Category Archives: biogeography

General Principles of Zoonotic Landscape Epidemiology

Zoonoses, pathogens with animal reservoirs, exist as part of a complex system of interactions between animal reservoirs, vectors, ecological factors and human interaction. Landscape epidemiology has existed as a field of study since Russian epidemiologist E.N. Pavlovsky coined the term and laid the groundwork in the 1960s. Landscape epidemiology is in essence the study of environmental foci of zoonotic disease, what Pavlovsky called a nidas. Many of the variables have been identified and studied in individual pathogen systems.

Each system seems so complex and unique that it can be easy to think that they each exist as separate entities with little to do with each other. It is necessary to develop some general principles to both see the bigger picture, and guide research and response to less studied and newly discovered pathogens. Lambin et al. set out to do just that by doing a meta-analysis of eight regional case studies of zoonotic diseases in Europe and East Africa: West Nile Virus in Senegal, Tick-borne Encephalitis in Latvia, Sandfly abundance (leishmaniasis vector) in the French Pyrenees, Rift Valley Fever in Senegal, West Nile Virus hosts in Camargue, Rodent-borne Puumala hantavirus in Belgium, human cases of Lyme borreliosis in Belgium, and risk of malaria re-emergence in Camargue. Obviously, as indicated, not all of these studies look at all factors involved in landscape epidemiology so validation is not solely based on the number of case studies that support each principle.

The ten proposed principles by Lambin et al are shown graphically below where they fit into the system of variables.

Graphical representation of the landscape determinants of disease transmission. The numbers refer to the ten propositions formulated in this paper. Lambin et al. International Journal of Health Geographics 2010 9:54   doi:10.1186/1476-072X-9-54
Graphical representation of the landscape determinants of disease transmission. The numbers refer to the ten propositions formulated in this paper.
Lambin et al. International Journal of Health Geographics 2010 9:54 doi:10.1186/1476-072X-9-54

Proposed general principles (Lambin et al, 2010):

  1. Landscape attributes may influence the level of transmission of an infection” This proposal is found in all case species. Features of the landscape influence vector and host distribution across the region of study. Distribution and type of water (fresh, brackish, or salt water) is a common landscape feature that influences density of insect vectors.
  2. Spatial variations in disease risk depend not only on the presence and area of critical habitats but also on their spatial configuration“.   The sheer size of the critical area is not the only or necessarily the most important characteristic to determine risk in an area. Some vectors like ticks thrive along border zones between ecosystems, like edges between woodland and grasslands.
  3. Disease risk depends on the connectivity of habitats for vectors and hosts” Creating contact zones or contiguous zones that create linked areas are also important. The spatial configuration can create corridors for disease persistence in harsh landscapes. Type and connectivity of  vegetation is as important as terrain for vector habitats. Connectivity between suitable habitat for rodents and insects allows the disease to spread from one patch to the next amplifying the pathogen to a level that increases risks of human transmission. Connections between patches of critical habitats allows for recolonization after local extinction.
  4. The landscape is a proxy for specific associations of reservoir hosts and vectors linked with the emergence of multi-host disease.” Their principle could be better fleshed out; their primary evidence coming from West Nile Virus (WNV). Like other multi-host pathogens, WNV has some hosts that are much more important than others for transmission across wide regions. In WNV migratory birds are a key to understanding its spread and epidemic dynamics. WNV is also an example of a disease with different proxies and amplification hosts in different regions of the world.
  5. To understand ecological factors influencing spatial variations of disease risk, one needs to take into account the pathways of pathogen transmission between vectors, hosts, and the physical environment.” Vector-borne diseases require direct contact between humans and the vector. For other zoonoses like hantavirus contact between humans and animal hosts can be via aerosols of material with rodent feces or dust containing rodent remains. For example, people have contracted hantavirus by vacuuming up rodent remains in homes. When estimating risk of transmission to humans, abiotic (non-living) environmental conditions that can preserve or transmit to humans have to be considered. Climate and moisture content of the soil are common abiotic factors to be concerned about. Additional support for this principle comes from the role of the rodent burrow system on plague (Yersinia pestis) hosts and vectors.
  6. The emergence and distribution of infection through time and space is controlled by different factors acting at multiple scales” In their discussion of this principle, they focus on human interaction with the environment and particularly urbanization altering disease risk. They note that climate change and natural environmental change do not account for all emerging and re-emerging disease but the activities of humans including urbanization and ecological change like deforestation. Ben-Ari et al‘s study on plague and climate change also looks at the many factors at all levels from micro to macro scales effect the abundance and likelihood of transmission of the plague.

