Mathematical models predict that community-wide access to HIV testing-and-treatment can rapidly and substantially reduce new HIV infections. Yet several large universal test-and-treat HIV prevention trials in high-prevalence epidemics demonstrated variable reduction in population-level incidence.To elucidate patterns of HIV spread in universal test-and-treat trials, we quantified the contribution of geographic-location, gender, age, and randomized-HIV-intervention to HIV transmissions in the 30-community Ya Tsie trial in Botswana. We sequenced HIV viral whole genomes from 5114 trial participants among the 30 trial communities.Deep-sequence phylogenetic analysis revealed that most inferred HIV transmissions within the trial occurred within the same or between neighboring communities, and between similarly aged partners. Transmissions into intervention communities from control communities were more common than the reverse post-baseline (30% [12.2 – 56.7] vs. 3% [0.1 – 27.3]) than at baseline (7% [1.5 – 25.3] vs. 5% [0.9 – 22.9]) compatible with a benefit from treatment-as-prevention.Our findings suggest that population mobility patterns are fundamental to HIV transmission dynamics and to the impact of HIV control strategies.This study was supported by the National Institute of General Medical Sciences (U54GM088558), the Fogarty International Center (FIC) of the U.S. National Institutes of Health (D43 TW009610), and the President’s Emergency Plan for AIDS Relief through the Centers for Disease Control and Prevention (CDC) (Cooperative agreements U01 GH000447 and U2G GH001911). The global number of new infections with HIV-1, the virus that causes AIDS, has gradually declined since the peak in 1997, yet population-level HIV incidence remains high in East and Southern Africa, especially among young women . DespiteFour large cluster-randomized trials were recently conducted in East (Kenya and Uganda) and Southern Africa  to evaluate the effect of universal HIV testing and treatment in reducing population-level HIV incidence . The triThe joint United Nations Programme on HIV/AIDS (UNAIDS) had set a global target of fewer than 500,000 new HIV infections by 2020 that was unmet. To stem the spread of new HIV infections we need to better understand patterns of HIV transmission in high-prevalence, generalized epidemics in sub-Saharan Africa. To obtain such understanding requires a better grasp of where and from whom those most at risk acquire infection and to whom they are likely to spread infection. Previous work in a high HIV burden setting in Kwa-Zulu Natal, South Africa identified age-disparate sexual partnerships between younger women under 25 years and older men as a primary contributor to HIV transmission and incidence in younger women .We hypothesized that geographic proximity, gender, age, and ready access to HIV care (randomized-HIV-intervention) contribute to shaping patterns of HIV transmission. To test these hypotheses, we performed a deep-sequence phylogenetic study of adults with HIV aged 16–64 years who consented to participate in the Botswana Ya Tsie HIV prevention trial to quantify the contribution of the above factors to HIV transmission patterns in Botswana adjusting for sampling heterogeneity.2 , with a population of about 2.3 million people. The Kalahari Desert occupies approximately 70% of the country, and the population is largely distributed along an eastern corridor bordered by South Africa, Zambia, and Zimbabwe  was a pair-matched community randomized trial to evaluate whether an optimized combination of effective HIV treatment and prevention interventions would substantially reduce population-level HIV incidence over 29 months. The trial was conducted from 2013 to 2018 in 30 rural and peri-urban communities (average population size: 5855) in the Central, South-East, and North/North-East regions of Botswana that represented an estimated total population of 175,664  . Among eTo better understand HIV transmission patterns among participating communities, residents with HIV-1 were invited to provide a sample for viral phylogenetic analyses. This included (1) all people with HIV-1 identified during a baseline survey of 20% of households randomly sampled from each trial community to establish an incidence follow-up cohort, (2) all people with HIV-1 identified through annual household surveys conducted in this 20% household sample in all 30 communities during the trial, (3) all people with HIV-1 who were enrolled in a community-wide survey in 100% of 6 communities, 3 in the intervention and 3 in the control arms (the ‘end of study survey’), (4) all newly identified people with HIV-1 discovered during an initial community-wide HIV testing and counseling campaign followed by more targeted HIV testing in the 15 intervention communities, (5) all people with HIV-1 in intervention communities who later presented at healthcare facilities after being identified during community-wide testing campaigns, and (6) all people with HIV-1 who were already receiving HIV care at health facilities in the 15 intervention communities. Altogether, 5114 participants consented to a blood draw for HIV-1 viral genotyping and their viral genomes were successfully deep-sequenced, representing the largest phylogenetics study conducted in Botswana .i in community, j were assumed to have a Poisson distribution with mean, To obtain HIV prevalence estimates we first fit a random-intercept Poisson regression model of HIV status, with age and gender as fixed-effects, using nonparametric maximum likelihood estimation in the gllamm program  in Statai in community, j  so that,Accordingly, Next, to account for heterogeneity in HIV prevalence across trial communities we computed empirical Bayes estimates (or posterior means) for the age-gender specific HIV prevalence in each community. The empirical Bayes estimate of HIV prevalence for the where Here,  program  in Stataj, the estimated number of people with HIV, We thereafter estimated the size of the population with HIV for each age-gender category in each trial community. Empirical Bayes estimates of the age-gender specific HIV prevalence for each trial community were standardized to the proportion of participants in the 2011 Botswana population and housing census that were in the same age and gender categories of these communities. Then for a specific community-age-gender combination, for example, females aged 16–24 years in community where.ion data .j.j.The age-gender estimates of the number of people with HIV in each trial community were used to adjust for sampling heterogeneity when estimating the proportions of HIV transmissions in the trial population . Population-size estimates of people with HIV were computed in R version 3.5.2 .Because most participants were virally suppressed at the time of sampling, deep sequencing of HIV-1 viral genomes was generally done on proviral DNA   see Mat and 2. FPaired-end deep-sequencing was performed with Illumina MiSeq and HiSeq instruments at two locations, the BioPolymers sequencing core facility at Harvard Medical School, Boston, United States (n = 4246 participants) , and at Our analysis was restricted to samples from 3832 participants  who had available demographic information and met minimum criteria on genome missingness (≥6300 nucleotides available) for inclusion in phylogenetic analyses. More precisely, individuals whose viral consensus genomes had fewer than 30% of the bases missing beyond the first 1000 nucleotides were retained for analysis. Sampling (or genotyping) density is defined as the estimated proportion of individuals living with HIV in a trial community that were included in our sample. Intervention communities were sampled more densely, 16%  than control communities, 12% ; and sampling densities among all 30 trial communities ranged from 2.7% to 36.2% —that 21%The HIV-incidence prospective cohort of the Botswana/Ya Tsie trial comprised a random sample of 20% of households in each community to evaluate the incidence of HIV infection and adoption of the intervention during the trial. The 3832 participants included in phylogenetic analyses included 85% (124 / 146) of the seroconverters in the HIV-incidence cohort; seroconverters in the HIV-incidence cohort were defined as individuals that acquired HIV infection during the trial . Seven ohttps://www.hiv.lanl.gov/content/sequence/VIRALIGN/viralign.html); genetic distances were then computed under the Tamura-Nei-1993 nucleotide substitution model  of the 3832 HIV-1 viral whole-genome consensus sequences available for phylogenetic analyses . Sequencon model  with TN9To save time and computational resources from evaluating sequences that were too distantly related, we first identified clusters of participants with genetically similar HIV-1 infections as a filtering step, before performing ancestral host state reconstruction with Phyloscanner to detect probable directed transmission pairs. Two clustering algorithms, HIV Transmission Cluster Engine (HIV-TRACE) v0.4.4  and Clusi and j are considered to be phylogenetically linked, more specifically, phylogenetic linkage is not excluded when the minimum patristic distance between their subgraphs, i and j are adjacent meaning that the shortest path connecting their subgraphs in a phylogeny does not pass through a third individual. Tree topology is used to infer the probable direction of transmission between a pair of individuals and refers to the number of subgraphs belonging to each individual in the pair that are ancestral-to or descendant-from those of the other individual. We classified pairs of individuals separated by a subgraph distance below 0.035 substitutions per site as phylogenetically linked based on previous work by We performed parsimony-based ancestral host state reconstruction of the mapped deep-sequencing short reads of participants in clusters of genetically similar HIV-1 infections (n = 525) with the Phyloscanner software v1.8.0  to identi and j had sufficient deep-sequence reads for phylogenetic inference; and i and j, for example, that the subgraphs of individual i are ancestral-to, descendent-from, sibling-to, or intermingled-with those of individual j; sibling and intermingled topologies are ones where there is insufficient evidence to make inferences about the probable direction of transmission (n = 42 (8400 / 200) non-overlapping alignments and deep-sequence trees along the genome; we excluded the first 1000 nucleotides of the HIV-1 genome from analysis as they are typically poorly sequenced corresponding to a genome length of 8400 bp. The linkage and direction of transmission score threshold, i and j exceed 80%, Probable transmission pairs identified with Phyloscanner were further classified on the strength of evidence for phylogenetic linkage, smission . We usedHIV-1 infections in southern Africa are more commonly transmitted through heterosexual contact between males and females, compared with sexual contact between same-sex couples. Thus, we can use the probability of inferring a phylogenetically linked male-male pair to calibrate an upper bound on the false discovery rate (FDR) of inferring a phylogenetically linked male-female pair, if we assume the probability of erroneously inferring direct transmission between a male-female pair is the same as the probability of inferring a phylogenetically linked male-male pair .Consider a deep-sequence