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-Effective population sizes during chronic infection. Diamonds represent COALESCE estimates, squares represent RECOMBINE estimates, triangles represent FLUCTUATE estimates, and the horizontal line represents the boundary between stochasticity and determinism. Solid lines with solid symbols represent samples derived from peripheral blood cell viral DNA and dotted lines with open symbols represent samples derived from plasma virus RNA. * indicates DNA samples and # indicates RNA samples that tested significant using the modified Nei-Gojobori method. † indicates DNA samples that tested significant by Tajima's D test. ‡ indicates DNA samples and § indicates RNA samples that tested significant by Fu and Li's D* test. indicates samples that tested significant by the test for recurrent mutation. The arrows point to the time of the first observed fixation event.
Source publication
Human immunodeficiency virus type 1 (HIV-1) has high replication and mutation rates that generate large census populations and high levels of genetic variation. We examined the roles of natural selection, population growth, random genetic drift, and recombination in shaping the variation in 1509 C2-V5 env sequences derived from nine men with chroni...
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... we can justify at minimum nine independent comparisons, We first determined which samples had no sites with more resulting in a correction of the significance level from 5% to than two cosegregating nucleotide states because the variation 0.55%. at such sites can be most parsimoniously explained by one Nucleotide sequence accession numbers: The GenBank acces- mutational event. For the remaining samples, we tested sion numbers are AF137629-AF138163, AF138166-AF138263, whether more sites than expected were unambiguously recur- AF138305-AF138703, and AF204402-AF204670 for the 1300 se- rently mutated (i.e., there were more than two cosegregating quences reported previously ( Shankarappa et al. 1999) and nucleotide states). To do this, we calculated how many sites AY348333-AY348528 and AY348532-AY348544 for the 209 fol- were expected to have been recurrently mutated by assuming low-up sequences reported herein. a Poisson mutational process with a mean conditioned upon the number of total sites and the number of variable sites. P values were generated from 10,000 independent replicates. RESULTS Estimating N e : Effective population sizes were estimated us- ing three coalescent-likelihood programs from the LAMARC Testing neutrality: Each of the three N e estimators we package ( Kuhner et al. 1995a). The first program, CO- employed assumed neutrality, so we first addressed this ALESCE, estimates assuming a single panmictic population assumption by applying three two-tailed statistical tests. (from participants 2 and 7) were found for which d n 10 5 /site/generation for point substitutions (Mansky 1996). exceeded d s (Figure 1, #). The samples for which neu- To qualitatively assess the effects of estimating for data with trality was rejected by this test did not tend to cluster at recombination under these three sets of assumptions, 10 inde- pendent replicates were generated under a neutral coalescent any particular time during infection (Figure 1). Further, model with recombination, using TREEVOLVE. We condi- the signal for selection tended to be sporadic, as evident radic excesses of nonsynonymous mutations in four of sions. Recombination can induce spuriously inferred parallel changes and reversions on the sample's phylog- the nine ...
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... we can justify at minimum nine independent comparisons, We first determined which samples had no sites with more resulting in a correction of the significance level from 5% to than two cosegregating nucleotide states because the variation 0.55%. at such sites can be most parsimoniously explained by one Nucleotide sequence accession numbers: The GenBank acces- mutational event. For the remaining samples, we tested sion numbers are AF137629-AF138163, AF138166-AF138263, whether more sites than expected were unambiguously recur- AF138305-AF138703, and AF204402-AF204670 for the 1300 se- rently mutated (i.e., there were more than two cosegregating quences reported previously ( Shankarappa et al. 1999) and nucleotide states). To do this, we calculated how many sites AY348333-AY348528 and AY348532-AY348544 for the 209 fol- were expected to have been recurrently mutated by assuming low-up sequences reported herein. a Poisson mutational process with a mean conditioned upon the number of total sites and the number of variable sites. P values were generated from 10,000 independent replicates. RESULTS Estimating N e : Effective population sizes were estimated us- ing three coalescent-likelihood programs from the LAMARC Testing neutrality: Each of the three N e estimators we package ( Kuhner et al. 1995a). The first program, CO- employed assumed neutrality, so we first addressed this ALESCE, estimates assuming a single panmictic population assumption by applying three two-tailed statistical tests. (from participants 2 and 7) were found for which d n 10 5 /site/generation for point substitutions (Mansky 1996). exceeded d s (Figure 1, #). The samples for which neu- To qualitatively assess the effects of estimating for data with trality was rejected by this test did not tend to cluster at recombination under these three sets of assumptions, 10 inde- pendent replicates were generated under a neutral coalescent any particular time during infection (Figure 1). Further, model with recombination, using TREEVOLVE. We condi- the signal for selection tended to be sporadic, as evident radic excesses of nonsynonymous mutations in four of sions. Recombination can induce spuriously inferred parallel changes and reversions on the sample's phylog- the nine ...
