The microbiome refers to the collection of microorganisms in an environment, such as the human gut. Clostridium difficile is a Gram-positive bacterium that was isolated from the human gut and was found to be responsible for gastric infections. This is frequently seen in hospitalized patients, particularly those who have received antibiotics.
Image Credit: Kateryna Kon/Shutterstock.com
Microbiome profiling seeks to characterize the species present in a microbiome, and this utilizes various techniques such as ribosomal RNA (rRNA) sequencing or sequencing of the whole microbiome DNA.
This not only allows for identification of C. difficile and other microbial species in a microbiome, but the information provided can be further analyzed to study C. difficile carriage and infections; for example, the study by Daquigan and co. found that there were instances where C. difficile was identified by microbiome profiling when the participant had not shown symptoms of C. difficile infection.
What is microbiome profiling?
Microbiome profiling is the process of understanding the species present in a microbial community, also known as the microbiome. The microbiome can be present in many different environments, including the human gut.
The starting point for microbiome profiling is the extraction of DNA, which is subsequently analyzed in two main ways; sequencing the ribosomal RNA (rRNA), or metagenomic sequencing where the entire extracted DNA is sequenced.
16S rRNA is present in all bacteria and archaea, and it has been shown that a small fragment of this sequence is sufficient to determine microbial species. Therefore, this approach can be successfully used in microbiome profiling. The development of DNA sequencing technology as well as improvements in databases and bioinformatic tools also facilitates the use of DNA sequencing in microbiome profiling.
What are Clostridium difficile infections?
C. difficile is a bacterium that can cause gastric infections in humans, which can be as severe as pseudomembranous colitis and toxic megacolon, and even lead to death. C. difficile is also known to produce toxins.
These toxins are monoglycosyltransferases, an enzyme that transfers glucose to Rho GTPases, and this disrupts the actin cytoskeleton. Consequently, this leads to the disruption of the gut barrier and apoptotic cell death.
C. difficile infections are a prominent problem in healthcare settings; often, infection with C. difficile is associated with antibiotic use, and it is thought that up to 20% of antibiotic-associated diarrhea cases are caused by C. difficile. Once diagnosed, treatment of C. difficile infections involves antibiotics, and when this fails a fecal microbiota transplant may be carried out.
Diagnosis of C. difficile infections typically involve enzyme immunoassays, glutamate dehydrogenase tests, and real-time PCR assays. Also, assays to test for the presence of C. difficile toxins can be used. While these tests identify C. difficile, it does not give information about the microbiome in general. So, how can microbiome profiling be applied to study how the human gut microbiome and C. difficile interact?
How can microbiome profiling be used to study C. difficile infections?
As the goal of microbiome profiling is to identify members of a microbiome, it should be possible to identify those who carry C. difficile in their gut microbiome using this approach. Studies have utilized microbiome profiling to successfully study C. difficile infections.
One such study is by Daquigan and co., which used a 16S rRNA sequencing microbiome profiling approach but went for higher resolution; studies had reported at the genus level, meaning that there was scope to identify species more accurately.
Here, the authors applied methodology that was capable of identification at the species level using a 16S rRNA database that contains 11,000 unique species alongside a hybrid global-local alignment strategy.
Rather than recruiting participants, the authors utilized 16S rRNA sequences that were already publicly available. Combining studies that investigated patients with C. difficile infection and those who did not have C. difficile infection, the authors found that there were cases where C. difficile was detected in participants who had no signs of C. difficile infection, meaning that C. difficile could be carried in the gut without symptoms. The authors also found that C. difficile was present in the gut of infants and that this could persist for the first year of the infants’ life.
Another study, by Pakpour and co., used the 16S rRNA sequencing microbiome profiling approach, focusing on the V4 region. Participants were adults who had their first diagnosed C. difficile infection, and stool samples were obtained at various time points during the C. difficile infection.
Once the DNA was extracted and the 16S rRNA sequenced, the resulting data was matched to a taxonomic database to identify the species present. This information was then correlated with data on whether the participants had relapsing C. difficile infections or not.
Overall, the authors found that the status of the microbiome before antibiotic exposure could be used to predict relapse of C. difficile infections; it is thought that variations in Veilonella dispar could be used as a marker off C. difficile infection relapse. Interestingly, there was no difference in the microbiome of participants after antibiotic exposure.
- Hamady, M., and Knight, R. (2009) Microbial community profiling for human microbiome projects: Tools, techniques, and challenges. Genome Research doi: 10.1101/gr.085464.108
- Theriot, C. M., and Young, V. B. (2015) Interactions Between the Gastrointestinal Microbiome and Clostridium difficile. Annu Rev Microbiol doi: 10.1146/annurev-micro-091014-104115
- Daquigan, N. et al. (2017) High-resolution profiling of the gut microbiome reveals the extent of Clostridium difficile burden. Npj Biofilms and Microbiomes https://doi.org/10.1038/s41522-017-0043-0
- Pakpour, S. et al. (2017) Identifying predictive features of Clostridium difficile infection recurrence before, during, and after primary antibiotic treatment. Microbiome https://doi.org/10.1186/s40168-017-0368-1