phylogenetic analysis where where Thereby affording a FDR estimate of,This represents an upper bound on the FDR of inferring direct transmission between males and females in phylogenetically linked male-female pairs.To estimate HIV transmission flows in the trial population within and between different age-gender groups and locations , accounting for variable rates of sampling, we used the method described by Consider a population of individuals with HIV, describes the counts of directed (source-recipient) transmission pairs identified in the sequenced sample within and between intervention and control communities, where be the number of distinct possible transmission pairs among individuals sampled from Estimates of probabilities of linkage between two individuals randomly sampled from within demographic groups are observed using the method of In accordance with where, We next estimate the relative probability that an HIV transmission event in the trial population occurred from sexual contact between partners residing within intervention communities or within control communities; compared with partners where one resides in an intervention community and the other a control community or vice versa. Accordingly, we estimate the conditional probability, i and j . Similarly, Here, describes estimated proportions of HIV transmissions in the trial population within and between intervention and control communities adjusted for differential sampling by trial arm.We use the Goodman method  to compuTo estimate Goodman’s CIs the estimated number of HIV transmission pairs in the trial population within and between intervention and control communities are scaled such that the sum of the weights is equal to the total number of probable male-female transmission pairs identified in the deep-sequenced sample (n = 82), and the weights are treated as known; broadly similar to calibration of weights in survey sampling. For example, the weighted counts of probable male-female transmission pairs within and between intervention and control communities are given by the vector,where This ensures that the estimated Goodman’s CIs of HIV transmission flows within and between groups/strata in the trial population reflect that amount of information in the sample.Let the weighted counts of the probable male-female transmission pairs identified within and between intervention and control communities, For example, where the lower confidence bounds are described by,and the upper confidence bounds are described by,Here, bumblebee that will be made available at the following URL: https://magosil86.github.io/bumblebee/ . A step-by-step tutorial on how to estimate HIV transmission flows with bumblebee and accompanying example data sets can be accessed at the following URL: https://github.com/magosil86/bumblebee/blob/master/vignettes/bumblebee-estimate-transmission-flows-and-ci-tutotial.md  mean age-gaps between males and females in (1) all inferred probable transmission pairs (n = 82), (2) male-to-female transmission events (n = 45), and (3) female-to-male transmission events (n = 37).Consider a deep-sequence phylogenetic analysis where Let Then the weighted mean age gap is given bywhich can be expanded as:Estimation of the weighted age gap variance: we compute the variance of the age-gaps between males and females in probable transmission pairs as:where p value was obtained as the fraction of iterations where the mean travel distance between permuted community pairs was smaller than that for community pairs where HIV transmission events were identified; similarly, a one-sided p value was derived for travel time. Travel distances and drive times were sourced from the google distance matrix API (application programming interface) with the mapsapi R package v0.4.2. and will be made available as an R data set. The permutation-test was performed using the R statistical software version 3.5.2.To evaluate whether people were more likely to form out-of-community sexual partnerships with partners from nearby communities than communities further away, we performed a permutation test under the null hypothesis that the mean travel distance (kilometers) and drive time (hours) between communities where HIV transmission events were identified was no different from that between any pair of randomly selected trial communities. We obtained null distributions for the mean travel distance and drive time, respectively by permuting the order of community pairs over 10,000 iterations. The underlying correlation structure of viral genetic linkage was preserved, for example, transmission events that occurred between members of two communities or between members of a community and two or more other communities. A one-sided n = 4410) individuals with HIV-1 infection were on antiretroviral therapy and virally suppressed (≤400 copies per ml of blood plasma). Compared to the 2011 Botswana census population of trial communities, our data set underrepresents men (n = 1475 vs. 3639) and individuals aged 16–24 years ; and of those 3832 participants, 2465 were sampled at baseline and 1367 sampled post-baseline. The mean ± standard deviation pairwise genetic distance between HIV-1 viral whole-genome consensus sequences of participants included in phylogenetic analyses was 12.5 ± 3.0%, and the maximum pairwise distance was 40% substitutions per site   of the participants  see Mat. To maxiThe sizes of genetic clusters ranged from 2 to 12 members, with 97% (230 / 236) of clusters comprising two or three members . The twoAbout 70% (366 / 525) of the HIV-1 viral consensus sequences in clusters were from women . This ren = 65) or men (n = 6), and the remaining were male-female pairs (n = 82). Considering that the predominant mode of HIV-1 transmission in Botswana and most of southern Africa is through heterosexual contact, that direct transmission between women is rare  with those of all other possible pairs of participating trial communities excluding same community pairs (n = 900 – 22 – 30 = 848) revealed a shorter drive time on average between genetically linked trial communities compared with what would be expected under random sexual mixing Trial communities could be broadly grouped into three geographical areas: in the North/North-East, South-East, and Central-East region of Botswana, which borders South Africa and Zimbabwe . The proAfter adjusting for differential sampling by community, an estimated 24% of transmissions to a resident of a trial community originated from another trial community  see Mat. FurtherThe estimated proportion of HIV transmissions in the trial population, after adjusting for differential sampling by geographic region, that occurred within the South-East region, 50% [95% CI: 29.6 – 70.5] was higher compared to that in the Central-East, 23% [9.7 – 45.4] and North-East regions, 18% [6.5 – 39.8], respectively  see Mat. The Soun = 33) or within control communities (n = 32) was 79% (65 / 82) compared with 21% (17 / 82) across intervention and control communities (n = 13 vs. 4).The proportion of inferred transmission events of HIV-1 infection in the deep-sequenced sample occurring within intervention , the proportion of inferred transmission events that occurred within intervention (n = 19) or within control communities (n = 25) was 86% (44 / 51) compared with 14% (7 / 51) across intervention and control communities (n = 4 vs. 3).Of the 82 male-female probable transmission pairs identified in the deep-sequenced sample, 51 involved a recipient that was first sampled during the period of baseline household survey activities in their community, that is, a transmission that could not have been affected by the intervention because it occurred before the start of the intervention. Among these , the proportion of inferred transmission events that occurred within intervention (n = 14) or within control communities (n = 7) was 68% (21 / 31) compared with 32% (10 / 31) across intervention and control communities (n = 9 vs. 1).By contrast, 31 of the 82 male-female probable transmission pairs inferred in the deep-sequenced sample involved a recipient that was first sampled about a year or more after the end of baseline household survey activities in their community; this subset reflects transmissions that could have in principle been affected by the intervention, as they may have occurred after the intervention began. Among these  HIV-TRACE clusters, (2) Cluster Picker clusters, (3) the overlap of HIV-TRACE and Cluster Picker clusters, and (4) the union of HIV-TRACE and Cluster Picker clusters . The secHIV incidence remains high in East and Southern Africa especially among young women. Promising HIV prevention intervention packages that combine population-level HIV testing coupled with strengthened linkage-to-care and early initiation of antiretroviral therapy, jointly termed universal test-and-treat, have yielded lower than expected reductions in the occurrence of new HIV-1 infections . This maIn this study, we focused on one of the four cluster/community-randomized trials, the Ya Tsie trial conducted in Botswana , to quanWe found that HIV transmissions in the trial were more likely to occur within communities or between neighboring communities than between distant trial communities, as well as between similarly aged partners. One of the most striking findings of our analysis was that an estimated 24% of transmissions involving residents of trial communities occurred between different communities in the trial. Given that the trial covered only 7.6% of the national population, this suggests that a large fraction of all transmissions involved a partner outside the trial.Furthermore, there was substantial sexual mixing between intervention and control communities. Overall, about one in every five HIV transmissions in the trial population involved sexual partnerships between residents of intervention communities and control communities. After baseline, that is, about a year or more after the introduction of the intervention one in three HIV transmissions occurred between intervention and control communities. HIV transmissions into intervention communities from control communities were similar to the reverse at baseline, and ten times more common post-baseline, concordant with a predicted benefit of a universal test-and-treat HIV prevention intervention, though dates of transmission were not identified.The extent of sexual mixing between residents of intervention and control communities highlights high mobility between the two arms of the Botswana/Ya Tsie trial. For the primary trial endpoint comparing HIV incidence in intervention versus control communities, this kind of mixing will tend to dilute the effect since some of the prevention value of the intervention would appear in control communities, while intervention communities would have incidence that comes from control communities and from communities outside the trial thus is not reachable by the effect of the intervention. Both sources of incidence, from control communities and from outside the trial, could dilute the intervention effect observed in the trial. Although we could not evaluate it in this study, it is possible that out-of-trial index cases would dilute the effect even more strongly than control-arm ones because the control arms comprise only about 5% of Botswana’s population, while about 90% of the population lives outside the trial.These findings