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Microbiome research has led to the discovery of important connections to human and environmental health. Sequencing has become a core investigational tool in microbiome research, a subject that we covered during a recent webinar. Our expert speakers shared a number of advancements including improved experimental workflows, research involving transmission dynamics, and invaluable analysis resources. This article recaps their informative presentations, offering insights and highlights from their discussions.
Avoiding PCR Bias Through a “Deconstructed” PCR Workflow
Stefan J. Green, Ph.D., Director of Core Laboratory Services at Rush University Medical Center, delivered a presentation on a method developed in his lab known as deconstructed PCR (DePCR)1,2. This technique was built to overcome common issues involved in traditional amplification strategies. In most sequencing-based microbiome research, conserved regions within the bacterial genome are amplified. The most common target is the 16S ribosomal RNA gene, which helps reflect the diversity of bacterial species. This process often utilizes a range of degenerate primers that tend to differ by just a single base, with the goal of amplifying the same genomic region across different bacterial types.
However, the complex mixture of primers and various DNA templates can introduce various types of bias, which Green calls “PCR mischief.” This bias can distort the bacterial species profile obtained from sequencing data compared to the actual sample composition. PCR biases have been recognized for some time3, and primer mismatches are known to be a leading factor. Yet, Green’s team identified another source of bias: an amplification efficiency discrepancy between linear copying and exponential amplification.
In reviewing the PCR process, Green pointed out that the first two cycles involve primers binding to genomic DNA and longer fragments to perform linear amplification. It isn’t until the third cycle that the desired amplicons are produced and exponential amplification begins. Green's DePCR method avoids the binding of degenerate primers to amplicons, addressing the recurring inefficiencies and reducing the potential for bias.
Separating Linear from Exponential Amplification
In the process of DePCR, as explained by Green, a standard PCR master mix is made, but it employs two distinct sets of primers (four primers total) to facilitate two separate stages of amplification. The initial stage utilizes the first primer set to perform four cycles of linear amplification. Following the fourth cycle, the reaction is stopped and the final amplicons contain the sequencing adapters, linkers, both forward and reverse primers, a unique barcode, and the target region. Importantly, these amplicons have never undergone exponential amplification.
Since each sample now has a sample-specific barcode, they can be easily pooled and purified together. A final amplification will then take place using the second set of primers tailored for sequencing—such as Illumina’s P5 and P7 primers—ensuring that while exponential amplification occurs, the primers are not binding within the region of interest and reduce any bias.
Green underscored two pivotal advantages of this approach. Firstly, DePCR separates the efficiencies associated with linear and exponential amplification. Linear amplification benefits from the primers' interaction with genomic DNA, producing one efficiency type, while exponential amplification interacts with another primer set, resulting in a different efficiency. By separating the processes, DePCR avoids the complication of dual efficiencies within the same reaction. Secondly, this method effectively "fossilizes" the original degenerate primers within the amplicon. This fossilization is important for showing which primers attached to the DNA template without altering the sequencing.
During the presentation, Green dove deeper into annealing temperatures and how this impacts the represented microbial diversity for his method and standard methods. His group also expanded on their original DePCR design and created novel ways to detect if there is any mismatch amplification between the primer and template.
Green finished his presentation by emphasizing that DePCR is recommended for reactions where complex primer pools are used or primer-template interactions may have low efficiency due to mismatches. Future evaluations with his team will assess additional primer-template systems to extend the utility of this method across various applications.
Person-to-Person Transmission Landscape of the Human Gut and Oral Microbiomes
Mireia Valles-Colomer, Ph.D., a Group Leader at the MELIS Department of the University Pompeu Fabra in Barcelona, Spain, delivered an insightful talk on the transmission of the microbiome. She began with a reflection on the human microbiome's critical role in human health and how alterations in its composition are linked to a range of health issues. Despite this growing awareness, the impact of social interactions on the microbiome's unique genetic structure and its distribution among individuals and groups remains largely underexplored.
Valles-Colomer asserted that understanding these transmission dynamics is important for a number of reasons. First, it addresses a fundamental question within microbiome research with profound implications. The microbiome's links to numerous diseases raise the possibility that many noncommunicable diseases could take on communicable characteristics through microbial transmission. Furthermore, a deeper understanding of this process expands our overall knowledge of the microbiome's involvement in health and disease. This could pave the way for better insights into microbiota modulations concerning both timing (from early life to adulthood) and composition (focusing on specific strains).
While it's well-known that infants inherit their mother's microbiome, research has also shown that cohabiting non-kin individuals also display microbiota similarities4, and there are demonstrations of horizontal microbiota transmission in households5. However, there were many questions about the types of transmissions microbes were capable of, which inspired Valles-Colomer and colleagues to perform a large-scale study of microbiome transmission.
Exploring Transmission Dynamics
Valles-Colomer’s research detailed the transmission dynamics of the human gut and oral microbiomes between individuals6. By running a comprehensive analysis of over 9,700 human metagenomes to uncover patterns of bacterial strain sharing, her work revealed distinct transmission pathways for gut and oral microbiomes. The study observed extensive mother-to-infant transmission of the gut microbiome, which remained significant during infancy and could be detected into older age.
For the oral microbiome, transmission occurred mainly through horizontal (non-familial) pathways and was also increased by the length of cohabitation. It was further demonstrated that cohabiting individuals share more strains than those who don't live together, with 12% and 32% median strain-sharing rates for the gut and oral microbiomes. Furthermore, cohabitation was found to have a more pronounced effect on strain sharing compared to genetics or age. In addition, strain sharing was found to recapitulate the host's population structure better than species-level profiles, indicating the importance of considering strain-level data in understanding human microbial ecology.