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... next addressed the infinite-sites assumption of eny. Furthermore, recombination is known to make Taj- ima's test conservative (Tajima 1989; Wall 1999; Tajima's and Fu and Li's tests. Thirty-three samples had no sites with more than two cosegregating nucleotide Schierup and Hein 2000). Figure 2 shows the distribu- tions of Tajima's test statistic for coalescent simulations states (data not shown). For the remaining 112 samples, 1 sample from participant 2 and 3 samples from partici- with and without recombination and demonstrates the reduction in variance induced by recombination. By pant 5 showed significantly more unambiguous recur- rent mutation than expected (Figure 1, ). This result comparing the 95% confidence intervals we calculated an average gain in power for Tajima's test of 11.8% by indicated that the assumption of a Poisson mutational process is largely valid for these samples. Furthermore, accounting for recombination. Using the null distributions with recombination, sig- because only 4 samples were found to have experienced significant excesses of unambiguous recurrent muta- nificant departures from the neutral expectation of Taji- ma's test (with excesses of low-frequency mutations) tion, diversifying selection was unlikely to have had a predominant role in the evolution of these ...
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... observed for 2 of the 145 samples, both from partic- ipant 3 (Figure 1, †). Only 1 sample that yielded signifi- The preceding analysis underestimates recurrent mu- tation, because it ignores parallel changes and rever- cance occurred after genetic diversity stabilized [taken viduals studied. For the other five individuals, the first fixation event happened before genetic diversity stabilized, the 2 samples from participant 6 occurred after genetic diversity stabilized. No departures from neutral expecta- as early as 1.5 years after seroconversion. In none of the individuals did the first fixation event happen after tions were detected by either test if recombination re- mained unacknowledged (data not shown). Accounting genetic diversity stabilized. The observed mean time to the first fixation event of 3.88 years is not significantly for recombination, therefore, revealed stronger evi- dence for nonneutral evolution in these ...
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... HIV-1 populations within infected individuals were from 311 to 4783 for COALESCE (Figure 1). RECOM- BINE, which relaxes the assumption of no recombina- experiencing recurring selective sweeps, we would ex- pect to detect fixation events occurring with high fre- tion, yielded estimates of N e from 326 to 2886 ( Figure 1). As expected, RECOMBINE yielded lower estimates quency. We therefore asked when the first fixation event occurred in each individual. The first fixation event than COALESCE. FLUCTUATE, which relaxes the as- sumption of a constant effective population size, is was defined as the first time a sample had a derived nucleotide state reach a frequency of 100% when it known to have an upward bias ( Kuhner et al. 1998) and yielded the highest estimates (from 439 to 1,063,776; started at 0% (sites that were initially polymorphic were disregarded because it was unknown when the initial Figure 1). The overestimates from FLUCTUATE were also consistent with sequences analyzed in the presence mutation events occurred). It is the neutral expectation that the first fixation event should occur after an average of unacknowledged recombination (Figure 3). All of the estimates of N e based on COALESCE and RECOMBINE, of 2N e generations, with variance on the order of N e 2 ( Rodrigo and Felsenstein 1999), which is also the and 88% of the estimates of N e based on FLUCTUATE, Figure 3.-Effects of estimating under vary- ing sets of assumptions. Sequences were simulated under a neutral coalescent model with recombi- nation. Solid bars represent Watterson's estimate of , open bars represent the estimate of from COALESCE, downward-hatched bars represent estimates of from FLUCTUATE, and upward- hatched bars represent estimates of from RE- COMBINE. The horizontal dashed line indicates the true parameter ...
