Biology subjects

Leitner, T.

Publications and source records attributed to Leitner, T..

11 recordsLinked to original sources

bioRxiv2026

wavess 1.2: Presenting an HLA-aware within-host virus sequence simulation framework

MotivationUnderstanding how virus sequences are shaped by selection can inform vaccine design and transmission inference. Modeling within-host evolution to interrogate these questions requires a detailed mechanistic framework that accurately captures sequence diversification. The CD8+ cytotoxic T-lymphocyte (CTL) response plays an important role in immune-mediated selection and can leave strong signatures in virus sequences; however, existing sequence-based within-host virus modeling frameworks do not explicitly include a human leukocyte antigen (HLA)-aware CTL response. ResultsWe extended our previously published within-host sequence evolution simulator, wavess, to include an explicit CTL response, and share a method for identifying HLA-specific CTL epitopes given a founder virus sequence. We also updated the model to permit a variable recombination rate, which allows for modeling non-adjacent genes, segmented genomes, and recombination hotspots. These extensions to wavess allow for more accurate simulation of viruses and virus genes, particularly in regions of the genome where the immune response is dominated by CTLs (rather than antibodies). It also provides the foundation for investigations of how these newly-added biological mechanisms influence within-host evolution. Availability and implementationThe core of wavess is written in Python 3, with helper functions written in R. It is available at https://github.com/MolEvolEpid/wavess.

bioinformatics↗

bioRxiv2024

Hypermut 3: Identifying specific mutational patterns in a defined nucleotide context that allows multistate characters

SummaryThe detection of APOBEC3F- and APOBEC3G-induced mutations in virus sequences is useful for identifying hypermutated sequences. These sequences are not representative of viral evolution and can therefore alter the results of downstream sequence analyses if included. We previously published the software Hypermut, which detects hypermutation events in sequences relative to a reference. Two versions of this method are available as a webtool. Neither of these methods consider multistate characters or gaps in the sequence alignment. Here, we present an updated, user-friendly web and command-line version of Hypermut with functionality to handle multistate characters and gaps in the sequence alignment. This tool allows for straightforward integration of hypermutation detection into sequence analysis pipelines. As with the previous tool, while the main purpose is to identify G to A hypermutation events, any mutational pattern and context can be specified. Availability and implementationHypermut 3 is written in Python 3. It is available as a command-line tool at https://github.com/MolEvolEpid/hypermut3 and as a webtool at https://www.hiv.lanl.gov/content/sequence/HYPERMUT/hypermutv3.html. Contacttkl@lanl.gov or seq-info@lanl.gov

bioinformatics↗

bioRxiv2023

Predicting Impacts of Contact Tracing on Epidemiological Inference from Phylogenetic Data

Robust sampling methods are foundational to inferences using phylogenies. Yet the impact of using contact tracing, a type of non-uniform sampling used in public health applications such as infectious disease outbreak investigations, has not been investigated in the molecular epidemiology field. To understand how contact tracing influences a recovered phylogeny, we developed a new simulation tool called SEEPS (Sequence Evolution and Epidemiological Process Simulator) that allows for the simulation of contact tracing and the resulting transmission tree, pathogen phylogeny, and corresponding virus genetic sequences. Importantly, SEEPS takes within-host evolution into account when generating pathogen phylogenies and sequences from transmission histories. Using SEEPS, we demonstrate that contact tracing can significantly impact the structure of the resulting tree, as described by popular tree statistics. Contact tracing generates phylogenies that are less balanced than the underlying transmission process, less representative of the larger epidemiological process, and affects the internal/external branch length ratios that characterize specific epidemiological scenarios. We also examined real data from a 2007-2008 Swedish HIV-1 outbreak and the broader 1998-2010 European HIV-1 epidemic to highlight the differences in contact tracing and expected phylogenies. Aided by SEEPS, we show that the data collection of the Swedish outbreak was strongly influenced by contact tracing even after downsampling, while the broader European Union epidemic showed little evidence of universal contact tracing, agreeing with the known epidemiological information about sampling and spread. Overall, our results highlight the importance of including possible non-uniform sampling schemes when examining phylogenetic trees. For that, SEEPS serves as a useful tool to evaluate such impacts, thereby facilitating better phylogenetic inferences of the characteristics of a disease outbreak. SEEPS is available at github.com/MolEvolEpid/SEEPS.

