Parallel-META 2.0: Enhanced Metagenomic Data Analysis with Functional Annotation, High Performance Computing and Advanced Visualization

Background: Gut flora has long been suspected to play a role in colorectal cancer (CRC) pathogenesis. Elegant experiments have substantiated such claims with the use of single pathogenic bacteria in germ free colon cancer mouse models. Here we run a comprehensive taxonomic, metagenomic and metabolomics analysis of stool samples from healthy and colon adenoma African American (AA) patients to define further this role. Patients & Methods: DNA extracts from stool samples of 6 healthy and 6 colon adenoma patients were used for the taxonomic and metagenomic analysis. The sequencing was done using a 454 pyrosequencer and the generated data was processed using informatics pipeline for phylogenetic bacterial affiliation and for bacterial functions assignment. For the metabolomics analysis, 10 fecal water extracts from healthy individuals and 10 from colon adenoma patients were analysed for their metabolites’ contents using NMR spectrometry. Results: The analysis of 16S-rDNA sequences revealed the existence of 22 groups of bacteria. A phylogenetic clustering revealed colon adenoma cases clustering together. Most predominant groups in both sets of samples were Clostridia and Bacteroides. No Lactobacilli or Bifidobacteria (good bacteria) were detected. The synergistetes_Dynergistia group -involved in gastrointestinal infections and Firmicutes phylum: Firmicutes group - involved in energy resorption and obesity were 20 and 10 folds more prevalent in adenoma patients, respectively. Bacteria TM7 group that contain active agents in IBD was 5 folds more represented in the control compared to the adenoma group. The metagenomic experiment have led to the generation of about 100,000 reads per sample, 80% of which were successfully annotated. Among the annotated functions, many were differentially present in the colon adenoma compared to healthy individuals. The metabolomics analysis led to a strong clustering of the adenoma samples metabolomes further consolidating the role of bacteria and their metabolites in colon cancer pathology. Conclusion: Our taxonomic, metagenomic and metabolomics analysis have led the presence of clear differential profiles in colon adenoma patients vs. healthy persons. Lack of detection of “good bacteria” (Lactobacilli and Bifidobacteria) in this study defines already a first level of intervention in this high risk CRC population. A comprehensive analysis of all the results is underway to bridge the Taxo, Meta and metabolomics data. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4414. doi:1538-7445.AM2012-4414

Simple SummaryNormally, researchers use feces or rectal swabs to characterize a gut microbiome, but because there is significant spatiotemporal variation across different intestinal segments there are marked differences in composition and function of microbiomes among various gut sites. Hence, a consensus has not been reached on the location for sampling for gut microbial metagenome sequencing. This study provides a comparative perspective on gut microbial function that takes into account different sampling resources when conducting a metagenomic sequence analysis, highlighting the differences in the choice of a gut microbiome sampling site, and investigating whether feces and rectal swab samples are efficient proxies for gut microbiome sampling.When conducting metagenomic analysis on gut microbiomes, there is no general consensus concerning the mode of sampling: non-contact (feces), noninvasive (rectal swabs), or cecal. This study aimed to determine the feasibility and comparative merits and disadvantages of using fecal samples or rectal swabs as a proxy for the cecal microbiome. Using broiler as a model, gut microbiomes were obtained from cecal, cloacal, and fecal samples and were characterized according to an analysis of the microbial community, function, and resistome. Cecal samples had higher microbial diversity than feces, while the cecum and cloaca exhibited higher levels of microbial community structure similarity compared with fecal samples. Cecal microbiota possessed higher levels of DNA replicative viability than feces, while fecal microbiota were correlated with increased metabolic activity. When feces were excreted, the abundance of antibiotic resistance genes like tet and ErmG decreased, but some antibiotic genes became more prevalent, such as fexA, tetL, and vatE. Interestingly, Lactobacillus was a dominant bacterial genus in feces that led to differences in microbial community structure, metabolism, and resistome. In conclusion, fecal microbiota have limited potential as a proxy in chicken gut microbial community studies. Thus, feces should be used with caution for characterizing gut microbiomes by metagenomic analysis.

