Metagenomics and its connection to microbial community organization

Hitac: a hierarchical taxonomic classifier for fungal ITS sequences compatible with QIIME2

BACKGROUND

Fungi play a key role in several important ecological functions, ranging from organic matter decomposition to symbiotic associations with plants. Moreover, fungi naturally inhabit the human body and can be beneficial when administered as probiotics. In mycology, the internal transcribed spacer (ITS) region was adopted as the universal marker for classifying fungi. Hence, an accurate and robust method for ITS classification is not only desired for the purpose of better diversity estimation, but it can also help us gain a deeper insight into the dynamics of environmental communities and ultimately comprehend whether the abundance of certain species correlate with health and disease. Although many methods have been proposed for taxonomic classification, to the best of our knowledge, none of them fully explore the taxonomic tree hierarchy when building their models. This in turn, leads to lower generalization power and higher risk of committing classification errors.

RESULTS

Here we introduce HiTaC, a robust hierarchical machine learning model for accurate ITS classification, which requires a small amount of data for training and can handle imbalanced datasets. HiTaC was thoroughly evaluated with the established TAXXI benchmark and could correctly classify fungal ITS sequences of varying lengths and a range of identity differences between the training and test data. HiTaC outperforms state-of-the-art methods when trained over noisy data, consistently achieving higher F1-score and sensitivity across different taxonomic ranks, improving sensitivity by 6.9 percentage points over top methods in the most noisy dataset available on TAXXI.

CONCLUSIONS

HiTaC is publicly available at the Python package index, BIOCONDA and Docker Hub. It is released under the new BSD license, allowing free use in academia and industry. Source code and documentation, which includes installation and usage instructions, are available at https://gitlab.com/dacs-hpi/hitac .

Short-Term Impact of Oxytetracycline Administration on the Fecal Microbiome, Resistome and Virulome of Grazing Cattle

Antimicrobial resistance (AMR) is an important public health concern around the world. Limited information exists about AMR in grasslands-based systems where antibiotics are seldom used in beef cattle. The present study investigated the impacts of oxytetracycline (OTC) on the microbiome, antibiotic resistance genes (ARGs), and virulence factor genes (VFGs) in grazing steers with no previous exposure to antibiotic treatments. Four steers were injected with a single dose of OTC (TREAT), and four steers were kept as control (CONT). The effects of OTC on fecal microbiome, ARGs, and VFGs were assessed for 14 days using 16S rRNA sequencing and shotgun metagenomics. Alpha and beta microbiome diversities were significantly affected by OTC. Following treatment, less than 8% of bacterial genera had differential abundance between CONT and TREAT samples. Seven ARGs conferring resistance to tetracycline (tet32, tet40, tet44, tetO, tetQ, tetW, and tetW/N/W) increased their abundance in the post-TREAT samples compared to CONT samples. In addition, OTC use was associated with the enrichment of macrolide and lincosamide ARGs (mel and lnuC, respectively). The use of OTC had no significant effect on VFGs. In conclusion, OTC induced short-term alterations of the fecal microbiome and enrichment of ARGs in the feces of grazing beef cattle.

Metabolomic analysis of halotolerant endophytic bacterium Salinivibrio costicola isolated from Suaeda maritima (L.) dumort

In this study, the Salinivibrio costicola strain was isolated from Suaeda maritima (L.) Dumort. collected in Sinan, Republic of Korea. The endophytic characteristics of the Gram-negative bacterium S. costicola were verified with metagenomics sequencing of S. maritima. S. costicola was cultivated for 3 days in a liquid medium with 3.3% sea salt and analyzed the metabolites produced by the strain cultured in five different bacterial cultivation media. From the bacterial cultures, polyhydroxybutyrate derivatives were detected using high-resolution mass spectrometry, and three major compounds were isolated by high-performance liquid chromatography. The chemical structures of the compounds were elucidated using nuclear magnetic resonance and MS analyses. The relationship between the compounds was confirmed with Global Natural Product Social Molecular Networking, which showed clustering of the compounds. From the S. maritima extract, polyhydroxybutyrate derivatives produced by S. costicola were detected as being accumulated in the host plant.

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.

