Recent Developments in Metagenomics

Metagenomics has improved the way researchers study microorganisms across diverse environments. Historically, studying microorganisms relied on culturing them in the lab, a method that limits the investigation of many species since most are unculturable¹. Metagenomics overcomes these issues by allowing the study of microorganisms regardless of their ability to be cultured or the environments they inhabit. Over time, the field has evolved, especially with the advent of next-generation sequencing (NGS) and numerous advancements in analytical techniques. In this article, we highlight several of these developments alongside recent research in metagenomics.

Enrichment Techniques
Earlier this year, scientists from the Icahn School of Medicine at Mount Sinai published a study on mEnrich-seq, a method designed to selectively enrich bacterial taxa of interest from microbiome samples based on their DNA methylation patterns². This approach works by using methylation-sensitive restriction enzymes that digest non-methylated DNA from microbes and host organisms, leaving the methylated DNA from the target bacteria intact. Then the undigested DNA is sequenced and analyzed to study specific bacterial genomes from complex samples.The authors noted that this method is versatile, applicable across various sequencing platforms, and can enrich low-abundance or difficult-to-culture species directly from microbiomes.

Wastewater Studies
Wastewater has been used in metagenomics research for years to monitor pathogens, explore microbial diversity, and assess human environmental impact. During the COVID-19 pandemic, wastewater studies increased to evaluate the spread of coronaviruses throughout different populations³. More recently, European researchers applied a metagenomics-based method for wastewater analysis across five cities⁴. This allowed them to discover over 1,300 novel microbial taxa from sewage samples, as well as identify cases of antimicrobial resistance and Vibrio cholerae⁵. In addition, the study provided helpful recommendations on computational analysis methods and time-specific sampling to mitigate common issues.

Long-Read Sequencing
Improvements in long-read sequencing technologies and their analysis methods are an important advancement in metagenomics. Two current reviews have covered the advantages and disadvantages of long reads, along with the development of this technology throughout the years^6,7. A recent study also explored how long-read sequencing improves bacterial metagenomic research by enhancing assembly quality, taxonomic classification, and metagenome-assembled genome (MAG) recovery rates⁸. It demonstrated that long-read sequencing provides greater accuracy in classifying microorganisms and improves the contiguity of genomic assemblies.
Long reads have enabled many discoveries in the field. For example, using long-read sequencing researchers were able to explore structural variations (SVs) in the human gut phageome⁹. This work demonstrated that phages in the gut exhibit significant genetic exchange with their bacterial hosts, and these variations, can provide insights into phage evolution and microbiome functionality.

Advances in Analysis
Successful metagenomics research depends on the analytical tools used to sift through extensive datasets and accurately identify microorganisms. Recent advancements in metagenomics have largely focused on developing new or improved analytical tools. For example, the new metagWGS enables efficient analysis of metagenomic data using either Illumina or PacBio HiFi reads¹⁰. The pipeline includes bin refinement with a new tool called "Binette," which surpasses existing tools in producing high-quality MAGs. It can also generate taxonomic and functional profiles for all contigs.

For viral metagenomics, researchers developed an integrated software platform known as Hecatomb¹¹. It was designed to manage important tasks quality control, host removal, and both read- and contig-based analysis. The platform also uses a tiered database search approach that minimizes false positives in viral annotations and can process short- and long-read sequencing data. This work demonstrated that Hecatomb improves virus discovery and genome assembly, as well as demonstrating effectiveness through the reanalysis of datasets.

Additionally, researchers evaluated the impact of different reference databases and confidence scores (CS) on Kraken2's performance for metagenomic classification¹². Using simulated datasets, they found that higher CS values reduced classification rates, especially with smaller databases. Larger databases, like GTDB r202, maintained better classification rates and improved precision and F1 scores at higher CS settings. The study demonstrated the importance of choosing the appropriate databases and CS values, suggesting moderate CS levels (0.2 to 0.4) improve metagenomic classification accuracy and sensitivity.

Despite these advances, there are still challenges in reconstructing genomes of abundant species due to high strain diversity and genome fragmentation. In a recent article in Genome Biology, Feng and Li proposed novel algorithms to recover missing bacterial genomes by identifying circular assembly subgraphs¹³. This approach offers more complete representations of microbial communities and improves genome binning accuracy. Their work also highlights the importance of completeness in metagenome assemblies.

Further Readings
Metagenomics is a broad and fascinating field, with several reviews providing valuable insights into the other important developments. One recent article discusses the integration of functional metagenomics to improve understanding of microbiome-immune interactions¹⁴. This review covers the advances in functional metagenomic methods, such as shotgun metagenomics, which allows for culture-independent analysis and provides insights into microbial functions. Furthermore, it emphasizes key opportunities for applying functional metagenomics to study immune responses in health and disease, including inflammatory bowel disease, cancer immunotherapy, and homeostatic immune processes.

