{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T18:45:30Z","timestamp":1769107530220,"version":"3.49.0"},"update-to":[{"DOI":"10.12688\/wellcomeopenres.17736.1","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T00:00:00Z","timestamp":1710374400000}},{"DOI":"10.12688\/wellcomeopenres.17736.2","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T00:00:00Z","timestamp":1710374400000}},{"DOI":"10.12688\/wellcomeopenres.17736.3","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T00:00:00Z","timestamp":1710374400000}}],"reference-count":41,"publisher":"F1000 Research Ltd","license":[{"start":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T00:00:00Z","timestamp":1710374400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01AI121558"],"award-info":[{"award-number":["R01AI121558"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["K24AI13499"],"award-info":[{"award-number":["K24AI13499"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01AI121558"],"award-info":[{"award-number":["R01AI121558"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Developing Excellence in Leadership, Training and Science (DELTAS) Africa Initiative","award":["DEL-15-003"],"award-info":[{"award-number":["DEL-15-003"]}]},{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"crossref","award":["107568"],"award-info":[{"award-number":["107568"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["wellcomeopenresearch.org"],"crossmark-restriction":false},"short-container-title":["Wellcome Open Res"],"abstract":"<ns3:p>Introduction Antimalarial therapeutic efficacy studies are routinely conducted in malaria-endemic countries to assess the effectiveness of antimalarial treatment strategies. Targeted amplicon sequencing (AmpSeq) uniquely identifies and quantifies genetically distinct parasites within an infection. In this study, AmpSeq of <ns3:italic>Plasmodium falciparum<\/ns3:italic> apical membrane antigen 1 (<ns3:italic>ama1<\/ns3:italic>), and multidrug resistance gene 1 (<ns3:italic>mdr1<\/ns3:italic>), were used to characterise the complexity of infection (COI) and drug-resistance genotypes, respectively. Methods <ns3:italic>P. falciparum<\/ns3:italic>-positive samples were obtained from a triple artemisinin combination therapy clinical trial conducted in 30 children under 13 years of age between 2018 and 2019 in Kilifi, Kenya. Nine of the 30 participants presented with recurrent parasitemia from day 26 (624h) onwards. The <ns3:italic>ama1<\/ns3:italic> and <ns3:italic>mdr1<\/ns3:italic> genes were amplified and sequenced, while <ns3:italic>msp1<\/ns3:italic>, <ns3:italic>msp2 and glurp<\/ns3:italic> data were obtained from the original clinical study. Results The COI was comparable between <ns3:italic>ama1<\/ns3:italic> and <ns3:italic>msp1<\/ns3:italic>, <ns3:italic>msp2 and glurp<\/ns3:italic>; overall, <ns3:italic>ama1<\/ns3:italic> detected more microhaplotypes. Based on ama1, a stable number of microhaplotypes were detected throughout treatment until day 3. Additionally, a recrudescent infection was identified with an <ns3:italic>ama1<\/ns3:italic> microhaplotype initially observed at 30h and later in an unscheduled follow-up visit. Using the relative frequencies of <ns3:italic>ama1<\/ns3:italic> microhaplotypes and parasitemia, we identified a fast (&lt;1h) and slow (&gt;5h) clearing microhaplotype. As expected, only two <ns3:italic>mdr1<\/ns3:italic> microhaplotypes (NF and NY) were identified based on the combination of amino acid polymorphisms at codons 86 and 184. Conclusions This study highlights AmpSeq as a tool for highly-resolution tracking of parasite microhaplotypes throughout treatment and can detect variation in microhaplotype clearance estimates. AmpSeq can also identify slow-clearing microhaplotypes, a potential early sign of selection during treatment. Consequently, AmpSeq has the capability of improving the discriminatory power to distinguish recrudescences from reinfections accurately.