Biology subjects

Almagro-Garcia, J.

Publications and source records attributed to Almagro-Garcia, J..

8 recordsLinked to original sources

bioRxiv2025

Pf-PeptideFilter: An Interactive Catalogue of Peptide Vaccine Candidates for Plasmodium falciparum

In the last decade, there has been substantial progress in vaccine development for malaria, with two WHO-recommended vaccines now available-RTS,S/AS01 (Mosquirix) and R21/Matrix-M. While these vaccines represent significant milestones, their partial efficacy underscores the need to develop next-generation vaccine approaches capable of broader, longer-lasting protection. Peptide vaccines are a promising strategy, as they focus on short, synthetic peptides specifically selected to trigger a T-cell immune response. However, developing effective peptide vaccines for malaria remains challenging, due to the extensive genetic diversity of Plasmodium falciparum. Currently, tools for comparing and prioritising immunogenic targets remain limited, and existing approaches overlook key factors, including genetic variation, lifecycle-stage expression, and peptide-level properties, each of which are critical for vaccine efficacy. To address these gaps, we developed Pf-PeptideFilter, an interactive web-based application and curated database designed for filtering peptide vaccine targets across the P. falciparum genome. By integrating population-scale genomic information (Pf7), liver-stage transcriptomics, and other biological annotations, Pf-PeptideFilter enables users to interactively filter potential vaccine candidates from the P. falciparum genome, based on several criteria relevant to vaccine design. The app allows users to prioritise candidate genes and peptides with immunogenic potential and export shortlists for incorporation into experimental workflows, offering an interactive platform for rational peptide vaccine design in P. falciparum. Author SummaryMalaria vaccines are challenging to design because the parasite responsible for most infections, Plasmodium falciparum, is highly genetically diverse. If a vaccine targets part of the parasite proteome that varies between strains, it may protect against only a subset of parasites and perform poorly across regions. Peptide vaccines offer a promising approach, by focusing immune responses on short, synthetic peptides selected to trigger T-cell immunity. However, identifying which peptides to include in a vaccine is difficult, as the parasite genome contains thousands of genes and huge numbers of potential peptide targets. Here, we introduce Pf-PeptideFilter, an interactive tool that helps researchers prioritise peptide vaccine candidates using a series of biologically relevant filters. We integrate population-scale genome variation from thousands of parasite samples with information on gene expression during liver infection, similarity to human proteins, and conservation across related parasite species. Users can adjust filtering thresholds and immediately see how these choices affect which genes and peptides are retained. Pf-PeptideFilter generates downloadable shortlists designed to support experimental follow-up, providing a robust and transparent platform for the early stages of vaccine design.

genomics↗

bioRxiv2024

Understanding the Global Spread of Artemisinin Resistance: Insights from over 100K Plasmodium falciparum Samples

Artemisinin partial resistance (ART-R) in Plasmodium falciparum is one of the most pressing threats to global malaria control. Over the last two decades, ART-R has spread widely across Southeast Asia, compromising public health strategies and hindering elimination efforts. As of 2024, ART-R has now emerged in East Africa, with the potential to dramatically increase human mortality in the region. Mitigating the spread of ART-R requires detailed genomic surveillance of point mutations in the kelch13 gene, the primary determinant of resistance to artemisinin derivatives. Although extensive surveillance data on these markers is available, it is distributed across many literature studies and open databases. In this literature review, we aggregate publicly available spatiotemporal data for 112,933 P. falciparum samples between 1980 - 2023 into a single resource, providing the most comprehensive overview of kelch13 markers to date. By synthesising insights from these samples over a global scale, we outline the history and current status of kelch13 mutations associated with ART-R, with particular reference to their emergence in Southeast Asia and recent emergence in East and Northeast Africa. Concerningly, we find their recent increases in frequency in these areas of Africa are comparable to those observed in Southeast Asia 10-15 years ago. We review several factors that may influence the spread of ART-R going forwards, such as fitness costs, treatment strategies, and local epidemiological dynamics, before articulating possible scenarios on how resistance may spread in Africa in coming years. In summary, this review provides a unified, comprehensive account of how the situation of ART-R has unfolded globally so far, highlighting insights both for researchers in the field and public health bodies which aim to reduce its negative effects. More broadly, we highlight the critical role genomic surveillance has had, and will continue to have in combating the spread of ART-R.