Microbiome is an ecological community of commensal, symbiotic, and pathogenic microorganisms that share the same environment. The study of microbiome, i.e., genetic material sampled directly from environmental samples is called metagenomics. In recent years, genome sequencing methods have dramatically improved and the number and variety of sequenced genomes has rapidly increased. New technology has significantly increased the variety and complexity of the microbiome research and ever-larger datasets present new challenges in analysis of metagenomic data. Two main tasks in metagenomic analysis are classification of sequenced metagenomic data into taxonomic group of any rank, such as a species, family, or class, and assembly of the data into longer contiguous sequences. The final aim of both tasks is to correctly identify species presented in the metagenomic sample. This has various applications in medicine (e.g. infectious disease diagnosis), development of biofuels, biotechnology, agriculture, and many other areas. In this paper, we present a description of common procedures and methods for metagenomic data analysis and challenges facing these procedures. We give an overview of existing software tools and a review of public genome databases used for metagenomic analysis. Finally, we explore possible improvements to the existing methods for metagenomic classification and assembly.

The application of shotgun sequencing to environmental samples has revealed a new universe of microbial community genomes (metagenomes) involving previously uncultured organisms. Metagenome analysis, which is expected to provide a comprehensive picture of the gene functions and metabolic capacity for microbial communities, needs to be conducted in the context of a comprehensive data management and analysis system. We present in this paper IMG/M, an experimental metagenome data management and analysis system that is based on the Integrated Microbial Genomes (IMG) system. IMG/M provides tools and viewers for analyzing both metagenomes and isolate genomes individually or in a comparative context. IMG/M is available at http://img.jgi.doe.gov/m.

Chicks are ideal to follow the development of the intestinal microbiota and to understand how a pathogen perturbs this developing population. Taxonomic/metagenomic analyses captured the development of the chick microbiota in unperturbed chicks and in chicks infected with Salmonella enterica serotype Typhimurium (STm) during development. Taxonomic analysis suggests that colonization by the chicken microbiota takes place in several waves. The cecal microbiota stabilizes at day 12 posthatch with prominent Gammaproteobacteria and Clostridiales. Introduction of S. Typhimurium at day 4 posthatch disrupted the expected waves of intestinal colonization. Taxonomic and metagenomic shotgun sequencing analyses allowed us to identify species present in uninfected chicks. Untargeted metabolomics suggested different metabolic activities in infected chick microbiota. This analysis and gas chromatography-mass spectrometry on ingesta confirmed that lactic acid in cecal content coincides with the stable presence of enterococci in STm-infected chicks. Unique metabolites, including 2-isopropylmalic acid, an intermediate in the biosynthesis of leucine, were present only in the cecal content of STm-infected chicks. The metagenomic data suggested that the microbiota in STm-infected chicks contained a higher abundance of genes, from STm itself, involved in branched-chain amino acid synthesis. We generated an ilvC deletion mutant (STM3909) encoding ketol-acid-reductoisomerase, a gene required for the production of l-isoleucine and l-valine. ΔilvC mutants are disadvantaged for growth during competitive infection with the wild type. Providing the ilvC gene in trans restored the growth of the ΔilvC mutant. Our integrative approach identified biochemical pathways used by STm to establish a colonization niche in the chick intestine during development. IMPORTANCE Chicks are an ideal model to follow the development of the intestinal microbiota and to understand how a pathogen perturbs this developing population. Using taxonomic and metagenomic analyses, we captured the development of chick microbiota to 19 days posthatch in unperturbed chicks and in chicks infected with Salmonella enterica serotype Typhimurium (STm). We show that normal development of the microbiota takes place in waves and is altered in the presence of a pathogen. Metagenomics and metabolomics suggested that branched-chain amino acid biosynthesis is especially important for Salmonella growth in the infected chick intestine. Salmonella mutants unable to make l-isoleucine and l-valine colonize the chick intestine poorly. Restoration of the pathway for biosynthesis of these amino acids restored the colonizing ability of Salmonella. Integration of multiple analyses allowed us to correctly identify biochemical pathways used by Salmonella to establish a niche for colonization in the chick intestine during development.

Metagenomics can be used to obtain sequence information on putative genes in a microbial community. However, it is difficult to identify genes with specific functions among the numerous predicted genes. In this study, we attempted to identify genes induced in cultured microbes by the addition of saccharides using metagenomic and metatranscriptomic analyses. A mixture of arabinoxylan and its derived oligosaccharides was used as the inducer in this study. Some genes were highly induced in the presence of additive saccharides and formed gene clusters for the utilization of additive saccharides, suggesting that metatranscriptomic and metagenomic analyses are useful for analyzing carbohydrate-responsive genes in microbial communities and screening novel carbohydrate-active enzymes.