<\/ns3:p>","DOI":"10.12688\/wellcomeopenres.17736.4","type":"journal-article","created":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T11:50:13Z","timestamp":1710417013000},"page":"95","update-policy":"https:\/\/doi.org\/10.12688\/wellcomeopenres.crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Targeted amplicon deep sequencing of ama1 and mdr1 to track within-host P. falciparum diversity throughout treatment in a clinical drug trial"],"prefix":"10.12688","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7721-5534","authenticated-orcid":false,"given":"Kevin","family":"Wamae","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2936-1059","authenticated-orcid":false,"given":"Leonard","family":"Ndwiga","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oksana","family":"Kharabora","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7157-1041","authenticated-orcid":false,"given":"Kelvin","family":"Kimenyi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Victor","family":"Osoti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zaydah","family":"de Laurent","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juliana","family":"Wambua","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jennifer","family":"Musyoki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8827-7685","authenticated-orcid":false,"given":"Caroline","family":"Ngetsa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Kalume","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gabriel","family":"Mwambingu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0266-1414","authenticated-orcid":false,"given":"Mainga","family":"Hamaluba","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rob","family":"van 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status","group":{"name":"current-referee-status","label":"Current Referee Status"}},{"value":"10.21956\/wellcomeopenres.19627.r49311, Ian M. Hastings, Liverpool School of Tropical Medicine, Liverpool, 31 Mar 2022, version 1, 2 approved with reservations","URL":"https:\/\/wellcomeopenresearch.org\/articles\/7-95\/v1#referee-response-49311","order":0,"name":"referee-response-49311","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"<b>Kevin Wamae<\/b>; \n<i>Posted: 27 Sep 2022<\/i>; 1. Please be accurate and consistent with use of the term \u201chaplotype\u201d and consider revising the manuscript accordingly.  \n<i>We have clarified the use of \u201chaplotype\u201d by settling on the term \u201cmicrohaplotype\u201d to mean the set of amino acid polymorphisms found on a single DNA amplicon. The term microhaplotype is now a preferred term in similar work.<\/i>  \n<b><i>Reference<\/i><\/b>\n<i>: <\/i>  \n<i>Tessema, S. K., et. al. (2020). Sensitive, Highly Multiplexed Sequencing of Microhaplotypes From the Plasmodium falciparum Heterozygome. The Journal of Infectious Diseases, 1\u201311.<\/i> \n<i>LaVerriere, E., et. al. (2022). Design and implementation of multiplexed amplicon sequencing panels to serve genomic epidemiology of infectious disease: A malaria case study. Molecular Ecology Resources, 22(6), 2285\u20132303.<\/i> \n<i>Taylor, A. R., Jacob, P. E., Neafsey, D. E., &amp; Buckee, C. O. (2019). Estimating Relatedness Between Malaria Parasites. Genetics, 212(4), 1337\u20131351.<\/i> \n<i>WHO: Informal consultation on methodology to distinguish reinfection from recrudescence in high malaria transmission areas. https:\/\/www.who.int\/publications\/i\/item\/9789240038363<\/i>     2. Note that in our publication1 we used the abbreviation \u201cAmpSeq\u201d, the current authors used \u201cTADS\u201d to mean the same thing. It would be nice to establish a consistent terminology noting the latest WHO recommendation also uses \u201cAmpSeq\u201d i.e. https:\/\/www.who.int\/publications\/i\/item\/9789240038363  If \u201cTADS\u201d is already in the literature I would request (but not demand) they consider changing TADS to Ampseq. Like the use of \u201chaplotype\u201d it is not essential but if a field starts off by using different anacronyms for the same thing it tends to persist which is in nobody\u2019s long-term interest. If \u201cTADS\u201d is not already in use in the literature it would be hard to justify&nbsp;using a new terms when one already exist.  \n<i>Throughout the manuscript, we have switched to using AmpSeq.<\/i>   \n<b>Methods.<\/b>  2.&nbsp; I have sat through seeming interminable technical discussions on amplicon sequencing, most of which went over my head. I do know enough to realise it is not as straightforward as most people think so suggest an expert on genotyping also reviews this manuscript. The authors noted that they could detect the minority allele in mixture of 95%:5% and regarded any reads less than 5% of total as noise. We previously found this threshold to be extremely important (i.e.