    Plague cycle including hosts and vectors with abiotic influences
    Plague cycle including hosts and vectors with abiotic influences (Ben-Ari et al, 2011).
  7. Landscape and meteorological factors control not just the emergence but also the spatial concentration and spatial diffusion of infection risk” This principle just adjusts the previous principles to take account of primarily rainfall by looking at temporary ponds or wetlands. This particularly affects mosquito abundance, but as the graphic above demonstrates also effects soil moisture.
  8. Spatial variation in disease risk depends not only on land cover but also on land use, via the probability of contact between, on one hand, human hosts and, on the other hand, infectious vectors, animal hosts or their infected habitats” Land use has been long known to affect mosquito abundance and disease transmission. Clearing land for settlements or agriculture always increases standing water in ditches, tire ruts, railroad ditches, animal troughs, incomplete building projects, and due to loss of water absorbing vegetation. A century of malaria research and management has focused on land use and the elimination of standing water.  Mature water management programs for cultivation or flood control can also alter vector abundance and human contact rates. For example flooding fields to grow rice not only provides habitat for mosquito production but also brings people into the fields to cultivate increasing contact rates. Irrigation canals would have a similar effect.
  9. The relationship between land use and the probability of contact between vectors and animal hosts and human hosts is influenced by land ownership” In Lambin et al, they looked at the contact rates between public (state) land and private ownership. In these studies state ownership increased access to forestland over private ownership.By the same token, state ownership could also prevent deforestation and urbanization by preserving the wilderness or reserving the land for other uses. Forest age and maturity also varies significantly between state forests and private land.
  10. Human behaviour is a crucial controlling factor of vector-human contacts, and of infection.”  Humans bring themselves into contact with vectors by risky behavior and can control exposure vectors and infections. Obviously, vaccination is one of the controlling factors of infection, although many zoonotic infections have either no or poor vaccines. Occupational and recreational exposure to vectors often explains gender difference in infection rates.

In conclusion these principles begin to mark out the three sides of a zoonotic triangle: biology of pathogen, vector and host; ecological system where they exist; and human behavior and ecological interaction. Human behavior including land use and constructed environments is as important as the other two sides of the triangle. Humans are not passive victims or collateral damage.

Reference:

Lambin, E. F., Tran, A., Vanwambeke, S. O., Linard, C., & Soti, V. (2010). Pathogenic landscapes: Interactions between land, people, disease vectors, and their animal hosts. International Journal of Health Geographics, 9(1), 54. doi:10.1186/1476-072X-9-54 [open access]

Ben-Ari T, Neerinckx S, Gage KL, Kreppel K, Laudisoit A, et al. (2011) Plague and Climate: Scales Matter. PLoS Pathog 7(9): e1002160. doi:10.1371/journal.ppat.1002160

Western Iranian Plague Foci Still Active, 2011-2012

In a letter in this month’s Emerging Infectious Diseases, an Iranian and French team of epidemiologists report that the old plague focus in western Iran bordering Kurdistan is still active. Between 1947 and 1966 there were nine human plague epidemics causing 156 human deaths.  The last recorded human case occurred in 1966 and in animals in 1978. No surveys for plague were conducted for the following 30 years. It is unlikely to be a coincidence that the Iranian Revolution also began in 1978.

During the summers of 2011 and 2012, the team captured and tested for the plague F1 antibody 98 rodents and counted their fleas, finding only one rodent with antibodies (1.08%). They also tested 117 sheepdogs finding 4 positive dogs or 3.42%.  In dogs, plague antibodies only last about six months suggesting that these sheepdogs must have had recent infections.  This is enough to suggest that the plague foci is still present in western Iran. Moreover, they believe the number of reservoir rodents and fleas per rodent (Xenopsylla species index 4.10) is “most favorable” circumstances for an epizootic. With plague antibodies found in the only area surveyed in 30 years, it is clear that surveillance needs to not only continue but expand extensively.