IntroductionMetagenomics, the genomic analysis of all species present within a mixed population, is an important tool used for the exploration of microbiomes in clinical and environmental microbiology. Whilst the development of next-generation sequencing, and more recently third generation long-read approaches such as nanopore sequencing, have greatly advanced the study of metagenomics, recovery of unbiased material from microbial populations remains challenging. One promising advancement in genomic sequencing from Oxford Nanopore Technologies (ONT) is adaptive sampling, which enables real-time enrichment or depletion of target sequences. As sequencing technologies continue to develop, and advances such as adaptive sampling become common techniques within the microbiological toolkit, it is essential to evaluate the benefits of such advancements to metagenomic studies, and the impact of methodological choices on research outcomes.Aim and methodsGiven the rapid development of sequencing tools and chemistry, this study aimed to demonstrate the impacts of choice of DNA extraction kit and sequencing chemistry on downstream metagenomic analyses. We first explored the quality and accuracy of 16S rRNA amplicon sequencing for DNA extracted from the ZymoBIOMICS Microbial Community Standard, using a range of commercially available DNA extraction kits to understand the effects of different kit biases on assessment of microbiome composition. We next compared the quality and accuracy of metagenomic analyses for two nanopore-based ligation chemistry kits with differing levels of base-calling error; the older and more error-prone (~ 97% accuracy) LSK109 chemistry, and newer more accurate (~ 99% accuracy) LSK112 Q20 + chemistry. Finally, we assessed the impact of the nanopore sequencing chemistry version on the output of the novel adaptive sampling approach for real-time enrichment of the genome for the yeast Saccharomyces cerevisiae from the microbial community.ResultsFirstly, DNA extraction kit methodology impacted the composition of the yield, with mechanical bead-beating methodologies providing the least biased picture due to efficient lysis of Gram-positive microbes present in the community standard, with differences in bead-beating methodologies also producing variation in composition. Secondly, whilst use of the Q20 + nanopore sequencing kit chemistry improved the base-calling data quality, the resulting metagenomic assemblies were not significantly improved based on common metrics and assembly statistics. Most importantly, we demonstrated the effective application of adaptive sampling for enriching a low-abundance genome within a metagenomic sample. This resulted in a 5-7-fold increase in target enrichment compared to non-adaptive sequencing, despite a reduction in overall sequencing throughput due to strand-rejection processes. Interestingly, no significant differences in adaptive sampling enrichment efficiency were observed between the older and newer ONT sequencing chemistries, suggesting that adaptive sampling performs consistently across different library preparation kits.ConclusionOur findings underscore the importance of selecting a DNA extraction methodology that minimises bias to ensure an accurate representation of microbial diversity in metagenomic studies. Additionally, despite the improved base-calling accuracy provided by newer Q20 + sequencing chemistry, we demonstrate that even older ONT sequencing chemistries can achieve reliable metagenomic sequencing results, enabling researchers to confidently use these approaches depending on their specific experimental needs. Critically, we highlight the significant potential of ONT’s adaptive sampling technology for targeted enrichment of specific genomes within metagenomic samples. This approach offers broad applicability for enriching target organisms or genetic elements (e.g., pathogens or plasmids) or depleting unwanted DNA (e.g., host DNA) in diverse sample types from environmental and clinical studies. However, researchers should carefully weigh the benefits of adaptive sampling against the potential trade-offs in sequencing throughput, particularly for low-abundance targets, where strand rejection can lead to pore blocking. These results provide valuable guidance for optimising adaptive sampling in metagenomic workflows to achieve specific research objectives.

An important problem in metagenomic analysis is to determine and quantify species (or genomes) in a metagenomic sample. The identification of phylogenetically related groups of sequence reads in a metagenomic dataset is often referred to as binning. Similarity-based binning methods rely on reference databases, and are unable to classify reads from unknown organisms. Composition-based methods exploit compositional patterns that are preserved in sufficiently long fragments, but are not suitable for binning very short next-generation sequencing (NGS) reads. Recently, several new metagenomic binning algorithms that can deal with NGS reads and do not rely on reference databases have been developed. However, all of them have difficulty with handling samples containing low-abundance species. We propose a new method to accurately estimate the abundance levels of species based on a novel probabilistic model for counting l-mer frequencies in a metagenomic dataset that takes into account frequencies of erroneous l-mers and repeated l-mers. An expectation maximization (EM) algorithm is used to learn the parameters of the model. Our algorithm automatically determines the number of abundance groups in a dataset and bins the reads into these groups. We show that our method outperforms the most recent abundance-based binning method, AbundanceBin, on both simulated and real datasets. We also show that the improved abundance-based binning method can be incorporated into a recent tool TOSS, which separates genomes with similar abundance levels and employs AbundanceBin as a preprocessing step to handle different abundance levels, to enhance its performance. We test the improved TOSS on simulated datasets and show that it significantly outperforms TOSS on datasets containing low-abundance genomes. Finally, we compare this approach against very recent metagenomic binning tools MetaCluster 4.0 and MetaCluster 5.0 on simulated data and demonstrate that it usually achieves a better sensitivity and breaks fewer genomes.Keywordsmetagenomicsnext-generation sequencingexpectation maximizationabundance-based binning

