Browsing by Author "Campino, Susana"

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    Flavivirus integrations in Aedes aegypti are limited and highly conserved across samples from different geographic regions unlike integrations in Aedes albopictus
    (Springer, 2021-06-26) Spadar, Anton; Phelan, Jody; Benavente, Ernest Diez; Campos, Monica; Gomez, Lara Ferrero; Mohareb, Fady; Clark, Taane G.; Campino, Susana
    Mosquitoes of the genus Aedes are the main vectors of many viruses, e.g. dengue and Zika, which affect millions of people each year and for which there are limited treatment options. Understanding how Aedes mosquitoes tolerate high viral loads may lead to better disease control strategies. Elucidating endogenous viral elements (EVEs) within vector genomes may give exploitable biological insights. Previous studies have reported the presence of a large number of EVEs in Aedes genomes. Here we investigated if flavivirus EVEs are conserved across populations and different Aedes species by using ~ 500 whole genome sequence libraries from Aedes aegypti and Aedes albopictus, sourced from colonies and field mosquitoes across continents. We found that nearly all flavivirus EVEs in the Ae. aegypti reference genome originate from four separate putative viral integration events, and that they are highly conserved across geographically diverse samples. By contrast, flavivirus EVEs in the Ae. albopictus reference genome originate from up to nine distinct integration events and show low levels of conservation, even within samples from narrow geographical ranges. Our analysis suggests that flaviviruses integrated as long sequences and were subsequently fragmented and shuffled by transposable elements. Given that EVEs of Ae. aegypti and Ae. albopictus belong to different phylogenetic clades and have very differing levels of conservation, they may have different evolutionary origins and potentially different functional roles.
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    Genomic variation in Plasmodium vivax malaria reveals regions under selective pressure
    (PLOS (Public Library of Science), 2017-05-11) Diez Benavente, Ernest; Ward, Zoe; Chan, Wilson; Mohareb, Fady R.; Sutherland, Colin J.; Roper, Cally; Campino, Susana; Clark, Taane
    Background Although Plasmodium vivax contributes to almost half of all malaria cases outside Africa, it has been relatively neglected compared to the more deadly P. falciparum. It is known that P. vivax populations possess high genetic diversity, differing geographically potentially due to different vector species, host genetics and environmental factors. Results We analysed the high-quality genomic data for 46 P. vivax isolates spanning 10 countries across 4 continents. Using population genetic methods we identified hotspots of selection pressure, including the previously reported MRP1 and DHPS genes, both putative drug resistance loci. Extra copies and deletions in the promoter region of another drug resistance candidate, MDR1 gene, and duplications in the Duffy binding protein gene (PvDBP) potentially involved in erythrocyte invasion, were also identified. For surveillance applications, continental-informative markers were found in putative drug resistance loci, and we show that organellar polymorphisms could classify P. vivax populations across continents and differentiate between Plasmodia spp. Conclusions This study has shown that genomic diversity that lies within and between P. vivax populations can be used to elucidate potential drug resistance and invasion mechanisms, as well as facilitate the molecular barcoding of the parasite for surveillance applications.
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    Global genetic diversity of var2csa in Plasmodium falciparum with implications for malaria in pregnancy and vaccine development
    (Nature Publishing Group, 2018-10-18) Diez Benavente, Ernest; Oresegun, Damilola R.; Florez de Sessions, Paola; Walker, Eloise M.; Roper, Cally; Dombrowski, Jamille G.; de Souza, Rodrigo M.; Marinho, Claudio R. F.; Sutherland, Colin J.; Hibberd, Martin L.; Mohareb, Fady R.; Baker, David A.; Clark, Taane G.; Campino, Susana
    Malaria infection during pregnancy, caused by the sequestering of Plasmodium falciparum parasites in the placenta, leads to high infant mortality and maternal morbidity. The parasite-placenta adherence mechanism is mediated by the VAR2CSA protein, a target for natural occurring immunity. Currently, vaccine development is based on its ID1-DBL2Xb domain however little is known about the global genetic diversity of the encoding var2csa gene, which could influence vaccine efficacy. In a comprehensive analysis of the var2csa gene in >2,000 P. falciparum field isolates across 23 countries, we found that var2csa is duplicated in high prevalence (>25%), African and Oceanian populations harbour a much higher diversity than other regions, and that insertions/deletions are abundant leading to an underestimation of the diversity of the locus. Further, ID1-DBL2Xb haplotypes associated with adverse birth outcomes are present globally, and African-specific haplotypes exist, which should be incorporated into vaccine design.