Total rRNA-Seq Analysis Gives Insight into Bacterial, Fungal, Protozoal and Archaeal Communities in the Rumen Using an Optimized RNA Isolation Method

Advances in high throughput, next generation sequencing technologies have allowed an in-depth examination of biological environments and phenomena, and are particularly useful for culture-independent microbial community studies. Recently the use of RNA for metatranscriptomic studies has been used to elucidate the role of active microbes in the environment. Extraction of RNA of appropriate quality is critical in these experiments and TRIzol reagent is often used for maintaining stability of RNA molecules during extraction. However, for studies using rumen content there is no consensus on (1) the amount of rumen digesta to use or (2) the amount of TRIzol reagent to be used in RNA extraction procedures. This study evaluated the effect of using various quantities of ground rumen digesta and of TRIzol reagent on the yield and quality of extracted RNA. It also investigated the possibility of using lower masses of solid-phase rumen digesta and lower amounts of TRIzol reagent than is used currently, for extraction of RNA for metatranscriptomic studies. We found that high quality RNA could be isolated from 2 g of ground rumen digesta sample, whilst using 0.6 g of ground matter for RNA extraction and using 3 mL (a 5:1 TRIzol : extraction mass ratio) of TRIzol reagent. This represents a significant savings in the cost of RNA isolation. These lower masses and volumes were then applied in the RNA-Seq analysis of solid-phase rumen samples obtained from 6 Angus X Hereford beef heifers which had been fed a high forage diet (comprised of barley straw in a forage-to-concentrate ratio of 70:30) for 102 days. A bioinformatics analysis pipeline was developed in-house that generated relative abundance values of archaea, protozoa, fungi and bacteria in the rumen and also allowed the extraction of individual rRNA variable regions that could be analyzed in downstream molecular ecology programs. The average relative abundances of rRNA transcripts of archaea, bacteria, protozoa and fungi in our samples were 1.4 ± 0.06, 44.16 ± 1.55, 35.38 ± 1.64, and 16.37 ± 0.65% respectively. This represents the first study to define the relative active contributions of these populations to the rumen ecosystem and is especially important in defining the role of the anaerobic fungi and protozoa.

High Prevalence of Gammaproteobacteria in the Sediments of Admiralty Bay and North Bransfield Basin, Northwestern Antarctic Peninsula

Microorganisms dominate most Antarctic marine ecosystems, in terms of biomass and taxonomic diversity, and play crucial role in ecosystem functioning due to their high metabolic plasticity. Admiralty Bay is the largest bay on King George Island (South Shetland Islands, Antarctic Peninsula) and a combination of hydro-oceanographic characteristics (bathymetry, sea ice and glacier melting, seasonal entrance of water masses, turbidity, vertical fluxes) create conditions favoring organic carbon deposition on the seafloor and microbial activities. We sampled surface sediments from 15 sites across Admiralty Bay (100–502 m total depth) and the adjacent North Bransfield Basin (693–1147 m), and used the amplicon 454-sequencing of 16S rRNA gene tags to compare the bacterial composition, diversity, and microbial community structure across environmental parameters (sediment grain size, pigments and organic nutrients) between the two areas. Marine sediments had a high abundance of heterotrophic Gammaproteobacteria (92.4% and 83.8% inside and outside the bay, respectively), followed by Alphaproteobacteria (2.5 and 5.5%), Firmicutes (1.5 and 1.6%), Bacteroidetes (1.1 and 1.7%), Deltaproteobacteria (0.8 and 2.5%) and Actinobacteria (0.7 and 1.3%). Differences in alpha-diversity and bacterial community structure were found between the two areas, reflecting the physical and chemical differences in the sediments, and the organic matter input.

Metagenomic Approaches to Identify Novel Organisms from the Soil Environment in a Classroom Setting

Molecular Microbial Metagenomics is a research-based undergraduate course developed at Georgia State University. This semester-long course provides hands-on research experience in the area of microbial diversity and introduces molecular approaches to study diversity. Students are part of an ongoing research project that uses metagenomic approaches to isolate clones containing 16S ribosomal ribonucleic acid (rRNA) genes from a soil metagenomic library. These approaches not only provide a measure of microbial diversity in the sample but may also allow discovery of novel organisms. Metagenomic approaches differ from the traditional culturing methods in that they use molecular analysis of community deoxyribonucleic acid (DNA) instead of culturing individual organisms. Groups of students select a batch of 100 clones from a metagenomic library. Using universal primers to amplify 16S rRNA genes from the pool of DNA isolated from 100 clones, and a stepwise process of elimination, each group isolates individual clones containing 16S rRNA genes within their batch of 100 clones. The amplified 16S rRNA genes are sequenced and analyzed using bioinformatics tools to determine whether the rRNA gene belongs to a novel organism. This course provides avenues for active learning and enhances students' conceptual understanding of microbial diversity. Average scores on six assessment methods used during field testing indicated that success in achieving different learning objectives varied between 84% and 95%, with 65% of the students demonstrating complete grasp of the project based on the end-of-project lab report. The authentic research experience obtained in this course is also expected to result in more undergraduates choosing research-based graduate programs or careers.

