| Literature DB >> 31921304 |
Abstract
Several aspects of the biology of the three players in a vector-borne disease that affect their evolutionary interactions are outlined. A model of the origin of a human-human cycle of vector-borne diseases is presented emphasizing the narrowing of the niche experienced by the pathogen and vector. Variation in the expected rates of evolution of the three players is discussed with the rapid rate of pathogen evolution providing them with advantages. Population sizes and fluctuations also affect the three players in very different ways. The time since the origin of a vector-borne disease likely determines how stable the interactions are and thus how easily the disease might be eliminated. Stability and variation are also linked. Human technological advances are rapidly upsetting the previously relatively slow coevolutionary adjustment of the three players. Finally, it is pointed out that development of quantitative coevolutionary models specifically addressing details of vector-borne diseases is needed to identify parameters most likely to break transmission cycles and thus control or eliminate diseases.Entities:
Keywords: evolution; genetics; mosquitoes; pathogens; vector-borne diseases
Year: 2019 PMID: 31921304 PMCID: PMC6929172 DOI: 10.3389/fgene.2019.01266
Source DB: PubMed Journal: Front Genet ISSN: 1664-8021 Impact factor: 4.599
Figure 1Schematic model of origin of human-human vector-borne disease.
Example of factors to consider in the evolution of vector-borne disease from the standpoint of the pathogen.
| What the pathogen wants: |
| From the vertebrate host: |
| 1. High density |
| 2. Susceptible |
| 3. Attractive to mosquito |
| 4. Infective for a long time, but not fatal |
| 5. Behave to be available to the mosquito |
| 6. Poor immune response to pathogen |
| From the mosquito: |
| 1. Frequent blood meals |
| 2. High competence to transmit, rapid transit from gut to saliva |
| 3. Innate immune system that does not inhibit pathogen growth |
| 3. Longevity |
Example of factors to consider in the evolution of vector-borne disease from the standpoint of the vertebrate/human host.
| What the vertebrate host (human) wants: |
| From the pathogen |
| 1. Low pathogenicity |
| 2. Short infections |
| 3. Short infectious period |
| 4. Easily recognized antigens for immune response and vaccine development |
| From the mosquito |
| 1. Low density |
| 2. Poor host seeking |
| 3. Low competence to transmit |
| 4. Short life time |
Example of factors to consider in the evolution of vector-borne disease from the standpoint of the mosquito vector.
| What the mosquito wants: |
| From the pathogen |
| 1. Low pathogenicity to mosquito, not affect fitness |
| 2. If it affects mosquito fitness, rapid passage through mosquito |
| From the vertebrate host (human) |
| 1. High density of blood sources |
| 2. Behave to be available to mosquito |
| 3. Blood with sufficient nutrients to produce as many eggs as possible |
| 4. Not harbor other pathogens that reduces mosquito fitness |
| 5. Not develop technologies to lower mosquito fitness |
Figure 2Relative rates of evolution of pathogens, vectors, and vertebrate hosts. (A) Natural situation. (B) Effect of human culture and technology.
Examples of vector competence studies on Aedes aegypti for three of the major viruses this species transmits, Zika, dengue, and yellow fever.
| Origin of mosquito strain | Virus | Infection rate | Reference |
|---|---|---|---|
| Salvador, Brazil | Zika DAK AR | 100% |
|
| Rio Grande, Texas | “ | 40% | |
| Singapore | Dengue Guinea C | 90% |
|
| Bangkok | “ | 10% | |
| Guatemala | Yellow Fever Asibi | 2% |
|
| Kwa Dzivo Kenya | “ | 57% |
The geographic origin of mosquitoes tested is in first column with the virus in the second. Infection rate (third column) is the percent of females that blood fed on infective blood that became infected. Note that in this table are presented studies using the same strain of virus and assayed in the same laboratory using identical methods on both strains of mosquitoes assayed.
Figure 3Population dynamics of a plasmodium infection. Yellow circles are estimated numbers at each stage. From Sindon (2017) with permission.