&nbsp;1) and termed it the Bioinformatics cut-off (BIC), and identified it as a vital piece of information to be included in publications, so it is nice to see it clearly defined here. However, on page 8 it states \u201cour analysis of rare haplotypes was limited to a 5% cut-off based on sequencing controls\u201d so some clarification is needed. Is it 5% of total reads or 5% of controls? If the latter, this could be explained much more clearly.  \n<i>We have clarified that this 5% is based on the proportion of the minor clone in the artificial mixture of the two laboratory clones, that is 95% 3D7 and 5% Dd2.<\/i>   4. It looks like the authors extracted their DNA from frozen blood samples. One of the things we previously identified as important was the \u201cblood sampling limit\u201d1&nbsp;i.e. the number or density of parasites below which they were unlikely to be detected because the blood volume entering the genotyping assay was so small that parasites of a given genotype were statistically unlikely to be present in the sample. \n<b>We made an estimate based on blood extracted from dried blood spots on filter paper<\/b>. It looks like the authors need to the same calculations based on the volume of blood they used. This is important for understanding the likely impact of gametocytes, and to interpret observed patterns (i.e., their figure 1) post-treatment; see comment (10) below. \n<b>They have total parasitaemia and relative frequencies of ama1 alleles in each patient so they can back-calculate likely number\/density of each clone and hence figure out whether it is in danger of falling below the sampling limit<\/b>. The calculations tend to be estimates but they need to do this before interpreting their figure 1 i.e. they need to be confident that all the ama1 signals in the patient are being detected and there is \n<b>no danger that, at later time points,&nbsp; one ama1 signal becoming so low (because the clone(s) containing it has fallen to very low density)&nbsp; that it is no longer detected in the assay despite its continued persistence in the patient<\/b>.  The blood sampling limit is also one plausible explanation for their observation (page 6) that\u201d In five individuals (PIDs 10, 32, 40, 59 and 60), there were sporadic detections of rare&nbsp;\n<i>ama1&nbsp;<\/i>haplotypes \u201d i.e. because parasitaemias of the clone containing that ama1 allele were around the sampling limit so that whether or not they were detected was a matter of chance. The second plausible reason is that it fluctuated around the 5% BIC so sporadically exceed the BIC. Discussion around these points would be good and it also emphasises why it is important to establish the sampling limit before interpreting the results.  \n<i>We have responded to this in comment 10, below.<\/i>  5.&nbsp; I am worried about these statements \u201cThe frequency of each haplotype in the population was calculated using the total number of samples that contained the haplotype over the total number of samples genotyped.\u201d This will calculate prevalence&nbsp;\n<i>not&nbsp;<\/i>frequencies (this has caused immense confusion in the past). The problem can be illustrated by a simple example: assume there are only two samples: sample #1 has alleles A,B and C, and sample #2 has A, B, and D. Then according to their statement the \u201cfrequencies\u201d of A, B, C and D will be 100%, 100%, 50% and 50% respectively which is obviously wrong as in this context the allele frequencies should sum to 100%. I assume the statement is written incorrectly and that they counted total number of clones in the two samples i.e. 3+3=6 then calculated the frequencies of A,B, C and D as 2\/6. 2\/6, 1\/6 and 1\/6 respectively i.e. ensuring frequencies sum to 100%. They then need, for reasons given below, &nbsp;to calculate expected heterozygosity (He) to quantify genetic variability ie. the probably that two randomly selected clones have different alleles.  \n<i>We agree with the suggestion. However, we have dropped the statement \u201cThe frequency of each haplotype in the population was calculated using the total number of samples that contained the haplotype over the total number of samples genotyped.\u201d This population frequency statistic was not used anywhere in the paper and was erroneously included.<\/i>  I also worry about their statement on page 10 when discussing table S2 i.e. \u201cThe frequencies were calculated by dividing the number of reads of each haplotype by the total number of reads obtained (116,187,131).