Scientists have been using genetic data to estimate when species first appeared for some time. The basic idea is to use differences between species and a guess as to how fast sequences change as a molecular clock, running it backward until they show the same sequence. The same trick can be done with viral genetic information. If you know the genetic sequence of a virus at one point in time and then at a later time you can make an estimate of how fast the clock is ticking. An analysis along these lines has just been done with a newly found lymph node sample from Kinshasa (Democratic Republic of the Congo) that was archived from a woman patient in 1960. It was the only one of 27 archived tissue blocks from patients that showed evidence of HIV infection. Lymph nodes are loaded with T cells, the cells in the immune system that HIV attacks. Using some new techniques the researchers have fished out some fragments of the HIV virus that were incorporated in the T-cells:
The samples had all been treated with harsh chemicals, embedded in paraffin wax and left at room temperature for decades. The acidic chemicals had broken the genome up into small fragments. Formalin, a chemical used to prepare samples for microscopy, had crosslinked nucleic acids with protein. “It’s as if you had a nice pearl necklace of DNA and RNA and protein and you clumped it together, drenched it in glue and then dried it out,” says [evolutionary biologist Michael Worobey of the University of Arizona in Tucson].
The team worked out a combination of methods that would allow them to sequence DNA and RNA from the samples; another lab at Northwestern University in Chicago, Illinois, confirmed the results, also finding traces of the HIV-1 genome in the lymph node biopsy. (Nature News)
Using estimates of the rate of clock ticking (in this case, the rate that one genetic letter is substituted for another), derived from a database of HIV sequences, it is possible to use the contemporary variation in HIV sequences to reconstruct the timing of the branchings caused by random substitutions in a common ancestor virus, i.e., the virus that first infected humans, presumably from some hunter eating infected chimpanzee meat. Using these data the archived sample was predicted to have arisen at about the documented time. This gives confidence in the clock rate.
This 1960 sample from the DRC is not the only such sample in existence. There is a 1959 Kinshasa sample as well, and comparison of the two shows that there was already substantial genetic divergence of the virus in that geographic area, divergence indicating that even by 1960 the virus had been present for many decades. Incorporating these two samples into the back calculation suggests that HIV was introduced into the human population sometime between the end of the 19th century and the 1930s, with the most likely data being in the first decade of the twentieth century. The use of the two 1959 and 1960 sequences reduced the uncertainty in the estimates and made them much less sensitive to a wide variety of assumptions that are required to do these kinds of analyses (various kinds of prior distributions in the Bayesian analysis).
The dates of emergence of HIV into the human population is interesting, again illustrating the effect of the social environment on disease. It was during this period that colonial administrative and trading centers such as Kinshasa (formerly Leopoldville, Belgian Congo) were established. In the region of West Africa where HIV is believed to have crossed the species barrier (current countries of Cameroon, Central African Republic, DRC and Gabon and Equatorial Guinea) there was not a single city larger than 10,000 until 1910. This analysis suggests that the population of infected people increased slowly until the explosion of the pandemic after the late 1970s. This suggests that the existence and growth of HIV at the epicenter of the AIDS pandemic was enabled by urbanization and spread by trade and travel routes once populations reached a threshold level for epidemicity.
Reconstructing the emergence of the AIDS virus in West Africa shows how larger sociopolitical and economic factors (colonialism, urbanization, trade, travel) conspired to produce one of today’s most horrific and devastating pandemics. This kind of insight is not especially empowering, since there is not much we can do directly about these events. We are at the mercy of a complex and interconnected “system.” We can’t stop it from happening. The best we can do is prepare to manage the consequences.
Which brings us back to where we almost always wind up, here. Managing the consequences means building a robust, resilient and flexible public health and social service infrastructure. We haven’t done it and we aren’t doing it. It isn’t clear we are even planning to do it or even thinking of doing it. So we will again surely reap the whirlwind that science, history and experience tell us happens with regularity.
When will we ever learn?