Accurately Measuring Recombination between Closely Related HIV-1 Genomes
Viral diversity is one of the major obstacles to the successful eradication of HIV 1 , 2 ., It arises due to the interplay between mutations introduced by error-prone reverse transcription 3 , high levels of viral turnover 4 , retroviral recombination 5 and strong diversifying selection pressure from the immune system 2 ., All retroviruses co-package two RNA genomes into each virion ., Retroviral recombination occurs when the reverse transcriptase ( RT ) enzyme switches between co-packaged RNAs during reverse transcription ( reviewed in 6 , 7 ) ., In HIV , recombination occurs much more frequently than mutation 8 , and is a major determinant of viral diversification ., Within infected individuals , recombination allows sequential rounds of viral escape of both antibody and T-cell recognition , resulting in loss of immune control 9 , 10 ., Furthermore , recombination can both promote and suppress the generation of multiple drug resistance , by creating or breaking linkages between drug resistance mutations 11–16 ., Therefore , an accurate measurement of recombination rates directly within the HIV genome is fundamental to our understanding of HIV ., Recombination has been studied extensively , by many groups , and is typically detected by monitoring the linking of marker points from co-packaged RNA genomes into a single DNA genome ., One popular method of measuring recombination is through the use of retroviral reporter systems ., These systems measure recombination within a ‘foreign’ gene insert , such as genes that code for antibiotic resistance proteins , surface protein markers , and/or fluorescent proteins 8 , 17–24 ., Retroviral reporter systems have the advantage of being able to readily quantify a large number of recombination events within the gene insert ., However , in vitro studies show that template sequence and nucleic acid structure are important determinants of the recombination process 25 , 26 ., Therefore , measurements of recombination rates within non-HIV ‘foreign’ gene sequences will not recapitulate recombination rates within HIV sequence ., Other groups utilize the genetic variation between and within HIV subtypes , and use sequencing to monitor recombination 8 , 21 , 22 ., These systems provide the foundation to reveal recombination events within the HIV genome ., However , the use of genetically divergent RNA templates does not reflect the situation in vivo , where the vast majority of infected individuals are infected with a single virus which rapidly diversifies into a viral quasispecies over the course of infection 27 ., The use of divergent RNA sequences can lead to confounding differences in parameters known to affect recombination , including: overall RNA homology 28 , 29 , RNA packaging 30 , 31 , and the amino acid sequence of viral proteins , such as reverse transcriptase 32–34 ., Therefore , the recombination events detected using divergent RNA sequences most likely reflect the special case of inter-subtype recombination ., Hence , there is a real need to develop a retroviral recombination system which mimics the recombination that occurs between closely related , yet genetically distinct , viruses found within an infected individual ., Recombination is detected by monitoring the linking of marker points from separate RNA genomes into a single DNA genome ., Regardless of the system in which it is measured , recombination is either detected or undetected between any two marker points ., This is generally interpreted as one or zero recombination events , respectively ., However , with increasing genomic distance and/or recombination rate , there is an increased likelihood that there will be multiple template switches between any two marker points which go undetected ., Consequently , with high rates of recombination and/or genomic distances between marker points , there is a greater chance of underestimating recombination rates due to multiple template switches ., These possibilities have been mentioned previously 20 , 24 , 35 , 36 ., However , there is no current standard method to calculate recombination rates over multiple genetic regions of varying lengths that also compensates for the possibility of multiple template switches between marker points ., Additionally there exists no theoretical estimate for the error when recombination is measured without compensating for multiple template switches , as is often the case ., Here , we present a novel experimental method based on limited codon modification of the HIV genome which does not change the infectivity of the virus or any viral protein ., This allows the measurement of recombination between closely related genomes analogous to those found in the quasispecies of an infected individual ., This system measures recombination in different gene segments , allowing the identification of possible recombination ‘hotspots’ , where template switches occur at higher frequencies ., We