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      The changing landscape of Plasmodium falciparum drug resistance in the Democratic Republic of Congo

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          Abstract

          Background

          Drug resistant malaria is a growing concern in the Democratic Republic of the Congo (DRC), where previous studies indicate that parasites resistant to sulfadoxine/pyrimethamine or chloroquine are spatially clustered. This study explores longitudinal changes in spatial patterns to understand how resistant malaria may be spreading within the DRC, using samples from nation-wide population-representative surveys.

          Methods

          We selected 552 children with PCR-detectable Plasmodium falciparum infection and identified known variants in the pfdhps and pfcrt genes associated with resistance. We compared the proportion of mutant parasites in 2013 to those previously reported from adults in 2007, and identified risk factors for carrying a resistant allele using multivariate mixed-effects modeling. Finally, we fit a spatial-temporal model to the observed data, providing smooth allele frequency estimates over space and time.

          Results

          The proportion of co-occurring pfdhps K540E/A581G mutations increased by 16% between 2007 and 2013. The spatial-temporal model suggests that the spatial range of the pfdhps double mutants expanded over time, while the prevalence and range of pfcrt mutations remained steady.

          Conclusions

          This study uses population-representative samples to describe the changing landscape of SP resistance within the DRC, and the persistence of chloroquine resistance. Vigilant molecular surveillance is critical for controlling the spread of resistance.

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          Most cited references37

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          Amodiaquine and artemether-lumefantrine select distinct alleles of the Plasmodium falciparum mdr1 gene in Tanzanian children treated for uncomplicated malaria.

          The artemisinin-based combination therapies artemether-lumefantrine (AL) and amodiaquine (AQ) plus artesunate have been adopted for treatment of Plasmodium falciparum malaria in many African countries. Molecular markers of parasite resistance suitable for surveillance have not been established for any of the component drugs in either of these combinations. We assessed P. falciparum mdr1 (Pfmdr1) alleles present in 300 Tanzanian children presenting with uncomplicated falciparum malaria, who were enrolled in a clinical trial of antimalarial therapy. Pfmdr1 genotype analysis was also performed with isolates from 182 children who failed AQ monotherapy and 54 children who failed AL treatment. Pfmdr1 alleles 86Y, 184Y, and 1246Y were more common among treatment failures in the AQ group than among pretreatment infections. The converse was found in the AL-treated group. Children presenting with the 86Y/184Y/1246Y Pfmdr1 haplotype and treated with AQ were significantly more likely to retain this haplotype if they were parasite positive during posttreatment follow-up than were children treated with AL (odds ratio, 33.25; 95% confidence interval, 4.17 to 1441; P, <0.001). We conclude that AL and AQ exert opposite within-host selective effects on the Pfmdr1 gene of P. falciparum.
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            Breaking new ground in mapping human settlements from space – The Global Urban Footprint

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              A systematic review and meta-analysis of evidence for correlation between molecular markers of parasite resistance and treatment outcome in falciparum malaria