\u201d These \u201cfrequencies\u201d are likely biased towards alleles in clones in high-parasitaemia patients. So, I think the calculation of frequency suggested above is better, especially because it can be used to calculate H\n<sub>e<\/sub>. &nbsp;  \n<i>This has been edited to \u201cThe frequencies were calculated by dividing the number of reads of each microhaplotype by the <b><u>total number of reads obtained per sample<\/u><\/b>\u201d.<\/i> &nbsp; \n<i>Therefore, we did not use just total reads but total reads per sample. Sequencing libraries from all samples were normalised to equimolar concentrations before pooling and sequencing to avoid having more input DNA from higher parasitaemia samples. Additionally, one of our cut-offs for identifying minor variants required that they were identified in other samples so this reduces bias due to high parasitaemia samples.<\/i>    6.&nbsp; I assume the genotyping at \n<i>msp1\/msp2\/glurp<\/i> was the standard WHO-recommended method of gel electrophoresis. As above, the sensitivity (equivalent to the BIC) used in their assay should be stated. Labs typically regard genotyping peaks less than 25% or 30% of the largest peak as noise but there is no consistency in this (and some labs apparently do it \u201cinformally\u201d) but this needs to be stated.  \n<i>We have included the following statement in the methods section:<\/i>  \n<b><i>\u201cmsp1\/msp2\/glurp genotyping was performed according to the WHO-recommended method of gel electrophoresis <\/i><\/b>\n<b><i>(WHO 2008)<\/i><\/b>\n<b><i>. These data were obtained from the original study, and the following was ensured during the analysis: Each PCR product had to have well-defined and easy to visualise, bands had to be bright and sharp to be of sufficient quality for scoring, PCRs were repeated if bands appeared in the negative control, the interpretation of results did not include products with less than 100 bp and did not account for faint bands or bands that formed smile-shaped patterns on the gel <\/i><\/b>\n<b><i>(Hamaluba et al. 2021)<\/i><\/b>\n<b><i>\u201d<\/i><\/b>   \n<b>Interpretation<\/b>  7. My main concern is that the authors seem to take no account of the possible impact of detecting alleles in gametocytes. We identified&nbsp; this as a key consideration as illustrated in Figure 2 of our main text and extensively discussed in our Supplementary Material Part 3: \u201cSimulating the potential impact of gametocytes on the accuracy of molecular correction\u201d.1&nbsp; The problem is that current drugs clear the asexual form of falciparum but not the gametocytes which continue to circulate and the increased sensitivity of deep sequencing allows detection of alleles in the gametocytes. Suppose a clone initially has 1% gametocytes then after treatment their proportion increases (roughly) 2 fold with every half-life of the asexual forms i.e. &nbsp;gametocytes increase (approx.) from 1% to 2% to 4% to 8% etc after 1,2,3,4, etc half-lives. The observed clearance half-life will therefore gradually decrease as the drug-insensitive gametocytes make up an increasing proportion of the clone. Whether the gametocytes will be observable in the initial test slide depends on the microscopy and on the clone size. So it\u2019s a two-stage calculation Firstly, the extent to which any putative gametocytes will gradually reduce observed clearance rates post-treatment. Secondly, the likely impact of gametocyte signals will also depend on the blood sampling limit (i.e. whether gametocytes are of sufficient density to de detectable) which I have suggested the authors calculate (cf Fig 2 of Jones&nbsp;\n<i>et al<\/i>.) &nbsp;  \n<i>This has been included as one of our limitations. None of the 30 individuals was gametocyte on day 0 and they remained negative up to day 42. One participant, however, was gametocyte positive on day 3 but negative thereafter so it\u2019s unlikely that gametocytaemia biased our findings.<\/i>   8. First paragraph of section \u201cPfama1 genetic diversity during and after treatment\u201d. The slight decrease in COI is presumably a consequence of clones dropping below the blood sampling limit (see above), or below the 5% BIC, &nbsp;and being lost. Given that monoclonal infections cannot lose diversity, it would be better to exclude these infections from the calculations to get a better idea of the drop in COI. &nbsp;  \n<i>After excluding monoclonal infections, we still see a decrease in COI from before treatment, at 0h the mean COI was 2.44, 0.5h to 72h the mean COI was 2.37 and 2 thereafter. These new values have been included in the text.