Metagenomic Classification Using an Abstraction Augmented Markov Model

The abstraction augmented Markov model (AAMM) is an extension of a Markov model that can be used for the analysis of genetic sequences. It is developed using the frequencies of all possible consecutive words with same length (p-mers). This article will review the theory behind AAMM and apply the theory behind AAMM in metagenomic classification.

Isolation and in silico characterization of novel esterase gene with β-lactamase fold isolated from metagenome of north western Himalayas

An esterase-producing clone Aph2 was isolated from the Apharwat soil metagenomic library, a mountain peak in NW Himalayas. ORF 2 (Est Ac) of clone Aph2 corresponds to 271 aa protein and showed 26 % sequence similarity to carboxylesterase gene of Synechococcus sp. JA-2-3B. Est Ac contains nucleophilic Ser in S68-X-X-K71 motif of β-lactamases with Tyr Y103. The conserved sequences are common with family VIII carboxylesterase and class C β-lactamase sequences. Phylogenetic analysis revealed that Est Ac sequence is closely related to esterase than to β-lactamases. In silico 3D protein structure of Est Ac was generated using MODELLER software (9.10 version). Model was generated on the basis of carboxylesterase template (PDB:1CI8) of Est B (Burkholderia gladioli) and the stereochemical parameters of the model generated were satisfactory. Docking with diisopropyl-fluorophosphate confirmed catalytic activity of Ser68 present in S-X-X-K motif.

16S classifier: a tool for fast and accurate taxonomic classification of 16S rRNA hypervariable regions in metagenomic datasets

The diversity of microbial species in a metagenomic study is commonly assessed using 16S rRNA gene sequencing. With the rapid developments in genome sequencing technologies, the focus has shifted towards the sequencing of hypervariable regions of 16S rRNA gene instead of full length gene sequencing. Therefore, 16S Classifier is developed using a machine learning method, Random Forest, for faster and accurate taxonomic classification of short hypervariable regions of 16S rRNA sequence. It displayed precision values of up to 0.91 on training datasets and the precision values of up to 0.98 on the test dataset. On real metagenomic datasets, it showed up to 99.7% accuracy at the phylum level and up to 99.0% accuracy at the genus level. 16S Classifier is available freely at http://metagenomics.iiserb.ac.in/16Sclassifier and http://metabiosys.iiserb.ac.in/16Sclassifier.

Random whole metagenomic sequencing for forensic discrimination of soils

Here we assess the ability of random whole metagenomic sequencing approaches to discriminate between similar soils from two geographically distinct urban sites for application in forensic science. Repeat samples from two parklands in residential areas separated by approximately 3 km were collected and the DNA was extracted. Shotgun, whole genome amplification (WGA) and single arbitrarily primed DNA amplification (AP-PCR) based sequencing techniques were then used to generate soil metagenomic profiles. Full and subsampled metagenomic datasets were then annotated against M5NR/M5RNA (taxonomic classification) and SEED Subsystems (metabolic classification) databases. Further comparative analyses were performed using a number of statistical tools including: hierarchical agglomerative clustering (CLUSTER); similarity profile analysis (SIMPROF); non-metric multidimensional scaling (NMDS); and canonical analysis of principal coordinates (CAP) at all major levels of taxonomic and metabolic classification. Our data showed that shotgun and WGA-based approaches generated highly similar metagenomic profiles for the soil samples such that the soil samples could not be distinguished accurately. An AP-PCR based approach was shown to be successful at obtaining reproducible site-specific metagenomic DNA profiles, which in turn were employed for successful discrimination of visually similar soil samples collected from two different locations.

Interactions in the microbiome: communities of organisms and communities of genes

A central challenge in microbial community ecology is the delineation of appropriate units of biodiversity, which can be taxonomic, phylogenetic, or functional in nature. The term 'community' is applied ambiguously; in some cases, the term refers simply to a set of observed entities, while in other cases, it requires that these entities interact with one another. Microorganisms can rapidly gain and lose genes, potentially decoupling community roles from taxonomic and phylogenetic groupings. Trait-based approaches offer a useful alternative, but many traits can be defined based on gene functions, metabolic modules, and genomic properties, and the optimal set of traits to choose is often not obvious. An analysis that considers taxon assignment and traits in concert may be ideal, with the strengths of each approach offsetting the weaknesses of the other. Individual genes also merit consideration as entities in an ecological analysis, with characteristics such as diversity, turnover, and interactions modeled using genes rather than organisms as entities. We identify some promising avenues of research that are likely to yield a deeper understanding of microbial communities that shift from observation-based questions of 'Who is there?' and 'What are they doing?' to the mechanistically driven question of 'How will they respond?'