There are frightening implications for any declaration of recovery from a viral disease. If Ebola can linger in serological backwaters, what other diseases can do the same?
>>If Ebola can linger in serological backwaters,
>>what other diseases can do the same?
We don’t know, but we are going to find out.
This is an article on Ebola mutation rate in humans.
Insight into the most recent Ebola outbreak could inform future intervention.by Roheeni Saxena - Jun 21, 2015 9:00am PDT
Sequencing genetic material from the blood of 179 Ebola patient blood samples has provided insights into the epidemiological and evolutionary course of the current Ebola epidemic. The analysis confirms the path that different viral lineages took through the human populations of West Africa. These findings are important because they can be used in conjunction with epidemiological data to retrospectively test the effectiveness of Ebola control measures.
The virus itself is really nothing special—until it gets inside a human.
For this study, viral genomes were sequenced from blood samples of Ebola infected patients. Each sample was linked to the following data: patient location, sample collection date, disease onset, and disease outcome. The median collection date was four days after the onset of symptoms. The viral gene sequence was derived from RNA sequencing of patient samples (Ebola is an RNA virus).
Phylogenetic analysis, investigating the evolution of viral gene sequences, showed the dynamic nature of the Ebola epidemic and the corresponding molecular changes in viral genome. The analysis included 179 previously unsequenced Ebola genomes from various locations in West Africa, and an additional group of previously sequenced genomes, 78 from Sierra Leone, 3 from Guinea, and 2 from Mali. During this analysis, several distinct genetic lineages of the virus were identified.
The first lineage was linked to early Guinean cases and a single Liberian sequence. This cluster of viral genomes is likely directly associated with the original outbreak in Guinea that was almost successfully contained in May 2014 via interventions from various health agencies. In Sierra Leone, there were two groupings of viral genome, which began to show overlap with viral genomes from both Guinea and Liberia, suggesting continued spread across borders during this time.
After the emergence of the primary lineage in Guinea, a second independent genetic lineage of Ebola spread into Guinea, Liberia, and Sierra Leone, where it become associated with the large epidemic that persisted into 2015. Though the scientists examined the possibility of one genetic strain being more virulent or deadline than another, the data didn’t show an increase or decrease in mortality associated with any particular virus cluster.
A probability analysis of gene evolution over time showed that the actual rate of genetic mutation in this outbreak was lower than the mutation rate that was initially reported, which means that the virus mutated at a slower rate than was initially projected.
In contrast, the observed mutation rate was higher than the non-outbreak rate of mutation for Ebola, meaning that the Ebola genome mutation rate during the outbreak was higher than it is when the virus is circulating in non-human hosts. This is to be expected because during an outbreak, the virus multiplies more rapidly as it spreads to new hosts, leading to a higher rate of mutation.
In considering the difference between in-outbreak mutation rates and non-outbreak mutation rates, it’s important to remember that the non-outbreak mutation rate for Ebola is entirely dependent on its existence in non-human virus reservoirs. During non-outbreak times, there is typically no infection in humans; instead Ebola replicates in primates and bats. The difference in host organism for during-outbreak and non-outbreak periods may mean that the in-outbreak and non-outbreak rates of mutation are not comparable.
The time-dependent genetic analysis of Ebola during this most recent series of infections provides an unprecedented peek into the evolution of an ongoing viral hemorrhagic fever outbreak. When paired with time-stamped data about the interventions that were being used in various affected areas, we could gain additional insight into the relationship between effective intervention and continued viral spread.