Typhoid in Kathmandu and Open Biology OA journal

And now for some shameless self-promotion…

A paper I’ve been working on for a few years on typhoid in Kathmandu yesterday had the honour of being the first paper ever published by the new open access journal of the Royal Society, Open Biology. I’m very keen on open access publishing and always try to submit to OA journals, but there is still a limited choice of truly OA journals. I love PLoS and BMC and submit to both regularly, but I think it’s really important to have a diverse range of OA journals – and therefore diversity in editors, editorial policies & styles, subject areas, etc – to make open access work for everyone.

So I’m excited to be have a paper in the new Open Biology, who publish under a Creative Commons 3 license (reuse/modify/distribute with attribution). Only time will tell how well the journal does, but it will only become great if us scientists are willing to submit good manuscripts. One incentive to do this is that Open Biology aims for a quick turn-around time of 4 weeks from submission to decision. Much as I love PLoS and BMC, they’ve never managed anywhere near that sort of turn-around. For info on Open Biology, see their ‘About’ page http://rsob.royalsocietypublishing.org/site/misc/about.xhtml.

So what is the paper? Thanks to OA, I can reproduce it here… (or you can read online or PDF)

Combined high-resolution genotyping and geospatial analysis reveals modes of endemic urban typhoid fever transmission

Stephen Baker1,2,*,†, Kathryn E. Holt3,4,†, Archie C. A. Clements5, Abhilasha Karkey2, Amit Arjyal2, Maciej F. Boni1,6, Sabina Dongol2, Naomi Hammond4, Samir Koirala2, Pham Thanh Duy1, Tran Vu Thieu Nga1, James I. Campbell1, Christiane Dolecek1,2, Buddha Basnyat2, Gordon Dougan4 and Jeremy J. Farrar1,2

Open Biol October 2011 1:110008; doi:10.1098/rsob.110008.

Basically, it uses genotyping and GPS to study typhoid fever in Kathmandu, Nepal. We examined 4-years worth of typhoid cases and looked at where the patients lived within the city (using GPS) and subtyped the bacteria responsible for their infections using high throughput SNP typing.

Firstly, we found that about 3/4 of the patients were infected with Salmonella Typhi and 1/4 were infected with Salmonella Paratyphi A. If you aren’t familiar with Salmonella, these are two serotypes of Salmonella enterica which, rather than causing gastrointestinal disease (ie food poisoning) like most Salmonella serotypes, cause the systemic infection known as typhoid. Typhi and Paratyphi A are quite different genetically, but have undergone convergent evolution to cause the same disease syndrome (see earlier paper in BMC Genomics).

Temporal distribution of Typhi (red) and Paratyphi A (blue) cases

Then we looked at the spatial distribution of the patients homes, and found that they were clustered in specific “hotspot” areas of Kathmandu:

Spatial risk model for Typhi infection (see paper for separate map for Paratyphi A risk)

Contrary to expectation, these hotspots weren’t the most densely populated areas…you might expect more people = more cases, but this wasn’t the case. Some complicated spatial statistics, done by Archie Clements at University of Queensland, confirmed that the hostpots weren’t associated with population density or hospital referral patterns, but were in low-elevation areas local people source their water from stone waterspouts.

Spatial distribution of Typhi cases, and location of water spouts

To see if the waterspouts could really be a source of typhoid transmission, we tested water samples for the presence of Typhi or Paratyphi A using culture and PCR. Culturing didn’t work, but it is notoriously difficult to culture Typhi from water samples that are not pre-enriched for bacteria…however PCR (using this method we published earlier in BMC Infectious Diseases) detected Typhi in 3/4 of water samples and Paratyphi A in 2/3.

Stone water spouts in Kathmandu (taken by co-author Stephen Baker)

We also looked at the population of bacteria causing the typhoid fever. We examined Salmonella Typhi isolated from the blood of typhoid fever patients, and used SNP typing to analyse the Typhi DNA and examine the population structure. We typed 113 SNPs (single nucleotide polymorphisms, ie point mutations) that we already knew about from previous variation discovery efforts. About 2/3 of isolates had the same haplotype, so to discriminate further within this local subgroup we sequenced 40 of the Typhi to identify novel SNPs arising in the local population (local microevolution) and typed these SNPs as well. Most of the Typhi belonged to the H58 lineage, which is common in other typhoid endemic zones we’ve looked at previously (Mekong Delta Vietnam – Holt & Dolecek 2011, PLoS NTD [OA]; Nairobi, Kenya – Kariuki 2010, J Clin Micro [free]; globally – Holt & Phan 2011, PLoS NTD [OA], Roumagnac 2006, Science [free in PMC]).

Typhi tree, red bars indicate frequency of genotypes in Kathmandu collection; red zones are H58 lineage and H58G sublineage

As the map above shows, the different Typhi genotypes were distributed randomly, with no spatial or temporal clustering. The exception was a probable outbreak in the west of the city, outside the hotspot zone, where 28 cases of infection with the same Typhi genotype were recorded in a two-month period – see yellow shaded area in map above, and zoomed in below:

Localised outbreak of Typhi genotype H58G-b4

Finally, we looked at what was happening in households from which multiple typhoid infections were studied. You might expect these household disease clusters to represent shared infections, which are transmitted between members of the household. However in most of these household typhoid clusters, the cases were caused by different organisms – either Paratyphi in one case and Typhi in the other, or multiple cases caused by different genotypes of Typhi. Cases with the same causative Typhi genotype are linked with dashed lines in this figure, you can see they are the exception rather than the rule:

Distribution of typhoid-causing bacteria in households with multiple typhoid cases

So, all in all we found typhoid fever infections clustered in spatial hotspots within Kathmandu, and that this clustering was explained not by population density but by low elevation and proximity to stone water spouts which are used to supply water. This implicates the water spouts in typhoid transmission via dissemination of Typhi and Paratyphi A around the city, supported by the detection of Typhi and Paratyphi A in the majority of water samples taken from these spouts. The diversity of Typhi genotypes we detected indicates that transmission occurs via water that is contaminated with a diverse population of Typhi, rather than point source outbreaks (with the exception of one outbreak, which actually occurred outside the hotspot zone). The diversity of Typhi genotypes within households suggests that this sort of transmission – ie dissemination via contamination of the water supply – contributes more to the overall typhoid burden than direct person-to-person transmission.

How does this contamination happen? Well, it is possible for people to carry Typhi and Paratyphi A in the gall bladder, without ever noticing an infection…for example, Typhoid Mary was a famous carrier of Typhi. Carriers shed the bacteria in their feces, so any food or water contaminated with their fecal material becomes a vehicle for typhoid transmission. Our study suggests that there are many Typhi and Paratyphi A carriers in Kathmandu who are unknowingly shedding the bacteria, so that whenever sewage seeps into the groundwater that feeds the stone water spout, the water becomes contaminated and can pass on the infection to those who drink the water. Most of the typhoid cases occur in the monsoon season, when flooding is likely to promote seepage of sewage into the underground aquifers that supply the water spouts. Hence the study suggests that endemic typhoid in Kathmandu is essentially a question of water infrastructure, and could potentially be dramatically reduced by supplying clean drinking water to people living in these few hotspot areas.