Without intervention, the virus almost always brakes through the host defenses and the patient will eventually develop AIDS. One of the most important and hard to battle characteristics of HIV-1 is its large adaptive potential. It is this potential that makes the virus escape from the host-immune system and resistant to anti-retroviral drugs.
In addition, recombination may be important, for example, in combining resistance mutations into a single genome to achieve drug-resistance [10] , [11].
The host may also affect the adaptive potential of the virus. For instance different target cell environments support differential viral replication rates [12] , generation time [13] and recombination rates [14].
Thus, the adaptive potential of HIV-1 is both shaped by the virus and the interaction between the virus and the host-environment. To which extent the host affects the adaptability of HIV-1, however, is still largely unexplored.
One viral trait that contributes to an unknown degree to the adaptive potential of HIV-1 is viral recombination. For effective recombination to occur in HIV-1, a single host-cell needs to be infected with two distinct viral strains. This double infected cell can then produce viral particles with a mixed dimeric RNA genome. Subsequently, cells infected by such heterozygous virions can obtain a hybrid provirus due to template switching during reverse transcription [15] , [16].
Recombination may be important, for example, in combining resistance mutations into a single genome to achieve fully drug-resistant strains [10] , [11].
However, until now the contribution of recombination to HIV-1 evolution has been predominantly shown in an indirect manner, through sequence analyses [17] — [19] and in silico studies [20] — [22]. It is not indisputably clear whether recombination always leads to a higher rate of adaptation.
The Fisher-Muller model predicts that in asexuals non-recombining individuals two beneficial mutations have to be fixed sequentially, whereas recombination can combine beneficial mutations that have evolved in parallel. Furthermore, since asexual organisms have genetically linked loci, in theory they are more prone to the accumulation of deleterious mutations. On the other hand recombination may be disadvantageous since the net effect may result in breaking up favorable combinations of mutations more often than combining beneficial mutations [23] — [27].
In addition, recombination may not be an essential process, since if the mutation rate is sufficiently high and the population size large, genomes carrying multiple beneficial mutations should appear even in asexual populations [28] , [29]. In this study we set out to determine whether the host-cell environment can affect the adaptability of HIV-1 in vitro.
Two HIV-1 strains were each cultured in two different T-cell lines MT4 and C for days with six replicas for each treatment; making a total of 24 serial passage lines. Relative viral fitness was determined at regular time intervals by letting the evolved viral strains compete against a reference strain. All viruses cultured in the MT4 T-cell line increased rapidly in fitness while viruses cultured in the C T-cell line did not show any increase in fitness.
Through sequence analyses and infections with HIV-1 strains expressing fluorescent protein we were able to relate the lack of adaptation in the C T-cell line to a decreased rate of recombination of HIV-1 in this T cell-line. To assess to what degree the host-cell environment influences the adaptability of HIV-1 we performed a large-scale selection experiment with two distinct HIV-1 strains in two T-cell lines MT4 and C MT4 and C are two genetically distinct T-cell lines, which have been immortalized by the human T-cell-leukemia-lymphoma virus type-I [32].
We have shown previously that mutations in the transcriptional promoter have different fitness effects when tested in the MT4 and C cell line [33]. This suggests that differences in cellular environment may affect the evolutionary outcome of a replicating virus.
Initial relative fitness and replication efficiency were determined for both viruses in the MT4 and C T-cell lines Table 1 and Supplemental Figure S1 , which indicates that viral population size and expansion were similar between cellular environments.
Wt and mt viruses were used to infect MT4 and C T-cells to establish 24 serial passage lines six replicates for each virus and cell line combination. Each time after serial passage the same initial viral density and number of host cells were used. Host cell populations typically expanded from 10 6 to 10 7 cells over 3—4 days, and no significant differences in growth between cell lines non-infected and infected were found.
Since the wt and mt virus have not been specifically adapted to either cell line we expected to observe a fitness increase for all serial passage lines [34]. Indeed, the six wt replicates cultured in the MT4 T-cell line wt-MT4 , increased rapidly in fitness in a seemingly linear fashion and reached a significantly higher fitness level of 1.
The six mt-MT4 serial passage lines initially also displayed a rapid fitness increase and they too reached a significant higher final fitness around 1. A The 6 wildtype replicates that are cultured on MT4 T-cells show a linear increase in fitness.
The inset shows a single representative example of replicate wt B The 6 mutant MT4 cultured replicates show an initial fitness increase that seems to reach a maximum at around a relative fitness of 1. The inset shows a single representative example of replicate mt-1 see Table 2 for relative fitness values at day C and D relative fitness of 6 wt and 6 mt C cultured viruses, respectively.
Fitness is relative to reference strain v13 also see materials and methods section. There are several potential explanations for this apparent lack of adaptation. First, the basic mutation rate might be low in the C T-cell line as compared to the MT4 T-cell line. This, however, is unlikely since the mutation rate depends mainly on the viral reverse transcriptase enzyme, which was identical in all situations. Besides, we confirmed by sequence analysis that the basic mutation rate was similar in both cell lines see next section.
Second, there may have been fewer adaptive mutations available in the C T-cell line, for instance, because the initial viral strains were already better adapted to the C T-cell line than to the MT4 cell line. These data illustrate that both wt and mt virus had a large adaptive potential. In spite of this potential, none of the C cultured viruses had increased their fitness, neither on C cells nor on MT4 cells. To determine whether there was a genetic basis for differences in adaptation we sequenced two domains of the HIV-1 genome; the LTR and viral Envelope Env.
