In this study, a three-stage single-chambered microbial fuel cell (SCMFC) biosensor inoculated with Exiguobacterium aestuarii YC211 was developed for in situ, real-time, and continuous Cr(VI) measurement. A negative linear relationship was observed between the Cr(VI) concentration (5–30 mg/L) and the voltage output using an SCMFC at 2-min liquid retention time. The theoretical Cr(VI) measurement range of the system could be extended to 5–90 mg/L by connecting three separate SCMFCs in series. The three-stage SCMFC biosensor could accurately measure Cr(VI) concentrations in actual tannery wastewater with low deviations (<7%). After treating the wastewater with the SCMFC, the original inoculated E. aestuarii remained dominant (>92.5%), according to the next-generation sequencing analysis. The stable bacterial community present in the SCMFC favored the reliable performance of the SCMFC biosensor. Thus, the three-stage SCMFC biosensor has potential as an early warning device with wide dynamic range for in situ, real-time, and continuous Cr(VI) measurement of tannery wastewater.