The complexity of microbial communities, combined with technical biases in next-generation sequencing, pose a challenge to metagenomic analysis. Here, we develop a set of internal DNA standards, termed “sequins” (sequencing spike-ins), that together constitute a synthetic community of artificial microbial genomes. Sequins are added to environmental DNA samples prior to library preparation, and undergo concurrent sequencing with the accompanying sample. We validate the performance of sequins by comparison to mock microbial communities, and demonstrate their use in the analysis of real metagenome samples. We show how sequins can be used to measure fold change differences in the size and structure of accompanying microbial communities, and perform quantitative normalization between samples. We further illustrate how sequins can be used to benchmark and optimize new methods, including nanopore long-read sequencing technology. We provide metagenome sequins, along with associated data sets, protocols, and an accompanying software toolkit, as reference standards to aid in metagenomic studies.

Caries is one of the most prevalent infectious diseases worldwide and is driven by the dysbiosis of dental biofilms adhering to tooth surfaces. The pits and fissured surfaces are the most susceptible sites of caries. However, information on the taxonomic composition and functional characteristics of the plaque microbiota in the pit and fissure sites is very limited. This study aimed to use metagenomic sequencing analyses to investigate the relationship between the plaque microbiome in the pit and fissure site and caries in adolescents. A total of 20 adolescents with active pit and fissure surface caries were involved as well as 20 age-matched, caries-free teenagers for control tests. Plaque samples were collected from the pit and fissure site and were subjected to metagenomic analyses, in which the microbial communities were investigated. Our results showed that the microbiota diversity was similar between those two groups. At the species level, the relative abundances of A. gerencseriae, P. acidifaciens, P. multisaccharivorax, S. oralis, S. mutans, and P. denticolens were higher in the caries-active group. N. elongata, C. hominis, and A. johnsonii were relatively more abundant in the caries-free groups. Functional analysis suggested that the metabolic pathway was the most abundant pathway, and the functional traits of the level 2 pathways included amino acid metabolism, metabolism of cofactors, and vitamins and carbohydrate metabolism. Our results also revealed that the caries group displayed several alterations in metabolic pathways, including enriched functions in carbohydrate digestion and absorption. This study suggested that in addition to the specific anatomical structures of the pit and fissured surfaces, the fundamental differences in the plaque microbiome may also be related to the susceptibility of pit and fissure caries.

BackgroundSkin is a complex ecosystem hosting a diverse microbial population as well as distinct environmental niches leading to hundreds of skin conditions that affect humans. There is an evident shift towards the metagenomic analysis from less efficient and strenuous culture-based techniques in biomedical research, thus creating a new dimension for dermatological study. A systematic and comprehensive study of skin microbiome appraises the dynamics between species, their interaction with the immune system, and composition in diseases.ResearchMetagenomics include research techniques like next-generation sequencing, sequencing of amplicon-based assays, shotgun metagenomics, gene prediction, metatranscriptomics, and statistical and comparative studies allowing us to access the functional and metabolic diversity of the skin microbiome and their role in host health. In disorders like acne, dandruff, seborrheic dermatitis, and bovine digital dermatitis, metagenomics provides information about the organisms present conferring the condition, inter-microbial interactions, and expression profiles of communities.ConclusionWe have enriched our understanding of the uncultured world resulting in a better understanding of microbe interaction with each other and their host. Metagenomic analysis provides glimpses into topographical and interpersonal complexity that defines the skin microbiome. It has led to an advanced study of dermatological disorders like acne, dandruff, seborrheic dermatitis, atopic dermatitis, bovine digital dermatitis, and psoriasis, and this knowledge is a breakthrough in dermatology research for creating better therapeutic solutions and personalized treatments.