might be relevant for the interpretation of the results of the other three universal test-and-treat HIV prevention trials conducted in Kenya, Uganda, South Africa, and Zambia. An important next step would be to quantify the extent to which the size of the observed effect in the Botswana/Ya Tsie trial was reduced by imported infections between partners in communities randomized to different HIV-intervention conditions and partners in communities outside the trial population.There was a deliberate effort to enroll men and young individuals in the Botswana/Ya Tsie study as these groups are less likely to be diagnosed with HIV and engaged in care. Following an adjustment for variable sampling by age and gender , we founPrior to this study, a recent consensus sequence phylogenetic study assessing age-disparate sexual partnerships in Kwa-Zulu Natal, South Africa identified men older than 25 years as an important source of viral transmission to younger women aged 15–25 years. Age disparate partnerships were defined as ones with an age-gap of more than 5 years. The Kwa-Zulu Natal study was based on a community sampling fraction of 4% that was not specifically targeted to people with HIV and assumed that the direction of viral transmission within clusters would be from older partners to younger ones . PhylogeTaken together, these findings might be relevant for understanding heterogeneity of HIV transmission in similarly designed phylogenetic studies in sub-Saharan Africa based on the sample size, demographic population, and adjustments for variable sampling.Participants with genetically similar HIV-1 infections in the Botswana/Ya Tsie trial tended to aggregate in small-sized (two- or three-person) clusters. This is consistent with phylogenetic clustering studies in generalized epidemics in Southern Africa but contrasts with larger clusters typically found in concentrated HIV epidemics in Europe and North America . The smaMost participants in the household survey of the Botswana/Ya Tsie trial indicated that they had not engaged in transactional sex in the last 12 months. This suggested that there was a limited contribution of sex workers to the transmission patterns identified in the trial.Our findings provide insight on HIV transmission patterns in Botswana over a 5-year period between 2013 and 2018 during which the Botswana/Ya Tsie trial was conducted. An important next step would be the integration of the large database of HIV viral genomes assembled by the Botswana/Ya Tsie trial into a real-time HIV genomic surveillance program to highlight emerging hubs of HIV spread and inform targeted studies of HIV prevention. Such an effort would be facilitated by samples routinely collected for HIV viral load monitoring and the continuously decreasing cost of high-throughput sequencing.Our study had several limitations. First, viral genomes from an estimated 14% of all people with HIV aged 16–64 years were sampled among participating trial communities. Comparatively, the sampling fraction of phylogenetic studies based on African genomes is commonly under 10% . AlthougOf the 82 probable male-female transmission pairs identified in the deep-sequenced sample there was a single individual involved in multiple transmission events; a male who transmitted to two females each residing in different communities from his own. This suggested that only a small degree of bias in estimation of CIs would arise from a simplifying assumption of independence between inferred transmission events; and allowed us to estimate CIs using methods for multinomial proportions .Second, although our estimates of HIV transmission flows in the trial population were adjusted for differential sampling we cannot exclude the possibility that unmeasured factors with influence on HIV transmission flows may have impacted our results. Most participants with HIV in this trial were virally suppressed, this might have biased our results in the sense that there might be some differences between the HIV transmission patterns we found and those that exist among viral genetic clusters largely populated by people with unsuppressed virus. Such heterogeneity in HIV transmission patterns might occur due to differences in risk behavior. Nevertheless, this study was based on a relatively representative sample of adults with HIV living in rural and peri-urban villages across Eastern Botswana.In sum, we identified several key factors in the Botswana/Ya Tsie trial that impacted HIV transmission dynamics with potential relevance to similar studies done in sub-Saharan Africa. We found that most HIV transmissions in the Botswana/Ya Tsie trial occurred between similarly aged partners within the same trial community or between trial communities in close proximity. Moreover, there was a greater flow of HIV transmissions into intervention communities from control communities than vice versa potentially reducing the observed effect-size of the trial, as would transmissions averted by the intervention that were not picked up because the recipient was not in a trial community.We recommend widely distributed and easily accessible HIV testing , treatment, and linkage-to-care to support people as they intermix within and across communities; augmented by targeted programs that might offer pre-exposure prophylaxis to younger women and the option of self-testing for men. Population-level genomic surveillance programs to identify communities with high flows of viral transmission within and between them would be helpful in spotting emerging hubs of transmission that can be prioritized for intervention. Taken together, these measures may reduce new HIV infections and shorten the time to epidemic control.bumblebee that will be made available at the following URL: https://magosil86.github.io/bumblebee. A step-by-step tutorial on how to estimate HIV transmission flows with bumblebee and accompanying example data sets can be accessed at: https://github.com/magosil86/bumblebee/blob/master/vignettes/bumblebee-estimate-transmission-flows-and-ci-tutotial.md  feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Miles Davenport as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Nadine Tschumi (Reviewer #1).Thank you for submitting your article "Deep-sequence phylogenetics to quantify patterns of HIV transmission in the context of a universal testing and treatment trial – BCPP/ Ya Tsie trial" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:Overall, we think the study was well designed and using a large sample size is highly valuable. The study provides detailed information on the methods including the release of a new R package for calculating transmission flow among populations. This is highly valuable for the interpretation of public health interventions, particularly test-and-treat. However, we all agree that it is important to add some clarifications of the methods used.1) The authors use a combination of HIV-TRACE and Cluster Picker as tools for cluster identification. As such, the authors implement a genetic threshold of 4.5% genetic diversity. This is the core outcome of the study; all the subsequent analyses are depended on the clusters identified. HIV-TRACE is often used instead of a phylogenetic tree, as the latter can be computational time-consuming with large data sets. However, the study also estimates a phylogenetic tree, thus we wish for more clarification on why both methods were used and how different the cluster outcomes are with each method.2) The cluster analysis resulted in many infection pairs between same-sex couples, indicating missing data in the transmission chains. The study excluded these pairs from subsequent analysis, resulting in a small number of overall pairs for the final transmission flow count. We think that such 'missing link' pairs could still be included in the analysis of estimating transmission flow between geographic regions. We were wondering if the study has investigated this and believe a constructive discussion in that regard could be favorable. See comments of reviewer 2.3) We agree that the study is overall well-presented and well-written, however, we think it would be useful to clarify some of the methods in the main text. We also agreed that the figures were overall informative and useful, however, we believe that some of the illustrations could be improved to aid with the understanding of the results. See comments of reviewer 3.Reviewer #1 (Recommendations for the authors):1. I think my concerns 8-11 above could be relatively easily addressed by sensitivity analyses showing that a change in respective definitions did not affect the main outcomes qualitatively.2. The estimated proportion of HIV transmissions in the trial population flowing into intervention communities from control communities and vice-versa was a key finding of the manuscript. Since the number of probable transmission pairs is low, the confidence intervals are wide and overlapping. I appreciated / think that it is important and correct that you only considered probable transmission pairs for the age gap analysis. However, for this analysis I wouldn't see a problem for including pairs with probable unsampled intermediates. Was this attempted / why not?Reviewer #2 (Recommendations for the authors):The methods describing the initial sequence quality control and alignment need to be expanded. Currently, it is unclear how the shiver assembly software was run. While the sequencing and bioinformatics pipelines used here have been used in previous studies the possible impact of sequencing errors should be discussed. Ideally, replicates would be used to determine the expected rate of sequencing errors. However, other approaches such as the proportion of minority variants at different coding locations for different variant frequency cutoffs could also be used . This is particularly important given that most trial participants were virally suppressed which could impact the overall quality of the sequencing data.Much of the downstream analysis and results relies on the initial separation of sequenced samples into putative transmission clusters. As a result, the choice of the 4.5% genetic distance threshold and the decision to combine the results of HIV-trace and cluster-picker needs much stronger motivation. This is particularly so as Supplementary Figures 7 and 8 suggests that the two methods give quite different results. Whilst I realise cluster picker is commonly used, HIV trace does not rely on the accuracy of a provided phylogeny and as the following transmission analysis only considers pairwise relationships, HIV trace seems more appropriate.I may have misunderstood but the method for calibrating the false discovery rate (FDR) for the inference of direct transmission described in Supplementary note 3 appears to consider all pairwise relationships (equation 6). This would only be possible if a person was able to be infected by multiple different transmitters. Assuming that the authors have only considered a single transmitter per infection then this would lead to an overestimate of the FDR. This should be clarified in the text.Finally, it would be useful if the authors could discuss what impact high risk communities such as sex workers could have on these results and the estimates of intercommunity transmission.– Many of the methods are described in the supplementary materials which makes the interpretation of the results challenging. The manuscript would be improved by moving some of the supplementary methods into the main text.