bioinformatics↗

bioRxiv2023

Last in first out: SIV proviruses seeded later in infection are harbored in short-lived CD4+ T cells

HIV can persist in a latent form as integrated DNA (provirus) in resting CD4+ T cells of infected individuals and as such is unaffected by antiretroviral therapy (ART). Despite being a major obstacle for eradication efforts, the genetic variation and timing of formation of this latent reservoir remains poorly understood. Previous studies on when virus is deposited in the latent reservoir have come to contradictory conclusions. To reexamine the genetic variation of HIV in CD4+ T cells during ART, we determined the divergence in envelope sequences collected from 10 SIV infected rhesus macaques. We found that the macaques displayed a biphasic decline of the viral divergence over time, where the first phase lasted for an average of 11.6 weeks (range 4-28 weeks). Motivated by recent observations that the HIV-infected CD4+ T cell population is composed of short- and long-lived subsets, we developed a model to study the divergence dynamics. We found that SIV in short-lived cells was on average more diverged, while long-lived cells harbored less diverged virus. This suggests that the long-lived cells harbor virus deposited starting earlier in infection and continuing throughout infection, while short-lived cells predominantly harbor more recent virus. As these cell populations decayed, the overall proviral divergence decline matched that observed in the empirical data. This model explains previous seemingly contradictory results on the timing of virus deposition into the latent reservoir, and should provide guidance for future eradication efforts. Significance statementHIV can persist in a latent reservoir unaffected by antiretroviral drugs. The genetic variation of this latent virus population is a major obstacle for eradication efforts, but also a clue to when HIV variants are deposited in the reservoirs. Unfortunately, previous studies assessing when the virus was deposited in latent reservoirs have come to contradictory conclusions. Here, we propose SIV proviral DNA exists in both short- and long-lived CD4+ T cells, and that these two cell subsets harbor different genetically diverged virus populations. Our model explains the contradictory findings and shows that when CD4+ T cells decay under effective drug treatment, which prevents virus replication, the resulting virus divergence decreases and recapitulates observed data. This knowledge should help in improving future eradication efforts.

microbiology↗

bioRxiv2023

Modeling the evolution of segment trees reveals deficiencies in current inferential methods for genomic reassortment

Reassortment is an evolutionary process common in viruses with segmented genomes. These viruses can swap whole genomic segments during cellular co-infection, giving rise to novel progeny formed from the mixture of parental segments. Because large-scale genome rearrangements have the potential to generate new phenotypes, reassortment is important to both evolutionary biology and public health research. However, statistical inference of the pattern of reassortment events from phylogenetic data is exceptionally difficult, potentially involving inference of general graphs in which individual segment trees are embedded. In this paper, we argue that, in general, the number and pattern of reassortment events are not identifiable from segment trees alone, even with theoretically ideal data. We call this fact the fundamental problem of reassortment, which we illustrate using the concept of the first-infection tree, a typically but not always counterfactual genealogy that would have been observed in the segment trees had no reassortment occurred. Further, we illustrate four additional problems that can arise logically in the inference of reassortment events and show, using simulated data, that these problems are not rare and can potentially distort our perception of reassortment even in small data sets. Finally, we discuss how existing methods can be augmented or adapted to account for not only the fundamental problem of reassortment but also the four additional situations that can complicate the inference of reassortment.