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... HIV-1 populations within infected individuals were from 311 to 4783 for COALESCE (Figure 1). RECOM- BINE, which relaxes the assumption of no recombina- experiencing recurring selective sweeps, we would ex- pect to detect fixation events occurring with high fre- tion, yielded estimates of N e from 326 to 2886 ( Figure 1). As expected, RECOMBINE yielded lower estimates quency. We therefore asked when the first fixation event occurred in each individual. The first fixation event than COALESCE. FLUCTUATE, which relaxes the as- sumption of a constant effective population size, is was defined as the first time a sample had a derived nucleotide state reach a frequency of 100% when it known to have an upward bias ( Kuhner et al. 1998) and yielded the highest estimates (from 439 to 1,063,776; started at 0% (sites that were initially polymorphic were disregarded because it was unknown when the initial Figure 1). The overestimates from FLUCTUATE were also consistent with sequences analyzed in the presence mutation events occurred). It is the neutral expectation that the first fixation event should occur after an average of unacknowledged recombination (Figure 3). All of the estimates of N e based on COALESCE and RECOMBINE, of 2N e generations, with variance on the order of N e 2 ( Rodrigo and Felsenstein 1999), which is also the and 88% of the estimates of N e based on FLUCTUATE, Figure 3.-Effects of estimating under vary- ing sets of assumptions. Sequences were simulated under a neutral coalescent model with recombi- nation. Solid bars represent Watterson's estimate of , open bars represent the estimate of from COALESCE, downward-hatched bars represent estimates of from FLUCTUATE, and upward- hatched bars represent estimates of from RE- COMBINE. The horizontal dashed line indicates the true parameter ...
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... HIV-1 populations within infected individuals were from 311 to 4783 for COALESCE (Figure 1). RECOM- BINE, which relaxes the assumption of no recombina- experiencing recurring selective sweeps, we would ex- pect to detect fixation events occurring with high fre- tion, yielded estimates of N e from 326 to 2886 ( Figure 1). As expected, RECOMBINE yielded lower estimates quency. We therefore asked when the first fixation event occurred in each individual. The first fixation event than COALESCE. FLUCTUATE, which relaxes the as- sumption of a constant effective population size, is was defined as the first time a sample had a derived nucleotide state reach a frequency of 100% when it known to have an upward bias ( Kuhner et al. 1998) and yielded the highest estimates (from 439 to 1,063,776; started at 0% (sites that were initially polymorphic were disregarded because it was unknown when the initial Figure 1). The overestimates from FLUCTUATE were also consistent with sequences analyzed in the presence mutation events occurred). It is the neutral expectation that the first fixation event should occur after an average of unacknowledged recombination (Figure 3). All of the estimates of N e based on COALESCE and RECOMBINE, of 2N e generations, with variance on the order of N e 2 ( Rodrigo and Felsenstein 1999), which is also the and 88% of the estimates of N e based on FLUCTUATE, Figure 3.-Effects of estimating under vary- ing sets of assumptions. Sequences were simulated under a neutral coalescent model with recombi- nation. Solid bars represent Watterson's estimate of , open bars represent the estimate of from COALESCE, downward-hatched bars represent estimates of from FLUCTUATE, and upward- hatched bars represent estimates of from RE- COMBINE. The horizontal dashed line indicates the true parameter ...
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... below the inverse mutation rate, with average esti- On the basis of the foregoing, we propose that the mates of 1933 from COALESCE and 1195 from RECOM- appropriate mutation rate to use when relating the least BINE, both between one and two orders of magnitude frequent haplotype to the product N e is the effective lower than the inverse mutation rate (Figure 1). The mutation rate at the two highly diverse sites in question median from all estimates was 1810. This, together with rather than an average across all sites. From the gamma the analyses of selection above, suggests that stochastic distribution of rate heterogeneity, we estimated the ef- forces are important influences on these populations fective mutation rate for the class of highly diverse sites and are sufficient to explain much of the observed ge- to be 14, relative to a mean across all sites of 1. Thus, netic ...