– The command line parameters used to run MAFFT and HIValign should be provided.– While I was able to successfully download and install the 'bumblebee' R package I could not get a small trial example to run. Example data and a short 'how to guide' should be included as part of the package.– Figure 7 is quite confusing and I think relatively little is gained by the use of an alluvial plot. I think it would be clearer to separate this plot into multiple bar plots of the total number of transmissions rather than the proportion within each category.– It would be better to combine Figures 9 and 10 into a multi panel figure– Lines 177, 184, 216, 219, 279, 342, 345 etc: The multiplication symbols look like a variable and should be removed.– Line 262: I think this should say that the Goodman method estimates CIs for the parameters of the multinomial distribution. Essential revisions:Overall, we think the study was well designed and using a large sample size is highly valuable. The study provides detailed information on the methods including the release of a new R package for calculating transmission flow among populations. This is highly valuable for the interpretation of public health interventions, particularly test-and-treat. However, we all agree that it is important to add some clarifications of the methods used.We thank the reviewers for their interest in this novel approach that uses deep-sequence phylogenetics to improve our understanding of HIV transmission patterns in the context of the universal HIV test-and-treat intervention.1) The authors use a combination of HIV-TRACE and Cluster Picker as tools for cluster identification. As such, the authors implement a genetic threshold of 4.5% genetic diversity. This is the core outcome of the study; all the subsequent analyses are depended on the clusters identified. HIV-TRACE is often used instead of a phylogenetic tree, as the latter can be computational time-consuming with large data sets. However, the study also estimates a phylogenetic tree, thus we wish for more clarification on why both methods were used and how different the cluster outcomes are with each method.We thank the reviewers for their comments and are happy to provide additional results that motivate our decision to use the union of clusters detected with HIV-TRACE and Cluster Picker to estimate HIV transmissions within and between demographic sub-groups in the Botswana – Ya Tsie trial population. The primary motivation was that a filtering step was required to save time and computational resources from evaluating sequences that were too distantly related, before applying the “gold standard” of Phyloscanner to detect directed (when possible) transmission pairs. Accordingly, clustering algorithms plus a distance threshold helped to achieve this filtering. Because we shared what we take to be the reviewers’ concerns about either of the algorithms alone, we sought to maximize the number of transmission pairs that could be identified between participants in the Botswana – Ya Tsie trial with Phyloscanner by using the union of clusters detected with HIV-TRACE and Cluster Picker. This also served as a sensitivity analysis that allowed us to evaluate the extent to which the clustering patterns observed were specific to a single algorithm.Furthermore, a previous study done by Rose and colleagues (PMID: 27824249) to compare the number and size of clusters identified with HIV-TRACE and Cluster Picker clustering algorithms revealed that HIV-TRACE generally identified larger but fewer clusters, compared with clusters identified with Cluster Picker that were typically more numerous and mostly small 2-person clusters ). This suggested that HIV-TRACE would be helpful in detecting potentially larger transmission chains and Cluster Picker would be valuable in revealing potential transmission events between pairs of individuals.Of the 236 genetic clusters detected with the two algorithms, we identified 19 full or partial clusters (including 41 sequences) that included members that were only detected with HIV-TRACE and 122 full or partial clusters (including 242 sequences) that were unique to Cluster Picker. Moreover, of the 82 directed male-female transmission pairs inferred from the sample, (n = 5) were from genetic clusters that were unique to HIV-TRACE compared with (n = 27) that were from clusters unique to Cluster Picker. Of the five transmission events unique to HIV-TRACE clusters, three occurred in intervention communities originating from control communities. By contrast, four of the twenty-seven transmission events unique to Cluster Picker clusters occurred in intervention communities from control communities.In summary, estimates of HIV transmissions in the trial population based on the full overlap of clusters detected with HIV-TRACE and Cluster Picker would have excluded 32 of the 82 male-female pairs used for the primary analysis. To improve clarity, we have updated the methods, results and discussion with the following statements respectively:“Consensus sequence phylogenetics to identify clusters of participants with genetically similar HIV-1 infections. To save time and computational resources from evaluating sequences that were too distantly related, we first identified clusters of participants with genetically similar HIV-1 infections as a filtering step, before performing ancestral host state reconstruction with Phyloscanner to detect probable directed transmission pairs. Two clustering algorithms, HIV Transmission Cluster Engine (HIV-TRACE) v0.4.4  and Cluster Picker v1.2.3 [28], were used to identify clusters of individuals whose HIV-1 viral whole genome consensus sequences were genetically similar -- suggesting they were probably members of a transmission chain . HIV-TRACE defines clusters based on pairwise genetic distances only; comparatively, Cluster Picker identifies clusters using pairwise genetic distances with the guidance of a phylogenetic tree. A multiple sequence alignment  was provided as input to HIV-TRACE and Cluster Picker. Additionally, for cluster picker, a corresponding phylogenetic tree inferred with FastTree2 v2.1.10 and boot-strap support values approximated with the Shimodaira-Hasegawa test [31] were provided as inputs. We defined genetic similarity clusters as groups of two or more participants whose viral whole genome consensus sequences were separated by a genetic distance at or smaller than 4.5% nucleotide substitutions per site--and, for Cluster Picker, a bootstrap support value of at least 80%. The genetic distance threshold of 4.5% nucleotide substitutions per site was motivated by the distribution of genetic distances separating HIV-1 subtype C viral whole genomes of epidemiologically-linked couples in the HIV Prevention Trials Network (HPTN) 052 trial  . A listing of parameters used for consensus-sequence phylogenetics with HIV-TRACE and Cluster Picker is provided in Supplementary file 1 Table 1B.”“About 1 in 7 genotyped participants included in consensus sequence phylogenetic analyses were assigned to genetic similarity clusters of people with closely related HIV-1 infections. … To maximize the number of transmission pairs that could be identified between participants in the Botswana – Ya Tsie trial with Phyloscanner we used the union of clusters detected with HIV-TRACE and Cluster Picker.”“We first used HIV-1 whole genome viral consensus sequences to identify clusters of trial participants with genetically similar HIV-1 infections. This was done as a filtering step to save time and computational resources by excluding distantly related sequences. Thereafter, we employed deep-sequence phylogenetics to resolve the probable order of HIV transmission events within each identified cluster.”2) The cluster analysis resulted in many infection pairs between same-sex couples, indicating missing data in the transmission chains. The study excluded these pairs from subsequent analysis, resulting in a small number of overall pairs for the final transmission flow count. We think that such 'missing link' pairs could still be included in the analysis of estimating transmission flow between geographic regions. We were wondering if the study has investigated this and believe a constructive discussion in that regard could be favorable. See comments of reviewer 2.We thank the reviewers for their suggestion and now report in the results the findings of a sensitivity analysis as follows:“A sensitivity analysis to evaluate the impact of probable transmission pairs with unsampled intermediates on the patterns of HIV transmission within and between intervention communities and control communities in the Botswana – Ya Tsie trial. The first set of sensitivity analyses were performed with highly supported directed same- and opposite-sex transmission pairs identified in: (1) HIV-TRACE clusters, (2) Cluster Picker clusters, (3) the overlap of HIV-TRACE and Cluster Picker clusters and (4) the union of HIV-TRACE and Cluster Picker clusters (Supplementary file 1 Table 1N). The second set of sensitivity analyses were performed for the same four categories but restricted to highly supported directed opposite-sex pairs only where the recipient in a transmission pair was first sampled about a year or more after the end of baseline household survey activities in their community (i.e. post-baseline) (Supplementary file 1 Table 1O). In both sets of sensitivity analyses the results were consistent with the primary analysis: Transmissions into intervention communities from control communities were more common than the reverse post-baseline (Supplementary file 1 Tables 1N and 1O) (see methods section on adjustment for variable sampling rates across different demographic groups or randomized-HIV-interventions). The signal was stronger as well as being more interpretable in the analysis restricted to opposite-sex pairs compared to the analysis that included same-sex pairs, which likely included one or more unsampled intermediates.”However, it is important to note that in a predominantly heterosexual epidemic same-sex pairs might represent pairs with one or more missing intermediates or false positives. Therefore, it would be challenging to interpret the estimated HIV transmission flows in the trial population without information on the number of missing intermediates, their gender, community and trial arm. For example, consider a directed female-female pair where female-1 was sampled from an intervention community (F1-intervention) and female-2 a control community (F2-control). Let the two sampled females be connected by a single unsampled male affording the following transmission chain, F1-intervention -> M – > F2 -control. If the male belongs to a control community the true transmission flow would be from intervention-to-control and control-to-control while if the male belongs to an intervention community then it would be intervention-to-intervention and intervention-to-control. These two are very different in terms of their implications, and we have no way to distinguish them. Thus same-sex pairs were excluded from the primary analyses to limit false positives and aid interpretation of the analysis.3) We agree that the study is overall well-presented and well-written, however, we think it would be useful to clarify some of the methods in the