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    Leveraging large-scale Mycobacterium tuberculosis whole genome sequence data to characterise drug-resistant mutations using machine learning and statistical approaches
    (Springer, 2024-12-01) Pruthi, Siddharth Sanjay; Billows, Nina; Thorpe, Joseph; Campino, Susana; Phelan, Jody E.; Mohareb, Fady; Clark, Taane G.
    Tuberculosis disease (TB), caused by Mycobacterium tuberculosis (Mtb), is a major global public health problem, resulting in > 1 million deaths each year. Drug resistance (DR), including the multi-drug form (MDR-TB), is challenging control of the disease. Whilst many DR mutations in the Mtb genome are known, analysis of large datasets generated using whole genome sequencing (WGS) platforms can reveal new variants through the assessment of genotype-phenotype associations. Here, we apply tree-based ensemble methods to a dataset comprised of 35,777 Mtb WGS and phenotypic drug-susceptibility test data across first- and second-line drugs. We compare model performance across models trained using mutations in drug-specific regions and genome-wide variants, and find high predictive ability for both first-line (area under ROC curve (AUC); range 88.3–96.5) and second-line (AUC range 84.1–95.4) drugs. To aggregate information from low-frequency variants, we pool mutations by functional impact and observe large improvements in predictive accuracy (e.g., sensitivity: pyrazinamide + 25%; ethionamide + 10%). We further characterise loss-of-function mutations observed in resistant phenotypes, uncovering putative markers of resistance (e.g., ndh 293dupG, Rv3861 78delC). Finally, we profile the distribution of known DR-associated single nucleotide polymorphisms across discretised minimum inhibitory concentration (MIC) data generated from phenotypic testing (n = 12,066), and identify mutations associated with highly resistant phenotypes (e.g., inhA − 779G > T and 62T > C). Overall, our work demonstrates that applying machine learning to large-scale WGS data is useful for providing insights into predicting Mtb binary drug resistance and MIC phenotypes, thereby potentially assisting diagnosis and treatment decision-making for infection control.
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    Robust detection of point mutations involved in multidrug-resistant Mycobacterium tuberculosis in the presence of co-occurrent resistance markers
    (PLOS (Public Library of Science), 2020-12-21) Libiseller-Egger, Julian; Phelan, Jody; Campino, Susana; Mohareb, Fady; Clark, Taane G.
    Tuberculosis disease is a major global public health concern and the growing prevalence of drug-resistant Mycobacterium tuberculosis is making disease control more difficult. However, the increasing application of whole-genome sequencing as a diagnostic tool is leading to the profiling of drug resistance to inform clinical practice and treatment decision making. Computational approaches for identifying established and novel resistance-conferring mutations in genomic data include genome-wide association study (GWAS) methodologies, tests for convergent evolution and machine learning techniques. These methods may be confounded by extensive co-occurrent resistance, where statistical models for a drug include unrelated mutations known to be causing resistance to other drugs. Here, we introduce a novel ‘cannibalistic’ elimination algorithm (“Hungry, Hungry SNPos”) that attempts to remove these co-occurrent resistant variants. Using an M. tuberculosis genomic dataset for the virulent Beijing strain-type (n=3,574) with phenotypic resistance data across five drugs (isoniazid, rifampicin, ethambutol, pyrazinamide, and streptomycin), we demonstrate that this new approach is considerably more robust than traditional methods and detects resistance-associated variants too rare to be likely picked up by correlation-based techniques like GWAS