Biology subjects

Kouyos, R.

Publications and source records attributed to Kouyos, R..

6 recordsLinked to original sources

bioRxiv2024

Comparative Evaluation of Bioinformatic Pipelines for Full-Length Viral Genome Assembly

The increasingly widespread application of next-generation sequencing (NGS) in clinical diagnostics and epidemiological research has generated a demand for robust, fast, automated, and user-friendly bioinformatic workflows. To guide the choice of tools for the assembly of full-length viral genomes from NGS datasets, we assessed the performance and applicability of four widely adopted bioinformatic pipelines (shiver - for which we created a user-friendly Dockerized version, referred to as dshiver; SmaltAlign, viral-ngs, and V-pipe) using both simulated datasets and real-world HIV-1 paired-end short- read sequences and default settings. All four pipelines produced high-quality consensus genome assemblies and minority variant calls when the reference sequence used for assembly had high similarity to the analyzed sample. However, while shiver and SmaltAlign showed robust performance also with more divergent samples (non-matching subtypes), viral-ngs and V-Pipe proved to be sensitive to genetic distance from the reference sequence. With empirical datasets, SmaltAlign and viral-ngs exhibited substantially shorter runtime compared to V-Pipe and shiver. In terms of applicability, V-Pipe provides the broadest functionalities; SmaltAlign and dshiver combine user-friendliness with robustness; while the use of viral-ngs requires a less computational resources compared to other tools. To conclude, all four pipelines can perform well in terms of quality metrics; however, the reference sequence needs to be adjusted to closely match the sample data for viral-ngs and V-Pipe. Differences in user-friendliness and runtime may guide the choice of the pipeline in a particular setting. The new Dockerized version of shiver offers ease of use in addition to the accuracy and robustness of the original pipeline.

bioinformatics↗

bioRxiv2020

Comparing mutational pathways to lopinavir resistance in HIV-1 subtypes B versus C

Although combination antiretoviral therapies seem to be effective at controlling HIV-1 infections regardless of the viral subtype, there is increasing evidence for subtype-specific drug resistance mutations. The order and rates at which resistance mutations accumulate in different subtypes also remain poorly understood. Here, we present a methodology for the comparison of mutational pathways in different HIV-1 subtypes, based on Hidden Conjunctive Bayesian Networks (H-CBN), a probabilistic model for inferring mutational pathways from cross-sectional genotype data. We introduce a Monte Carlo sampling scheme for learning H-CBN models on a large number of resistance mutations and develop a statistical test to assess differences in the inferred mutational pathways between two groups. We apply this method to the temporal progression of mutations conferring resistance to the protease inhibitor lopinavir in a large cross-sectional data set of South African individuals living with HIV-1 subtype C, as well as a genotype data set of subtype B infections derived from the Stanford HIV Drug Resistance Database and the Swiss HIV Cohort Study. We find strong support for different initial mutational events in the protease, namely at residue 46 in subtype B and at residue 82 in subtype C. Our results also show that mutations can accumulate along various alternative paths within subtypes, as opposed to a unique total temporal ordering. Furthermore, the maximum likelihood mutational networks for subtypes B and C share only 7 edges (Jaccard distance 0.802) and imply many different evolutionary pathways. Beyond HIV drug resistance, the statistical methodology is applicable more generally for the comparison of inferred mutational pathways between any two groups. Author summaryThere is a disparity in the distribution of infections by HIV-1 subtype in the world. Subtype B is predominant in America, Western Europe and Australia, and most therapeutic strategies are based on research and clinical studies on this subtype. However, non-B subtypes represent the majority of global HIV-1 infections; e.g., subtype C alone accounts for nearly half of all HIV-1 infections. We present a statistical framework enabling the comparison of patterns of accumulating mutations in different HIV-1 subtypes. Specifically, we study lopinavir resistance pathways in HIV-1 subtypes B versus C, but the methodology can be generally applied to compare the temporal ordering of genetic events in different subgroups.

evolutionary biology↗

bioRxiv2020

ColTapp, an automated image analysis application for efficient microbial colony growth dynamics quantification

Phenotypic heterogeneity occurs in a population of genetically identical bacteria due to stochastic molecular fluctuations and environmental variations. In extreme cases of phenotypic heterogeneity, a fraction of the bacterial population enters dormancy, and these metabolically inactive or non-dividing bacteria persist through most antibiotic challenges. These subpopulations of persister cells are difficult to study in patient samples. However, the proportion of persisters in a sample can be accessed by physically separating bacteria on a plate measuring the time until colonies become visible as dormant bacteria resume growth later than their active counterparts and form smaller colonies. Here, we present ColTapp (Colony Time-lapse app), an application dedicated to bacterial colony growth quantification, freely available for download together with its MATLAB source code or as a MacOS/Windows executable. ColTapps intuitive graphical user interface allows users without prior coding knowledge to analyze endpoint or time-lapse images of colonies on agar plates. Colonies are detected automatically, and their radius can be tracked over time. Downstream analyses to derive colony lag time and growth rate are implemented. We demonstrate here the applicability of ColTapp on a dataset of Staphyloccocus aureus colony time-lapse images. Colonies on dense plates reached saturation early, biasing lag time estimation from endpoint images. This bias can be reduced by considering the area available to each colony on a plate. By facilitating the analysis of colony growth dynamics in clinical settings, this application will enable a new type of diagnostics, oriented towards personalized antibiotic therapies.