<\/i>  9. I am extremely curious about the assertion made in the caption to figure 2 i.e. \u201cPID32 had the fastest clearing (0.8h)&nbsp;\n<i>ama1&nbsp;<\/i>haplotype V9.\u201d. Looking at Figure 1, it appears that PID32 was largely monoclonal except for a single detection of another ama1 allele at time 8hours which may well be spurious. More explanation is needed. &nbsp;  \n<i>Regarding the single ama1 microhaplotype detected at 8h, we have shown in extended data table S3 that it was detected above the blood sampling limit. Still, we have no reason to believe that PID32\u2019s infection being clonal would have biased our estimate of determining parasite clearance. <\/i>  10. So once background calculation have been made for blood sampling limit and impact of gametocytes there are (as far as I can see) three plausible patterns post treatment. &nbsp;  Immediate and sustained&nbsp; difference in clearance rates between clones in the same patient. PID27 therefore looks the most interesting to me as is suggested significant differences in clearance between the two clones immediately post-treatment (Figure 1). Figure 2B also shows that the two clearance rates in this patient are clearly distinguishable on the Y axis unlike the other patients . Note&nbsp; this could be due to differences in drug sensitivity or, less likely, to differential impact of immunity acting on the two clone.  \n<i>We did not see a difference in the sensitivity of the drugs; hence we hypothesize that acquired immunity may have had a larger role to play in the difference in clearing times for PID27.<\/i>    &nbsp; 10. An impact of gametocytes. All clones would retain roughly the same proportions immediately post-treatment, but one eventually rises. The one that increases in proportion could arise as gametocytes become a significant proportion of its biomass (see point 7 above) which are not cleared by the drug . PID08 seems to show this pattern. \n<i>As mentioned above, all participants in the current study were gametocyte negative throughout treatment, thus it\u2019s likely that gametocytaemia biased our findings.<\/i> Impact of a sampling limit. All clones maintain same proportions until the lower one falls below the sampling limit and disappears, hence the 100% has to be retained by increased proportions on those remaining above the sampling limit. This would be an artefact. Interesting, I can\u2019t see evidence of this of Figure 1 so I\u2019m guessing sampling limit was not a big issue (the requested background calculation should confirm this).  \n<i>We set our arbitrary blood sampling limit to 10 parasites\/\u03bcl based on the lower limit used in the PMID:<\/i> \n<i>34252299. In multiclonal infections, some microhaplotypes started to fall below this limit, however, for the seven individuals with microhaplotypes that were detected sporadically, only one (PID60) had a microhaplotype below this limit.<\/i> &nbsp; \n<i>We have included this comment in the discussion, and we recommended future studies to include artificial sequencing-controls with decreasing parasitaemia, e.g. 10,000 down to 1 parasite\/\u03bcl to determine their limit of detection.<\/i>   So these are three paradigms. There may be variation caused by factors such as sequestration, &nbsp;but given that the authors chose to present and discuss the profiles, I strongly suggest they improve their discussion to include the putative\/likely impact of gametocytes and sampling limits.  \n<i>We had mentioned earlier that the sporadic detections of rare microhaplotypes could have originated from changes in parasite densities, sequestered parasites, and lingering nucleic material from dead parasites.<\/i> &nbsp; \n<i>We have also included in our limitations that these rare microhaplotypes could have originated from gametocytes too. However, this was unlikely in this study since all participants were gametocyte negative throughout treatment.<\/i>  11. Page 9 they state \u201cHence,&nbsp;\n<i>ama1&nbsp;<\/i>is a highly discriminatory marker with a larger number of variants with a prevalence of &lt;5%, providing a higher resolution to better distinguish new from recrudescent infections in moderate to high transmissions settings where polyclonal infections are common.