Microbiomes are the future. Versions of this statement have been uttered time and again by some of the most influential minds in science. The CALS Stewards of the future seminar at NCSU echoed this call in late October with an entire day focused on studying the microbiome, the implications these studies have on the future of science, and how collaboration is needed to succeed. The talks ranged from basic to applied studies and emphasized how this technology could be applied to agricultural fields. Continue reading
A 2015 USDA publication estimated the yearly economic burden of 15 foodborne illnesses on the U.S. population at $15.5 billion. Right off the bat it should be clarified that economic burden is NOT the same thing as out-of-pocket expenses. The economic burden is a result of factors such as the illness’ “frequency, severity, and health impact”1. The report states that “Conceptually, economists measure the economic burden of a disease as the sum of the willingness to pay by all individuals in society to reduce its incidence or likelihood”1. Campylobacter spp. ranks 5th in this list for the greatest contributor to economic burden at $1.9 billion.
Campylobacter accounts for 9% of illnesses where a biological agent can be identified. Illnesses are self-limiting, but ~1% of illnesses will require hospitalization. 15% of all foodborne illnesses that require hospitalization are caused by Campylobacter. Mild cases may go away in two to five days. However, severe cases are assumed to take six days in the hospital and three days of at-home recuperation. Guillain-Barre Syndrome (GBS) is a rare but severe autoimmune disease that may follow illnesses with certain pathogens. And, an estimated 40% of GBS cases are triggered by Campylobacter. Approximately 56% of the $1.9 billion economic burden from Campylobacter is actually a result of GBS. GBS occurs in less than 0.25% of Campylobacter cases, but those cases account for over 50% of the economic burden. Campylobacter deaths result in 34% of the burden, and 10% is from both non-hospitalized and hospitalized illnesses.
While Campylobacter may not be as well-known as other foodborne pathogens, the costs of the illness are staggering. Handling or eating raw or undercooked poultry is still a major risk-factor for developing campylobacteriosis. Handwashing and avoiding cross-contamination are the best ways to keep oneself from developing this illness.
- Hoffmann, Sandra, Bryan Maculloch, and Michael Batz. Economic Burden of Major Foodborne Illnesses Acquired in the United States, EIB-140, U.S. Department of Agriculture, Economic Research Service, May 2015.
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