main text. We also agreed that the figures were overall informative and useful, however, we believe that some of the illustrations could be improved to aid with the understanding of the results. See comments of reviewer 3.We thank the reviewers for their suggestion. To improve clarity we have moved all Supplementary Notes to the methods section and we now also provide a detailed listing of parameters used for: Shiver, MAFFT, HIValign, HIV-TRACE, Cluster Picker and FASTTREE2 in Supplementary file 1 Table 1B. Furthermore, we have replaced the alluvial plot with a barplot  and combined Figures that show the estimated transmission flows of HIV-1 infection in the Botswana – Ya Tsie trial population within and between geographical regions and trial arms into a single multi-panel figure  in line with the reviewer’s suggestions.Reviewer #1 (Recommendations for the authors):1. I think my concerns 8-11 above could be relatively easily addressed by sensitivity analyses showing that a change in respective definitions did not affect the main outcomes qualitatively.2. The estimated proportion of HIV transmissions in the trial population flowing into intervention communities from control communities and vice-versa was a key finding of the manuscript. Since the number of probable transmission pairs is low, the confidence intervals are wide and overlapping. I appreciated / think that it is important and correct that you only considered probable transmission pairs for the age gap analysis. However, for this analysis I wouldn't see a problem for including pairs with probable unsampled intermediates. Was this attempted / why not?Kindly refer to the responses to essential revision numbers 1 and 2.Reviewer #2 (Recommendations for the authors):The methods describing the initial sequence quality control and alignment need to be expanded. Currently, it is unclear how the shiver assembly software was run. While the sequencing and bioinformatics pipelines used here have been used in previous studies the possible impact of sequencing errors should be discussed. Ideally, replicates would be used to determine the expected rate of sequencing errors. However, other approaches such as the proportion of minority variants at different coding locations for different variant frequency cutoffs could also be used . This is particularly important given that most trial participants were virally suppressed which could impact the overall quality of the sequencing data.Kindly refer to the response to essential revision number 3. Furthermore, we agree with the reviewer that the impact of sequencing errors for sequences generated from proviral DNA is an important issue to discuss. Accordingly, we have expanded the methods as follows:“Paired-end deep-sequencing of HIV viral genomes for phylogenetic analyses. … Moreover, the quality of sequencing was assessed with standard metrics for deep (or next-generation) sequencing data, however, we cannot exclude the potential for sequencing errors arising from hypermutations. The shiver sequence assembly software [20] was used to assemble and map each participant’s deep-sequencing short reads to a de-novo reference sequence tailored to the participant’s viral population. A listing of command-line parameters used to assemble HIV viral whole genomes with Shiver is provided in Supplementary file 1 Table 1B.”Studying the impact of hypermutations on the quality of proviral DNA sequences is an interesting question that we prefer to reserve for future study, not least as it is not trivial.Much of the downstream analysis and results relies on the initial separation of sequenced samples into putative transmission clusters. As a result, the choice of the 4.5% genetic distance threshold and the decision to combine the results of HIV-trace and cluster-picker needs much stronger motivation. This is particularly so as Supplementary Figures 7 and 8 suggests that the two methods give quite different results. Whilst I realise cluster picker is commonly used, HIV trace does not rely on the accuracy of a provided phylogeny and as the following transmission analysis only considers pairwise relationships, HIV trace seems more appropriate.Kindly refer to the response to essential revision number 1.I may have misunderstood but the method for calibrating the false discovery rate (FDR) for the inference of direct transmission described in Supplementary note 3 appears to consider all pairwise relationships (equation 6). This would only be possible if a person was able to be infected by multiple different transmitters. Assuming that the authors have only considered a single transmitter per infection then this would lead to an overestimate of the FDR. This should be clarified in the text.We are happy to clarify for the reviewer that the estimated false discovery rate is indeed an upper bound as described in the results.“Deep-sequence phylogenetics to infer the probable order of transmission events within identified clusters of genetically similar HIV-1 infections … We used the probability of inferring a phylogenetically-linked directed male-male pair in the sample to calibrate an upper bound on the false-discovery rate of inferring direct transmission between males and females in phylogenetically-linked male-female pairs; the estimated number of linked male-female pairs in the sample with unsampled intermediates was approximately 30, corresponding to a false discovery rate of 36% (30 / 82) . This estimated false positive rate is likely inflated given that two individuals would need to be missing from the sequenced sample to incorrectly infer transmission in a male-female pair, whereas only a single female would need to be missing to erroneously infer transmission in a male-male pair.”In line with the reviewer’s suggestion this point is further emphasized in the methods section on estimating error rates in phylogenetic inference of direct transmission between sampled males and females as follows:“This represents an upper bound on the false-discovery rate of inferring direct transmission between males and females in phylogenetically-linked male-female pairs.”Finally, it would be useful if the authors could discuss what impact high risk communities such as sex workers could have on these results and the estimates of intercommunity transmission.In line with the reviewer’s suggestions we have updated the discussion as follows:“Most participants in the household survey of the Botswana – Ya Tsie trial indicated that they had not engaged in transactional sex in the last twelve months. This suggested that there was a limited contribution of sex workers to the transmission patterns identified in the trial.”– Many of the methods are described in the supplementary materials which makes the interpretation of the results challenging. The manuscript would be improved by moving some of the supplementary methods into the main text.We thank the reviewer for their suggestion and would like them to kindly note that all previous Supplementary Notes are now presented in the methods section.– The command line parameters used to run MAFFT and HIValign should be provided.A detailed listing of parameters used to run MAFFT and HIVAlign is now provided in Supplementary file 1 Table 1B.– While I was able to successfully download and install the 'bumblebee' R package I could not get a small trial example to run. Example data and a short 'how to guide' should be included as part of the package.We thank the reviewer for their comment and apologize that this was insufficiently clear in the paper. To improve clarity we have updated the code availability section and methods as follows:“A step-by-step tutorial on how to estimate HIV transmission flows with bumblebee and accompanying example datasets can be accessed at: https://github.com/magosil86/bumblebee/blob/master/vignettes/bumblebee-estimate-transmission-flows-and-ci-tutotial.md ”.– Figure 7 is quite confusing and I think relatively little is gained by the use of an alluvial plot. I think it would be clearer to separate this plot into multiple bar plots of the total number of transmissions rather than the proportion within each category.Kindly refer to the response to essential revision number 3.– It would be better to combine Figures 9 and 10 into a multi panel figureKindly refer to the response to essential revision number 3.– Lines 177, 184, 216, 219, 279, 342, 345 etc: The multiplication symbols look like a variable and should be removed.We thank the reviewer for their suggestion and have updated the manuscript accordingly.– Line 262: I think this should say that the Goodman method estimates CIs for the parameters of the multinomial distribution.We thank the reviewer for their suggestion and have updated the manuscript accordingly.


In MR-Linac treatment, manual annotation of organs-at-risk (OARs) and clinical volumes requires a significant clinician interaction and is a major challenge. Currently, there is a lack of available pre-annotated MRI data for training supervised segmentation algorithms. This study aimed to develop a deep learning (DL)-based framework to synthesize pelvic T1 weighted MRI scans.MRI synthesis was performed using UNet++ and cycle-consistent generative adversarial network (Cycle-GAN), and the predictions were compared qualitatively and quantitatively against a baseline UNet model using pixel-wise and perceptual loss functions. Additionally, the Cycle-GAN predictions were evaluated through qualitative expert testing (4 radiologists), and a pelvic bone segmentation routine based on a UNet architecture was trained on synthetic MRI using CT-propagated contours and subsequently tested on real pelvic TIn our experiments, Cycle-GAN generated sharp images for all pelvic slices whilst UNet and UNet++ predictions suffered from poorer spatial resolution within deformable soft-tissues . Qualitative radiologist assessment showed inter-expert variabilities in the test scores; each of the four radiologists correctly identified images as acquired/synthetic with 67%, 100%, 86% and 94% accuracy. Unsupervised segmentation of pelvic bone on T1-weighted images was successful in a number of test casesPelvic MRI synthesis is a challenging task due to the absence of soft-tissue contrast on CT. Our study showed the potential of deep learning models for synthesizing realistic MR images from CT, and transferring cross-domain knowledge which may help to expand training datasets for 21 development of MR-only segmentation models. Computed tomography (CT) is conventionally used for the delineation of the gross tumor volume (GTV) and subsequent clinical/planning target volumes (CTV/PTV), along with organs-at-risk (OARs) in radiotherapy (RT) treatment planning. Resultant contours allow optimization of treatment plans by delivering the required dose to PTVs whilst minimizing radiation exposure of the OARs by ensuring that spatial dose constraints are not exceeded. Magnetic resonance imaging (MRI) offers excellent soft-tissue contrast and is generally used in conjunction with CT to improve visualization of the GTV and surrounding OARs during treatment planning. However, manual definition of these regions is repetitive, cumbersome and may be subject to inter- and/or intra-operator variabilities . The recDeep learning (DL) is a sub-category of artificial intelligence (AI), inspired by the human cognition system. In contrast to traditional machine learning approaches