In a phase 3 trial in African infants and children, the RTS,S/AS01 vaccine (GSK) showed moderate efficacy against clinical malaria. We sought to further understand RTS,S/AS01-induced immune responses associated with vaccine protection.Applying the blood transcriptional module (BTM) framework, we characterized the transcriptomic response to RTS,S/AS01 vaccination in antigen-stimulated (and vehicle control) peripheral blood mononuclear cells sampled from a subset of trial participants at baseline and month 3 (1-month post-third dose). Using a matched case–control study design, we evaluated which of these ‘RTS,S/AS01 signature BTMs’ associated with malaria case status in RTS,S/AS01 vaccinees. Antigen-specific T-cell responses were analyzed by flow cytometry. We also performed a cross-study correlates analysis where we assessed the generalizability of our findings across three controlled human malaria infection studies of healthy, malaria-naive adult RTS,S/AS01 recipients.+ T-cell responses. There were few RTS,S/AS01-associated BTMs whose month 3 levels correlated with malaria risk. In contrast, baseline levels of BTMs associated with dendritic cells and with monocytes (among others) correlated with malaria risk. The baseline dendritic cell- and monocyte-related BTM correlations with malaria risk appeared to generalize to healthy, malaria-naive adults.RTS,S/AS01 vaccination was associated with downregulation of B-cell and monocyte-related BTMs and upregulation of T-cell-related BTMs, as well as higher month 3 (vs. baseline) circumsporozoite protein-specific CD4A prevaccination transcriptomic signature associates with malaria in RTS,S/AS01-vaccinated African children, and elements of this signature may be broadly generalizable. The consistent presence of monocyte-related modules suggests that certain monocyte subsets may inhibit protective RTS,S/AS01-induced responses.Funding was obtained from the NIH-NIAID (R01AI095789), NIH-NIAID (U19AI128914), PATH Malaria Vaccine Initiative (MVI), and Ministerio de Economía y Competitividad . The RNA-seq project has been funded in whole or in part with Federal funds from the National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under grant number U19AI110818 to the Broad Institute. This study was also supported by the Vaccine Statistical Support (Bill and Melinda Gates Foundation award INV-008576/OPP1154739 to R.G.). C.D. was the recipient of a Ramon y Cajal Contract from the Ministerio de Economía y Competitividad (RYC-2008-02631). G.M. was the recipient of a Sara Borrell–ISCIII fellowship (CD010/00156) and work was performed with the support of Department of Health, Catalan Government grant (SLT006/17/00109). This research is part of the ISGlobal’s Program on the Molecular Mechanisms of Malaria which is partially supported by the Fundación Ramón Areces and we acknowledge support from the Spanish Ministry of Science and Innovation through the ‘Centro de Excelencia Severo Ochoa 2019–2023’ Program (CEX2018-000806-S), and support from the Generalitat de Catalunya through the CERCA Program. Malaria remains a serious public health problem, with an estimated 241 million cases and 627,000 related deaths in 2020 . DespitePlasmodium falciparum circumsporozoite protein (CSP)  . This rein (CSP) .ClinicalTrials.gov NCT00866619)  , RTS,S/A0866619) , and it 0866619) . Evidenc0866619) .+ T cells  in protection in this population  and wanes substantially within the first 18 months . The ideThe MAL067 study, an ancillary study to the RTS,S/AS01 phase 3 trial, was conducted to address key knowledge gaps of RTS,S-induced immune responses and their correlation with protection against natural exposure in the field. Using RNA-sequencing (RNA-seq) data from antigen- or vehicle-stimulated peripheral blood mononuclear cell (PBMC) obtained at baseline and 1-month postfinal primary vaccination dose from infants and children enrolled in Bagamoyo, Tanzania and Manhiça, Mozambique, we aimed to identify baseline and/or RTS,S/AS01-induced signatures associated with clinical malaria risk. Postvaccination anti-CSP antibody levels, cytokine profiles, and T-cell responses, the latter of which were additionally assessed in samples from participants enrolled in Lambaréné, Gabon, were also examined as correlates of clinical malaria and/or of RTS,S/AS01-induced transcriptional responses.The major finding of our study is that prevaccination