evolutionary biology↗

bioRxiv2023

Inferring viral transmission time from phylogenies for known transmission pairs

When the time of an HIV transmission event is unknown, methods to identify it from virus genetic data can reveal the circumstances that enable transmission. We developed a single-parameter Markov model to infer transmission time from an HIV phylogeny constructed of multiple virus sequences from people in a transmission pair. Our method finds the statistical support for transmission occurring in different possible time slices. We compared our time-slice model results to previously-described methods: a tree-based logical transmission interval, a simple parsimony-like rules-based method, and a more complex coalescent model. Across simulations with multiple transmitted lineages, different transmission times relative to the sources infection, and different sampling times relative to transmission, we found that overall our time-slice model provided accurate and narrower estimates of the time of transmission. We also identified situations when transmission time or direction was difficult to estimate by any method, particularly when transmission occurred long after the source was infected and when sampling occurred long after transmission. Applying our model to real HIV transmission pairs showed some agreement with facts known from the case investigations. We also found, however, that uncertainty on the inferred transmission time was driven more by uncertainty from time-calibration of the phylogeny than from the model inference itself. Encouragingly, comparable performance of the Markov time-slice model and the coalescent model--which make use of different information within a tree--suggests that a new method remains to be described that will make full use of the topology and node times for improved transmission time inference.

evolutionary biology↗

bioRxiv2023

Migration coupled with recombination explains disparate HIV-1 anatomical compartmentalization signals

The evolution of HIV-1 in a host is shaped by many evolutionary forces, including recombination of virus genomes and the potential isolation of viruses into different tissues with compartmentalized evolution. Recombination and compartmentalization have opposite effects on viral diversification, with the former causing global mixing and the latter countering it through spatial segregation of the virus population. Therefore, recombination and compartmentalization together give rise to complex evolutionary dynamics that convolutes their individual effects in standard, bifurcating phylogenetic trees. Although there are various theoretical methods available to infer the presence of recombination or compartmentalization individually, there is little knowledge of their combined effect. To study their interaction and whether that could explain the many disparate results that have been described in the HIV-1 literature, we developed an age-structured forward-time evolutionary model that includes compartments, migration, and recombination. By tracking the evolutionary history of individual virus variants in an Ancestral Recombination Graph (ARG) and resolving the ARG into standard bifurcating trees, we reexamined 771 anatomical tissue pairs infected with HIV-1. Remarkably, we found that recombination can make the resulting bifurcating tree appear more compartmentalized than the virus population actually is. However, we found migration between all 771 tissue pairs, typically at 2.8-4.9x10-3 taxa-1 day-1. Thus, while different point mutations may arise in different parts of the body, migration eventually brings these variants together and recombination merges them into a relatively homogeneous cloud of a universally evolving quasispecies. Modelling this process, we explain the many different results previous research found among distinct anatomical tissues. We also show that the popular Slatkin-Maddison test comes to different results about compartmentalization at the same migration rate depending on the sample size. SIGNIFICANCEWhether HIV-1 sampled in different anatomical tissues are compartmentalized or not has been a long-standing quest because it relates to both clinical and biological insight. Previous studies of this question have reported disparate results from the same as well as different tissue comparisons. Here, we consolidate and explain these contrasting results with a new evolutionary model that includes compartments, migration, and recombination. We show that no anatomical tissues are fully compartmentalized, and that the migration of HIV-1 between the tissues allows recombination to homogenize the diversity that may arise in separate tissues of an infected person.

evolutionary biology↗

bioRxiv2022

Recombination smooths the time-signal disrupted by latency in within-host HIV phylogenies

Within-host HIV evolution involves latency and re-activation of integrated provirus that has the potential to disrupt the temporal signal induced by the evolutionary race between host immune responses and viral evolution. Yet, within-host HIV phylogenies tend to show clear, ladder-like trees structured by the time of sampling. Recombination complicates this dynamic by allowing latent HIV viruses to re-integrate as fragments in the genomes of contemporary virus populations. That is, recombination violates the fundamental assumption made by the phylogenetic methods typically used to study within-host HIV sequence data that evolutionary history can be represented by a single bifurcating tree. In this paper we develop a coalescent-based simulator of within-host HIV evolution that includes, latency, recombination, and population dynamics that allows us to study the relationship between the true, complex genealogy of within-host HIV, encoded as an Ancestral Recombination Graph (ARG), and the observed phylogenetic tree. We show how recombination recovers the disruption of the temporal signal of within-host HIV evolution caused by latency by mixing fragments of ancestral, latent genomes into the contemporary population through recombination. In effect, recombination averages over extant heterogeneity, whether it stems from mixed time-signals or population bottlenecks. Further, we establish that the signals of latency and recombination can be observed in phylogenetic trees despite being an incorrect representation of the true evolutionary history. Using an Approximate Bayesian Computation method, we develop a set of statistical probes to tune our simulation model to nine longitudinally-sampled within-host HIV phylogenies, finding evidence for recombination rates at the lower end of published estimates and relatively small latent pool sizes ranging from about 1000 to 2500 cells.