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... et al. (1991) suggested that stably inte- amount of diversity, as shown in Figure 1, implies that the strength of natural selection remains equally stable grated proviral DNA may turn over more slowly than plasma viral RNA, thus providing a reason to analyze through chronic infection. It seems highly unlikely that the strength of natural selection should remain constant sequences from these two sources separately. In our previous study (Shankarappa et al. 1999), we found no through time and across individuals. The estimates of N e derived from COALESCE and differences in the development of diversity within time points or divergence from the founder strain between RECOMBINE were remarkably consistent within and among individuals, regardless of the presence or ab- cell-associated viral DNA and cell-free viral RNA sam- ples. Similarly, in this study, we found no differences in sence of a signal for selection. This suggests that the methods were relatively robust to violations of the as- the patterns of neutrality or the estimates of N e between viral DNA and RNA samples. This lack of difference sumption of no selection in these sequences. The higher may be explained by the fact that our DNA sampling estimates from FLUCTUATE were consistent with inap- protocol does not differentiate between more labile, propriately attempting to explain recombination by a intracellular but unintegrated, viral DNA and more sta- model of population growth. The rate heterogeneity we ble, integrated, proviral DNA. Furthermore, all three invoked to correct Rouzine and Coffin's estimate to 10 3 of these genomic forms probably turn over substantially can be explained by selection and/or recombination faster than the 6-month interval at which the samples (Yang 1994;Schierup and Hein 2000). We further were obtained (Ho et al. 1995;Wei et al. 1995; Perelson suggest that if one had adequate data to estimate Mittler et al. 1999;Ramratnam et al. ...
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Chromosomal rearrangements have a relevant role in organismic evolution. However, little is known about the mechanisms that lead different phylogenetic clades to have different chromosomal rearrangement rates. Here, we investigate the causes behind the wide karyotypic diversity exhibited by mammals. In particular, we analyzed the role of metabolic,...
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Citations
... Virus mutations produce biological diversity, which is subject to the competing forces of variety and irregular genetic drift, which are both inspired by the size of the virus population [51]. ...
Artificial intelligence (AI) and Deep Learning Algorithms are potential methods for preventing the alarmingly widespread RNA viruses and ensuring pandemic safety, they have become an integrative part of the modern scientific methodology, offering automated procedures for the prediction of a phenomenon based on past observations, unraveling underlying patterns in data and providing insights about the problem. With the continuous growth in the number of RNA Virus COVID-19 patients, likely, doctors and healthcare personnel won’t be helpful in treating every case. Thus, data scientists can help in the battle against RNA Viruses Mutations by implementing more innovative solutions in order to accomplish controlling severe acute respiratory syndrome quickly RNA Viruses are viruses that are made up of strands of RNA. This work studies the induction of machine learning models and motivating their design and purpose whenever possible. In the second part of this work, we analyze and discuss the biological data in the eyes of deep learning models. The core of our contributions rests in the role of machine learning in viruses pandemics.KeywordsMachine learning algorithmsDeep learning algorithmsRNA viruses mutation problemsCorona virusSpike proteinRNA genome
... As a consequence, typical intra-host phylogenies are ladder-like, describing sequential replacement of pathogenic variants over time where only a few lineages coexist at any time-point but there is a rapid emergence of lineages though time (e.g., Bush et al., 1999;Lemey et al., 2006). In addition, sometimes the fate of intra-host pathogen populations can also be influenced by their effective population sizes (and their dynamics) and the generation time, especially in evolutionary scenarios driven by genetic drift (Frost et al., 2000;Lemey et al., 2006;Salemi, 2013;Shriner et al., 2004b). ...
... However, due to the transmission bottlenecks [18], genetic drift may be another viable explanation for the observed time trends in the frequency of certain amino acid variants. The role of genetic drift in HIV evolution is debated and has been quantified to some extent at the level of intra-patient evolution and for known transmission pairs rather than on inter-patient level [60,61]. Genetic drift on interpatient level requires inheritance of selectively neutral substitutions. ...
... Interestingly, all of these substitutions decreased in frequency over time. Ad (ii): First, we performed Tajima's D test [60,61] with a result of D = −1.44, by this indicating negative selection. ...
... by this indicating negative selection. Next, we calculated the ratio of nonsynonymous over synonymous mutations (dN/dS ratio) [60,61], finding that most regions of the integrase were under strong negative selection, including sites 72, 154, 165, and 265 (Additional file 2: Figure S1). In summary, we could not observe a clear contribution of genetic drift to the time trends of the examined substitutions. ...
Background
Detailed knowledge of the evolutionary potential of polymorphic sites in a viral protein is important for understanding the development of drug resistance in the presence of an inhibitor. We therefore set out to analyse the molecular evolution of the HIV-1 subtype B integrase at the inter-patient level in Germany during a 20-year period prior to the first introduction of integrase strand inhibitors (INSTIs).
