There’s an amazing article out now that describes how whole genome shotgun sequencing (WGS) has contributed to the epidemiology of Campylobacter jejuni, leading cause of gastroenteritis in Europe. Our friends at barfblog (check them out here) sent this out the other day and it an example of how this revolutionary technology is contributing to the increased understanding and research into C. jejuni. Even GenomeTrackr (WGS FDA facility- we talk about them in our other post here), is looking into adding some C. jejuni sequences into their database.
The article is a review of how WGS has helped researchers gain a better understanding of the evolution and epidemiology of C. jejuni. As these high throughput methods become better understood, they are rapidly replacing older molecular methods due to their greater specificity and how easily they they can be shared via open access databases (like MG-RAST, QIIME, GenomeTrackr, etc). C. jejuni WGS, when used in conjunction with epidemiological methods, has helped identify sources of outbreaks and transmission pathways. WGS has served to majorly advance detection and drastically improve surveillance of C. jejuni infections.
However, Campylobacter is not as well-known as other pathogens such as Listeria and Salmonella (see here and here). This factor makes it hard to interpret the data that WGS outputs for C. jejuni. We do not currently have a comprehensive understanding or picture of the genetic lineages of C. jejuni, which makes it difficult to determine genetic relationships between strains. There is also not yet a consensus on what analysis pipeline is best to use or what cut off values for quality should be used within a pipeline.
But these problems are seen in all high throughput methodology (16S, functional metagenomics, etc), and are not necessarily exclusive to C. jejuni WGS. Despite these obstacles it is exciting to see WGS being applied to C. jejuni outbreak epidemiology and research in general. It will allow new resolution of C. jejuni genetics and certainly revolutionize the detection and response time for outbreaks.
Read the pre-pub: http://biorxiv.org/content/early/2016/10/01/078550