Each of the 24 serial passage lines were analyzed by sequencing ten clones for each region and this was repeated for four time points, whereas for a fifth time point at 61 days only Env was sequenced. On average, nucleotide diversity in both cell lines was similar, which underpins our previous assumption that the basic mutation rate in both cell lines was similar Table 4. There was no significant difference in linkage disequilibrium between MT4 and C cultures, possibly due to the overall relatively low genetic variation.
There was no obvious bias in the observed mutations; all cultures had a similar number of transitions and transversions and also synonymous and non-synonymous changes in Env were not significantly different between cultures Table 5. After days of culture four MT4 cultures contained a single point mutation that had gone to fixation.
This lack of mutations that have achieved fixation might be partly due to coexistence of beneficial mutants. Also adaptive mutations in parts of the genome that we did not sequence might have contributed to the observed increase in fitness. From the sequence analyses two distinct patterns emerge. In other words, MT4 cultured viruses show no linkage in genetic variation between the LTR and Env region, which is in accordance with strong selection.
In contrast, C cultured viruses show a strong correlation between genetic variation in the LTR and Env region, which is indicative of the variation being nearly neutral. For each culture, ten clones of both the LTR and Env region were sequenced and this was repeated for five points in time. Thus, each data point corresponds to the amount of genetic variation within a single serial passage line at a certain point in time. Genetic variation Kimura 2 parameter is plotted separately for the two cell-lines.
A second pattern emerges from the level of viral polymorphism in each culture. We observed that the polymorphism level for the wt-C viruses was twice as high compared to wt-MT4 viruses 0. The same pattern was present for mt viruses; the polymorphism level being twice as high in mt-C viruses compared to mt-MT4 viruses 0. In combination with the similar levels of nucleotide diversity between environments this implies that singletons were at least two times more common in the MT4 cultures data not shown.
We were interested in how different cellular environments may drive differences in viral adaptation. We had minimized the chance that differences in adaptation would be triggered by differences in: i population size; both viral population size, cell population size and expansion size were similar between cultures, ii bottlenecks; the same amount of cells were infected with the same amount of virus, and iii mutation rate; the mutation rate of HIV-1 is mainly driven by the erroneous nature of the reverse transcriptase RT enzyme, which is produced by the virus itself.
The RT gene was identical in all viruses, and consequently the basic mutation rate should be similar across viruses. We show in Table 4 that, indeed, the accumulated nucleotide diversity, which is the average number of nucleotide differences per site, was similar between the two cell types. Nevertheless, we found a strong effect of cellular environment on adaptation, with strong fitness increase in MT4 and no fitness increase in C We think this difference in adaptive potential is possibly caused by differences in recombination rate between the two cell lines.
Recombination can alleviate this restriction by combining beneficial mutations into a single genome and thus circumvent competition among strains [45] — [47]. A second important process that can slow down adaptation or even result in a decrease in fitness is Muller's ratchet, which can be triggered by repeated bottlenecks [48] — [51]. Every four days well before peak of infection virus was harvested and a new batch of cells was infected. FACS analysis was performed after two and three rounds of serial passage after 8 and 12 days, respectively.
We observed 36x and x more double infected cells in the MT4 cultures compared to the C cultures, after eight and twelve days, respectively. These data confirm that effective recombination occurs less frequently in C cells than in MT4 cells, which is the result of a lower number of double infections in C cells. In this paper we set out to determine whether differences in the host-cell environment can drive divergent evolution of HIV After days of culture we obtained some very interesting results.
First, we showed that viruses when cultured on MT4 cells evolved to a higher fitness level. Interestingly, the wild type virus showed a continual linear increase in fitness, whereas the adaptive rate of the mutant virus slowed down and a fitness plateau was reached. As a consequence, the mutant virus obtained a lower fitness level than the wildtype virus. Turner On the biological success of viruses. Annu Rev Microbiol Wasik, B. Evolution 70 2 : Zimmer, C. STAT online news. Your email address will not be published.
Home » Research Talks » Microbiology. Audience: Researcher Educators of Adv. Speaker: Paul Turner. All Talks in Evolution. Leave a Reply Cancel reply Your email address will not be published. Scientists studying the evolutionary history of HIV found that it is closely related to other viruses.
Those viruses include SIVs simian immunodeficiency viruses , which infect primates, and the more distantly related FIVs the feline strains , which infect cats. If scientists can figure out how non-human primates and wild cats are able to live with these viruses, they may learn how to better treat HIV infections or prevent them altogether.
The diagram shows some of the evolutionary history of HIV as we know it today. An ancestral virus bottom evolved into strains that infected chimpanzees SIV. Over time, new strains began to infect humans HIV. Why are some people resistant to HIV? HIV is by no means the first plague that human populations have weathered. Many pathogens have deeply affected our evolutionary history. In fact, the human genome is littered with the remnants of our past battles with pathogens — and one of these remnants, a mutation to a gene called CCR5, may lead researchers to a new treatment for HIV.
The mutant CCR5 allele probably began to spread in northern Europe during the past years when the population was ravaged by a plague. It may have been bubonic plague or some other pathogen; research on this topic continues. The mutant CCR5 probably made its bearers resistant to the disease, and so its frequency increased. However, the populations of Asia and Africa were not exposed to the same epidemics; very few Asians and Africans now carry the allele see map above.
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