Biology subjects

de Oliveira, T.

Publications and source records attributed to de Oliveira, T..

At least 19 recordsLinked to original sources

bioRxiv2026

Anniemap: Vector Search for Viral Short Read Alignment

Background: The process of aligning sequencing reads to a reference genome is a foundational step in genomic analysis, underpinning tasks from variant detection to pathogen surveillance. In viral genomics, however, this problem becomes substantially more challenging: viral sequences are often present at low abundance within host-dominated samples and can differ markedly from available references due to rapid mutation and population heterogeneity. These characteristics reduce the effectiveness of conventional seed-and-extend aligners, which typically rely on long exact or near-exact matches to anchor alignments. Even modest sequence divergence or sequencing errors can disrupt such seeds, particularly for short reads, leading to missed alignments. The central challenge in this setting is maintaining robust alignment under high divergence without sacrificing efficiency. Results: We introduce Anniemap, a vector search based approach to viral short-read sequence alignment. Anniemap represents reads and reference sequences as binary vectors and performs approximate nearest-neighbour search using Facebook AI Similarity Search (FAISS) to efficiently identify candidate mappings. Anniemap was compared with the well-established alignment tools Bowtie2 and BWA-MEM2 across a diverse set of viral genomes and read lengths using both simulated and real sequencing data. Anniemap achieved higher sensitivity and throughput in almost all evaluated scenarios, with the most substantial improvements in sensitivity observed for highly divergent genomes, such as Hepatitis C virus (HCV) and Human Immunodeficiency Virus (HIV). Conclusions; By measuring vector similarity rather than relying on long exact seed matches, Anniemap provides greater robustness to sequencing errors and genomic mutations. This property is particularly advantageous for viral genomes, where substantial sequence divergence is common. Further work is required to efficiently extend vector-based search for read alignment beyond viral genomes.

genomics↗

bioRxiv2025

Disaggregation Regression and Multi-Model Evaluation for Predicting Dengue Risk in Africa

Dengue risk mapping is essential for estimating disease burden, and informing targeted surveillance and control efforts. Current approaches to risk mapping vary widely in their methodology, data sources, output metrics and applications. Many existing approaches focus on predicting ecological suitability and produce high-resolution risk maps based on environmental conditions, yet high-resolution incidence maps remain scarce, leaving a critical gap in guiding precise, location-specific interventions. The prediction of disease incidence or transmission intensity remains relatively uncommon in disease ecology, largely due to data limitations, reporting biases, and the inherent complexity that arises from transmission dynamics. In this study, we applied disaggregation regression modelling to downscale aggregated dengue case data from 14 countries in Central and South America, generating fine-resolution incidence estimates that we subsequently projected onto the African continent. We then compared the resulting predictions from the incidence-based risk map with three widely used approaches: vector suitability index, dengue environmental suitability index, and mechanistic transmission potential (Index P). The disaggregation model achieved relatively strong predictive accuracy within the training region (mean correlation = 0.72) and showed partial alignment with reported burden across Africa (Spearman {rho} = 0.33). Other risk maps exhibited similar or weaker correlations with reported cases in Africa, including {rho} = 0.33 for dengue environmental suitability, {rho} = 0.32 for transmission potential and {rho} = 0.23 for Aedes aegypti suitability. Disaggregation regression offers a valuable tool for translating reported case data into spatially explicit estimates of burden, bridging the gap between ecological risk and epidemiological relevance. While spatial agreement was high in parts of coastal West Africa across the different risk map approaches, notable divergences highlight the distinct assumptions underlying each framework. Authors SummaryDengue is a mosquito-borne viral disease with expanding global impact. Accurately mapping dengue risk is essential for identifying areas of high transmission and targeting interventions effectively. Most current approaches to mapping dengue risk focus on environmental suitability for the virus or its mosquito vector, rather than estimating actual disease burden. In this study, we used an incidence-based approach; disaggregation regression, to estimate dengue cases at high spatial resolution using national and regional case data from Latin America. We then applied the model to Africa, where surveillance data are limited, and compared its predictions to three other common types of dengue risk maps. Our results showed that while all approaches provided some insight into geographic risk patterns, they often highlighted different areas as priorities. Our incidence-based model captured both where dengue might occur and how intense transmission may be, helping bridge the gap between environmental and ecological suitability of transmission and real-world disease burden. This approach can support more informed decision-making in areas with limited surveillance and guide targeted control efforts.