then develop statistical tools to calculate an ‘optimal recombination rate’ that reproduces observed recombination frequencies , taking into account multiple template switches ., These tools demonstrate the error in calculating crude recombination rates ( that do not consider multiple template switches ) and emphasize the necessity for careful data analysis ., These tools also provide the basis to quantify statistical differences in recombination rates in various regions of the HIV genome , under different conditions , or infection with different target cells ., Finally , our analysis allows for testing and subsequent validation of some inherent assumptions and sources of error in the experimental design ., We compare our analytic procedure with previously published studies and find that our approach avoids some of the potential pitfalls of using reporter gene inserts ., Recombination is measured by analysing the cDNA that results from infection with non-identical ( heterozygous ) co-packaged RNA genomes ., The positions in which the RNA genomes differ are called marker points ., Recombination is detected only when the resulting cDNA contains a mixture of marker points from both RNA strands ., It is tempting to conclude that one template switch has occurred every time recombination is detected between a set of marker points , and that no template switches occurred elsewhere ., However , any even number of template switches between two fixed marker points will lead to us observing no recombination , and any odd number will result in us observing a single recombination event ( Figure 1A ) ., An important consequence of this fact is that the probability of observing a recombination event is a function of the genomic distance between the markers and the overall recombination rate ., We created a model of recombination which takes into account the possibility of not detecting recombination events ( see Materials and Methods ) ., Our recombination rate calculation ( denoted ‘optimal’ recombination rate ) reveals the relationship between the overall recombination rate , distance between marker points and the probability of observing a recombination event ( Figures 1B and 1C ) ., We show that for each genomic distance and overall rate of recombination , there is a unique probability of observing recombination ., Furthermore , with high overall rates of recombination and large genomic distances , it becomes much more difficult to calculate the recombination rate accurately ., Indeed , these probabilities eventually converge until it becomes impossible to derive the true rate of recombination because there is an equal chance of observing or not observing a recombination event ., To demonstrate the consequences of ignoring multiple template switches , we utilized a simple equation ( denoted ‘crude’ recombination rate calculation ) : r\u200a=\u200ac/nl , where r is the rate of recombination events per nucleotide per round of infection ( REPN ) , c is the number of template switches detected , n is the number of sequences , and l is the genomic distance over which recombination is measured ., This crude formula assumes that between marker points , at most one template switch can occur ., To calculate the theoretical expected error of the ‘crude’ recombination rate we first use the ‘optimal’ recombination rate equation ( Eq . A ) to determine the probability of observing recombination over different genomic distances ., We then use the ‘crude’ recombination rate calculation on these probabilities and find that this calculation consistently underestimates the real recombination rate ., At an actual recombination rate of 0 . 001 REPN ( lower than the median recombination rate measured in T-cells in this study ) , the calculated crude recombination rate is 9% lower when measured over a distance of 100 nucleotides , and 37% lower when measured over a distance of 500 nucleotides ( Figure 1D ) ., This error is even larger when the real recombination rate is 0 . 003 , where the crude rate is 25% and 68% lower than the actual rate when measured over a distance of 100 nucleotides and 500 nucleotides respectively ( Figure 1E ) ., This error is a direct result of not considering multiple template switches , emphasizing the need for our optimal recombination rate calculation ., We sought to measure the rate of recombination directly in the HIV genome ., To this end , we made a marker virus ( MK ) by introducing 6 codon modifications into the gag gene of wild-type ( WT ) HIV ., This creates 5 regions ( varying in length from 77 to 398 nucleotides ) over which we can directly measure the recombination rate of a full length HIV genome ( Figure 2A and 2B ) ., These modifications neither affect the infectivity of the virus nor alter the amino acid sequence of any viral protein ( Figure S1 ) ., The recombination process depends greatly on template sequence , RNA structure , the overall homology between sequences and the viral proteins involved in reverse