Reference:

Esamaeili S, Azadmanesh K, Naddaf SR, Rajerison M, Carniel E, & Mostafavi E (2013). Serologic survey of plague in animals, Western Iran. Emerging infectious diseases, 19 (9) PMID: 23968721

Fleshing out Yersinia pestis

Up until a few months ago there were a few representative samples of the Yersinia pestis genome. Important windows into its secrets, but windows none the less. In January a Chinese group remedied this situation by expanding the number of fully sequenced genomes from 15 to 133 (Cui et al, 2013).  China supplied 107 genomes selected from over 900 genotyped specimens collected since 1955 to represent bacterial and host diversity. To these, 11 additional isolates from Mongolia, Myanmar (Burma), the former Soviet Union, and Madagascar were fully sequenced. For the analysis, the previously sequenced 15 genomes were added bringing the total up to 133 including the ancient specimens from 14th century London.

The Core-Genome and the Pan-Genome

Even for a bacterium like Yersinia pestis that is considered to have little genetic diversity, its genome is more elastic than any eukaryote (everything but bacteria). The bacterial genome can be divided into its core genome, found in all members of the species, and the accessory genome, sequences found only in some strains. Plasmids are part of the accessory genome but not all of it. Extra genes are also found on the bacterial chromosome. The core genome is 3.53 Mb long with 3450 genes; the accessory genome has 1.92 Mb with 1249 genes (including 451 on the six known plasmids) (Cui et al, 2013, Table S1). So the accessory genome contains 26% of genes found in the species. This may seem like a lot, but more promiscuous species like Escherishia coli (E. coli) have many more accessory genes than core genes. With E. coli the more specimens that are sequenced, the larger the accessory genome gets with no end in sight.

Combining all of the genes found in Yersinia pestis (core and accessory genome), we have the pan-genome. The pan-genome is 5.46 Mb with 4699 genes (Cui et al, 2013).  No one strain has all of these genes. So different strains do have significant differences in their functions but, as far as I know, there are no significant differences in human prognosis. Hopefully, there will be more study in the future that cross-references strain type  or particular genes with human prognosis, transmission routes (% bubonic vs pneumonic), hosts etc.

Branching Out

Using known and new SNPs, the phylogenetic tree has finally been fleshed out into a healthy looking tree . We couldn’t keep the sickly looking Charlie Brown tree of the past forever! Even so, the tree below represents only the main branches.

Click to enlarge, (Cui et al, PNAS, 2013)
Click to enlarge, (Cui et al, PNAS, 2013)

To my mind, the most important aspect of the new tree is that nodes of increased diversity are much more apparent. The authors are the most excited by node 7 where there is a four-way branch, adding two new branches  (3.ANT1 and 4.ANT1) to the main stem of the tree. They refer to this diversity point as the ‘big bang’. This node gains the most attention because the 14th century London genomes are just one step off of node 7 down the 1.ANT1 branch. So it stands the reason that node 7 represents a period of diversity that produced the second pandemic. Yet, looking at their diagram, other locations like node 12 have greater diversity. The 1.IN strains are intermediary on the same lineage between the second the third pandemic. Node 14 is the initial diversity that produced the third pandemic.  Calling node 7 a ‘big bang’ seems to me to have more to do with it producing the second pandemic rather than the diversity at the node itself. The new third and fourth branch (3.ANT and 4.ANT) are concentrated in Mongolia, putting emphasis on the importance of doing such deep sequencing in other Central Asian regions. It is impossible to tell which host species these bursts of diversity occurred within, almost certainly not humans. It’s not that diversity can’t be generated in humans especially during a pneumonic plague, but since it is not endemic in humans,  it must make it back to a reservoir to be preserved anywhere other than in ancient DNA.

Biogeography shows clustering of related strains in regions as would be expected, though they are fairly well mixed within the circled zone in the map above. Samples seem to follow ancient roads, although keep in mind all of these strains have been isolated within the last 60 years.   I do wonder why they were not able to identify a route for the eastern branch two isolates. All of the branch two isolates appear to be running along a fairly straight line from southwest to northeast China (extending trade route III to Manchuria). The 107 Chinese specimens were chosen from > 900 strains identified from 5000 isolates for their diversity revealed by genotyping, host diversity and geography (Cui et al, 2013). It would have been interesting to see a map with all 5000 on it as a measure of abundance (with or without typing).