ecology↗

bioRxiv2025

Craft: A Machine Learning Approach to Dengue Subtyping

MotivationThe dengue virus poses a major global health threat, with nearly 390 million infections annually. A recently proposed hierarchical dengue nomenclature system enhances spatial resolution by defining major and minor lineages within genotypes, aiding efforts to track viral evolution. While current subtyping tools - Genome Detective, GLUE, and NextClade - rely on computationally intensive sequence alignment and phylogenetic inference, machine learning presents a promising alternative for achieving accurate and rapid classification. ResultsWe present Craft (Chaos Random Forest), a machine learning framework for dengue subtyping. We demonstrate that Craft is capable of faster classification speeds while matching or surpassing the accuracy of existing tools. Craft achieves 99.5% accuracy on a hold-out test set and processes over 140 000 sequences per minute. Notably, Craft maintains remarkably high accuracy even when classifying sequence segments as short as 700 nucleotides. Contactdanielvanzyl@sun.ac.za Supplementary informationA supplemental table acknowledging the authors of the GISAID dengue sequences is available at Bioinformatics online.

genomics↗

bioRxiv2025

Identifying genomic surveillance gaps in Africa for the global public health response to West Nile Virus

BackgroundWest Nile Virus (WNV) is a zoonotic flavivirus of significant One Health relevance and is classified as a priority pathogen with a high-risk of causing public health emergencies of global concern. WNV is endemic to Africa; however, the availability of genomic sequences from the continent remains limited. MethodsWe review the extent of polymerase chain reaction testing and genomic sequencing of WNV conducted across Africa. Using phylogeographic methods, we map the spatiotemporal spread of the virus across the continent and globally. FindingsOur study shows that WNV has been detected in 39 African countries (including Comoros, Seychelles, and Mauritius), the Canary Islands, and Reunion Island. Publications including molecular data originate from 24 countries; however, genomic sequences are publicly available for only 16 countries. We identify regions with detected viral circulation but lacking molecular surveillance. Further, we list such regions that overlap with Key Biodiversity Areas (sites harbouring significant bird diversity) as they may host high viral circulation, and high human population density that may be susceptible to spillover. InterpretationWe recognise significant knowledge gaps on the true disease burden, molecular epidemiology, and distribution of WNV in Africa. Addressing these gaps requires an integrated One Health surveillance approach which is challenging to establish. We propose three key surveillance needs as potential starting points to improve our understanding of the virus in Africa to strengthen the global public health response to this disease. FundingRockefeller Foundation, the National Institute of Health USA, Institute of Human Virology Nigeria, Global Health EDCTP3 Joint Undertaking, the Health Emergency Preparedness and Response Umbrella Program, managed by the World Bank Group, the Medical Research Foundation, and the Wellcome Trust.

molecular biology↗

bioRxiv2024

Alignment Free Viral Sequence Classification

BackgroundThe rapid increase in nucleotide sequence data generated by next-generation sequencing (NGS) technologies demands efficient computational tools for sequence comparison. Alignment-based methods, such as BLAST, are increasingly overwhelmed by the scale of contemporary datasets due to their high computational demands for classification. This study evaluates alignment-free (AF) methods as scalable and rapid alternatives for viral sequence classification, focusing on identifying techniques that maintain high accuracy and efficiency when applied to extremely large datasets. ResultsWe employed six established AF techniques to extract feature vectors from viral genomes, which were subsequently used to train Random Forest classifiers. Our primary dataset comprises 297,186 SARS-CoV-2 nucleotide sequences, categorized into 3502 distinct lineages. Furthermore, we validated our models using dengue and HIV sequences to demonstrate robustness across different viral datasets. Our AF classifiers achieved 97.8% accuracy on the SARS-CoV-2 test set, and 99.8% and 89.1% accuracy on dengue and HIV test sets, respectively. ConclusionDespite the high-class dimensionality, we show that word-based AF methods effectively represent viral sequences. Our study highlights the practical advantages of AF techniques, including significantly faster processing compared to alignment-based methods and the ability to classify sequences using modest computational resources.

