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

ABSTRACT: 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.

SUBMITTER: Rahman SJ

PROVIDER: S-EPMC5134946 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Soil-transmitted helminths (STHs) are parasitic intestinal worms that infect almost a fifth of the global population. Sustainable control of STHs requires understanding the complex interaction of factors contributing to transmission. Identifying risk factors has mainly relied on logistic regression models where the underlying assumption of independence between variables is not always satisfied. Previously demonstrated risk factors including water, sanitation and hygiene (WASH) access and behaviours, and socioeconomic status are intrinsically linked. Similarly, environmental factors including climate, soil and land attributes are often strongly correlated. Alternative methods such as recursive partitioning and Bayesian networks can handle correlated variables, but there are no published studies comparing these methods with logistic regression in the context of STH risk factor analysis. Baseline cross-sectional data from school-aged children in the (S)WASH-D for Worms study were used to compare risk factors identified from modelling the same data using three different statistical techniques. Outcomes of interest were infection with Ascaris spp. and any hookworm species (Necator americanus, Ancylostoma duodenale, and Ancylostoma ceylanicum). Mixed-effects logistic regression identified the fewest risk factors. Recursive partitioning identified the most WASH and demographic risk factors, while Bayesian networks identified the most environmental risk factors. Recursive partitioning produced classification trees that visualised potentially at-risk population sub-groups. Bayesian networks helped visualise relationships between variables and enabled interactive modelling of outcomes based on different scenarios for the predictor variables of interest. Model performance was similar across all techniques. Risk factors identified across all techniques were vegetation for Ascaris spp., and cleaning oneself with water after defecating for hookworm. This study adds to the limited body of evidence exploring alternative data modelling approaches in identifying risk factors for STH infections. Our findings suggest these approaches can provide novel insights for more robust interpretation.

Project description:Soil ecosystems harbor diverse microorganisms and yet remain only partially characterized as neither single-cell sequencing nor whole-community sequencing offers a complete picture of these complex communities. Thus, the genetic and metabolic potential of this "uncultivated majority" remains underexplored. To address these challenges, we applied a pooled-cell-sorting-based mini-metagenomics approach and compared the results to bulk metagenomics. Informatic binning of these data produced 200 mini-metagenome assembled genomes (sorted-MAGs) and 29 bulk metagenome assembled genomes (MAGs). The sorted and bulk MAGs increased the known phylogenetic diversity of soil taxa by 7.2% with respect to the Joint Genome Institute IMG/M database and showed clade-specific sequence recruitment patterns across diverse terrestrial soil metagenomes. Additionally, sorted-MAGs expanded the rare biosphere not captured through MAGs from bulk sequences, exemplified through phylogenetic and functional analyses of members of the phylum Bacteroidetes Analysis of 67 Bacteroidetes sorted-MAGs showed conserved patterns of carbon metabolism across four clades. These results indicate that mini-metagenomics enables genome-resolved investigation of predicted metabolism and demonstrates the utility of combining metagenomics methods to tap into the diversity of heterogeneous microbial assemblages.IMPORTANCE Microbial ecologists have historically used cultivation-based approaches as well as amplicon sequencing and shotgun metagenomics to characterize microbial diversity in soil. However, challenges persist in the study of microbial diversity, including the recalcitrance of the majority of microorganisms to laboratory cultivation and limited sequence assembly from highly complex samples. The uncultivated majority thus remains a reservoir of untapped genetic diversity. To address some of the challenges associated with bulk metagenomics as well as low throughput of single-cell genomics, we applied flow cytometry-enabled mini-metagenomics to capture expanded microbial diversity from forest soil and compare it to soil bulk metagenomics. Our resulting data from this pooled-cell sorting approach combined with bulk metagenomics revealed increased phylogenetic diversity through novel soil taxa and rare biosphere members. In-depth analysis of genomes within the highly represented Bacteroidetes phylum provided insights into conserved and clade-specific patterns of carbon metabolism.

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

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