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Drug resistance inPlasmodium falciparumis a major threat to malaria control efforts. We analyzed data from two decades (2000-2020) of continuous molecular surveillance ofP. falciparumparasite strains in Senegal to determine how historical changes in drug administration policy may have affected parasite evolution. We profiled several known drug resistance markers and their surrounding haplotypes using a combination of single nucleotide polymorphism (SNP) molecular surveillance and whole-genome sequence (WGS) based population genomics. We observed rapid changes in drug resistance markers associated with the withdrawal of chloroquine and introduction of sulfadoxine-pyrimethamine in 2003. We also observed a rapid increase inPfcrtK76T and decline inPfdhpsA437G starting in 2014, which we hypothesize may reflect changes in resistance or fitness caused by seasonal malaria chemoprevention (SMC). Parasite populations evolve rapidly in response to drug use, and SMC preventive efficacy should be closely monitored.

Code and barcode sequences associated with the manuscript "RH: A genetic metric for measuring intra-host Plasmodium falciparum relatedness and distinguishing cotransmission from superinfection" published in PNAS Nexus

Additional file 1. Schematic of Richard Toll sample analysis. A total of 759 samples were received and genotyping yielded 649 samples that passed genotyping (5 or fewer missing alleles in the 24-SNP barcode). Of these 649 samples, 473 were monogenomic (0, or 1 mixed allele in the 24-SNP barcode).

Additional file 8. Map of infections. A. Map of infections collected in 2015 and relative location of Thiès. The distance between Richard Toll and Thiès is approximately 150 miles. The sites of households from which samples were collected in 2015 are shown, with red indicating infections in individuals with a travel history and blue indicating infections from individuals with no travel history (see B). No household locations were collected in any other year. B. Inset from panel A is shown enlarged to indicate the relative locations of households from which parasite infections were identified in 2015. Infections identified in individuals who reported a travel history are shown in red, while those from individuals who reported no recent travel are shown in blue. Tear drop markers indicate infections identified through reactive active case detection (RACD), with index case marked red for positive travel history or blue for no travel history. All of these RACD households with a travel history (red teardrops) had parasites that were different from one another; and all these RACD households with no travel history (blue teardrops) shared genotype infections, with one exception being the household in dark blue in the center of Richard Toll (identified with an asterisk). This household had parasites that were genetically distinct but the index case reported no recent travel. C. Location of passive case detection infections that share a parasite genotype. To provide an example of household location where shared infections were identified by passive case detection at four households with the same parasite genotype are shown in blue teardrops, and a different parasite genotype found among two households are shown in red teardrops. The two infections from the red household were both detected on the same date at the clinic. The infections for the blue households occurred on different dates: (1) September 7; (2) September 12; (3) November 8; (4) November 10. Note that household 4 was positive for additional infections and corresponds to the household in panel B as indicated by the red arrow. The geographic distance between household 1 and 2 is 3.3 miles; between household 2 and 3 is 14 miles and between household 3 and 4 is 0.8 miles.

Additional file 3. SNP genetic barcode; Genotyped SNPs arrange according to the place of collection.

Consumers are often confused and reluctant to purchase products from fresh retail meat counters that exhibit significant color variations. This hesitation to purchase results in annual profit loss to the pork industry. Processes to minimize color variation remain a major focus of the industry and many meat science programs across the globe. Previously, we found inherent muscle characteristics contribute to variations in pork quality but these characteristics fail to explain the high frequency of two-toning and other pork quality defects routinely occurring in many hog processing facilities. Therefore, we hypothesized harvesting practices, such as scald alter color across muscles of the ham. Scald time was initially investigated using 32 carcasses subjected to either a 4 (n=16) or 8 (n=16) min scald time. Samples were collected before or after scalding and at 24 hrs. A 50% reduction in scald time resulted in (p < 0.0001) lighter muscle color (L*) early postmortem, although the 8 min scald treatment was lighter (p < 0.005) at 24 hrs. Although differences in pH (p < 0.0001) and color were noted, ultimate carcass temperature was not affected. To that end, we moved to validate our hypothesis in an industrial plant setting. Carcasses (n=200) were assigned treatments of 6.5 or 7.5 min scald times, and SM muscle samples were collected at 24 hrs. Surprisingly, the shorter scald time resulted in (p < 0.05) a lighter color, contradicting our first study. To explore this color issue further, we uncoupled scald from the dehairing process. To achieve this goal, carcasses (n=24) were assigned to either an 8 or 16 min time to dehair, with or without scalding. Protracted time to dehair resulted in higher ultimate pH (p < 0.005) and less color variation across the muscle (p < 0.05). Though a color gradient remained, the variation across the muscle was reduced by increasing time to dehair. These data show time to dehair affects pork quality development and suggest that delaying time to physical manipulation of the carcass may improve pork color, thus increasing consumer acceptance. Master of Science

Additional file 1: Table S1. Number of samples by year of PfRh2b polymorphism in Thiès and Western Gambia. Column N shows the number of samples analysed. n = the number of samples of each allele. PfRh2bdel = deletion present; PfRh2bfull = full-length sequence; Mix = PfRh2bDel/PfRh2bfull.

Additional file 1. Participant questionnaire.

Additional file 2: Figure S2. Plasmodium falciparum detection in Kédougou by health center, 2013–2014 (n = 475).