that use hand-engineered image-processing routines, DL is able to learn complex information from large datasets. In recent years, DL-based approaches have shown great promise in medical imaging applications, including image synthesis , 4 and a1-weighted (T1W) MR images from pelvic CT scans as a basis for training algorithms for OARs and tumor delineation on acquired MRI datasets. We include in our analysis the use of state-of-the-art UNet  before and after treatment  and whole-body TWe investigated three DL architectures for MR image synthesis: (i) UNet, (ii) UNet++, and (iii) Cycle-GAN. UNet is one of the most popular DL architectures for image-to-image translations, with initial applications in image segmentation . In esse1W MR and CT images. The high-level schematic of the Cycle-GAN network is shown in GANs are the state-of-the-art approaches for generating photo-realistic images based on the principles of game theory . In imagFor segmentation, we propose a framework that first generates synthetic T1W MR images from CT, propagates ground-truth CT contours and outputs segmentation contours on acquired T1W MR images. To examine the capability of our fully-automated DL framework for knowledge transfer from CT to MRI, we generate ground-truth contours of the bones using a Gaussian mixture model proposed by Blackledge etal.  and tran1W MR slices from the anatomical pelvic station in each patient were resampled using a 2D affine transformation followed by non-rigid registration using multi-resolution B-Spline free-form deformation   . The reseneities  and sign1) and mean squared error (MSE or L2) between the target domain and the synthetic output. However, such loss functions ignore complex image features such as texture and shape. Therefore, for UNet/UNet++ models, we compared L1 loss in the image space with L1 loss calculated based on the features extracted from a previously-trained object classification network, deriving the “perceptual loss”. For this purpose, the VGG-19 classification network was used  (defined as L1 – SSIM) was used as the loss to govern the cycle consistency, whilst L1 and L2 losses were used for the generator and the discriminator respectively. For segmentation training, the Dice loss  was used to perform binary division of bone on MR images.Common loss functions in image synthesis are mean absolute error  to blindly classify randomly-selected test images as synthetic or acquired, and outline reasoning for answers (18 synthetic and 18 acquired test MR slices), and (ii) to describe key differences between synthetic and acquired test T1W MR images when the input CT and ground truth acquired MR images were also provided (10 sets of images from 3 test patients). This test was completed by 4 radiologists (two with <5 years and two with >5 years of experience). The segmentation network was trained on Cycle-GAN generated synthetic MR images  for 600 epochs using the Adam optimizer (learning rate = 1e-4) and batch size of 1. To avoid overfitting, random linear shear and rotation  were applied to images during training.The dataset was split to 981, 150 and 116 images from 11, 3 and 3 patients for training, validation and testing respectively. All models were trained for 150 epochs using the Adam optimizer  on a NVIDIA RTX6000 GPU   scored 67% and 100% whilst experts 3 and 4 (>5 years of experience) correctly identified 86% and 94% of total 36 test images. Hence, no particular correlation was observed between the percentage scores and the participants’ years of experience . The outcome from various pelvic slices across 8 patients from our in-house cohort are presented in The bone segmentation results using our fully-automated approach showed that our proposed framework successfully performed unsupervised segmentation of the bone from acquired T1W MR image synthesis from pelvic CT scans as a data generative tool for training of segmentation algorithms for MR-Linac RT treatment planning. Our results suggested that the Cycle-GAN network generated synthetic images with the greatest visual fidelity across all pelvic slices whilst the synthetic images from UNet and UNet++ appeared less sharp, which is likely due to soft-tissue misalignments during the registration process. The observed disparity in contrast in Cycle-GAN images for bladder, bone marrow and bowel loops may be due to large variabilities in our relatively small training dataset. Although the direct impact of these contrast discrepancies on MRI segmentation performance is yet to be evaluated, the Cycle-GAN predictions appeared more suitable for CT contour propagation to synthetic MRI than UNet and UNet++ images due to distinctive soft-tissue boundaries and high-resolution synthesis.One major limitation in adaptive RT on the MR-Linac system is the need for manual annotation of OARs and tumors on patient scans for each RT fraction which requires significant clinician interaction. DL-based approaches are promising solutions to automate this task and reduce burden on clinicians. However, the development of these algorithms is hindered by the paucity of pre-annotated MRI datasets for training and validation. In this study, we developed paired and unpaired training for T1 and L2 differences are useful measures when comparing images, they may not directly correspond to photo-realistic network outcome. This was evident in quantitative evaluation of the images generated from the UNet and UNet++ models trained with L1 loss in the image space against UNet with VGG-19 perceptual loss and Cycle-GAN predictions. Therefore, expert clinician qualitative assessments may provide a more reliable insight into the performance of medical image generative networks. In this study, our expert evaluation test based on Cycle-GAN predictions suggested that despite a number of suboptimal soft-tissue contrast predictions , there were differences in radiologist accuracies for correctly identifying synthetic from acquired MR images. The fact that 3/4 radiologists were unable to accurately identify synthetic images in all cases highlights the capability of our model to generate realistic medical images that may be indistinguishable from acquired MRI.Quantitative analysis of all model predictions indicated that the imaging metrics did not fully conform with the output image visual fidelity and apparent sharpness. This finding was in fact in line previous studies comparing paired and unpaired MRI synthesis , 22. CT-Our segmentation results demonstrated the capability of our fully-automated framework in segmenting bones on acquired MRI images with no manual MR contouring. Domain adaptation offers a significant clinical value in transferring knowledge from previously-contoured OARs by experts on CT to MR-only treatment planning procedures. Additionally, it potentially enables expanding medical datasets which are essential for training supervised DL models. Such a technique is also highly valuable outside the context of radiotherapy, as body MRI has increasing utility for monitoring patients with secondary bone disease from primary prostate  and breaGANs are notoriously difficult to train due to their large degree of application-based hyper-parameter optimization and non-standardized training techniques. However, this study showed that even when trained on relatively small datasets, GANs may have the potential to generate realistic images to overcome the challenge of medical image data shortage. Therefore, fut ure studies will investigate the performance of the proposed framework on larger datasets and alternative pelvic OARs, as well as exploring novel techniques to enforce targeted organ contrast during GAN and segmentation training. Additionally, future research will examine the performance sensitivity on the level of manual MRI contours required for training cross-domain DL algorithms.matthew.Blackledge@icr.ac.uk.The data analyzed in this study is subject to the following licenses/restrictions: The datasets presented in this article are not readily available due to patient confidentiality concerns. Requests to access these datasets should be directed to The studies involving human participants were reviewed and approved by the Committee for Clinical Research at the Royal Marsden Hospital. The patients/participants provided written informed consent to participate in this study.All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.This project represents independent research funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at The Royal Marsden NHS Foundation Trust and the Institute of Cancer Research, London, United Kingdom.The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Genomic imprinting is an epigenetic mechanism that results in allele-specific expression (ASE) based on the parent of origin. It is known to play a role in the prenatal and postnatal allocation of maternal resources in mammals. ASE detected by whole transcriptome RNA-seq (wht-RNAseq) has been widely used to analyze imprinted genes using reciprocal crosses in mice to generate large numbers of informative SNPs. Studies in humans are more challenging due to the paucity of SNPs and the poor preservation of RNA in term placentas and other tissues. Targeted RNA-seq (tar-RNAseq) can potentially mitigate these challenges by focusing sequencing resources on the regions of interest in the transcriptome. Here, we compared tar-RNAseq and wht-RNAseq in a study of ASE in known imprinted genes in placental tissue collected from a healthy human cohort in Mali, West Africa. As expected, tar-RNAseq substantially improved the coverage of SNPs. Compared to wht-RNAseq, tar-RNAseq produced on average four times more SNPs in twice as many genes per sample and read depth at the SNPs increased fourfold. In previous research on humans, discordant ASE values for SNPs of the same gene have limited the ability to accurately quantify ASE. We show that tar-RNAseq reduces this limitation as it unexpectedly increased the concordance of ASE between SNPs of the same gene, even in cases of degraded RNA. Studies aimed at discovering associations between individual variation in ASE and phenotypes in mammals and flowering plants will benefit from the improved power and accuracy of tar-RNAseq. In humans, about 100 imprinted genes have been identified . The highest proportion of imprinted genes were expressed in embryonic, extra-embryonic and brain tissues  based on parent of origin. Many imprinted genes are found in the nutritive tissue of placental and marsupial mammals as well as flowering plants .Informed consent or assent was obtained from participants depending on whether they were adults or children. Institutional Review Boards (IRB) approval was obtained from the University of Michigan IRBMED (HUM00043670) and from La Faculté de Médecine de Pharmacie et d'Odontostomatologie (FMPOS) de Bamako in Mali (Nn = 67), and genes with consistent high placental expression in the wht-RNAseq dataset . ERCC spike-in controls were also targeted except for nine transcripts that spanned the expression range. A genetic variation identified in the cohort was taken into account by including 100 bp capturing oligonucleotides containing the alternate allele for all SNPs spaced more than 50 bp apart (n = 4634). The targeting regions were evaluated by the NimbleDesign Software and oligonucleotides covering 2,797,406 bases were synthesized using the Roche SeqCap RNA Developer platform (Supplementary .bed file).Sample collection, nucleic acid purification, and wht-RNAseq were described previously (n = 227) and F1 saliva samples (n = 189) was performed with targeted DNA sequencing (Roche: SeqCap EZ Choice). The region genotyped for the tar-RNAseq samples overlapped the region of the wht-RNAseq samples (1.4 Mb) and included more genes for a total of 3.9 Mb. The analysis presented here is limited to the 75 genes that had RNAseq data in both datasets. This subset of genes mapped to 0.58 Mb and 0.48 Mb of the regions genotyped in the tar-RNAseq and the wht-RNAseq samples, respectively. Library preparation and hybridization captures were performed at the University of Michigan Advanced Genomics Core following the manufacturer’s protocols.Genotyping of F2 umbilical cord tissues  were included in all library preparations according to the manufacturer’s guidelines. Six indexed cDNA libraries were pooled for each capture reaction totaling 1 µg of cDNA. In cases where Kapa RNA HyperPrep plus RiboErase libraries were multiplexed with Kapa RNA HyperPrep (nonrRNA-depleted) libraries, the amount of the rRNA-depleted library was adjusted to10-fold less than nondepleted RNA libraries in these mixtures. Libraries were sequenced on an Illumina NovaSeq (S4). RNAs with RIN ≤ 3.8 and DV200 ≤ 70 were generally selected for KAPA RNA Prep Plus RiboErase library preparations. In total, 236 RNA samples from 227 F2 individuals were sequenced.The University of Michigan Advanced Genomics Core prepared KAPA RNA HyperPrep Kit (Roche KK8540) libraries or KAPA RNA HyperPrep with RiboErase Kit (Roche KK8560) libraries from 1000 ng, DNaseI digested, placental total RNA using conditions adapted to each sample’s RNA quality. Initially, only samples with poorly defined rRNA bands on Agilent traces (RIN < 2.5) were depleted of rRNA with RiboErase prior to fragmentation. Later, samples with intermediate RNA (RIN < 6.0) were also processed with RiboErase as the cost was not prohibitive. Fragmentation conditions were established based on each sample’s Agilent Bioanalyzer DV®II First Strand cDNA Synthesis Kit  and random hexamer primers. Qiagen PyroMark Assay Design 2.0 software was used to design pyrosequencing primers and amplicons were generated with PyroMark PCR Kit  and sequenced using a PyroMark Q96 MD workstation. Nineteen SNPs were pyrosequenced in 3 placentas. The Pearson correlation coefficient for major allele frequency between RNAseq and pyrosequencing was 0.98 (P = 9.6 × 10−19).Allelic expression of select heterozygous SNPs was validated by pyrosequencing. cDNA synthesis by RT was performed immediately after DNaseI digestion of placental RNA with the ProtoScriptet al. et al. 2018); (3) variants falling in the ENCODE Blacklist regions , maternally expressed (MEGs), and complexly expressed (CEGs) genes were considered, which limited the comparison to 75 genes . SNP-level Pat-Freq was calculated as the ratio of the paternal allele read count to the total read count. For gene-level Pat-Freq, we summed the paternal allele read counts and total read counts from all the SNPs of the gene and calculated their ratio. To determine the ASE correlations between SNPs at the gene or exon level, Pearson correlation coefficients were calculated across all pairwise combinations of SNPs mapping to the same gene or exon.e.g., agilent.com, arborbiosci.com, illumina.com, idtdna.com, qiagen.com, thermofisher.com, twistbioscience.com).A comparison between the cost of wht-RNAseq and tar-RNAseq (Supplementary Table S3) on a per sample basis was made to weigh increases in sequencing depth and coverage against costs of adding a target capture step to the library preparation method. Library preparation service costs reflect pricing as of May 7, 2020 at the University of Michigan Advanced Genomics Core. All other reagents reflect pricing at the time of purchase. The custom Roche SeqCap Target Enrichment System employed for tar-RNAseq has been discontinued but similar products are currently offered by multiple suppliers . SNPs were annotated as in our earlier study  . By combThe agreement between datasets is further illustrated by the correlation of gene-level Pat-Freq between tar-RNAseq and wht-RNAseq data for the placenta that was assayed both ways . The meaP < 2 × 10−303, n = 2) . Thus, similar to the well-documented high reproducibility of whole Exome targeting , tar-RNAseq delivers repeatable ASE estimates and tolerates the inclusion of RiboErase. Furthermore, validation of a subset of 19 SNPs in 3 samples revealed high correlation of Ref-Freq between ASE determined by pyrosequencing and tar-RNAseq , similar to what is observed using wht-RNAseq in high RIN samples in mice  —far less than the mean number of reads per sample of 269 × 106 for wht-RNAseq (SD 110 × 106). Stated in terms of the number of total SNPs covered per billion bases sequenced, tar-RNAseq produced 54 SNPs/109 bases while wht-RNAseq delivered only 2 SNPs/109 bases. This improved use of sequencing resources was expected from the enrichment of the RNA fragments of interest. In our tar-RNAseq dataset, the percentage of bases that aligned to the targeted region was 85%, on average, indicative of successful enrichment . In principle, increasing sequencing depth could overcome the coverage deficits of wht-RNAseq. We calculate that the per sample cost would increase 35-fold, which for most projects is prohibitive especially in the context of a population study.Even though ASE measurements between the two datasets were congruent, we obtained substantial improvement in SNP coverage from tar-RNAseq compared to wht-RNAseq. After removing the SNPs that had fewer than 10 total read counts, wht-RNAseq was able to cover only 79 (or 20%) of the SNPs, on average, across the samples, while tar-RNAseq covered up to 337 (or 80%) of the SNPs  in the 7While both datasets had at least one SNP in the 75 genes analyzed here, each gene had informative SNPs in more samples in tar-RNAseq than in wht-RNAseq. Specifically, after quality filtration, 24 genes per sample had at least one SNP in wht-RNAseq with each gene carrying, on average, 3 SNPs; these values increased to 55 genes with 6 SNPs per gene in tar-RNAseq . In addivs wht-RNAseq) and counts of genotyped exonic hetSNPs per sample . Tar-RNAseq yielded a huge improvement in SNP coverage relative to wht-RNAseq at all observed numbers of genotyped SNPs. Moreover, even in the samples that had about 500 hetSNPs using wht-RNAseq, the SNP coverage was lower than in the samples that had about 300 hetSNPs using tar-RNAseq . The mean number of genotyped SNPs was 418 for tar-RNAseq and 386 for wht-RNAseq, but evidently this difference could not underlie the improvement in SNP coverage using tar-RNAseq. In sum, our findings are not sensitive to the difference in the samples used in the two sequencing approaches.Arguably, the comparison could be confounded by the fact that different samples were analyzed between our tar-RNAseq and wht-RNAseq datasets as only one individual was sequenced by both technologies. However, it is unlikely that the difference in the samples between the two datasets was responsible for the large difference in the SNP coverage described above. Three sample-level QC metrics that could contribute to SNP coverage are number of genotyped SNPs, RIN, and maternal contamination. We show that RIN and maternal contamination did not differ significantly between the two datasets (Supplementary Table S2). We show that the choice of sequencing approach is more important than the number of genotyped SNPs through linear regressions in which the dependent variables were five different measures relevant to SNP coverage and the independent variables were tar-RNAseq (Low SNP read coverage can limit the concordance of ASE between the SNPs from the same gene . The con200 ∼50%).RNA degradation contributed to the reduced SNP coverage in the wht-RNAseq samples and inefficient rRNA removal is a factor known to interfere with the complexity of sequencing libraries . The comAlthough the focus of our efforts was on ASE and not relative expression . Placentas were not included in GTEx, but human placental tissue has been analyzed by other groups using wht-RNAseq and analyzed for ASE . A complete dataset would have sufficient reads at every SNP in every person, which is a goal that was more closely achieved by tar-RNA seq than by wht-RNAseq. Having a richer dataset will enable us to determine the interindividual variation in ASE for more genes across more individuals, so that we can better query the association between genomic imprinting and growth phenotypes in our cohort study.Maternal contamination is a potential confounder unique to placental tissue and is a limiting factor in molecular analyses . We were successful in combining random primed library preparation with rRNA depletion to generate libraries from degraded RNA for tar-RNAseq. Little to no loss of coverage was observed with degraded RNA from most samples, and we salvaged samples with DV200 as low as 50%. The critical role for efficient rRNA depletion in preparing libraries from degraded RNA is well known  samples  and showed that our conclusion about the superiority of tar-RNAseq was not sensitive to any of these parameters. We also note that library preparation reagents for wht-RNAseq libraries and tar-RNAseq libraries were from different manufacturers. Although we did not try to estimate ASE in relation to the cellular composition of the fetal compartment of the placenta, a recent single cell study showed that placental samples collected using a protocol similar to ours were mostly comprised of trophoblast and syncytiotrophoblast cells of the fetus (In conclusion, we compared tar-RNAseq and wht-RNAseq in a study of ASE in 75 known imprinted genes in placental tissue collected from a healthy human cohort. Tar-RNAseq covered more SNPs of interest and at greater depth. In previous research on humans, discordant ASE values for SNPs of the same gene have limited the ability to accurately quantify ASE. We show that Tar-RNAseq improved the reliability of ASE detection by greatly increasing the concordance of ASE measurement between the SNPs from the same gene. In combination with rRNA depletion, tar-RNAseq performed well even in cases of degraded RNA. The advantages of tar-RNAseq go beyond the savings on sequencing costs alone and include higher accuracy in ASE estimates in samples with varying RNA quality, as is typical for field collections. Targeted sequencing will benefit the study of associations between individual variation in ASE and phenotypes in humans or in other species where growth phenotypes are of interest, such as domesticated animals. The data we presented here originated from field samples and provide metrics to inform the design of such projects.