expression of immune-related blood transcriptional modules (BTMs), including BTMs related to dendritic cells and monocytes, correlated positively with malaria risk in RTS,S/AS01-vaccinated African children; moreover, the dendritic cell- and monocyte-related elements of this signature appeared to generalize to malaria-naive RTS,S/AS01-vaccinated healthy adults.ClinicalTrials.gov identifier NCT00866619; 6 PBMC in liquid nitrogen, the remaining cells were used for fresh antigen stimulations onsite and the cell pellets were collected. Therefore, only vaccinees with enough PBMC for cryopreservation and additional stimulations (at least 6.6 × 106 PBMC) were included in this study for RNA-seq.During the MAL055 phase 3 study  in the 12 months of follow-up after month 3.5, identified by passive case detection  during all phase 3 trial follow-up. Data were collected during the clinical trial. Controls were participants who did not have any clinical malaria case during the 12 months of follow-up. Controls were matched to cases based on site, age group, and time of vaccination and follow-up. 5 freshly isolated PBMC seeded in duplicates were rested for 12 hr and then incubated 12 hr at 37°C with 1 μg/ml antigens in 96-well plates. Plates were then centrifuged for 5 min at 250 × g at room temperature and cell pellet duplicates were resuspended and pooled in RLT buffer  at Bagamoyo or RNAprotect  at Manhiça, transferred into a 96-well V-bottomed plate  and sealed with adhesive foil  and cryopreserved at −80°C until RNA extraction.PBMC collection and stimulation with dimethyl sulfoxide , apical membrane antigen , CSP (peptide pools), or HBS (peptide pools) is described in 2 incubator overnight. PBMC were stimulated for 6 hr with the same peptide pools as above, DMSO (vehicle control), and Staphylococcal enterotoxin B  as a positive control.For stimulation before ICS, cryopreserved PBMC were thawed and then rested in a 37°C, 5% CO6 cells) were used for leukocyte phenotyping. The flow cytometry panel and staining protocol used are described in Leftover cryopreserved PBMC thawed for ICS  and had an average RIN score of 7.4. RNA samples were distributed in 384-well plates for library preparation. Samples from the same individuals were in the same plate and key study variables  were checked for balance across plates to avoid batch effects.RNA was extracted at the Center for Global Infectious Disease Research, Seattle Children's Research Institute ( Seattle) using the Promega SV96 Total RNA Isolation kit  following the manufacturer’s protocol. Samples kept in RNAprotect were centrifuged at 4000 × An optimized version of Digital Gene Expression (DGE) was used, based on the Single Cell Barcoding and Sequencing method described by NANP-, HBS-, and C-terminal domain of CSP (C-term)-specific antibody data from previous studies were analyzed for correlations with BTM expression as described below. IgG titers (EU/ml) against NANP and against HBS were obtained from the MAL055 trial database . IgG conPreprocessing: Preprocessing of RNAseq data was done by Broad Technology Labs. In brief, reads were aligned using BWA Aln version 0.7.10 using UCSC RefSeq (Human 19) with mitochondrial genes added. Quantified samples were then quality controlled using mapping summary statistics to remove low quality samples based on predetermined minimum values for the total number of mapped reads, percent of mapped reads mapped to the human genome, etc. Downstream analysis was applied only to reads that mapped uniquely to a UMI and only mapped to isoforms of the same gene (UMI.unq).Normalization: The TMM normalization method  was applQuality control: In a pilot study, we found that sample libraries that exhibit less than 75,000 total RNAseq reads per sample were of low quality. Thus, such libraries were removed from the study. Genes that had less than 20 samples (around 10%) with read counts greater than 5 were also removed. Multidimensional scaling (MDS) plots as implemented in the plotMDS function of the limma package were used to visualize variability across samples and identify potential sources of variability  or patterns of biological interest .Differential expression: Differential expression was assessed using module-based (using voom and camera ) approacBTM analysis: BTMs used were from Analysis of antigen (Ag)-specific T-cell transcriptional responses: When analyzing Ag-specific T