genomics↗

bioRxiv2024

Pf-HaploAtlas: an interactive web app for spatiotemporal analysis of P. falciparum genes

Monitoring the genomic evolution of Plasmodium falciparum - the most widespread and deadliest of the human-infecting malaria species - is critical for making decisions in response to changes in drug resistance, diagnostic test failures, and vaccine effectiveness. The MalariaGEN data resources are the worlds largest whole genome sequencing databases for Plasmodium parasites. The size and complexity of such data is a barrier to many potential end users in both public health and academic research. A user-friendly method for accessing and interpreting data on the genetic variation of P. falciparum would greatly enable efforts in studying and controlling malaria. We developed Pf-HaploAtlas, a web application enabling exploration of genomic variation without requiring advanced technical expertise. The app provides analysis-ready data catalogues and visualisations of amino acid haplotypes for all 5,102 core P. falciparum genes. Pf-HaploAtlas facilitates comprehensive spatial and temporal analyses of genes and variants of interest by using data from 16,203 samples, from 33 countries, and spread between the years 1984 and 2018. The scope of Pf-HaploAtlas will expand with each new MalariaGEN Plasmodium data release. Pf-HaploAtlas is available online for public use at https://apps.malariagen.net/pf-haploatlas, which allows users to download the underlying amino acid haplotype data, and its source code is freely available on GitHub under the MIT licence at https://github.com/malariagen/pf-haploatlas.

genomics↗

bioRxiv2024

A complex Plasmodium falciparum cryptotype circulating at low frequency across the African continent

The population structure of the malaria parasite Plasmodium falciparum can reveal underlying demographic and adaptive evolutionary processes. Here, we analyse population structure in 4,376 P. falciparum genomes from 21 countries across Africa. We identified a strongly differentiated cluster of parasites, comprising [~]1.2% of samples analysed, geographically distributed over 13 countries across the continent. Members of this cluster, named AF1, carry a genetic background consisting of a large number of highly differentiated variants, rarely observed outside this cluster, at a multitude of genomic loci distributed across most chromosomes. At these loci, the AF1 haplotypes appear to have common ancestry, irrespective of the sampling location; outside the shared loci, however, AF1 members are genetically similar to their sympatric parasites. AF1 parasites sharing up to 23 genomic co-inherited regions were found in all major regions of Africa, at locations over 7,000 km apart. We coined the term cryptotype to describe a complex common background which is geographically widespread, but concealed by genomic regions of local origin. Most AF1 differentiated variants are functionally related, comprising structural variations and single nucleotide polymorphisms in components of the MSP1 complex and several other genes involved in interactions with red blood cells, including invasion and erythrocyte antigen export. We propose that AF1 parasites have adapted to some as yet unidentified evolutionary niche, by acquiring a complex compendium of interacting variants that rarely circulate separately in Africa. As the cryptotype spread across the continent, it appears to have been maintained mostly intact in spite of recombination events, suggesting a selective advantage. It is possible that other cryptotypes circulate in Africa, and new analysis methods may be needed to identify them.

genomics↗

bioRxiv2022

Nanopore sequencing for real-time genomic surveillance of Plasmodium falciparum

Malaria is a global public health priority causing over 600,000 deaths annually, mostly young children living in Sub-Saharan Africa. Molecular surveillance can provide key information for malaria control, such as the prevalence and distribution of antimalarial drug resistance. However, genome sequencing capacity in endemic countries can be limited. Here, we have implemented an end-to-end workflow for P. falciparum genomic surveillance in Ghana using Oxford Nanopore Technologies, targeting antimalarial resistance markers and the leading vaccine antigen circumsporozoite protein (csp). The workflow was rapid, robust, accurate, affordable and straightforward to implement, and could be deployed using readily collected dried blood spot samples. We found that P. falciparum parasites in Ghana had become largely susceptible to chloroquine, with persistent sulfadoxine-pyrimethamine (SP) resistance, and no evidence of artemisinin resistance. Multiple Single Nucleotide Polymorphism (SNP) differences from the vaccine csp sequence were identified, though their significance is uncertain. This study demonstrates the potential utility and feasibility of malaria genomic surveillance in endemic settings using Nanopore sequencing.

