Biology subjects

Poon, A. F.

Publications and source records attributed to Poon, A. F..

6 recordsLinked to original sources

bioRxiv2021

Revisiting the recombinant history of HIV-1 group M with dynamic network community detection

A new abundance of full-length HIV-1 genome sequences provides an opportunity to revisit the standard model of HIV-1/M diversity that clusters genomes into largely non-recombinant subtypes, which is not consistent with recent evidence of deep recombinant histories for SIV and other HIV-1 groups. Here we develop an unsupervised non-parametric clustering approach, which does not rely on predefined non-recombinant genomes, by adapting a community detection method developed for dynamic social network analysis. We show that this method (DSBM) attains a significantly lower mean error rate in detecting recombinant breakpoints in simulated data (quasibinomial GLM, P < 8 x 10-8), compared to other reference-free recombination detection programs (GARD, RDP4 and RDP5). Applied to a representative sample of n = 525 actual HIV-1 genomes, we determined k = 25 as the optimal number of DSBM clusters, and used change point detection to estimate that at least 95% of these genomes are recombinant. Further, we identified both known and novel recombination hotspots in the HIV-1 genome, and evidence of inter-subtype recombination in HIV-1 subtype reference genomes. We propose that clusters generated by DSBM can provide an informative new framework for HIV-1 classification.

evolutionary biology↗

bioRxiv2020

Tuning Intrinsic Disorder Predictors for Virus Proteins

Many virus-encoded proteins have intrinsically disordered regions that lack a stable folded threedimensional structure. These disordered proteins often play important functional roles in virus replication, such as down-regulating host defense mechanisms. With the widespread availability of next-generation sequencing, the number of new virus genomes with predicted open reading frames is rapidly outpacing our capacity for directly characterizing protein structures through crystallography. Hence, computational methods for structural prediction play an important role. A large number of predictors focus on the problem of classifying residues into ordered and disordered regions, and these methods tend to be validated on a diverse training set of proteins from eukaryotes, prokaryotes and viruses. In this study, we investigate whether some predictors outperform others in the context of virus proteins. We evaluate the prediction accuracy of 21 methods, many of which are only available as web applications, on a curated set of 126 proteins encoded by viruses. Furthermore, we apply a random forest classifier to these predictor outputs. Based on cross-validation experiments, this ensemble approach confers a substantial improvement in accuracy, e.g., a mean 36% gain in Matthews correlation coefficient. Lastly, we apply the random forest predictor to SARS-CoV-2 ORF6, an accessory gene that encodes a short (61 AA) and moderately disordered protein that inhibits the host innate immune response.

microbiology↗

bioRxiv2019

Detection of novel HIV-1 drug resistance mutations by support vector analysis of deep sequence data and experimental validation

The global HIV-1 pandemic comprises many genetically divergent subtypes. Most of our understanding of drug resistance in HIV-1 derives from subtype B, which predominates in North America and western Europe. However, about 90% of the pandemic represents non-subtype B infections. Here, we use deep sequencing to analyze HIV-1 from infected individuals in Uganda who were either treatment-naive or who experienced virologic failure on ART without the expected patterns of drug resistance. Our objective was to detect potentially novel associations between mutations in HIV-1 integrase and treatment outcomes in Uganda, where most infections are subtypes A or D. We retrieved a total of 380 archived plasma samples from patients at the Joint Clinical Research Centre (Kampala), of which 328 were integrase inhibitor-naive and 52 were raltegravir (RAL)-based treatment failures. Next, we developed a bioinformatic pipeline for alignment and variant calling of the deep sequence data obtained from these samples from a MiSeq platform (Illumina). To detect associations between within-patient polymorphisms and treatment outcomes, we used a support vector machine (SVM) for feature selection with multiple imputation to account for partial reads and low quality base calls. Candidate point mutations of interest were experimentally introduced into the HIV-1 subtype B NL4-3 backbone to determine susceptibility to RAL in U87.CD4.CXCR4 cells. Finally, we carried out replication capacity experiments with wild-type and mutant viruses in TZM-bl cells in the presence and absence of RAL. Our analyses not only identified the known major mutation N155H and accessory mutations G163R and V151I, but also novel mutations I203M and I208L as most highly associated with RAL failure. The I203M and I208L mutations resulted in significantly decreased susceptibility to RAL (44.0-fold and 54.9-fold, respectively) compared to wild-type virus (EC50=0.32 nM), and may represent novel pathways of HIV-1 resistance to modern treatments.\n\nAuthor summaryThere are many different types of HIV-1 around the world. Most of the research on how HIV-1 can become resistant to drug treatment has focused on the type (B) that is the most common in high-income countries. However, about 90% of infections around the world are caused by a type other than B. We used next-generation sequencing to analyze samples of HIV-1 from patients in Uganda (mostly infected by types A and D) for whom drug treatment failed to work, and whose infections did not fit the classic pattern of adaptation based on B. Next, we used machine learning to detect mutations in these virus populations that could explain the treatment outcomes. Finally, we experimentally added two candidate mutations identified by our analysis to a laboratory strain of HIV-1 and confirmed that they conferred drug resistance to the virus. Our study reveals new pathways that other types of HIV-1 may use to evolve resistance to drugs that make up the current recommended treatment for newly diagnosed individuals.