evolutionary biology↗

bioRxiv2021

A deep learning approach to real-time HIV outbreak detection using genetic data

Pathogen genomic sequence data are increasingly made available for epidemiological monitoring. A main interest is to identify and assess the potential of infectious disease outbreaks. While popular methods to analyze sequence data often involve phylogenetic tree inference, they are vulnerable to errors from recombination and impose a high computational cost, making it difficult to obtain real-time results when the number of sequences is in or above the thousands. Here, we propose an alternative strategy to outbreak detection using genomic data based on deep learning methods developed for image classification. The key idea is to use a pairwise genetic distance matrix calculated from viral sequences as an image, and develop convolutional neutral network (CNN) models to classify areas of the images that show signatures of active outbreak, leading to identification of subsets of sequences taken from an active outbreak. We showed that our method is efficient in finding HIV-1 outbreaks with R0[≥] 3, and overall a specificity exceeding 85% and sensitivity better than 70%. We validated our approach using data from HIV-1 CRF01 in Europe, containing both endemic sequences and a well-known dual outbreak in intravenous drug users. Our model accurately identified known outbreak sequences in the background of slower spreading HIV. Importantly, we detected both outbreaks early on, before they were over, implying that had this method been applied in real-time as data became available, one would have been able to intervene and possibly prevent the extent of these outbreaks. This approach is scalable to processing hundreds of thousands of sequences, making it useful for current and future real-time epidemiological investigations, including public health monitoring using large databases and especially for rapid outbreak identification. Author summaryThe analysis of pathogen genomic data to analyze epidemics at scale is constrained by the computational cost associated with phylogenetic tree reconstruction. As a fast and efficient alternative, we employed convolutional neural networks to analyze evolutionary pairwise distance matrices as images to perform classifications of the current epidemiological situation of a growing public health sequence database. We used simulated data to train and test our model, and as validation we accurately mapped the start and end of two linked well-documented HIV-1 outbreaks in the backdrop of ongoing slower HIV spread. Thus, our new approach is efficient, accurate, scalable, and can analyze data in real time.

bioinformatics↗

bioRxiv2021

Combining biomarker and virus phylogenetic models improves epidemiological source identification

