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Novel bioinformatic methods and machine learning approaches reveal candidate biomarkers of the intensity and timing of past exposure to Plasmodium falciparum
Accurately quantifying the burden of malaria over time is an important goal of malaria surveillance efforts and can enable effective targeting and evaluation of interventions. Malaria surveillance methods capture active or recent infections which poses several challenges to achieving malaria surveil...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10395840/ https://www.ncbi.nlm.nih.gov/pubmed/37531325 http://dx.doi.org/10.1371/journal.pgph.0001840 |
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author | Bérubé, Sophie Kobayashi, Tamaki Norris, Douglas E. Ruczinski, Ingo Moss, William J. Wesolowski, Amy Louis, Thomas A. |
author_facet | Bérubé, Sophie Kobayashi, Tamaki Norris, Douglas E. Ruczinski, Ingo Moss, William J. Wesolowski, Amy Louis, Thomas A. |
author_sort | Bérubé, Sophie |
collection | PubMed |
description | Accurately quantifying the burden of malaria over time is an important goal of malaria surveillance efforts and can enable effective targeting and evaluation of interventions. Malaria surveillance methods capture active or recent infections which poses several challenges to achieving malaria surveillance goals. In high transmission settings, asymptomatic infections are common and therefore accurate measurement of malaria burden demands active surveillance; in low transmission regions where infections are rare accurate surveillance requires sampling large subsets of the population; and in any context monitoring malaria burden over time necessitates serial sampling. Antibody responses to Plasmodium falciparum parasites persist after infection and therefore measuring antibodies has the potential to overcome several of the current obstacles to accurate malaria surveillance. Identifying which antibody responses are markers of the timing and intensity of past exposure to P. falciparum remains challenging, particularly among adults who tend to be re-exposed multiple times over the course of their lifetime and therefore have similarly high antibody responses to many Plasmodium antigens. A previous analysis of 479 serum samples from individuals in three regions in southern Africa with different historical levels of P. falciparum malaria transmission (high, intermediate, and low) revealed regional differences in antibody responses to P. falciparum antigens among children under 5 years of age. Using a novel bioinformatic pipeline optimized for protein microarrays that minimizes between-sample technical variation, we used antibody responses to Plasmodium antigens as predictors in random forest models to classify samples from adults into these three regions of differing historical malaria transmission with high accuracy (AUC = 0.99). Many of the most important antigens for classification in these models do not overlap with previously published results and are therefore novel candidate markers for the timing and intensity of past exposure to P. falciparum. Measuring antibody responses to these antigens could lead to improved malaria surveillance. |
format | Online Article Text |
id | pubmed-10395840 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-103958402023-08-03 Novel bioinformatic methods and machine learning approaches reveal candidate biomarkers of the intensity and timing of past exposure to Plasmodium falciparum Bérubé, Sophie Kobayashi, Tamaki Norris, Douglas E. Ruczinski, Ingo Moss, William J. Wesolowski, Amy Louis, Thomas A. PLOS Glob Public Health Research Article Accurately quantifying the burden of malaria over time is an important goal of malaria surveillance efforts and can enable effective targeting and evaluation of interventions. Malaria surveillance methods capture active or recent infections which poses several challenges to achieving malaria surveillance goals. In high transmission settings, asymptomatic infections are common and therefore accurate measurement of malaria burden demands active surveillance; in low transmission regions where infections are rare accurate surveillance requires sampling large subsets of the population; and in any context monitoring malaria burden over time necessitates serial sampling. Antibody responses to Plasmodium falciparum parasites persist after infection and therefore measuring antibodies has the potential to overcome several of the current obstacles to accurate malaria surveillance. Identifying which antibody responses are markers of the timing and intensity of past exposure to P. falciparum remains challenging, particularly among adults who tend to be re-exposed multiple times over the course of their lifetime and therefore have similarly high antibody responses to many Plasmodium antigens. A previous analysis of 479 serum samples from individuals in three regions in southern Africa with different historical levels of P. falciparum malaria transmission (high, intermediate, and low) revealed regional differences in antibody responses to P. falciparum antigens among children under 5 years of age. Using a novel bioinformatic pipeline optimized for protein microarrays that minimizes between-sample technical variation, we used antibody responses to Plasmodium antigens as predictors in random forest models to classify samples from adults into these three regions of differing historical malaria transmission with high accuracy (AUC = 0.99). Many of the most important antigens for classification in these models do not overlap with previously published results and are therefore novel candidate markers for the timing and intensity of past exposure to P. falciparum. Measuring antibody responses to these antigens could lead to improved malaria surveillance. Public Library of Science 2023-08-02 /pmc/articles/PMC10395840/ /pubmed/37531325 http://dx.doi.org/10.1371/journal.pgph.0001840 Text en © 2023 Bérubé et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Bérubé, Sophie Kobayashi, Tamaki Norris, Douglas E. Ruczinski, Ingo Moss, William J. Wesolowski, Amy Louis, Thomas A. Novel bioinformatic methods and machine learning approaches reveal candidate biomarkers of the intensity and timing of past exposure to Plasmodium falciparum |
title | Novel bioinformatic methods and machine learning approaches reveal candidate biomarkers of the intensity and timing of past exposure to Plasmodium falciparum |
title_full | Novel bioinformatic methods and machine learning approaches reveal candidate biomarkers of the intensity and timing of past exposure to Plasmodium falciparum |
title_fullStr | Novel bioinformatic methods and machine learning approaches reveal candidate biomarkers of the intensity and timing of past exposure to Plasmodium falciparum |
title_full_unstemmed | Novel bioinformatic methods and machine learning approaches reveal candidate biomarkers of the intensity and timing of past exposure to Plasmodium falciparum |
title_short | Novel bioinformatic methods and machine learning approaches reveal candidate biomarkers of the intensity and timing of past exposure to Plasmodium falciparum |
title_sort | novel bioinformatic methods and machine learning approaches reveal candidate biomarkers of the intensity and timing of past exposure to plasmodium falciparum |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10395840/ https://www.ncbi.nlm.nih.gov/pubmed/37531325 http://dx.doi.org/10.1371/journal.pgph.0001840 |
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