bioinformatics↗

bioRxiv2017

Dissecting HIV Virulence: Heritability Of Setpoint Viral Load, CD4+ T Cell Decline And Per-Parasite Pathogenicity

Pathogen strains may differ in virulence because they attain different loads in their hosts, or because they induce different disease-causing mechanisms independent of their load. In evolutionary ecology, the latter is referred to as \"per-parasite pathogenicity\". Using viral load and CD4+ T cell measures from 2014 HIV-1 subtype B infected individuals enrolled in the Swiss HIV Cohort Study, we investigated if virulence -- measured as the rate of decline of CD4+ T cells -- and per-parasite pathogenicity are heritable from donor to recipient. We estimated heritability by donor-recipient regressions applied to 196 previously identified transmission pairs, and by phylogenetic mixed models applied to a phylogenetic tree inferred from HIV pol sequences. Regressing the CD4+ T cell declines and per-parasite pathogenicities of the transmission pairs did not yield heritability estimates significantly different from zero. With the phylogenetic mixed model, however, our best estimate for the heritability of the CD4+ T cell decline is 17% (5%-30%), and that of the per-parasite pathogenicity is 17% (4%-29%). Further, we confirm that the set-point viral load is heritable, and estimate a heritability of 29% (12%-46%). Interestingly, the pattern of evolution of all these traits differs significantly from neutrality, and is most consistent with stabilizing selection for the set-point viral load, and with directional selection for the CD4+ T cell decline and the per-parasite pathogenicity. Our analysis shows that the viral genetype affects virulence mainly by modulating the per-parasite pathogenicity, while the indirect effect via the set-point viral load is minor.

evolutionary biology↗

bioRxiv2016

Easy and Accurate Reconstruction of Whole HIV Genomes from Short-Read Sequence Data

Next-generation sequencing has yet to be widely adopted for HIV. The difficulty of accurately reconstructing the consensus sequence of a quasispecies from reads (short fragments of DNA) in the presence of rapid between- and within-host evolution may have presented a barrier. In particular, mapping (aligning) reads to a reference sequence leads to biased loss of information; this bias can distort epidemiological and evolutionary conclusions. De novo assembly avoids this bias by effectively aligning the reads to themselves, producing a set of sequences called contigs. However contigs provide only a partial summary of the reads, misassembly may result in their having an incorrect structure, and no information is available at parts of the genome where contigs could not be assembled. To address these problems we developed the tool shiver to preprocess reads for quality and contamination, then map them to a reference tailored to the sample using corrected contigs supplemented with existing reference sequences. Run with two commands per sample, it can easily be used for large heterogeneous data sets. We use shiver to reconstruct the consensus sequence and minority variant information from paired-end short-read data produced with the Illumina platform, for 65 existing publicly available samples and 50 new samples. We show the systematic superiority of mapping to shivers constructed reference over mapping the same reads to the standard reference HXB2: an average of 29 bases per sample are called differently, of which 98.5% are supported by higher coverage. We also provide a practical guide to working with imperfect contigs.

bioinformatics↗

bioRxiv2016

Phylodynamics on local sexual contact networks

Phylodynamic models are widely used in infectious disease epidemiology to infer the dynamics and structure of pathogen populations. However, these models generally assume that individual hosts contact one another at random, ignoring the fact that many pathogens spread through highly structured contact networks. We present a new framework for phylodynamics on local contact networks based on pairwise epidemiological models that track the status of pairs of nodes in the network rather than just individuals. Shifting our focus from individuals to pairs leads naturally to coalescent models that describe how lineages move through networks and the rate at which lineages coalesce. These pairwise coalescent models not only consider how network structure directly shapes pathogen phylogenies, but also how the relationship between phylogenies and contact networks changes depending on epidemic dynamics and the fraction of infected hosts sampled. By considering pathogen phylogenies in a probabilistic framework, these coalescent models can also be used to estimate the statistical properties of contact networks directly from phylogenies using likelihood-based inference. We use this framework to explore how much information phylogenies retain about the underlying structure of contact networks and to infer the structure of a sexual contact network underlying a large HIV-1 sub-epidemic in Switzerland.

epidemiology↗