The human microbiome—the collection of microbes that live on and in us—is integral to our wellness. Bacteria in our gut break down complex carbohydrates into accessible forms of nutrition. Our skin hosts microbes that exclude pathogens and fine-tune the immune system. High-throughput sequencing and culturing have uncovered some of our microbial partners, but many are unidentified. This equates to, in the words of Jackson Laboratory Assistant Professor Julia Oh, Ph.D., our “microbial dark matter.” This dark matter undoubtedly contains key information about how our microbiome contributes to human health and disease. But how do you study something you can’t see?
Skin is particularly rich in dark matter; up to 96% of a skin sample will contain non-human DNA that is unidentifiable by standard methods. Dr. Oh is tackling this problem by combining sequencing tools and novel computational analyses, described in a recent paper published in mBio. The work was performed while Oh was a fellow at the National Institutes of Health, but it provides the springboard from which she is developing her own laboratory at JAX-GM.
In the analysis, Oh and colleagues sequenced the same skin sample twice. One sequencing run used Illumina technology, which generates many short sequences of DNA, and the other used PacBio technology, which generates fewer but much longer DNA sequences. Using standard, reference-based approaches, they found both platforms identified only about 20% of the DNA sequences from a sample taken from the foot, which aligned to nearly 700 total archaea, bacteria, fungi and viruses. They then applied novel computational techniques to knit together the roughly 80% of PacBio reads that were microbial “dark matter” based on their similarity to each other. This metagenome reconstruction improved the alignment rate for the original community from about 20% to 85%. They determined that the presence of so much unknown microbial matter in the original sample was largely due to the inability to identify one species of bacteria, Clostridium simulans.
By combining the information from both sequencing platforms, they determined that the newly identified C. simulans population was dominated by one strain. Further, they uncovered a novel toxin gene encoded by these bacteria, which most closely resembled the septicolysin toxin originally identified in Clostridium septicum (the causative agent of gas gangrene). Additionally, the researchers identified a novel phage in the C. simulans metagenome. Phages are viruses that infect bacteria and often shuttle genes, such as antibiotic resistance and detoxification genes, between bacteria. As such they can contribute to genetic variation among bacterial species, which is one way bacterial populations can acquire resistance to antibiotic treatment. Thus, the elucidation of microbial dark matter may provide insight into how our microbiome may contribute to the maintenance or deterioration of our health and have implications for clinical therapeutics.
The paper is freely available and found on the mBio website here.