Another interesting review discusses genome-resolved metagenomics as an important tool in metagenomics, particularly in microbiome medicine¹⁵. It examines the limitations of traditional 16S rRNA sequencing and highlights how genome-resolved metagenomics reconstructs microbial genomes directly from whole metagenome sequencing data. This approach enables high-resolution taxonomic and functional profiling, potentially leading to discoveries in microbial genetics, species diversity, and personalized medicine. Overall, the review emphasizes its potential to revolutionize the understanding and treatment of human diseases through microbiome research.

Lastly, a review of how deep learning (DL) methods are advancing the field of metagenomics explains how DL techniques, such as convolutional networks and autoencoders, are being used for microbial sequence classification, disease prediction, and functional annotation of metagenomic data¹⁶. The authors note that these methods address challenges in traditional bioinformatics and are enabling more accurate analysis of microbiomes, particularly for human health applications.

References

1. Handelsman J. Metagenomics: Application of Genomics to Uncultured Microorganisms. Microbiology and Molecular Biology Reviews. 2004;68(4):669-685. doi:https://doi.org/10.1128/mmbr.68.4.669-685.2004
2. Cao L, Kong Y, Fan Y, et al. mEnrich-seq: methylation-guided enrichment sequencing of bacterial taxa of interest from microbiome. Nature Methods. 2024;21(2):236-246. doi:https://doi.org/10.1038/s41592-023-02125-1
3. Farkas K, Hillary LS, Malham SK, et al. Wastewater and public health: the potential of wastewater surveillance for monitoring COVID-19. Environmental Health: COVID19. 2020;17:14-20. doi:https://doi.org/10.1016/j.coesh.2020.06.001
4. Becsei Á, Fuschi A, Otani S, et al. Time-series sewage metagenomics distinguishes seasonal, human-derived and environmental microbial communities potentially allowing source-attributed surveillance. Nature Communications. 2024;15(1):7551. doi:https://doi.org/10.1038/s41467-024-51957-8
5. Brinch C, Otani S, Munk P, van, Franz E, Aarestrup FM. Discovery of Vibrio cholerae in Urban Sewage in Copenhagen, Denmark. Microbial Ecology. 2024;87(1):102. doi:https://doi.org/10.1007/s00248-024-02419-7
6. Kim C, Pongpanich M, Porntaveetus T. Unraveling metagenomics through long-read sequencing: a comprehensive review. Journal of Translational Medicine. 2024;22(1):111. doi:https://doi.org/10.1186/s12967-024-04917-1
7. Agustinho DP, Fu Y, Menon VK, at al. Unveiling microbial diversity: harnessing long-read sequencing technology. Nature Methods. 2024;21(6):954-966. doi:https://doi.org/10.1038/s41592-024-02262-1
8. Greenman N, Hassouneh SA-D, Abdelli LS, et al. Improving Bacterial Metagenomic Research through Long-Read Sequencing. Microorganisms. 2024;12(5). doi:https://doi.org/10.3390/microorganisms12050935
9. Lai S, Wang H, Bork P, Chen W, Zhao X. Long-read sequencing reveals extensive gut phageome structural variations driven by genetic exchange with bacterial hosts. Science Advances. 10(33):eadn3316. doi:https://doi.org/10.1126/sciadv.adn3316
10. Mainguy J, Vienne M, Fourquet J, et al. metagWGS, a comprehensive workflow to analyze metagenomic data using Illumina or PacBio HiFi reads. bioRxiv. Published online January 1, 2024:2024.09.13.612854. doi:https://doi.org/10.1101/2024.09.13.612854
11. Roach MJ, Beecroft SJ, Mihindukulasuriya KA, et al. Hecatomb: an integrated software platform for viral metagenomics. Gigascience. 2024;13:giae020. doi:https://doi.org/10.1093/gigascience/giae020
12. Liu Y, Ghaffari MH, Ma T, et al. Impact of database choice and confidence score on the performance of taxonomic classification using Kraken2. aBIOTECH. Published online 2024. doi:https://doi.org/10.1007/s42994-024-00178-0
13. Feng X, Li H. Evaluating and improving the representation of bacterial contents in long-read metagenome assemblies. Genome Biology. 2024;25(1):92. doi:https://doi.org/10.1186/s13059-024-03234-6
14. Sardar P, Almeida A, Pedicord VA. Integrating functional metagenomics to decipher microbiome–immune interactions. Immunol Cell Biol. 2024;102(8):680-691. doi:https://doi.org/10.1111/imcb.12798
15. Kim N, Ma J, Kim W, Kim J, Belenky P, Lee I. Genome-resolved metagenomics: a game changer for microbiome medicine. Experimental & Molecular Medicine. 2024;56(7):1501-1512. doi:https://doi.org/10.1038/s12276-024-01262-7
16. Roy G, Prifti E, Belda E, et al. Deep learning methods in metagenomics: a review. Microbial Genomics. 2024;10. doi:https://doi.org/10.1099/mgen.0.001231