transcription ., Therefore , this system mimics the situation in vivo , where recombination occurs in the context of a quasispecies of highly related , yet genetically distinct viruses ., In our experimental system , a template switch observed in the DNA provirus is most likely to be the result of viral recombination during reverse transcription of the two RNA molecules co-packaged in a heterozygous virion ., However , it is possible that recombination could also have occurred during a number of steps in sample preparation and sequencing ., To determine the potential bias within our experimental system , we quantified experimentally-induced recombination , as follows: Firstly , we tested the possibility of transfection-induced recombination which can occur via homologous recombination in the producer cell 37 ., We measured this by direct sequencing of plasmid DNA extracted from co-transfected 293T cells ( Figure 2C ) ., Of 182 sequences of plasmid DNA extracted from transfected cells we observed zero recombination events , suggesting that this is not a source of error in our system ( Table 1 ) ., Secondly , we tested for PCR-induced recombination that may occur if the polymerase switches templates during PCR amplification of the viral sequences prior to sequencing ., We measured this by performing two separate infections with either WT homozygous virus or MK homozygous virus ( Figure 2D ) ., In this case , recombination occurs at the usual rate between co-packaged HIV RNA strands , but template switching between these identical copies of RNA cannot be detected ., These homozygous samples are mixed prior to PCR ., Thus , any observable recombination can be inferred to be an artifact of the PCR ., 125 sequences were obtained and 3 recombination events were detected ( Table 1 ) ., Finally , we measured the rate of ‘inter-virion’ recombination that may have occurred if the target cells were multiply infected , and retroviral recombination was occurring between the RNA molecules of different virions ., To do this we co-infected cells with homozygous WT and homozygous MK virions ., Thus , any intra-virion recombination would be undetected , but both inter-virion recombination and PCR-induced recombination would be detected ( Figure 2E ) ., 128 sequences were obtained and 2 recombination events were detected ( Table 1 ) ., To measure the biological rate of recombination we generated a mixture of heterozygous and homozygous virus by co-transfection ., When equal amounts of two HIV plasmids are co-transfected , co-packaging of RNA into virions is random 38 ., Therefore , when we co-transfected equal amounts of WT and MK plasmid , we expect 50% heterozygous virions , 25% homozygous WT virions and 25% homozygous MK virions ( Figure 2F ) ., This mix was used to infect primary T-cells ., 118 sequences were obtained and 58 recombination events were detected ( Table 1 ) ., In determining recombination rates , it is easy to assume that transfection of equal amounts of WT and MK plasmid leads to the production of 50% heterozygous and 50% homozygous virus ., However , variations in the level of co-transfection will lead to the production of a different proportion of heterozygous virions than expected ., This will bias the calculation of recombination rates ., Our design allows us to estimate the proportion of heterozygous virions in our experiments directly from the data ( as described in Materials and Methods ) ., The estimated proportion of heterozygous virus was approximately 50% in our studies ( 48 . 6% , 45 . 1% , 49 . 7% and 46 . 1% for transfection , PCR , between virion and T-cell experiments respectively ) , indicating that there is no bias in infection rates between WT and MK virus or in the production of our heterozygous virions ., We then calculated the recombination rates for each of our experimental conditions , using both our crude and optimal recombination rate calculations ( Table 1 ) ., As we detected no recombination events in our transfection-induced recombination control , the crude and optimal recombination rates were 0 REPN ., From 125 and 128 sequences for the PCR-induced recombination control and the PCR-induced plus inter-virion control , we observe 3 and 2 recombination events respectively ., This corresponds to an optimal recombination rate of approximately 0 . 1×10−3 REPN ., For our biological sample , the crude recombination rate was calculated to be 0 . 81×10−3 REPN , and the optimal recombination rate to be 1 . 