\u201d This is probably incorrect and definitely incomplete. Msp1, msp2 and glurp are all highly variable, possible more so than ama1: they would need to report H\n<sub>e<\/sub> values for ama1 to substantiate this assertion of greater genetic diversity. The most important point about deep sequencing vs msp1\/msp2\/glurp is sequencing has much better ability to detect minor clones\u2026 they need to cite our previous work (i..e Jones\n<i>&nbsp;<\/i>\n<i>et al<\/i>.) here as we explicitly discussed the impact of improved sensitivity in detail.  \n<i>This section has been amended to include the high H<sub>e<\/sub> we obtained for the ama1 locus we genotyped. Additionally, we have added that amplicon sequencing is more sensitive than msp1\/msp2\/glurp genotyping because it can better capture minority variants. The section now reads as follows:<\/i>  \n<b><i>\u201chence, the ama1 locus genotyped in this study is a highly discriminatory marker with a larger number of variants with a prevalence of &lt;5% and a high expected heterozygosity value (0.96). Coupled with the high sensitivity of amplicon sequencing to capture minor variants, this assay provides a higher resolution to better distinguish reinfections from recrudescent infections in moderate to high transmissions settings where polyclonal infections are common.\u201d<\/i><\/b>","URL":"https:\/\/wellcomeopenresearch.org\/articles\/7-95\/v4#referee-comment-5128","order":1,"name":"referee-comment-5128","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.21956\/wellcomeopenres.20663.r53729, Mita Toshihiro, Department of Tropical Medicine and Parasitology, Juntendo University, Tokyo, 20 Jan 2023, version 2, indexed","URL":"https:\/\/wellcomeopenresearch.org\/articles\/7-95\/v2#referee-response-53729","order":2,"name":"referee-response-53729","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.21956\/wellcomeopenres.20663.r61601, Eniyou Cheryll Oriero, Medical Research Council Unit The Gambia at LSHTM, Fajara, 14 Jul 2023, version 2, indexed","URL":"https:\/\/wellcomeopenresearch.org\/articles\/7-95\/v2#referee-response-61601","order":3,"name":"referee-response-61601","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"<b>Kevin Wamae<\/b>; \n<i>Posted: 22 Sep 2023<\/i>; \n<i><b>We want to thank Dr. Eniyou for agreeing to review this article, and we respond to her questions below:<\/b><\/i>  \n<i><b>Parasite clearance: The authors have shown half-life graphs of parasite clearance estimates, adding a Kaplan-Meier curve of parasite clearance by microscopy and PCR (if data is available) will support parasite genotyping at the 72hr time point post-treatment, with evidence of PCR detectable parasites at that time point:&nbsp;<\/b><\/i>  The information presented in Figure S2C demonstrates that all individuals had eliminated the parasites by day 7 (hour 168). Therefore, the Kaplan-Meier (KM) analysis does not give us more information beyond this and is better suited to the parent study that included a larger sample size to conduct the KM analysis. It's noteworthy that parasites were undetectable by microscopy beyond 72 hours, and we acknowledge that we encountered challenges with PCR and sequencing failures from this point onward, which we discussed as one of the limitations in our study's discussion section.&nbsp;  \u201c\n<i>\u2026The low parasitaemia following treatment minimised the generation of good quality AmpSeq data, impacting the sample size. Consequently, there were limited samples to examine the changes in microhaplotypes throughout the study period\u2026<\/i>\u201d &nbsp; &nbsp; \n<b>Though the manuscript is a proof-of-concept study with added advantage of simultaneous drug resistance genotyping, it has not sufficiently shown superiority of AmpSeq over msp1\/msp2\/glurp genotyping for PCR correction and determining complexity of infection. The authors may want to comment further on the cost-effectiveness and availability of expertise (e.g. bioinformatics) for AmpSeq in LMICs:<\/b>&nbsp;  \u200b\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u200bWe have amended the manuscript to acknowledge this observation in the last three paragraphs of the discussion, as indicated below:&nbsp;&nbsp;  \n<i>\u201c...While this study serves as a proof of concept, the sample size employed was not aimed at establishing the clear superiority of AmpSeq over msp1\/msp2\/glurp genotyping for tasks such as PCR correction and determining infection complexity. Moreover, the adoption of AmpSeq in low and middle-income countries (LMICs) is challenged by its current cost and the requisite molecular biology and bioinformatics expertise needed for effective implementation. Nevertheless, the constantly declining costs of next-generation sequencing, coupled with emerging technologies like the Oxford Nanopore platform that offer reduced buy-in and maintenance expenses, hint at a potentially more accessible AmpSeq in the future.