genomics↗

bioRxiv2022

Selection analysis identifies unusual clustered mutational changes in Omicron lineage BA.1 that likely impact Spike function

Among the 30 non-synonymous nucleotide substitutions in the Omicron S-gene are 13 that have only rarely been seen in other SARS-CoV-2 sequences. These mutations cluster within three functionally important regions of the S-gene at sites that will likely impact (i) interactions between subunits of the Spike trimer and the predisposition of subunits to shift from down to up configurations, (ii) interactions of Spike with ACE2 receptors, and (iii) the priming of Spike for membrane fusion. We show here that, based on both the rarity of these 13 mutations in intrapatient sequencing reads and patterns of selection at the codon sites where the mutations occur in SARS-CoV-2 and related sarbecoviruses, prior to the emergence of Omicron the mutations would have been predicted to decrease the fitness of any genomes within which they occurred. We further propose that the mutations in each of the three clusters therefore cooperatively interact to both mitigate their individual fitness costs, and adaptively alter the function of Spike. Given the evident epidemic growth advantages of Omicron over all previously known SARS-CoV-2 lineages, it is crucial to determine both how such complex and highly adaptive mutation constellations were assembled within the Omicron S-gene, and why, despite unprecedented global genomic surveillance efforts, the early stages of this assembly process went completely undetected.

evolutionary biology↗

bioRxiv2021

SARS-CoV-2 cell-to-cell spread occurs rapidly and is insensitive to antibody neutralization

Viruses increase the efficiency of close-range transmission between cells by manipulating cellular physiology and behavior, and SARS-CoV-2 uses cell fusion as one mechanism for cell-to-cell spread. Here we visualized infection using time-lapse microscopy of a human lung cell line and used live virus neutralization to determine the sensitivity of SARS-CoV-2 cell-to-cell spread to neutralizing antibodies. SARS-CoV-2 infection rapidly led to cell fusion, forming multinucleated cells with clustered nuclei which started to be detected at 6h post-infection. To compare sensitivity of cell-to-cell spread to neutralization, we infected either with cell-free virus or with single infected cells expressing on their surface the SARS-CoV-2 spike protein. We tested two variants of SARS-CoV-2: B.1.117 containing only the D614G substitution, and the escape variant B.1.351. We used the much smaller area of single infected cells relative to infection foci to exclude any input infected cells which did not lead to transmission. The monoclonal antibody and convalescent plasma we tested neutralized cell-free SARS-CoV-2, with the exception of B.1.351 virus, which was poorly neutralized with plasma from non-B.1.351 infections. In contrast, cell-to-cell spread of SARS-CoV-2 showed no sensitivity to monoclonal antibody or convalescent plasma neutralization. These observations suggest that, once cells are infected, SARS-CoV-2 may be more difficult to neutralize in cell types and anatomical compartments permissive for cell-to-cell spread.

microbiology↗

bioRxiv2021

Naturally-acquired immunity in Syrian Golden Hamsters provides protection from re-exposure to emerging heterosubtypic SARS-CoV-2 variants B.1.1.7 and B.1.351

The ability of acquired immune responses against SARS-CoV-2 to protect after subsequent exposure to emerging variants of concern (VOC) such as B1.1.7 and B1.351 is currently of high significance. Here, we use a hamster model of COVID-19 to show that prior infection with a strain representative of the original circulating lineage B of SARS-CoV-2 induces protection from clinical signs upon subsequent challenge with either B1.1.7 or B1.351 viruses, which recently emerged in the UK and South Africa, respectively. The results indicate that these emergent VOC may be unlikely to cause disease in individuals that are already immune due to prior infection, and this has positive implications for overall levels of infection and COVID-19 disease.

microbiology↗

bioRxiv2021

SARS-CoV-2 501Y.V2 (B.1.351) elicits cross-reactive neutralizing antibodies

Neutralization escape by SARS-CoV-2 variants, as has been observed in the 501Y.V2 (B.1.351) variant, has impacted the efficacy of first generation COVID-19 vaccines. Here, the antibody response to the 501Y.V2 variant was examined in a cohort of patients hospitalized with COVID-19 in early 2021 - when over 90% of infections in South Africa were attributed to 501Y.V2. Robust binding and neutralizing antibody titers to the 501Y.V2 variant were detected and these binding antibodies showed high levels of cross-reactivity for the original variant, from the first wave. In contrast to an earlier study where sera from individuals infected with the original variant showed dramatically reduced potency against 501Y.V2, sera from 501Y.V2-infected patients maintained good cross-reactivity against viruses from the first wave. Furthermore, sera from 501Y.V2-infected patients also neutralized the 501Y.V3 (P.1) variant first described in Brazil, and now circulating globally. Collectively these data suggest that the antibody response in patients infected with 501Y.V2 has a broad specificity and that vaccines designed with the 501Y.V2 sequence may elicit more cross-reactive responses.

