Biology subjects

Olabode, A. S.

Publications and source records attributed to Olabode, A. S..

7 recordsLinked to original sources

bioRxiv2025

Evolutionary fingerprinting of protein-coding genes in RNA viruses

RNA viruses evolve rapidly to adapt to changing host environments. Much of this adaptation occurs at the level of protein-coding genes. Thus, some of the most well-characterized examples of rapid adaptation have been found in virus proteins that are exposed on the surface of the viral particle, where they mediate host receptor binding and cell entry. To investigate whether surface-exposed proteins and other proteins encoded by viruses exhibit different patterns of evolution under selection, we analyzed 244 protein-coding genes from 28 species of RNA viruses representing 15 taxonomic families. First, we show that gene-wide rates of non-synonymous (dN) and synonymous (dS) substitutions do not differentiate between categories of proteins. To provide a more detailed comparison between genes, we inferred for each alignment the bivariate posterior distribution over a fixed grid of codon site-specific dN and dS values. This distribution is the genes evolutionary fingerprint. Next, we computed the Wasserstein distance for every pair of fingerprints, which is analogous to amount of work required to reshape one distribution to another. After compensating for differences in genetic variation among viruses and proteins, we found that surface-exposed proteins could not be distinguished from non-exposed proteins in the space induced by the Wasserstein distance matrix. However, surface-exposed proteins from enveloped viruses were significantly clustered apart from their counterparts in non-enveloped viruses. In contrast, there was no significant separation between these categories of viruses for proteins with polymerase activity. We show that this pattern is more consistent with relaxed purifying selection than adaptive evolution in proteins associated with viral envelopes.

evolutionary biology↗

bioRxiv2023

Tracking SARS-CoV-2 variants of concern in wastewater: an assessment of nine computational tools using simulated genomic data

Wastewater-based surveillance (WBS) is an important epidemiological and public health tool for tracking pathogens across the scale of a building, neighbourhood, city, or region. WBS gained widespread adoption globally during the SARS-CoV-2 pandemic for estimating community infection levels by qPCR. Sequencing pathogen genes or genomes from wastewater adds information about pathogen genetic diversity which can be used to identify viral lineages (including variants of concern) that are circulating in a local population. Capturing the genetic diversity by WBS sequencing is not trivial, as wastewater samples often contain a diverse mixture of viral lineages with real mutations and sequencing errors, which must be deconvoluted computationally from short sequencing reads. In this study we assess nine different computational tools that have recently been developed to address this challenge. We simulated 100 wastewater sequence samples consisting of SARS-CoV-2 BA.1, BA.2, and Delta lineages, in various mixtures, as well as a Delta-Omicron recombinant and a synthetic "novel" lineage. Most tools performed well in identifying the true lineages present and estimating their relative abundances, and were generally robust to variation in sequencing depth and read length. While many tools identified lineages present down to 1% frequency, results were more reliable above a 5% threshold. The presence of an unknown synthetic lineage, which represents an unclassified SARS-CoV-2 lineage, increases the error in relative abundance estimates of other lineages, but the magnitude of this effect was small for most tools. The tools also varied in how they labelled novel synthetic lineages and recombinants. While our simulated dataset represents just one of many possible use cases for these methods, we hope it helps users understand potential sources of noise or bias in wastewater sequencing data and to appreciate the commonalities and differences across methods.

bioinformatics↗

bioRxiv2021

CoVizu: Rapid analysis and visualization of the global diversity of SARS-CoV-2 genomes

Phylogenetics has played a pivotal role in the genomic epidemiology of SARS-CoV-2, such as tracking the emergence and global spread of variants, and scientific communication. However, the rapid accumulation of genomic data from around the world -- with over two million genomes currently available in the GISAID database -- is testing the limits of standard phylogenetic methods. Here, we describe a new approach to rapidly analyze and visualize large numbers of SARS-CoV-2 genomes. Using Python, genomes are filtered for problematic sites, incomplete coverage, and excessive divergence from a strict molecular clock. All differences from the reference genome, including indels, are extracted using minimap2, and compactly stored as a set of features for each genome. For each Pango lineage (https://cov-lineages.org), we collapse genomes with identical features into variants, generate 100 bootstrap samples of the feature set union to generate weights, and compute the symmetric differences between the weighted feature sets for every pair of variants. The resulting distance matrices are used to generate neigihbor-joining trees in RapidNJ and converted into a majority-rule consensus tree for the lineage. Branches with support values below 50% or mean lengths below 0.5 differences are collapsed, and tip labels on affected branches are mapped to internal nodes as directly-sampled ancestral variants. Currently, we process about million genomes in approximately nine hours on 34 cores. The resulting trees are visualized using the JavaScript framework D3.js as beadplots, in which variants are represented by horizontal line segments, annotated with beads representing samples by collection date. Variants are linked by vertical edges to represent branches in the consensus tree. These visualizations are published at https://filogeneti.ca/CoVizu. All source code was released under an MIT license at https://github.com/PoonLab/covizu.

bioinformatics↗

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

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↗