Additional file 1: Figure S1. Plasmodium spp. detection in Kédougou, 2013–2014 (n = 475 samples).

Table S1. Prevalence of dhfr and dhps alleles in Comoros over time. Prevalences in dhfr and dhps mutations were compared from Rebaudet et al. in 2010 and this study in 2012â 2013.

Additional file 1. Additional tables and figures.

Additional file 1. Additional tables and figures.

Additional file 1. Culture-adaptation of TRAC parasites.

Additional file 5. Association between percent survival RSA0â 3h and clearance time. Correlation between clearance time on the x-axis and percent survival RSA0â 3h on the y-axis. Each dot represents an isolate. Samples represented by color were used for in vitro drug testing and for pfmdr1 copy number variation determinationâ green for ART sensitive; blue for discordant; and red for ART resistant parasites. The grey dot marks the isolate with an E270K mutation. The dotted lines represent the cut off used to discriminate between resistant and sensitive parasites (1% for RSA0â 3h, 4 or 5% for clearance).

Additional file 1. Culture-adaptation of TRAC parasites.

Additional file 2. Parallel amplicon sequencing error correction (PASEC) workflow.

Additional file 4. Distribution of C580Y mutations in Cambodian isolates. Parasites from Pursat (round symbols) or Pailin (square symbols) were classified according to their Pfkelch13 alleles. Comparison of clearance half-life values (hours, h) from parasites harboring the wild-type (C) or mutant (Y) allele at amino acid position 580 in the Pfkelch13 locus, with culture-adapted parasites represented by black outlined symbols. C580 parasites harboring a mutation in the Pfkelch13 propeller domain other than C580Y are indicated by grey filled symbols. An unpaired t test was performed between the C580 and C580Y carrying parasites from Pursat (p

Table S1. Prevalence of dhfr and dhps alleles in Comoros over time. Prevalences in dhfr and dhps mutations were compared from Rebaudet et al. in 2010 and this study in 2012â 2013.

Additional file 5. Association between percent survival RSA0â 3h and clearance time. Correlation between clearance time on the x-axis and percent survival RSA0â 3h on the y-axis. Each dot represents an isolate. Samples represented by color were used for in vitro drug testing and for pfmdr1 copy number variation determinationâ green for ART sensitive; blue for discordant; and red for ART resistant parasites. The grey dot marks the isolate with an E270K mutation. The dotted lines represent the cut off used to discriminate between resistant and sensitive parasites (1% for RSA0â 3h, 4 or 5% for clearance).

Additional file 3. Schematic shows distribution of parasites obtained from Pursat (blue star on map, KH1 parasites indicated in blue) and Pailin (red star, KH4 parasites indicated in red) from Cambodia (map modified from https://commons.wikimedia.org/wiki/File:Cambodia_provinces_en.svg ).

Additional file 2. Parallel amplicon sequencing error correction (PASEC) workflow.

Additional file 4. Distribution of C580Y mutations in Cambodian isolates. Parasites from Pursat (round symbols) or Pailin (square symbols) were classified according to their Pfkelch13 alleles. Comparison of clearance half-life values (hours, h) from parasites harboring the wild-type (C) or mutant (Y) allele at amino acid position 580 in the Pfkelch13 locus, with culture-adapted parasites represented by black outlined symbols. C580 parasites harboring a mutation in the Pfkelch13 propeller domain other than C580Y are indicated by grey filled symbols. An unpaired t test was performed between the C580 and C580Y carrying parasites from Pursat (p

Background: The malaria parasite Plasmodium falciparum exhibits abundant genetic diversity, and this diversity is key to its success as a pathogen. Previous efforts to study genetic diversity in P. falciparum have begun to elucidate the demographic history of the species, as well as patterns of population structure and patterns of linkage disequilibrium within its genome. Such studies will be greatly enhanced by new genomic tools and recent large-scale efforts to map genomic variation. To that end, we have developed a high throughput single nucleotide polymorphism (SNP) genotyping platform for P. falciparum. Results: Using an Affymetrix 3,000 SNP assay array, we found roughly half the assays (1,638) yielded high quality, 100% accurate genotyping calls for both major and minor SNP alleles. Genotype data from 76 global isolates confirm significant genetic differentiation among continental populations and varying levels of SNP diversity and linkage disequilibrium according to geographic location and local epidemiological factors. We further discovered that nonsynonymous and silent (synonymous or noncoding) SNPs differ with respect to within-population diversity, inter-population differentiation, and the degree to which allele frequencies are correlated between populations. Conclusions: The distinct population profile of nonsynonymous variants indicates that natural selection has a significant influence on genomic diversity in P. falciparum, and that many of these changes may reflect functional variants deserving of follow-up study. Our analysis demonstrates the potential for new high-throughput genotyping technologies to enhance studies of population structure, natural selection, and ultimately enable genome-wide association studies in P. falciparum to find genes underlying key phenotypic traits., National Institutes of Health (U.S.), Broad Institute of Harvard and MIT, Burroughs Wellcome Fund, Bill & Melinda Gates Foundation, National Institute of Allergy and Infectious Diseases (U.S.). Microbial Sequencing Center, Ellison Medical Foundation, Fogarty International Center, Exxon Mobil Foundation, National Center for Research Resources (U.S.) (Grant U54 RR020278)