immunology↗

bioRxiv2021

Reduced antibody cross-reactivity following infection with B.1.1.7 than with parental SARS-CoV-2 strains

We examined the immunogenicity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant B.1.1.7 that arose in the United Kingdom and spread globally. Antibodies elicited by B.1.1.7 infection exhibited significantly reduced recognition and neutralisation of parental strains or of the South Africa B.1.351 variant, than of the infecting variant. The drop in cross-reactivity was more pronounced following B.1.1.7 than parental strain infection, indicating asymmetric heterotypic immunity induced by SARS-CoV-2 variants.

immunology↗

bioRxiv2021

Chikungunya virus ECSA lineage reintroduction in the northeasternmost region of Brazil

The Northeast region of Brazil registered the second highest incidence proportion of chikungunya fever in 2019. In that year an outbreak consisting of patients presented with febrile disease associated with joint pain were reported by the public primary health care service in the city of Natal, Rio Grande do Norte state, in March 2019. At first, the aetiological agent of the disease was undetermined. Since much is still unknown about chikungunya virus (CHIKV) genomic diversity and evolutionary history in this northeasternmost state, we used a combination of portable whole genome sequencing, molecular clock, and epidemiological analyses that revealed the re-introduction of the CHIKV East-Central-South-African (ECSA) lineage into Rio Grande do Norte. We estimated CHIKV ECSA lineage was first introduced into Rio Grande do Norte in early June 2014, while the 2019 outbreak clade diverged around April 2018 during a period of increased chikungunya incidence in the Southeast region, which might have acted as a source of virus dispersion towards the Northeast region. Together, these results confirm the ECSA lineage continues to spread across the country through interregional importation events likely mediated by human mobility. HIGHLIGHTSCHIKV ECSA lineage introduction into Rio Grande do Norte state, Northeast Brazil, was estimated to early June 2014 At least two CHIKV importation events occurred in Rio Grande do Norte state, Brazil The 2019 chikungunya outbreak in Rio Grande do Norte was likely caused by a second event of CHIKV introduction imported from Rio de Janeiro state.

genomics↗

bioRxiv2021

West Nile virus detection in horses in three Brazilian states.

We report genetic evidence of WNV circulation from southern and northeastern Brazilian states isolated from equine red blood cells. In the northeastern state the tenth human case was also detected, presenting neuroinvasive disease compatible with WNV infection. Our analyses demonstrate that much is still unknown on the virus local epidemiology. We advocate for a shift to active surveillance, to ensure adequate control for future epidemics with spill-over potential to humans.