45×10−3 REPN ., Thus , the crude recombination rate underestimates the optimal rate by approximately 44% ., This underlines the importance of calculating recombination rates using our ‘optimal’ recombination rate calculation instead of the ‘crude’ method commonly used in the literature , which does not compensate for multiple template switches ., Using the above approach we are able to directly estimate the recombination rate from an experimental data set ., However , the error of this estimate is affected by the number of sequences sampled , and their distribution ., In order to determine confidence intervals for these estimates we generated probability distributions by bootstrapping the sequence data ( see Materials and Methods ) ., The 95% confidence intervals of these distributions are calculated with the Percentile Method and are shown in Table 1 ., Due to the high number of samples ( >118 for all datasets ) and relative symmetry of the bootstrap distributions ( data not shown ) , we assume very good coverage of these confidence intervals ., We conclude that the recombination rates are significantly different ( at the 0 . 05 level ) when the 95% confidence intervals do not overlap ., These distributions show that the recombination rate is not significantly different between PCR induced recombination and PCR induced plus inter-virion recombination ., Thus , inter-virion recombination is not a significant factor in our experimental setup ., However , recombination rates were significantly different between our controls and the rate of HIV RT-induced recombination in the biological sample ., The true HIV RT-induced recombination rate was then calculated with a control correction method ( see Materials and Methods ) , that is approximately a subtraction of the two recombination rates ., The RT-induced recombination rate alone is calculated to be 1 . 35×10−3 REPN in primary T-cells ., Our experimental system allows recombination to be measured between closely related viral genomes ., However , most recent recombination assays involve the insertion of fluorescent proteins into the HIV genome ., In these systems two distinct defective genes , encoding a fluorescent protein , are inserted into different HIV genomes ., A recombination event that eliminates the deactivating mutations recreates a functional fluorescent encoding gene ., Recombination can then be measured via FACS analysis of infected and fluorescent protein expressing cells ., This technique is capable of producing large quantitative datasets and has shown to be an effective tool to compare recombination rates under varying conditions ., Generally , the extent of recombination in these systems is measured as a function of the multiplicity of infection ( MOI ) of fluorescent protein expressing cells and the MOI of viral infection ., However these calculations are not easily comparable to calculations made for marker points separated by different genomic lengths ., A clearer approach is to calculate the recombination rate in terms of ‘recombination events per nucleotide per round of infection’ ( REPN ) , as this rate allows the prediction of the number of recombination events that will occur over any length of RNA ., To demonstrate how our recombination rate calculation method can be applied to fluorescent protein studies , and to make a direct comparison of these recombination rates to our own , we analysed the data from Rhodes et al . 2005 24 ., Table 2 , 3 , 4 , and 5 from Rhodes 2005 lists the total number of cells , infected cells , and green fluorescent protein positive ( GFP+ ) cells when the recombination is measured over a genomic distance of 588 , 300 , 288 and 103 base pairs , respectively ., From these ratios the GFP+ MOI/infection MOI ( ratio denoted as M ) is calculated ., This ratio represents the probability of a single infection event resulting in the reconstruction of a functional GFP protein ( see Materials and Methods ) ., Recombination is only detectable from 50% of the virions ( those that are heterozygous ) ., Thus , the probability that a heterozygous infection recreates a functional GFP is 2M ., A functional GFP is only created when the two inactivating mutations are eliminated via recombination ., However , the two inactivating mutations being ‘joined’ via recombination is equally likely ., Thus , the probability that a heterozygous infection results in mosaic cDNA ( from a nucleotide sequence perspective ) is 4M ., Using equation ( A ) ( Materials and Methods ) with R ( L ) =\u200a4M converts M into the required recombination rate measured in REPN ., Thus , taking into account the possibility of multiple template switches , the recombination rates for the data in Rhodes 2005 ranges from 0 . 49×10−3 to 0 . 97×10−3 ( table 2 ) ., Note that the calculated optimal recombination rates in Rhodes 2005 are similar regardless of the genomic distance over which recombination is measured ., This is because our analytical recombination rate calculation compensates for genomic distance when calculating the probability of multiple template switches ., Our conversion of the data in Rhodes 2005 to a recombination rate per nucleotide per round of infection is in line with previous conversions by Suryavanshi and Dixit 36 , who used curve fitting techniques to estimate an average recombination rate over the different lengths ., The advantage of our technique is that our procedure can be applied with a standard calculator and requires no curve fitting experience or software ., Crossover sites of the HIV-1 RT may consist of RNA sequence determinants that direct the RT to switch templates , and it has been suggested that RNA-RNA interactions can promote recombination in vitro 28 , 29 ., Unlike systems that measure recombination over only one region , our experimental design allows recombination to be studied in five gene segments in gag , which cover a total genomic distance of 917 base pairs ., Our analytical recombination rate calculation allows us to calculate the optimal constant recombination rate that best describes our experimental data and compensates for the possibility of multiple template switches ., This system allows us to determine:, ( i ) if the variation in recombination along the gene is significantly different than that expected by random variation ( indicating whether recombination is a random event ) ;, ( ii ) the optimal location for a recombination rate change ( determining the marker point that separates any recombination ‘hotspots’ and ‘coldspots’ ) ; and, ( iii ) whether a two recombination rate model better describes the observed experimental recombination data ., Together , these analyses will help us to determine whether recombination occurs randomly across the viral genome ., We first use a chi-squared goodness of fit test to determine if the observed frequency of recombination and the expected frequency ( calculated from our optimal recombination rate and compensating for multiple template switches , equation ( B ) Materials and Methods ) are significantly different in each gene segment ., Figure 3A profiles the experimentally observed and expected number of detected template switches that were recorded over the different sections of gag ., The experimental data displays significant variation from the expected frequencies of recombination to the observed frequencies ( p\u200a=\u200a0 . 02 ) suggesting that recombination rates vary along the gene segments ., We then adjusted our mathematical model of HIV recombination to fit two optimal recombination rates along the gene segment ., This was achieved by splitting the gene segment into two , and calculating each subsegments optimal recombination rate ., The location of the split was optimised along marker positions 2 to, 5 . We find that the recombination rate is higher towards the marker site 1 end and lower towards marker site, 6 . The optimal location for recombination rate switch was at marker site 4 ( 1 . 95×10−3 and 0 . 49×10−3 REPN from sites 1–4 and 4–6 respectively ) ( Figure 3B ) ., Comparing the dual recombination rate model to the original model with an F-test did not produce a significant p value ( p\u200a=\u200a0 . 30 , Figure 3B ) , indicating that the dual recombination rate model did not fit significantly better to justify the additional parameters ( second recombination rate and switch location ) ., To address this further , we analyzed a second set of data and sequenced 192 cDNA strands ., Again , we found that recombination is higher towards marker site 1 and lower towards marker site 6 ( Figure 3C and 3D ) ., However , an F-test comparing the one and two recombination rate models in this dataset , but this time applying the same switch location estimated in the first experiment ( one less parameter in the two recombination rate model ) , still did not achieve significance ( p\u200a=\u200a0 . 068 ) ., Thus , our data support a difference in recombination rate across the gene , but was unable to identify the precise ‘hotspots’ of higher recombination ., The assumption of an equal recombination rate amongst all sequences predicts that the frequency of multiple recombination events should be Poisson distributed ., However , due to the possibility of multiple template switches occurring between markers of varying genomic distances , and the possibility of varying recombination rate across the gag gene , the frequency of multiple detectable template switches does not follow a Poisson distribution ., We calculated this distribution to compute the expected frequency of multiple detectable template switches and compare this to our experimental results ( Figure 4 ) ., This calculation compensates for multiple crossovers and uses the individual recombination rate observed in each region ., Our data indicates some variation from the expected frequency of multiple template switches , however this was not significant ( p\u200a=\u200a0 . 096 ) ., Finally , it is possible that the limited introduction of marker points into the HIV genome altered the RNA structure in such a way as to bias the recombination process ., For example , reverse transcription commencing on the MK genome may be more likely to result in recombination than reverse transcription on the WT genome due to our codon modifications ., This predicts that the probability of recombination will be different when the RT is reverse transcribing a WT or MK marker point ., Therefore , we compared the proportion of recombination events where recombination occurred from MK to WT , versus from WT to MK in our sequences ., Of the 90 template switches observed in the pooled dataset , 42 were MK to WT and 48 were WT to MV , consistent with the null expectation of 50∶50 ( p\u200a=\u200a0 . 