Methods
We determined 337 HIV-1 integrase subtype B sequences (amino acids 1–278) from stored plasma samples of antiretroviral treatment-naïve individuals newly diagnosed with HIV-1 between 1986 and 2006. Shannon entropy was calculated to determine the variability at each amino acid position. Time trends in the frequency of amino acid variants were identified by linear regression. Direct coupling analysis was applied to detect covarying sites.
Results
Twenty-two time trends in the frequency of amino acid variants demonstrated either single amino acid exchanges or variation in the degree of polymorphy. Covariation was observed for 17 amino acid variants with a temporal trend. Some minor INSTI resistance mutations (T124A, V151I, K156 N, T206S, S230 N) and some INSTI-selected mutations (M50I, L101I, T122I, T124 N, T125A, M154I, G193E, V201I) were identified at overall frequencies >5%. Among these, the frequencies of L101I, T122I, and V201I increased over time, whereas the frequency of M154I decreased. Moreover, L101I, T122I, T124A, T125A, M154I, and V201I covaried with non-resistance-associated variants.
Conclusions
Time-trending, covarying polymorphisms indicate that long-term evolutionary changes of the HIV-1 integrase involve defined clusters of possibly structurally or functionally associated sites independent of selective pressure through INSTIs at the inter-patient level. Linkage between polymorphic resistance- and non-resistance-associated sites can impact the selection of INSTI resistance mutations in complex ways. Identification of these sites can help in improving genotypic resistance assays, resistance prediction algorithms, and the development of new integrase inhibitors.
Electronic supplementary material
The online version of this article (10.1186/s12985-017-0887-1) contains supplementary material, which is available to authorized users.
... As a consequence, typical intra-host phylogenies are ladder-like, describing sequential replacement of pathogenic variants over time where only a few lineages coexist at any time-point but there is a rapid emergence of lineages though time (e.g., Bush et al., 1999;Lemey et al., 2006). In addition, sometimes the fate of intra-host pathogen populations can also be influenced by their effective population sizes (and their dynamics) and the generation time, especially in evolutionary scenarios driven by genetic drift (Frost et al., 2000;Lemey et al., 2006;Salemi, 2013;Shriner et al., 2004b). ...
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Background
The existing literature about HCV association with, and replication in mosquitoes is extremely poor. To fill this gap, we performed cellular investigations aimed at exploring (i) the capacity of HCV E1E2 glycoproteins to bind on Aedes mosquito cells and (ii) the ability of HCV serum particles (HCVsp) to replicate in these cell lines. Methods
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These results demonstrate for the first time that natural HCV could really replicate within Aedes mosquitoes, a discovery which may have major consequences for public health as well as in vaccine development.
... Furthermore, during the pathogenesis of HIV infection, the virus evolves, in part, due to genetic drift of neutral mutations followed by brief episodes of natural selection during the infectious process, changing with it the cell subtype preference of the virus, which is dictated by coreceptors expressed by target cells. 17 A complete understanding of the different molecules that HIV uses to enter particular immune cells is crucial to develop effective strategies to inhibit the virus at precise stages of infection. Hence, while it is easier to focus on a single cell type or subtype for new HIV prevention strategies, an overview of HIV receptor usage on different target cells merits discussion as effective strategies will need to focus on multiple targets. ...
In the last three decades, extensive research on human immunodeficiency virus (HIV) has highlighted its capability to exploit a variety of strategies to enter and infect immune cells. Although CD4(+) T cells are well known as the major HIV target, with infection occurring through the canonical combination of the cluster of differentiation 4 (CD4) receptor and either the C-C chemokine receptor type 5 (CCR5) or C-X-C chemokine receptor type 4 (CXCR4) coreceptors, HIV has also been found to enter other important immune cell types such as macrophages, dendritic cells, Langerhans cells, B cells, and granulocytes. Interestingly, the expression of distinct cellular cofactors partially regulates the rate in which HIV infects each distinct cell type. Furthermore, HIV can benefit from the acquisition of new proteins incorporated into its envelope during budding events. While several publications have investigated details of how HIV manipulates particular cell types or subtypes, an up-to-date comprehensive review on HIV tropism for different immune cells is lacking. Therefore, this review is meant to focus on the different receptors, coreceptors, and cofactors that HIV exploits to enter particular immune cells. Additionally, prophylactic approaches that have targeted particular molecules associated with HIV entry and infection of different immune cells will be discussed. Unveiling the underlying cellular receptors and cofactors that lead to HIV preference for specific immune cell populations is crucial in identifying novel preventative/therapeutic targets for comprehensive strategies to eliminate viral infection.