genomics↗

bioRxiv2020

HIV cell-to-cell spread slows evolution of drug resistance

Many enveloped viruses such as HIV have evolved to transmit by two infection modes: cell-free infection and cell-to-cell spread. Cell-to-cell spread is highly efficient as it involves directed viral transmission from the infected to the uninfected cell. In contrast, cell-free infection relies on chance encounters between the virion and cell. Despite the higher efficiency of cell-to-cell spread, there is substantial transmission by cell-free infection in conjunction with cell-to-cell spread. A possible reason is that cell-free infection offers a selective advantage by increasing sensitivity to factors interfering with infection, hence accelerating evolution of resistance relative to cell-to-cell spread alone. Here we investigated whether a combination of cell-free infection and cell-to-cell spread confers a selective advantage in experimental evolution to an antiretroviral drug. We maintained HIV infection using coculture of infected with uninfected cells in the face of moderate inhibition by the reverse transcriptase inhibitor efavirenz. We tested the effect on the rate of drug resistance evolution of replacing one coculture infection cycle with an infection cycle involving cell-free infection only, and observed earlier evolution of drug resistance mutations to efavirenz. When we increased selective pressure by adding a second reverse transcriptase inhibitor, emtricitabine, infection with the cell-free step consistently evolved multidrug resistance to both drugs and was able to replicate. In contrast, infection without a cell-free step mostly failed to evolve multidrug resistance. Therefore, HIV cell-to-cell spread decreases the ability of HIV to rapidly evolve resistance to inhibitors, which is conferred by cell-free infection. Author summaryCell-to-cell spread of HIV differs from cell-free, diffusion-based HIV infection in that viral transmission is directed from the infected to the uninfected cell through cellular interactions. Cell-to-cell spread has been recognized as a highly efficient infection mode that is able to surmount inhibition by antibodies and antiretroviral drugs. However, the effect of HIV cell-to-cell spread on the rate of evolution of viral resistance to infection inhibitors has not been studied. Here we used experimental evolution to investigate the effect of cell-to-cell spread versus cell-free infection on the emergence of drug resistance mutations to one or a combination of antiretroviral drugs. We found that replacing one infection cycle in experimental evolution with cell-free infection, where the filtered supernatant from infected cells, but not the cellular fraction, is used as the viral source, results in more rapid evolution of resistance. The consequences are that multidrug resistance consistently evolves with a cell-free viral cycle, but not when infection is solely by coculture of infected and uninfected cells. A possible consequence is that in environments where HIV cell-to-cell spread may predominate and some residual viral replication occurs in the face of ART, the emergence of drug resistance mutations would be delayed.

evolutionary biology↗

bioRxiv2020

Whole Genome Sequencing of SARS-CoV-2: Adapting Illumina Protocols for Quick and Accurate Outbreak Investigation During a Pandemic

The COVID-19 pandemic spread very fast around the world. A few days after the first detected case in South Africa, an infection started a large hospital outbreak in Durban, KwaZulu-Natal. Phylogenetic analysis of SARS-CoV-2 genomes can be used to trace the path of transmission within a hospital. It can also identify the source of the outbreak and provide lessons to improve infection prevention and control strategies. In this manuscript, we outline the obstacles we encountered in order to genotype SARS-CoV-2 in real-time during an urgent outbreak investigation. In this process, we encountered problems with the length of the original genotyping protocol, reagent stockout and sample degradation and storage. However, we managed to set up three different library preparation methods for sequencing in Illumina. We also managed to decrease the hands on library preparation time from twelve to three hours, which allowed us to complete the outbreak investigation in just a few weeks. We also fine-tuned a simple bioinformatics workflow for the assembly of high-quality genomes in real-time. In order to allow other laboratories to learn from our experience, we released all of the library preparation and bioinformatics protocols publicly and distributed them to other laboratories of the South African Network for Genomics Surveillance (SANGS) consortium.

genomics↗

bioRxiv2020

Complementation can maintain a quasispecies of drug sensitive and resistant HIV

HIV exists as multiple genotypes in a single infected individual referred to as a quasispecies. Here we reproduced a quasispecies by moderate selective pressure using an HIV reverse transcriptase inhibitor. The drug resistant genotype never completely supplanted the drug sensitive genotype, which stabilized at about 20 percent of viral sequences. Single-cell sequencing showed that resistant genotype frequency plateaued when cells were co-infected with sensitive and resistant genotypes, suggesting a sharing of viral proteins in co-infected cells (complementation) which masks genotypic differences. To test if complementation can confer phenotypic drug resistance, we co-transfected fluorescently labelled molecular clones of sensitive and resistant HIV and observed that genotypically sensitive virus from co-transfected cells was drug resistant. Resistant virus preferentially infected cells in tandem with drug sensitive HIV, explaining how co-infections of sensitive and resistant genotypes were initiated. Modelling this effect, we observed that a stable quasispecies could form at the experimental multiplicities of infection for the drug resistant and drug sensitive virus, showing that complementation can lead to a quasispecies in an HIV evolution experiment.

evolutionary biology↗

bioRxiv2020

Genome Detective Coronavirus Typing Tool for rapid identification and characterization of novel coronavirus genomes