60 , binomial distribution ) ., This illustrates that our codon-modified markers have not significantly altered the RNA structure so as to bias the observed recombination rate ., Recombination plays an instrumental role in the evolution of HIV 39 , 40 and continues to shape the global pandemic 41 ., Despite the excellent progress made in understanding inter-subtype recombination 30 , 42 , 43 , the study of recombination occurring between closely related genomes within an infected individual has been hampered by the lack of an appropriate model ., Existing recombination systems are based on foreign reporter sequences , inter-subtype HIV genomes and/or intra-subtype HIV genomes with variation in amino acid sequences ., Therefore , we have developed a novel marker system and associated mathematical tools that:, ( i ) measures the recombination rate directly on the HIV genome;, ( ii ) controls for background recombination;, ( iii ) corrects for multiple template switching ., Our HIV recombination marker system uses genetic marker points based on the codon modification of the authentic full length HIV genome without altering the amino acid sequences ., Other groups have previously measured recombination rates on the HIV genome using the divergent RNA sequences found between or within HIV subtypes or within non-viral reporter sequences ., Our procedure has several advantages over previously published methods ., First , we rationally introduced marker points into the HIV genome at well defined locations , by avoiding RNA sequences that are known to be important for HIV replication ., These marker points allow recombination to be monitored , but do not affect the HIV replication cycle , even over multiple rounds of replication ( Figure S1 ) ., This is in contrast to recombination systems using divergent RNA sequences from different viral strains , where the differential replication capacities of the virus may bias the outcome of recombination ., Second , our marker system retains every virion protein and these are expressed in their correct biological context ., In the case of the retroviral reporter systems , it is common to completely knockout one or more HIV proteins by replacing them with non-viral reporter protein sequence ., Therefore , these reporter systems , even when attempts are made to reintroduce these proteins back into the virion , do not recapitulate the exact biological conditions occurring in the full length virus 22 , 24 ., Third , our silent modifications do not change the amino acid sequence of the viral proteins ., This is important in light of reports that the amino acid sequence of the HIV RT affects the rate of template switching 32 , 34 and that mutations in the Gag polyprotein can affect RNA packaging and recombination 44 ., Therefore , it seems likely that variations in the amino acid sequence of any viral protein involved in either assembly or reverse transcription of the virus could have unintentional consequences on the rate of recombination ., This would limit the utility of divergent RNA sequences , even from within the same subtype 45 ., Fourth , by limiting our modifications to targeted regions of the genome , we aim to maintain overall RNA structure and homology , which are critical determinants of recombination 28 , 29 , 46 ., We demonstrated that recombination occurred at an equal rate on our WT and MK genome; hence , our modifications do not change the rate of recombination ., This indicates that the variations in the recombination rate we observe are due to differences in the RNA sequence between marker points , not to the marker points themselves ., We acknowledge that there are experimental complexities associated with the direct measurement of recombination by sequencing that can lead to the inclusion of non-viral recombination artifacts ., Therefore , we carefully controlled for transfection-induced recombination , PCR-induced recombination and the effects of co-infection due to inter-virion recombination ., In our study , transfection-induced recombination can be excluded as a source of error ., We also show that inter-virion recombination , due to multiple infections of a cell , is not a significant source of error ., By contrast , most retroviral reporter systems are biased by multiple infections ., That is , in most retroviral reporter systems , multiple infections cannot be distinguished from single infections ., This decreases the apparent total number of infection events , which is required to accurately calculate the recombination rate ., To overcome this , these systems make use of MOI calculations which compensates for multiple infections ., However , MOI calculations assume that infection events are independent and random ., This is problematic in light of reports