microbiology↗

bioRxiv2018

sierra-local: A lightweight standalone application for secure HIV-1 drug resistance prediction

Genotypic resistance interpretation systems for the prediction and interpretation of HIV-1 antiretroviral resistance are an important part of the clinical management of HIV-1 infection. Current interpretation systems are generally hosted on remote webservers that enable clinical laboratories to generate resistance predictions easily and quickly from patient HIV-1 sequences encoding the primary targets of modern antiretroviral therapy. However they also potentially compromise a health providers ethical, professional, and legal obligations to data security, patient information confidentiality, and data provenance. Furthermore, reliance on web-based algorithms makes the clinical management of HIV-1 dependent on a network connection. Here, we describe the development and validation of sierra-local, an open-source implementation of the Stanford HIVdb genotypic resistance interpretation system for local execution, which aims to resolve the ethical, legal, and infrastructure issues associated with remote computing. This package reproduces the HIV-1 resistance scoring by the web-based Stanford HIVdb algorithm with a high degree of concordance (99.997%) and a higher level of performance than current methods of accessing HIVdb programmatically.

bioinformatics↗

bioRxiv2018

Tree Shape-based approaches for the Comparative study of Cophylogeny

Cophylogeny is the congruence of phylogenetic relationships between two different groups of organisms due to their long-term interaction, such as between host and pathogen species. Discordance between host and pathogen phylogenies may occur due to pathogen host-switch events, pathogen speciation within a host species, and extinction. Here, we investigated the use of tree shape distance measures to quantify the degree of cophylogeny for the comparative analysis of host-pathogen interactions across taxonomic groups.\n\nWe firstly implemented a coalescent model to simulate pathogen phylogenies within a fixed host tree, given the cospeciation probability, migration rate between hosts, and pathogen speciation rate within hosts.\n\nNext, we used simulations from this model to evaluate 13 distance metrics between these trees and the host tree, including Robinson-Foulds distance and two kernel distances that we developed for labeled and unlabeled trees, which use branch lengths and can accommodate trees of different sizes. Finally, we used these distance metrics to revisit actual datasets from published cophylogenetic studies across all taxonomic groups, where authors described the observed associations as representing a high or low degree of cophylogeny.\n\nOur simulation analyses demonstrated that some metrics are more informative than others with respect to specific coevolution parameters. For example, the Sim metric was the most responsive to variation in coalescence rates, whereas the unlabeled kernel metric was the most responsive to cospeciation probabilities. We also determined that distance metrics were more informative about the model parameters when the underlying parameter values did not assume extreme values, e.g., rapid host switching. When applied to real datasets, projection of these trees associations into a parameter space defined by the 13 distance metrics revealed some clustering of studies reporting low concordance. This suggested that different investigators are describing concordance in a consistent way across biological systems, and that these expert subjective assessments can be at least partly quantified using distance metrics.\n\nOur results support the hypothesis that tree distance measures can be useful for quantifying host and pathogen cophylogeny. This motivates the usage of distance metrics in the field of coevolution and supports the development of simulation-based methods, i.e., approximate Bayesian computation, to estimate coevolutionary parameters from the discordant shapes of host and pathogen trees. [tree shape; cophylogeny; codivergence; coevolution; host switching; tree metrics; kernel]

evolutionary biology↗

bioRxiv2018

Evidence for a Recombinant Origin of HIV-1 group M from Genomic Variation

Reconstructing the early dynamics of the HIV-1 pandemic can provide crucial insights into the socioeconomic drivers of emerging infectious diseases in human populations, including the roles of urbanization and transportation networks. Current evidence indicates that the global pandemic comprising almost entirely of HIV-1/M originated around the 1920s in central Africa. However, these estimates are based on molecular clock estimates that are assumed to apply uniformly across the virus genome. There is growing evidence that recombination has played a significant role in the early history of the HIV-1 pandemic, such that different regions of the HIV-1 genome have different evolutionary histories. In this study, we have conducted a dated-tip analysis of all near full-length HIV-1/M genome sequences that were published in the GenBank database. We used a sliding window approach similar to the bootscanning method for detecting breakpoints in intersubtype recombinant sequences. We found evidence of substantial variation in estimated root dates among windows, with an estimated mean time to the most recent common ancestor (tMRCA) of 1922. Estimates were significantly autocorrelated, which was more consistent with an early recombination event than with stochastic error variation in phylogenetic reconstruction and dating analyses. A piecewise regression analysis supported the existence of at least one recombination breakpoint in the HIV-1/M genome with interval-specific means around 1929 and 1913, respectively. This analysis demonstrates that a sliding window approach can accommodate early recombination events outside the established nomenclature of HIV-1/M subtypes, although it is difficult to incorporate the earliest available samples due to their limited genome coverage.

evolutionary biology↗