<\/i> &nbsp; \n<i>Regarding its performance, our study illustrates that AmpSeq yields results comparable to those obtained from msp1\/msp2\/glurp genotyping. However, AmpSeq boasts additional benefits over msp1\/mps2\/glurp genotyping. It demonstrates scalability advantages and heightened resolution based on sequence identity, surpassing the limitations of msp1\/msp2\/glurp genotyping, such as the reliance on labour-intensive gel electrophoresis-based band size analysis or fragment analysis that can be subject to interpretation errors and the inability to detect low-density parasite clones. An additional AmpSeq's strength lies in its potential for multiplexing, enabling the simultaneous identification of drug resistance markers alongside genotyping. This multiplexing capability enhances the assay's versatility and could offer valuable insights into the parasite's drug resistance profiles.<\/i> &nbsp; \n<i>In light of these considerations, while the current prerequisites of expertise and cost might impede the immediate integration of AmpSeq in LMICs, the evolving landscape of next-generation sequencing technologies and cost reductions inspire optimism regarding AmpSeq's future accessibility. Therefore, this study <\/i>\n<i>recognises<\/i>\n<i> the prospective value and adaptability of AmpSeq for tasks like PCR correction and determining infection complexity, even though broader adoption may necessitate ongoing advancements in affordability and the availability of expertise\u2026\u201d<\/i>&nbsp; &nbsp;&nbsp; &nbsp; \n<b>The authors used artificial mixtures of two parasite strains to generate sequencing controls with artificial microhaplotypes similar to natural mixed infections. The authors should comment if they were able to determine copy number variation (particularly for mdr1) using read counts obtained from the AmpSeq data:<\/b>&nbsp;  \u200b\u200b\u200b\u200b\u200b\u200b\u200bIn our study, we employed AmpSeq, a method characterised by deep sequencing of targeted regions but with shorter read lengths than whole-genome sequencing. While AmpSeq excels in the depth of sequencing within specified zones, the comprehensive detection of CNVs demands a wider genomic scan to pinpoint duplications or deletions. Concerning the \n<i>mdr1<\/i> gene, our sequencing strategy was not tailored for CNV identification. As such, while AmpSeq might provide preliminary indications of CNVs through read-depth analysis, robust and precise CNV assessments necessitate other techniques, and would require the inclusion of controls that include mdr1 genes with CNVs for comparison. This was not an objective of our study, and we primarily focused on characterising microhaplotypes for drug resistance.","URL":"https:\/\/wellcomeopenresearch.org\/articles\/7-95\/v4#referee-comment-5456","order":4,"name":"referee-comment-5456","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.21956\/wellcomeopenres.20663.r61598, Aurel Holzschuh, University of Notre Dame, Notre Dame, Indiana, 25 Jul 2023, version 2, indexed","URL":"https:\/\/wellcomeopenresearch.org\/articles\/7-95\/v2#referee-response-61598","order":5,"name":"referee-response-61598","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"This work was supported by Developing Excellence in Leadership, Training and Science  (DELTAS) Africa Initiative (grant number: DEL-15-003) and a Wellcome Trust Intermediate Fellowship awarded to L.I.O.-O (grant no. 107568\/Z\/15\/Z). J.A.B and J.J.J received support from the National Institutes of Health (R01AI121558 to JAB and K24AI13499 and R01AI121558 to JJJ). The DELTAS Africa Initiative is an independent funding scheme of the African Academy of Sciences (AAS)'s Alliance for Accelerating Excellence in Science in Africa (AESA) and supported by the New Partnership for Africa's Development Planning and Coordinating Agency (NEPAD Agency) with funding from the Wellcome Trust (107769\/Z\/10\/Z) and the UK government.\r\nThe views expressed in this publication are those of the authors and not necessarily those of AAS, NEPAD Agency, Wellcome Trust, or the UK government. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","order":6,"name":"grant-information","label":"Grant Information"},{"value":"This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","order":0,"name":"copyright-info","label":"Copyright"}]}}