Despite the success of antiretroviral therapy (ART) in suppressing HIV-1 replication and preventing disease progression, the high costs, the burden of daily medication, toxicity and the development of resistance underscore the need for new therapeutic approaches.Over the past decade, broadly HIV-1 neutralizing antibodies (bNAbs) were discovered that are up to a 1000-fold more potent than HIV-1-reactive antibodies previously described. About 10 years after the first identification of these broadly neutralizing antibodies, bNAbs that effectively target multiple HIV-1 variants with a high potency have been found for most of the immunological important epitopes on the HIV-1 envelope-trimer like the CD4 binding site, the V1/V2 loop, the V3-glycan, the membrane-proximal external region (MPER), the interface region with the fusion peptide and the so called ‘silent face’. Some of these bNAbs have been demonstrated to safely suppress viremia and delay viral rebound after interruption of antiretroviral therapy (ART) in HIV-1-infected individuals. Moreover, bNAbs have been demonstrated to prevent infection in animal models and prevention studies where bNAbs are tested for their effectivity as passive immunization in humans are currently ongoing. Thus, bNAbs represent a promising novel approach for effective HIV-1 immunotherapy and prevention. However, infusions of single bNAbs drive the emergence of viral escape mutations and some patients harbor pre-existing resistance in their proviral or circulating HIV-1 quasispecies. Furthermore, the recently completed proof-of-concept Antibody Mediated Prevention (AMP) phase 2b trials showed that much higher bNAb titers or more potent and broader bNAbs, especially for single bNAbs, would be required for HIV-1 prevention in real-world settings. Thus, in order to restrict HIV-1 escape mechanisms and for improved antibody-mediated HIV-1 prevention, future regimens will require novel antibodies, antibody combinations or novel concepts like e.g. bi- or trispecific antibodies.In this Research Topic, we aim to bring together new studies and comprehensive reviews that advance the field of bNAbs and their future clinical use for treatment and prevention of HIV-1.Cardozo-Ojeda and Perelson have used different mathematical models to explain the observed viral load dynamics in the VRC01 phase 1 clinical trial. A model containing reversible bNAb binding to virions and clearance of virus-antibody complexes by a two-step process explains best the observed viral loads. First, VRC01 induces an enhancement of virus clearance by a phagocytic mechanism, but due to saturation this process slows down and the long-term viral decline is due to neutralization. However, selection pressure may lead to the outgrowth of a less-susceptible virus population to VRC01 which is reflected in the viral load final rebound.A better understanding of the effect of antibody treatment on viral loads will guide future antibody dosing strategies as well as gain insights on the mechanisms of viral clearance by the antibody. Wesley et al. describe an assay to simultaneously quantify the respective physiological concentrations of passively infused bnAb cocktails in human serum to ultimately define the threshold needed for protection from HIV-1 infection.An important clinical trial outcome is the accurate measurement of the passively infused bnAbs to determine effective doses for therapy and/or prevention. in vivo bNAb activity. Nonetheless, the delivery of human-derived IgG in heterologous species such as rhesus macaques can limit their success due the animals developing antidrug antibodies (ADA) to human IgG. Such ADA responses restrict the number, frequency and doses of bNAbs given to non-human primates. Lee et al. extend these observations to the pigtailed macaque model. They show that such ADA responses were positively correlated with the number of doses and target the constant region of therapeutic bNAb, and not the variable region, resulting in cross-reactivity with either human control IgG1 antibody as well as another bNAb not delivered to the animals. Most notably, stronger ADA responses correlated with more precipitous decline of plasma bNAb concentrations and were significantly associated with worse control of simian HIV (SHIV). This study therefore outlines the caution that should be exercised in future studies of bNAb activity in pigtail macaques, and by extending the ADA observations to pigtail macaques, suggest that similar mechanisms could restrict study of bNAbs in other immunocompetent animal models.Non-human primate models of passively transferred bNAbs for SHIV/SIV prevention and therapy have been critical for evaluating the Wilson et al. present compelling data present compelling data addressing these questions. They show that the latent reservoir can harbor aNAb resistant viruses and the latent viral Env diversity, presumably created by escape from aNAbs, can lead to resistance to certain therapeutic bNAbs, but not others. Clinical studies such as this can thus begin to address the key question of how many and which bNAbs will be needed to prevent viral breakthrough in analytical treatment interruption (ATI) studies and to ultimately succeed in HIV-1 therapy.The diversity of replication competent HIV-1 latent proviruses and their susceptibility to therapeutic bNAbs are critical to successful bNAb-mediated HIV-1 therapy. Most HIV-1 infected induce robust autologous neutralizing antibodies (aNAbs) that drive viral Env escape, and this raises the following interesting questions: how do such autologous antibodies impact the composition of the latent reservoir, and how does escape from such autologous antibodies impact resistance to therapeutic bNAbs?  explored whether combinations of scFvs targeting different HIV-1 Env epitopes can improve the breadth and potency against HIV-1 isolates. They show using experimental and theoretical approaches that combinations of scFv can significantly enhance breadth and potency over individual scFvs and that combinations of 2-3 scFvs could cover majority of viruses tested with high potency. By demonstrating that combinations of scFvs have favorably broad and potent in vitro neutralization profiles, this study lays the groundwork for further in vivo testing and development of scFv combinations as promising novel antibody-based prophylactics and therapeutics against HIV-1.Single chain variable fragments (scFv) antibodies comprise of heavy and light chain variable fragments connected by glycine linkers in the same gene construct. Their smaller size as compared to full-length IgG can provide substantial advantages such as improved penetration of tissue, especially mucosa, and practical considerations such as expression by nucleic acids and viral vectors. However, the lack of Fc regions results in lower half-lives for scFv versus IgG and in the absence of antibody effector functions. The scFv molecules also lose some neutralization potency and breadth as compared to IgG due to loss of bivalent binding and/or subtly different paratope structures, albeit scFvs were shown to retain substantial activity of the parental antibodies. In this study, Richardson et al. describe the change in Fc-effector function profiles of HIV-1 bNAbs when expressed as IgG3 isotypes. Some HIV-1 reactive bnAbs expressed as IgG3 demonstrated enhanced binding to Fc receptors and improved FC-mediated effector functions while maintaining their neutralization activity. Thus, the antiviral effect of already very broad and potent bNAbs can even be enhanced by manipulations of the constant regions. These insights are important for the field given the current focus on using HIV-1 bNAbs for passive immunization strategies.Tuyishime et al. They investigated the effect of HIVxCD3 DART® Molecules that have broadly-neutralizing and non-neutralizing activities on SHIV infected cells and found that these molecules effectively eliminated SHIV infected cells. Thus, these findings can be crucial for futures HIV-1 cure strategies since it is here shown that HIVxCD3 DART® Molecules can leverage the host immune system for treatment of HIV-1 infection.A potential future role of dual-affinity re-targeting Antibodies (DART) for treatment of HIV-1 is elegantly shown by Griffith and McCoy provide a comprehensive overview of the most promising currently available bNAbs, Hsu et al., Phelps and Balazs, and Umotoy and de Taeye review and outline the use of these bNAbs in future HIV-1 cure, treatment and prevention approaches.This Research Topic hosts articles that review currently available bNAbs and their potential future use. While Griffith and McCoy describe a possible definition of antibody features that are required by a specific antibody to be classified as “broadly neutralizing”. To this end they review and compare the neutralizing profiles, mutations, genetic features and the targeted epitopes of an array of currently known neutralizing antibodies. Through this effort the authors provide a comprehensive and unbiased overview of the currently available bNAbs of which several are or will be tested in clinical studies.Hsu et al. concisely and yet thoroughly review the potential role of HIV bNAbs in strategies to achieve long term virologic control in the absence of ART. The authors comprehensively review this complicated area of research with clinical studies from many different groups. They discuss a plethora of preclinical and clinical studies that aim to determine if bNAbs can act alone, in combination, and/or together with other drugs to control HIV infection and eliminate or decrease the viral reservoir.In this review Phelps and Balazs review key concepts of effector functions mediated by bNAbs and the potential for vectored immunoprophylaxis as a means of producing bNAbs in patients to overcome the need for constant re-administration in order to maintain steady-state bNAb concentrations.While neutralization is a crucial component of therapeutic efficacy, numerous studies have demonstrated that bNAbs can also mediate effector functions. Umotoy and de Taeye readers will get a thorough and historical perspective on the use antibody conjugates (ACs) in HIV research. The authors describe the concept of an antibody-based carrier of anti-HIV-molecules and why specifically bNAbs could be the ideal carrier. The anti-HIV-molecules include toxins fused to the antibody, antibodies conjugated by radionuclides (radioimmunotherapy) and small drugs or oligonucleotides conjugated to antibodies. In contrast to anticancer treatment, for which several constructs are already FDA-approved, no AC is currently approved for treatment of viral infections. However, with new potent bNAbs available ACs have a huge potential for future HIV-1 cure strategies which is discussed in detail by the authors.By reading the review of Walsh and Seaman. To this end, the authors summarize key clinical trials like the recently published AMP-trial, based on their advantage and how they contributed to the field. Besides recent clinical studies, the authors also discuss strategies that will lead to an improved breadth, potency and durability of antiviral protection in future clinical trials.The current status of clinical studies evaluating bNAbs for HIV-1 prevention is reviewed by Altogether, bNAbs show promise for successful HIV-1 prevention, therapeutic control and potential towards HIV-1 cure, especially when they are further tailored to improve breadth, potency, bioavailability and increased killing of infected cells through protein engineering. For future evaluation of bNAb prophylaxis and therapy in clinical trials it is crucial to have well validated quantitative assays with well-predicting antibody distribution and viral load models.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.KW was supported by the Comprehensive Antibody Vaccine Immune Monitoring Consortium (CAVIMC) (Grant: OPP1032144 from the Bill & Melinda Gates Foundation.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.