SummaryGenome Detective is a web-based, user-friendly software application to quickly and accurately assemble all known virus genomes from next generation sequencing datasets. This application allows the identification of phylogenetic clusters and genotypes from assembled genomes in FASTA format. Since its release in 2019, we have produced a number of typing tools for emergent viruses that have caused large outbreaks, such as Zika and Yellow Fever Virus in Brazil. Here, we present The Genome Detective Coronavirus Typing Tool that can accurately identify novel coronavirus (2019-nCoV) sequences isolated in China and around the world. The tool can accept up to 2,000 sequences per submission and the analysis of a new whole genome sequence will take approximately one minute. The tool has been tested and validated with hundreds of whole genomes from ten coronavirus species, and correctly classified all of the SARS-related coronavirus (SARSr-CoV) and all of the available public data for 2019-nCoV. The tool also allows tracking of new viral mutations as the outbreak expands globally, which may help to accelerate the development of novel diagnostics, drugs and vaccines. AvailabilityAvailable online: https://www.genomedetective.com/app/typingtool/cov * Contactkoen@emweb.be and deoliveira@ukzn.ac.za Supplementary informationSupplementary data is available online.

bioinformatics↗

bioRxiv2019

Yellow fever virus spread in Rio de Janeiro and Espirito Santo, 2016-2019: Phylodynamic assessment to improve intervention strategies

The recent re-emergence of yellow fever virus (YFV) in Brazil has raised serious concerns due to the virus rapid dissemination in the southeastern region. To better understand YFV genetic diversity and dynamics during the recent outbreak in southeastern Brazil we generated 18 complete and near-complete genomes from the peak of the epidemic curve from non-human primates (NHPs) and human infected cases across Espirito Santo and Rio de Janeiro states. Genomic sequencing of 18 YFV genomes revealed the timing, source and likely routes of yellow fever virus transmission and dispersion during the one of the largest outbreaks ever registered in Brazil. We showed that the recent YFV epidemic spillover southwards several times from Minas Gerais to Espirito Santo and Rio de Janeiro states in 2016 to 2019. The quick production and analysis of data from portable sequencing could identify the corridor of spread of YFV. These findings reinforce that real-time and continued genomic surveillance strategies can assist in the monitoring and public health responses of arbovirus epidemics.\n\nIMPORTANCEArbovirus infections in Brazil including Yellow Fever, Dengue, Zika and Chikungunya result in considerable morbidity and mortality and are pressing public health concerns. However, our understanding of these outbreaks is hampered by limited availability of real time genomic data. In this study, we investigated the genetic diversity and spatial distribution of YFV during the current outbreak in southeastern Brazil. To gain insights into the routes of YFV introduction and dispersion, we tracked the virus by sequencing YFV genomes sampled from non-human primates and infected patients from the southeastern region. Our study provides an understanding of how YFV initiates transmission in new Brazilian regions and illustrates that near-real time genomics in the field can augment traditional approaches to infectious disease surveillance and control.

microbiology↗

bioRxiv2019

The CNS in the face of ART contains T cell origin HIV which can lead to drug resistance

HIV persists despite antiretroviral therapy (ART) in cellular reservoirs thought to occur in distinct anatomical compartments. Therapy failure may occur because of incomplete ART adherence and possibly viral replication at some reservoir sites. The CNS may serve as a reservoir site due to lowered ART penetration and virus production from long-lived tissue resident macrophages. Compelling evidence for the CNS as a reservoir is the existence of individuals where HIV is suppressed below limit of detection in blood but detectable in the cerebrospinal fluid (CSF), termed CSF Escape. Here, we asked whether HIV in CSF Escape individuals is derived from macrophages or persists due to lowered ART. We used cell surface markers on the HIV envelope to determine the cellular source of HIV. We verified detection using in vitro derived virus from infected macrophages and T cells and tested CSF from CSF Escape individuals. We observed host surface markers consistent with T cell origin. We also measured ART concentrations in the CSF and plasma. We found a dramatic decrease in CSF ART concentrations described previously, but no significant difference between CSF Escape versus fully suppressed individuals. To examine the effect of the observed CSF ART concentrations on HIV replication, we used long-term infection with ART in cell culture. CSF Escape ART levels led to either HIV suppression or evolution of drug resistance, but not replication of drug sensitive HIV. These observations argue that persistent CNS viremia despite ART can be T cell generated and may result in drug resistance and therapy failure.

microbiology↗