Metagenomics — A unique view into the unculturable microbial communities

Metagenomics refers to the study of entire nucleotide sequences isolated from all the organisms (typically microbes) in a bulk sample. It is often used to study a specific community of microorganisms, such as those residing on human skin, in the soil, or in a water sample (NIH). Metagenomics is more versatile than genomics where (1) the organism(s) needs to be cultured in the lab and (2) generally involves sequencing only one organism at a time. With metagenomics, genetic material recovered from environmental or clinical samples can be directly sequenced.

Metagenomics data can be shotgun or targeted (16s rRNA, or 18s rRNA). Both of these are commonly done using next-generation sequencing technologies. This blog is regarding the analysis of shotgun metagenomics data. If interested in learning how to analyze 16S rRNA sequencing data and how BatchX can help with that please refer to this post. Shotgun metagenomics data can further be divided between marker gene-based analysis and genome-resolved metagenomics. This post explains the difference between both of them, mentions their utility, and describes how BatchX can serve as a useful platform for running these analyses.

Marker gene-based analysis

Marker gene-based analysis involves comparing each read to a reference database of taxonomically or phylogenetically informative sequences (i.e., marker genes), using a classification algorithm that determines if the read aligns with one of these reference sequences. These marker genes can then be used for taxonomic and functional profiling. A number of studies have identified that gut microbiome diversity can be linked to human health, with lower levels of diversity associated with several acute and chronic diseases. Additionally, comparing microbiome composition profiles between healthy and unhealthy individuals has helped link microbiome alterations with different pathologies such as inflammatory bowel disease, hepatocellular carcinoma, diabetes, and obesity.

Marker gene-based analysis enables the analysis of a large number of samples from different conditions quickly and at a lower cost compared to genome-resolved metagenomics. Since this type of analysis uses reads directly as input (as opposed to genome-resolved metagenomics where the reads need to be first assembled together) it is often also referred to as read-based analysis.

A small caveat while doing marker-based analysis is that the results are only as good as the reference database. If some function or taxonomy is not represented in the database it won’t be identified by the software. Fortunately, gut microbiome is one of the most commonly used databases in clinics and it has comprehensive reference databases available (Human Microbiome Project Data Portal). However, marker gene-based analysis might not be the best approach while studying environmental samples, where most of the genomes are novel.

Picture credits: Human Microbiome Project Data Portal

Use cases of marker gene-based analysis

Marker gene-based analysis has found widespread applications in clinical studies. An example of a real-life use case has been documented by a study in which researchers tracked clade-specific marker genes to better understand the effect of antibiotics in gut microbial composition. They used marker gene-based analysis and identified that antibiotic treatment caused a remarkable reduction in gut bacterial microbiota diversity while enriching certain types of antibiotic resistance genes [1].

In another study, researchers identified new ways to help diagnose Crohn’s disease. The disease is characterized by chronic inflammation of the gastrointestinal tract. It is well known that the gut microbiome is altered in patients suffering from this disease. However, through marker-based metagenomics, they demonstrated that even the oral microbiome is altered in patients suffering from this disease. This change in oral microbiome composition translates into a faster and more convenient method for Crohn’s disease diagnosis [2].

Genome resolved metagenomics

Genome-resolved metagenomics analysis involves the reconstruction of bacterial genomes using shotgun reads. The most important use case of genome-resolved metagenomics is that it enables the study of previously unknown bacteria in detail. Marker gene-based analysis is generally based on a database of marker genes, which generally assumes that each bacteria belonging to a particular species is the same. However, it is now known that two bacteria belonging to the same species can be very different from each other. As most of the binning algorithms (used in genome-resolved analyses) do not depend completely on the database of known organisms they are useful for identifying a previously unknown bacterium. This is critical for discovering new organisms and genes as well as reducing the amount of microbial dark matter.

Genome-resolved metagenomics typically starts by first assembling the reads into longer contiguous sequences called contigs. Each of these contigs is then assigned to its respective genome during a process called binning, which uses clustering algorithms to assemble contigs together in Metagenome Assembled Genomes (MAGs). MAGs are extremely useful for analyzing bacterial genomes in detail. Open reading frames (ORFs) can be called for each MAG and then annotated to study their metabolic capacity. Furthermore, since genome-resolved metagenomics involves assembling reads into longer sequences it enables the identification of novel genes and even gene clusters.