genomics↗

bioRxiv2018

The origins and relatedness structure of mixed infections vary with local prevalence of P. falciparum malaria

Individual malaria infections can carry multiple strains of Plasmodium falciparum with varying levels of relatedness. Yet, how local epidemiology affects the properties of such mixed infections remains unclear. Here, we develop an enhanced method for strain deconvolution from genome sequencing data, which estimates the number of strains, their proportions, identity-by-descent (IBD) profiles and individual haplotypes. Applying it to the Pf3k data set, we find that the rate of mixed infection varies from 29% to 63% across countries and that 51% of mixed infections involve more than two strains. Further-more, we estimate that 47% of symptomatic dual infections contain sibling strains likely to have been co-transmitted from a single mosquito, and find evidence of mixed infections propagated over successive infection cycles. Finally, leveraging data from the Malaria Atlas Project, we find that prevalence correlates within Africa, but not Asia, with both the rate of mixed infection and the level of IBD.

epidemiology↗

bioRxiv2017

Origins of the current outbreak of multidrug resistant malaria in Southeast Asia: a retrospective genetic study

BackgroundAntimalarial failure is rapidly spreading across parts of Southeast Asia where dihydroartemisinin-piperaquine (DHA-PPQ) is used as first line treatment. The first published reports came from western Cambodia in 2013. Here we analyse genetic changes in the Plasmodium falciparum population of western Cambodia in the six years prior to that.\n\nMethodsWe analysed genome sequence data on 1492 P. falciparum samples from Southeast Asia, including 464 collected in western Cambodia between 2007 and 2013. Different epidemiological origins of resistance were identified by haplotypic analysis of the kelch13 artemisinin resistance locus and the plasmepsin 2-3 piperaquine resistance locus.\n\nFindingsWe identified over 30 independent origins of artemisinin resistance, of which the O_SCPCAPKELC_SCPCAP1 lineage accounted for 91% of DHA-PPQ-resistant parasites. In 2008, O_SCPCAPKELC_SCPCAP1 combined with O_SCPCAPPLAC_SCPCAP1, the major lineage associated with piperaquine resistance. By 2012, the O_SCPCAPKELC_SCPCAP1/O_SCPCAPPLAC_SCPCAP1 co-lineage had reached over 60% frequency in western Cambodia and had spread to northern Cambodia.\n\nInterpretationThe O_SCPCAPKELC_SCPCAP1/O_SCPCAPPLAC_SCPCAP1 co-lineage emerged in the same year that DHA-PPQ became the first line antimalarial drug in western Cambodia and spread aggressively thereafter, displacing other artemisinin-resistant parasite lineages. These findings have significant implications for management of the global health risk associated with the current outbreak.\n\nFundingWellcome Trust, Bill & Melinda Gates Foundation, Medical Research Council, UK Department for International Development, and Intramural Research Program of the US National Institute of Allergy and Infectious Diseases, National Institutes of Health.

evolutionary biology↗

bioRxiv2017

Deconvoluting multiple infections in Plasmodium falciparum from high throughput sequencing data

MotivationThe presence of multiple infecting strains of the malarial parasite Plasmodium falciparum affects key phenotypic traits, including drug resistance and risk of severe disease. Advances in protocols and sequencing technology have made it possible to obtain high-coverage genome-wide sequencing data from blood samples and blood spots taken in the field. However, analysing and interpreting such data is challenging because of the high rate of multiple infections present.\n\nResultsWe have developed a statistical method and implementation for deconvolving multiple genome sequences present in an individual with mixed infections. The software package DEploid uses haplotype structure within a reference panel of clonal isolates as a prior for haplotypes present in a given sample. It estimates the number of strains, their relative proportions and the haplotypes presented in a sample, allowing researchers to study multiple infection in malaria with an unprecedented level of detail.\n\nAvailability and implementationThe open source implementation DEploid is freely available at https://github.com/mcveanlab/DEploid under the conditions of the GPLv3 license. An R version is available at https://github.com/mcveanlab/DEploid-r.\n\nContactjoe.zhu@well.ox.ac.uk or mcvean@well.ox.ac.uk

bioinformatics↗