The oldest strain 0.PE7 is found only on the Qinghai-Tibet plateau in China, an area framed by the ancient trade routes along which most of the western strains are found. This has led Cui et al, 2013 to postulate that the  Qinghai-Tibet plateau as the origin of  Yersinia pestis.

Unsteady Molecular Clocks

Estimating ages from genetics can be a very risky business. To estimate years since the last common ancestor, it requires a steady molecular clock , measured in base changes per unit of time. In theory all of the genes from the core genome should have changed to the same degree from the common ancestor, but that is not the case at all. The number of SNPs in the Yersinia pestis core genome varies greatly. Even excluding the most divergent Angola (0.PE3) strain, there is “a nearly 40 fold difference between the slowest and the fastest evolving branches” (Cui et al, 2013). An unsteady molecular clock was also suggested by previous data from Madagascar, though the discussion was buried in the supplementary material (Morelli et al, 2010, p. S10-s18). Mutator phenotypes do occur (Rajanna et al, 2013), though Cui et al, 2013 assure us that none of these strains are mutators.  On the other hand, a Georgian group suggest that the mutator phenotype, a single point mutation, could naturally reverse (back mutate) altering the predictability of the lineage age (Rajanna et al, 2013). The Chinese group concluded that the faster clock rates for some branches are due to a higher reproduction rate, probably due to more or larger epidemics in the lineage (Cui et al, 2013). The types of genetic changes (SNPs) indicate neutral selection, so the increased reproduction rate is not due to the genetic changes.

While I understand that calculating divergence dates an important exercise to people who focus on phylogenetics, for the understanding of historical plague it is not useful. It is not solid or specific enough to base historical events upon alone. Predictions are just that; all of these groups have been proven wrong, sometimes later by themselves, too often.  Most importantly, it appears that it will eventually be trumped by ancient DNA analysis with an archaeological and/or documentary context. As far as I’m concerned, the Angola strain is a genetic and geographic outlier of uncertain provenance. We don’t know important factors like how long it was kept in active culture before it was made into a stock or the conditions of storage. Both of these can effect mutation rates and the molecular clock (Rajanna et al, 2013).  I’m sure the Angola strain’s story is interesting but unlikely to be useful for understanding the whole species unless it turns up in ancient DNA.

Gaining and Loosing Diversity

Returning to these starburst points on the tree, called polytomys, where multiple lineages share the same ancestor, we have some of the most valuable information in the new phylogenetic tree. Epidemics (and presumably epizootics) are believed to have an increased reproduction rate over enzootic plague. Since the mutation rate is directly tied to the reproduction rate, increased reproduction rates predict an increased mutation rate and, therefore, production of genetic diversity.  The team predicts that “higher clock rates are an indicator of epidemic disease, even in the absence of historical evidence” (Cui et al, 2013). It is unclear how an epidemic can be differentiated from an epizootic by genetics alone. We know from modern observations that not all epizootics spill over into the human population. Yet, major polytomys can at least be used to estimate how many bursts of growth the bacterium has gone through in China. We should see other polytomys with increased sequencing of other Central Asian regions.

While these polytomys show a starburst of new lineages, there is also a loss of diversity during every epidemic. Most of the new lineages produced during an epidemic (or epizootic) will die out (become extinct) when the epidemic ends. If the changes are truly neutral, then which lineage survives to endure in the reservoir will be completely random (as will be the number of surviving lineages). We should also remember that clinical isolates  during an epidemic and ancient DNA can preserve lineages that become extinct (and this is normal). In the four individuals they sequenced from 14th century East Smithfield, they found two different clones, with the second being derivative of the first. Both of these clones may only be found in ancient DNA, not in any living specimen. The more time that passes the greater the likelihood that the minor lineages will become extinct. This tends to make the earlier sections of the pylogenetic tree look cleaner by stripping off side branches.