Soil microorganisms are critical to the occurrence of Cordyceps sinensis (Chinese Cordyceps), a medicinal fungi used in Traditional Chinese Medicine. The over-collection of Chinese Cordyceps has caused vegetation degradation and impacted the sustainable occurrence of Cordyceps. The effects of Chinese Cordyceps collection on soil microorganisms have not been reported. Metagenomic analysis was performed on the soil of collecting and non-collecting areas of production and non-production areas, respectively. C. sinensis collection showed no alteration in alpha-diversity but significantly affected beta-diversity and the community composition of soil microorganisms. In Cordyceps production, Thaumarchaeota and Crenarchaeota were identified as the dominant archaeal phyla. DNA repair, flagellar assembly, propionate metabolism, and sulfur metabolism were affected in archaea, reducing the tolerance of archaea in extreme habitats. Proteobacteria, Actinobacteria, Acidobacteria, Verrucomicrobia, and Nitrospirae were identified as the dominant bacterial phyla. The collection of Chinese Cordyceps enhanced the bacterial biosynthesis of secondary metabolites and suppressed ribosome and carbon metabolism pathways in bacteria. A more complex microbial community relationship network in the Chinese Cordyceps production area was found. The changes in the microbial community structure were closely related to C, N, P and enzyme activities. This study clarified soil microbial community composition and function in the Cordyceps production area and established that collection clearly affects the microbial community function by altering microbial community structure. Therefore, it would be important to balance the relationship between cordyceps production and microbiology.

Current information from conventional microbiological methods on the microbial diversity of table olives is insufficient. Next-generation sequencing (NGS) technologies allow comprehensive analysis of their microbial community, providing microbial identity of table olive varieties and their designation of origin. The purpose of this study was to evaluate the bacterial and yeast diversity of fermented olives of two main Greek varieties collected from different regions—green olives, cv. Halkidiki, from Kavala and Halkidiki and black olives, cv. Konservolia, from Magnesia and Fthiotida—via conventional microbiological methods and NGS. Total viable counts (TVC), lactic acid bacteria (LAB), yeast and molds, and Enterobacteriaceae were enumerated. Microbial genomic DNA was directly extracted from the olives’ surface and subjected to NGS for the identification of bacteria and yeast communities. Lactobacillaceae was the most abundant family in all samples. In relation to yeast diversity, Phaffomycetaceae was the most abundant yeast family in Konservolia olives from the Magnesia region, while Pichiaceae dominated the yeast microbiota in Konservolia olives from Fthiotida and in Halkidiki olives from both regions. Further analysis of the data employing multivariate analysis allowed for the first time the discrimination of cv. Konservolia and cv. Halkidiki table olives according to their geographical origin.

Exploring characteristics of value-added production of anthraquinones in rhubarb via fermentation: Compartmental modelling and molecular docking analysis

BackgroundMicrobial communities associated with roots play a crucial role in the growth and health of plants and are constantly influenced by plant development and alterations in the soil environment. Despite extensive rhizosphere microbiome research, studies examining multi-kingdom microbial variation across large-scale agricultural gradients remain limited.ResultsThis study investigates the rhizosphere microbial communities associated with soybean across 13 diverse geographical locations in China. Using high-throughput shotgun metagenomic sequencing on the BGISEQ T7 platform with 10 GB per sample, we identified a total of 43,337 microbial species encompassing bacteria, archaea, fungi, and viruses. Our analysis revealed significant site-specific variations in microbial diversity and community composition, underscoring the influence of local environmental factors on microbial ecology. Principal coordinate analysis (PCoA) indicated distinct clustering patterns of microbial communities, reflecting the unique environmental conditions and agricultural practices of each location. Network analysis identified 556 hub microbial taxa significantly correlated with soybean yield traits, with bacteria showing the strongest associations. These key microorganisms were found to be involved in critical nutrient cycling pathways, particularly in carbon oxidation, nitrogen fixation, phosphorus solubilization, and sulfur metabolism. Our findings demonstrate the pivotal roles of specific microbial taxa in enhancing nutrient cycling, promoting plant health, and improving soybean yield, with significant positive correlations (r = 0.5, p = 0.039) between microbial diversity and seed yield.ConclusionThis study provides a comprehensive understanding of the diversity and functional potential of rhizosphere microbiota in enhancing soybean productivity. The findings underscore the importance of integrating microbial community dynamics into crop management strategies to optimize nutrient cycling, plant health, and yield. While this study identifies key microbial taxa with potential functional roles, future research should focus on isolating and validating these microorganisms for their bioremediation and biofertilization activities under field conditions. This will provide actionable insights for developing microbial-based agricultural interventions to improve crop resilience and sustainability.BGDCKW7bTctQq4sh1kxH_XVideo