Genome-resolved metagenomics has great utility in understanding novel microbial communities, however, it’s time-consuming and costly compared to 16S rRNA or marker gene-based analysis. It’s important to consider high memory and CPU requirements when doing genome-resolved metagenomics.

Use cases of genome-resolved metagenomics

A real-life use case comes from the fact that most of the drugs that are used today, including antibiotics like streptomycin, tetracycline, vancomycin, and erythromycin are produced by bacteria. However, it is estimated that less than 1% of the bacteria can be cultured in the lab. Therefore, being able to explore the information contained in this 99% uncultured bacteria would translate to the discovery of many new clinical drugs and treatments. This has now begun to be possible through genome-resolved metagenomics. There have been numerous studies [3–6] where researchers have used genome-resolved metagenomics to identify potential gene clusters with the genetic information to synthesize a drug of interest as well as the specific bacterium having those genes.

One such example comes from a study involving Forcepia sp., a marine sponge. In previous studies, Lasonolide A (LSA), a promising anticancer drug, was isolated from this sponge. However, it was unclear why this drug was present in the sponge and how it was being produced. Sponges harbor a lot of microbial diversity and previous studies have hinted at these microbial species to be the probable producers of drugs. Through genome-resolved metagenomics, researchers from this study were able to identify the gene cluster responsible for the production of LSA as well as the bacterium with the gene cluster coded in their genome. Surprisingly, the bacterium with the LSA gene cluster was a completely new bacterium that could even require defining a new genus to be properly classified [3]. This study clearly illustrates the power of genome-resolved metagenomics in discovering novel bacteria.

Metagenomics at BatchX

BatchX provides pipelines for metagenomics analysis based on open-source reference bioinformatics tools which are considered standards for the analysis of metagenomics data.

• Pipeline for marker gene-based analysis — link
• Pipeline for genome resolved metagenomics analysis — link
• Pipeline for 16S rRNA analysis — link

BatchX is a no-code-needed platform for bioinformatics analysis. Tools in the catalog are ready to be used and can easily be run by simply providing the data needed to be analyzed. BatchX also provides the flexibility to scale any analysis to meet its computational demands.

Summary

Shotgun metagenomics offers a unique view into the unculturable microbial community. It enables sequencing an entire microbiome community without culturing restrictions. Two of the main approaches to analyzing this data were described here, marker-based analysis and genome-resolved metagenomics. Marker-based analysis is useful for clinical studies where a good reference database is available. It is more affordable and quicker than genome-resolved metagenomics. On the other hand, genome-resolved metagenomics is useful when dealing with environmental samples where a comprehensive reference database is not available. It’s also convenient when dealing with previously unknown bacteria, genes, or pathways. BatchX provides a low-cost and easy-to-use solution for running both of these metagenomics analyses. It provides a scalable infrastructure, with curated tools and a dedicated support team for users, so they can focus on what they are best at!

References

1. Xu L, Surathu A, Raplee I, Chockalingam A, Stewart S, Walker L, et al. The effect of antibiotics on the gut microbiome: a metagenomics analysis of microbial shift and gut antibiotic resistance in antibiotic treated mice. BMC Genomics. 2020;21: 263.

2. Hu S, Mok J, Gowans M, Ong DEH, Hartono JL, Lee JWJ. Oral Microbiome of Crohn’s Disease Patients With and Without Oral Manifestations. J Crohns Colitis. 2022;16: 1628–1636.

3. Uppal S, Metz JL, Xavier RKM, Nepal KK, Xu D, Wang G, et al. Uncovering Lasonolide A Biosynthesis Using Genome-Resolved Metagenomics. MBio. 2022;13: e0152422.

4. Lopera J, Miller IJ, McPhail KL, Kwan JC. Increased Biosynthetic Gene Dosage in a Genome-Reduced Defensive Bacterial Symbiont. mSystems. 2017. doi:10.1128/msystems.00096–17

5. Flórez LV, Scherlach K, Miller IJ, Rodrigues A, Kwan JC, Hertweck C, et al. An antifungal polyketide associated with horizontally acquired genes supports symbiont-mediated defense in Lagria villosa beetles. Nat Commun. 2018;9: 2478.

6. Kwan JC, Donia MS, Han AW, Hirose E, Haygood MG, Schmidt EW. Genome streamlining and chemical defense in a coral reef symbiosis. Proc Natl Acad Sci U S A. 2012;109: 20655–20660.