To identify and stop active HIV transmission chains new epidemiological techniques are needed. Here, we describe the development of a multi-biomarker augmentation to phylogenetic inference of the underlying transmission history in a local population. HIV biomarkers are measurable biological quantities that have some relationship to the amount of time someone has been infected with HIV. To train our model, we used five biomarkers based on real data from serological assays, HIV sequence data, and target cell counts in longitudinally followed, untreated patients with known infection times. The biomarkers were modeled with a mixed effects framework to allow for patient specific variation and general trends, and fit to patient data using Markov Chain Monte Carlo (MCMC) methods. Subsequently, the density of the unobserved infection time conditional on observed biomarkers were obtained by integrating out the random effects from the model fit. This probabilistic information about infection times was incorporated into the likelihood function for the transmission history and phylogenetic tree reconstruction, informed by the HIV sequence data. To critically test our methodology, we developed a coalescent-based simulation framework that generates phylogenies and biomarkers given a specific or general transmission history. Testing on many epidemiological scenarios showed that biomarker augmented phylogenetics can reach 90% accuracy under idealized situations. Under realistic within-host HIV evolution, involving substantial within-host diversification and frequent transmission of multiple lineages, the average accuracy was at about 50% in transmission clusters involving 5-50 hosts. Realistic biomarker data added on average 16 percentage points over using the phylogeny alone. Using more biomarkers improved the performance. Shorter temporal spacing between transmission events and increased transmission heterogeneity reduced reconstruction accuracy, but larger clusters were not harder to get right. More sequence data per infected host also improved accuracy. We show that the method is robust to incomplete sampling, and we evaluate real HIV-1 transmission clusters. The technology presented here could allow for better prevention programs by providing data for locally informed and tailored strategies. AUTHOR SUMMARYWhile phylogenetic methods have been successfully used to infer transmission patterns on many scales, substantial uncertainty about the corresponding transmission history has remained. Here, we introduce a formal inference framework for the simultaneous use of HIV biomarkers and HIV sequences derived from persons who may have infected each other. We created a flexible system that can use up to five biomarkers to estimate the time of infection of each person, with the appropriate uncertainty given by an empirical probability distribution. These time-of-infection distributions were jointly modelled with the HIV phylogenetic tree estimation to produce possible transmission histories. We show that adding biomarkers substantially limits the possible transmission histories. This makes it possible to identify transmission risks, to assess confidence in source attributions, and to make efficient resource allocations to prevent further transmissions in local epidemics.

evolutionary biology↗

bioRxiv2018

Getting more from heterogeneous HIV-1 surveillance data in a high immigration country: estimation of incidence and undiagnosed population size using multiple biomarkers

BackgroundMost HIV infections originate from individuals who are undiagnosed and unaware of their infection. Estimation of this quantity from surveillance data is hard because there is incomplete knowledge about i) the time between infection and diagnosis (TI) for the general population and ii) the time between immigration and diagnosis for foreign-born persons.\n\nDevelopmentWe developed a new statistical method for estimating the number of undiagnosed people living with HIV (PLHIV) and the incidence of HIV-1 based on dynamic modeling of heterogenous HIV-1 surveillance data. We formulated a Bayesian non-linear mixed effects model using multiple biomarkers to estimate TI accounting for biomarker correlation and individual heterogeneities. We explicitly model the probability that an HIV-1 infected foreign-born person was infected either before or after immigration to distinguish between endogenous and exogeneous incidence. The incidence estimator allows for direct calculation of the number of undiagnosed persons.\n\nApplicationThe model was applied to surveillance data in Sweden. The dynamic biomarker model was trained on longitudinal data from 31 treatment-naive patients with well-defined TI, using CD4 counts, BED serology, polymorphisms in HIV-1 pol sequences, and testing history. The multiple-biomarker model was more accurate than single biomarkers (mean absolute error 1.01 vs [≥] 1.95). We estimate that 813 (95% CI 780-862) PLHIV were undiagnosed in 2015, representing a proportion of 10.8% (95% CI 10.4-11.3%) of all PLHIV.\n\nConclusionsThe proposed methodology will enhance the utility of standard surveillance data streams and will be useful to monitor progress towards and compliance with the 90-90-90 UNAIDS target.\n\nKey messagesO_LICombined heterogeneous HIV-1 surveillance data and biomarker data can be used to estimate both local incidence and the number of undiagnosed people living with HIV.\nC_LIO_LIExplicit modeling of the dynamics, heterogeneity, and correlation of multiple biomarkers over time improved estimation of time between infection and diagnosis.\nC_LIO_LIExplicit modeling of the probability that foreign-born persons were infected before or after immigration improves accuracy of estimates of endogenous incidence and undiagnosed persons living with HIV.\nC_LIO_LIThe endogenous incidence of HIV-1 in Sweden is declining, despite continued immigration of HIV-1 infected persons.\nC_LIO_LIThe proportion of undiagnosed PLHIV decreased over 2010-2015 and was estimated to be 10.8% (95% CI, 10.4-11.3%) in 2015.\nC_LI

epidemiology↗