that double-infection occurs more frequently than predicted from random chance alone 47 , 48 , although this effect has been challenged by other data 22 and mathematical analysis 49 ., Nevertheless , our system has the advantage that the recombination rate calculations are not affected by the occurrence of multiple infections ., Finally , we did detect some recombination due to PCR-induced recombination but were able to optimize our PCR cycling conditions to minimize its effects ., In addition , our recombination rate calculation corrects for this background to reveal the true rate of recombination ., This highlights the necessity of including appropriate controls , as the effect of PCR-induced recombination has been ignored in similar studies 8 , 22 , 50 , 51 ., As recombination is measured by observing the linking of genetic marker points , all recombination systems are potentially biased by the occurrence of multiple template switches ., A potential solution is to reduce the genomic distance between marker points and to evenly space them on the HIV genome ., This effectively eliminates multiple template switches and any bias due to variations in genomic distance between marker points ., However , it is impossible to modify the HIV genome in this way without drastically affecting the replication cycle ., As a result , modifications that do not affect important RNA sequences or vary the amino acid sequences of viral proteins will always be unevenly spaced ., Furthermore , increasing the frequency of marker points increases the genetic diversity between co-packaged RNAs ., This is expected to decrease the observed recombination rate , as high levels of sequence identity between templates is required for efficient template switching 46 , 52 ., Thus , whilst reducing the genomic distance between markers can improve the ability to detect recombination , it also biases the observation by decreasing the likelihood of template switching in the first place ., As multiple template switches between any two marker points occur , by definition , between identical sequences , these switches take place under optimal conditions for 
Retroviral recombination is thought to play an important role in the generation of immune escape and multiple drug resistance by shuffling pre-existing mutations in the viral population ., Current estimates of HIV-1 recombination rates are derived from measurements within reporter gene sequences or genetically divergent HIV sequences ., These measurements do not mimic the recombination occurring in vivo , between closely related genomes ., Additionally , the methods used to measure recombination make a variety of assumptions about the underlying process , and often fail to account adequately for issues such as co-infection of cells or the possibility of multiple template switches between recombination sites ., We have developed a HIV-1 marker system by making a small number of codon modifications in gag which allow recombination to be measured over various lengths between closely related viral genomes ., We have developed statistical tools to measure recombination rates that can compensate for the possibility of multiple template switches ., Our results show that when multiple template switches are ignored the error is substantial , particularly when recombination rates are high , or the genomic distance is large ., We demonstrate that this system is applicable to other studies to accurately measure the recombination rate and show that recombination does not occur randomly within the HIV genome .
HIVs ability to generate and maintain high genetic diversity leads to multiple drug resistances and evasion from the immune system , eventually leading to immune failure and progression to AIDS ., HIV maintains this diversity with a process of mutation ( incorrect copying of genetic information in viral replication ) and recombination ( mixing two viral genomes in the creation of viral offspring ) ., Recombination is generally studied by inserting genes encoding non-viral fluorescent proteins ., However , recombination in such modified HIV genomes may not accurately reflect the level of recombination occurring within a patient infected with HIV ., Additionally , recombination will go undetected in regions where the parental genomes are identical , and this effect is often ignored ., We have developed a novel experimental system which allows recombination to be measured between two very closely related HIV genomes ., We have also developed statistical tools to accurately calculate the recombination rate , compensating for undetectable recombination in identical regions of the parental genomes ., We show that our experimental system bypasses some of the pitfalls of fluorescent recombination experiments and our tools provide a strong quantitative foundation for future studies in this area .
infectious diseases/hiv infection and aids, mathematics/statistics, virology/viral replication and gene regulation, computational biology