Expanding on this, Valles-Colomer explained how the group identified certain bacterial taxa as efficient spreaders across different transmission modes, which were associated with various bacterial phenotypes that may influence survival outside the host. This highlights the role of bacterial transmission in studies of the human microbiome, particularly concerning non-infectious, microbiome-associated diseases.
In her concluding remarks, Valles-Colomer emphasized several key points. She emphasized how the microbiome is transmitted both vertically and horizontally. Valles-Colomer also highlighted the significant influence of shared environments (cohabitation) and social interactions that play a key role in shaping individual microbiomes. Additionally, she pointed out distinct transmission patterns observed in gut and oral microbiomes. Finally, she drew attention to the presence of specific microbes that were shown to transmit more frequently, showing that these transmissions were not left to chance.
Generating Bio-Specific Genome Catalogs from Metagenomic Data
Microbiome research is a complex field, with the analysis and interpretation of sequencing data typically representing the most difficult aspect. To assist with some of these challenges, Tatiana Gurbich, Ph.D., Bioinformatician for the Microbiome Informatics Team at EMBL-EBI, introduced the analysis resource known as MGnify7. This tool provides biome-specific, non-redundant microbial genome catalogs.
The primary focus of Gurbich's team is to create these customized catalogs for each biome. This work is performed by searching for user-submitted metagenome-assembled genomes (MAGs) in GenBank. Occasionally, this includes submitted whole-genome sequencing data that can be converted to MAGs by the bioinformatics team. Despite making up a smaller amount of this data, the group also utilizes isolate genomes for this process. The current listings only include prokaryotic organisms; however, the group plans to build catalogs for eukaryotic genomes in the future.
Since these catalogs will be utilized across the research community, the bioinformatics team ensures the genomes integrated into the catalog are of high quality. They start by filtering out data based on contamination and completeness, and genomes likely to be chimeric are also removed. After the data has passed a rigorous QC process, species annotation and clustering are performed.
During this process, Gurbich discussed how the catalogs are organized into species clusters, prioritizing isolate genomes over MAGs for quality representation. Then much of the genome is annotated using a variety of tools. Finally, with each catalog, the following additional information is included: protein catalog, gene catalog, Kraken 2 database, phylogeny, and metadata. All of this information is generated from a standardized pipeline that is publicly available and reproducible.
Leveraging MGnify for Research
Currently, over 300,000 genomes representing 11,048 non-redundant species have been cataloged, including taxa not currently represented by cultured genomes. These catalogs assist in the visualization and analysis of genomic data and annotations, allowing researchers to compare their sequences with existing data.
Some of these available catalogs include those for the human gut and cow rumen. Gurbich highlighted the composition of these biomes and the novelty they present at various taxonomic levels. Notably, the novelty of these genomes is significant. In many cases, they represent species or higher taxonomic ranks not previously known, which demonstrates the potential of metagenomic data to reveal unexplored microbial diversity.
Gurbich went on to review some of the use cases of this resource. Many researchers are using this tool with their own data to understand what organisms are present and what they are doing. The taxonomy and functional annotation provided by MGnify assist with answering these questions. Furthermore, there are additional tools that allow for functional comparisons with other biomes. For example, researchers can use these tools to investigate the differences in species composition between two biomes.
This resource can also be used as a reference database where researchers can identify the taxonomy of their data. Additionally, MGnify provides a search function where researchers can search their own genomes against the catalogs. Another important use is investigating microbial dark matter where users can explore novel taxa and uncharacterized proteins.
Lastly, Gurbich explained that the catalogs are updated with new data, ensuring the resource remains current and comprehensive. It is also important to note that users can contribute by uploading their own data. The requirements are that the data must be public and submitted to either ENA, NCBI, or DDBJ. By submitting this information, researchers can help with the development of this valuable resource and share their data with the community. MGnify has continued to grow and facilitate access to a vast array of genomic data, expanding our understanding of microbial diversity and aiding in the exploration of uncharacterized species.
Don't worry if you missed it; you can still watch the microbiome webinar on-demand here! Watch it at your own convenience, and if you liked it, don’t forget to check out the other great SEQanswers webinars.
References
- Green, S. J., Venkatramanan, R., & Naqib, A. (2015). Deconstructing the polymerase chain reaction: understanding and correcting bias associated with primer degeneracies and primer-template mismatches. PloS one, 10(5), e0128122.
- Naqib, A., Poggi, S., & Green, S. J. (2019). Deconstructing the Polymerase Chain Reaction II: an improved workflow and effects on artifact formation and primer degeneracy. PeerJ, 7, e7121.
- Suzuki, M. T., & Giovannoni, S. J. (1996). Bias caused by template annealing in the amplification of mixtures of 16S rRNA genes by PCR. Applied and environmental microbiology, 62(2), 625-630.
- Song, S. J., Lauber, C., Costello, E. K., Lozupone, C. A., Humphrey, G., Berg-Lyons, D., ... & Knight, R. (2013). Cohabiting family members share microbiota with one another and with their dogs. elife, 2, e00458.
- Browne, H. P., Neville, B. A., Forster, S. C., & Lawley, T. D. (2017). Transmission of the gut microbiota: spreading of health. Nature Reviews Microbiology, 15(9), 531-543.
- Valles-Colomer, M., Blanco-Míguez, A., Manghi, P., Asnicar, F., Dubois, L., Golzato, D., ... & Segata, N. (2023). The person-to-person transmission landscape of the gut and oral microbiomes. Nature, 614(7946), 125-135.
- Gurbich, T. A., Almeida, A., Beracochea, M., Burdett, T., Burgin, J., Cochrane, G., ... & Finn, R. D. (2023). MGnify Genomes: a resource for biome-specific microbial genome catalogues. Journal of Molecular Biology, 168016.