... Furthermore, during the pathogenesis of HIV infection, the virus evolves, in part, due to genetic drift of neutral mutations followed by brief episodes of natural selection during the infectious process, changing with it the cell subtype preference of the virus, which is dictated by coreceptors expressed by target cells. 17 A complete understanding of the different molecules that HIV uses to enter particular immune cells is crucial to develop effective strategies to inhibit the virus at precise stages of infection. Hence, while it is easier to focus on a single cell type or subtype for new HIV prevention strategies, an overview of HIV receptor usage on different target cells merits discussion as effective strategies will need to focus on multiple targets. ...
... Several studies have estimated the effective population size of HIV-1 quasispecies intrapatient. 19,[20][21][22] In this study, calculation of the Bayes factor selected the BSP over the constant population growth in all datasets, while exponential growth was selected in only a few datasets, showing that the BSP is a better fitting model to estimate the effective population size. The population growth estimate using different models indicates that the chronically infected pair has a more constant growth. ...
Characterizing the impact of HIV transmission routes on viral genetic diversity can improve the understanding of the mechanisms of virus evolution and adaptation. The HIV vertical transmission can occur in utero, during delivery or while breastfeeding. The present study investigated the phylodynamics of the HIV-1 env gene in mother-to-child transmission, by analyzing one chronically infected pair from Brazil and three acutely infected pairs from Zambia, with three to five time points. Sequences from 25 clones from each sample were obtained and aligned using Clustal X. ML trees were constructed in PhyML using the best evolutionary model. Bayesian analyses testing the relaxed and strict molecular clock were performed using BEAST and Bayesian Skyline Plot (BSP) was construed. The genetic variability of previously described epitopes were investigated and compared between each individual time points and between mother and child sequences. The relaxed molecular clock was the best-fitted model for all datasets. The tree topologies did not show differentiation in the evolutionary dynamics of the virus circulating in the mother from the child viral population. In the BSP, the effective population size was more constant along the time in the chronically infected patients while in the acute patients it was possible to detect bottlenecks. The genetic variability within viral epitopes recognized by the human immune system was considerably higher among the chronically infected pair in comparison with acutely infected pairs. These results contribute to the better understanding of the HIV-1 evolutionary dynamics in mother to child transmission.
... Therefore recombination should be taken into account for analyzing and understanding HIV-1 evolution. The role of recombination on the emergence of drug resistance mutations is not yet clear and it can be difficult to assess because other processes may also influence its evolutionary consequences (i.e., cellular superinfection [52][53][54], random genetic drift, and viral population size [55,56] or fitness selection of the newly generated viral forms [57,58]) and because the detection of recombination can be problematic under low levels of nucleotide diversity [59]. Contradictory effects of recombination during HIV-1 antiviral therapy can be found in the literature. ...
A variety of enzyme inhibitors have been developed in combating HIV-1, however the fast evolutionary rate of this virus commonly leads to the emergence of resistance mutations that finally allows the mutant virus to survive. This review explores the main genetic consequences of HIV-1 molecular evolution during antiviral therapies, including the viral genetic diversity and molecular adaptation. The role of recombination in the generation of drug resistance is also analyzed. Besides the investigation and discussion of published works, an evolutionary analysis of protease-coding genes collected from patients before and after treatment with different protease inhibitors was included to validate previous studies. Finally, the review discusses the importance of considering genetic consequences of antiviral therapies in models of HIV-1 evolution that could improve current genotypic resistance testing and treatments design.
... There is disagreement in the literature in regard to the modelling of HIV-1 evolutionary dynamics under stochastic or deterministic processes (Nijhuis et al. 1998;Rouzine and Coffin 1999;Achaz et al. 2004;Shriner et al. 2004). The predicament dwells in the observation that the actual effective population size N e for HIV-1 is often smaller than the total population size (Kouyos et al. 2006). ...