              Background An assessment of the correlation between anti-malarial treatment outcome and molecular markers would improve the early detection and monitoring of drug resistance by Plasmodium falciparum. The purpose of this systematic review was to determine the risk of treatment failure associated with specific polymorphisms in the parasite genome or gene copy number. Methods Clinical studies of non-severe malaria reporting on target genetic markers (SNPs for pfmdr1, pfcrt, dhfr, dhps, gene copy number for pfmdr1) providing complete information on inclusion criteria, outcome, follow up and genotyping, were included. Three investigators independently extracted data from articles. Results were stratified by gene, codon, drug and duration of follow-up. For each study and aggregate data the random effect odds ratio (OR) with 95%CIs was estimated and presented as Forest plots. An OR with a lower 95th confidence interval > 1 was considered consistent with a failure being associated to a given gene mutation. Results 92 studies were eligible among the selection from computerized search, with information on pfcrt (25/159 studies), pfmdr1 (29/236 studies), dhfr (18/373 studies), dhps (20/195 studies). The risk of therapeutic failure after chloroquine was increased by the presence of pfcrt K76T (Day 28, OR = 7.2 [95%CI: 4.5–11.5]), pfmdr1 N86Y was associated with both chloroquine (Day 28, OR = 1.8 [95%CI: 1.3–2.4]) and amodiaquine failures (OR = 5.4 [95%CI: 2.6–11.3, p < 0.001]). For sulphadoxine-pyrimethamine the dhfr single (S108N) (Day 28, OR = 3.5 [95%CI: 1.9–6.3]) and triple mutants (S108N, N51I, C59R) (Day 28, OR = 3.1 [95%CI: 2.0–4.9]) and dhfr-dhps quintuple mutants (Day 28, OR = 5.2 [95%CI: 3.2–8.8]) also increased the risk of treatment failure. Increased pfmdr1 copy number was correlated with treatment failure following mefloquine (OR = 8.6 [95%CI: 3.3–22.9]). Conclusion When applying the selection procedure for comparative analysis, few studies fulfilled all inclusion criteria compared to the large number of papers identified, but heterogeneity was limited. Genetic molecular markers were related to an increased risk of therapeutic failure. Guidelines are discussed and a checklist for further studies is proposed.
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                Author and article information

                Contributors
                301-675-4599 , mollydf1@live.unc.edu
                Journal
                BMC Infect Dis
                BMC Infect. Dis
                BMC Infectious Diseases
                BioMed Central (London )
                1471-2334
                22 October 2019
                22 October 2019
                2019
                : 19
                : 872
                Affiliations
                [1 ]ISNI 0000 0001 1034 1720, GRID grid.410711.2, Department of Epidemiology, Gillings School of Global Public Health, , University of North Carolina, ; Chapel Hill, USA
                [2 ]ISNI 0000 0004 1936 9094, GRID grid.40263.33, Department of Pathology and Laboratory Medicine, , Brown University, ; Providence, RI USA
                [3 ]ISNI 0000 0004 1936 8294, GRID grid.214572.7, Department of Geographical & Sustainability Sciences, , University of Iowa, ; Iowa City, IA USA
                [4 ]ISNI 0000 0001 2113 8111, GRID grid.7445.2, Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, , Imperial College London, ; London, UK
                [5 ]ISNI 0000 0000 9927 0991, GRID grid.9783.5, Ecole de Santé Publique, , Faculté de Médecine, , University of Kinshasa, ; Kinshasa, Democratic Republic of Congo
                [6 ]ISNI 0000 0004 1936 7961, GRID grid.26009.3d, Division of Infectious Diseases and Duke Global Health Institute, , Duke University, ; Durham, NC USA
                [7 ]ISNI 0000000122483208, GRID grid.10698.36, Division of Infectious Diseases, , University of North Carolina at Chapel Hill, ; Chapel Hill, USA
                [8 ]ISNI 0000000122483208, GRID grid.10698.36, Curriculum in Genetics and Molecular Biology, , University of North Carolina at Chapel Hill, ; Chapel Hill, USA
                Author information
                http://orcid.org/0000-0001-6191-930X
                Article
                4523
                10.1186/s12879-019-4523-0
                6805465
                31640574
                fd4c3d1d-c341-4997-b71d-26197558c169
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 28 June 2019
                : 30 September 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000009, Foundation for the National Institutes of Health;
                Award ID: R01AI107949
                Award ID: K24AI134990
                Award ID: R21AI121465
                Award ID: R01AI139520
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001713, European and Developing Countries Clinical Trials Partnership;
                Award ID: NA
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: NA
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2019

                Infectious disease & Microbiology
                malaria,drug resistance,spatial-temporal modeling
                Infectious disease & Microbiology
                malaria, drug resistance, spatial-temporal modeling

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