Another recent study by Vogler et al (2013), supports their scenario on a finer scale during the 9 year epidemic in a port town of Mahajana,  Madagascar from 1991 to 1999. Over a decade we can compare the incidence of plague vs. the genetic diversity. Yersinia pestis evolution can be plotted with great precision. In the lower diagram, clones are color coded to the year of isolation. From 1995 to 1999 it is possible to see the next year’s primary clone emerge in the previous year’s epidemic, which supports local cycling within the city. At the same time, most of the diversity generated is not represented in later outbreaks.

Vogler et al, 2013
F3.large
Vogler et al, 2013

Host Diversity

Host genus vs Y. pestis strain collected (Cui et al, 2013).

The hosts of these 107 strains give us a glimpse into the host diversity for Yersinia pestis within China (Cui et al, 2013). The figure to the right gives an indication of strain diversity within each host but does not tell us abundance or location within China. What jumps out at me, is that humans and marmots have the most strain diversity. The high strain diversity in humans including 0.PE7, the strain closest to the most recent common ancestor, suggests to the Chinese team that Yersinia pestis has been pathogenic to humans since it evolved (Cui et al, 2013). Thus, at no point in its evolution did it gain the ability to infect humans. The few strains that can not infect humans are hypothesized to have lost their ability to infect humans possibly as a function of purifying selection for voles as hosts. It is interesting that the 1.ORI strains of the third pandemic are only found in humans, rats and mice.  We have to be careful about taking this figure to represent abundance or importance of a particular host. The great gerbil, Rhombomys opimus, is a primary host throughout central Asia is is represented by only one strain in this figure.

Studies published this winter have moved us significantly down the road to fleshing out Yersinia pestis. The genetic survey of Y. pestis in China provides a firm foundation to build on as more ancient DNA becomes available and extensive sequencing is done in other regions. Madagascar continues to be the best laboratory for plague ecology and epidemiology, while the Georgian study begins to address unintended intra-laboratory evolution that may shed light on Y. pestis in the wild. I’ll return to these papers again soon as I continue to examine Y. pestis from different perspectives and ruminate on answers to other questions.

References:

Cui, Y., Yu, C., Yan, Y., Li, D., Li, Y., Jombart, T., Weinert, L., Wang, Z., Guo, Z., Xu, L., Zhang, Y., Zheng, H., Qin, N., Xiao, X., Wu, M., Wang, X., Zhou, D., Qi, Z., Du, Z., Wu, H., Yang, X., Cao, H., Wang, H., Wang, J., Yao, S., Rakin, A., Li, Y., Falush, D., Balloux, F., Achtman, M., Song, Y., Wang, J., & Yang, R. (2013). Historical variations in mutation rate in an epidemic pathogen, Yersinia pestis Proceedings of the National Academy of Sciences, 110 (2), 577-582 DOI: 10.1073/pnas.1205750110

Morelli G, Song Y, Mazzoni CJ, Eppinger M, Roumagnac P, Wagner DM, Feldkamp M, Kusecek B, Vogler AJ, Li Y, Cui Y, Thomson NR, Jombart T, Leblois R, Lichtner P, Rahalison L, Petersen JM, Balloux F, Keim P, Wirth T, Ravel J, Yang R, Carniel E, & Achtman M (2010). Yersinia pestis genome sequencing identifies patterns of global phylogenetic diversity. Nature genetics, 42 (12), 1140-3 PMID: 21037571

Rajanna C, Ouellette G, Rashid M, Zemla A, Karavis M, Zhou C, Revazishvili T, Redmond B, McNew L, Bakanidze L, Imnadze P, Rivers B, Skowronski EW, O’Connell KP, Sulakvelidze A, & Gibbons HS (2013). A Strain of Yersinia pestis With a Mutator Phenotype from the Republic of Georgia. FEMS microbiology letters PMID: 23521061

Vogler, A., Chan, F., Nottingham, R., Andersen, G., Drees, K., Beckstrom-Sternberg, S., Wagner, D., Chanteau, S., & Keim, P. (2013). A Decade of Plague in Mahajanga, Madagascar: Insights into the Global Maritime Spread of Pandemic Plague mBio, 4 (1) DOI: 10.1128/mBio.00623-12

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