cells, vehicle-only stimulations (DMSO) were used to determine the effect of Ag stimulation over vehicle stimulation for each PBMC sample. The comparison was performed using the limma package  in R as Equations used were: BTM correlations with immunogenicity: For each module, a score was calculated for each RTS,S recipient at months 3 and 0 based on the average normalized expression level of all genes in the modules, on the log scale. Spearman’s rank correlation was used to assess association between gene expression, antibody and cellular responses. Each correlation was tested (Spearman correlation test) and a p value was obtained. p values were adjusted within each response ; significance was defined as an adjusted p value ≤0.2.Correlates analysis: We identified BTMs significantly associated with protection using the limma package. All analyses controlled for plate, total reads, and age (as described above). This model was applied to each BTM and stimulation condition identified in the downselection process. Resulting p values were adjusted for multiple testing with an FDR cutoff of 0.2.Equations used were: NCT00075049), which randomly assigned participants to receive RTS,S/AS02A or RTS,S/AS01B at months 0, 1, and 2 (NCT01857869), which randomly assigned participants to receive a full dose of RTS,S/AS01B at months 0, 1, and 2 or a full dose of RTS,S/AS01B at months 0 and 1, followed by a fractional dose at month 7  data from MAL067 (vehicle-stimulated PBMC only). In brief, month 3 or 0 data for every BTM were tested and FDR adjustment was done across all BTMs. Only those BTMs with FDR < 0.2 in MAL067 were examined as potential correlates of challenge outcome in the CHMI studies, with FDR adjustment performed within each study. The three CHMI studies used in the cross-study immune correlates were: WRAIR 1032 .The transcriptional response to RTS,S/AS01 vaccination was assessed in control-stimulated PBMC as well as Ag-stimulated PBMC. Through this approach, we hypothesized that we would see recall responses of Ag-specific T cells activated in vitro, as well as responses of other cell types to the secreted cytokines/chemokines. Of note, the sampling schedule at MAL067 was designed for evaluation of acquired immune responses to the vaccine and not ex vivo responses. Our motivation was that in healthy, malaria-naive adults, the transcriptional response to RTS,S/AS01 has been shown to largely wane by week 3 postfinal dose , implyinTwo comparisons were done to characterize the transcriptional response to RTS,S/AS01 vaccination: Comparison 1: comparing gene expression in month 3 samples from RTS,S/AS01 vs. comparator recipients ; and Comparison 2: comparing gene expression in months 3 vs. 0 from RTS,S/AS01 recipients . Each comparison has its own advantages: Comparison 1allows the identification of RTS,S/AS01-specific responses while taking into account other environmental factors to which the children are exposed, such as malaria exposure . MoreovA BTM-based approach was taken to reduce dimensionality, avoid paying a high penalty for multiple testing, and aid results interpretability. For Comparison 1, there were 68 significantly differentially expressed (FDR cutoff ≤0.2) BTMs across all antigen stimulation conditions . The majFor Comparison 2, only RTS,S/AS01 recipients for whom months 0 and 3 samples were available were included in the analysis . There were a larger number (131) of significantly differentially expressed (FDR cutoff ≤0.2) BTMs across all stimulation conditions; again, the majority (90) were found in vehicle-stimulated PBMC . In vehiTo preserve statistical power in the immune correlates analysis, only BTMs differentially expressed after RTS,S/AS01 vaccination according to Comparison 1 (any stimulation) were down selected. We define these 68 BTMs as the ‘RTS,S/AS01 signature BTMs’ . We nextIn vehicle-stimulated PBMC, seven BTMs were significantly differently expressed in RTS,S/AS01 cases vs. controls . Three wThe antigen-specific transcriptional modules associated with clinical malaria risk differed from those seen in vehicle-stimulated PBMC. In CSP- and HBS-stimulated cells adjusted by vehicle stimulation, there were 0 and 1 correlate BTMs, respectively. In AMA1-stimulated cells adjusted by vehicle stimulation, distinct and opposite correlations were seen, for example correlation with protection for ‘enriched in activated dendritic cells/monocytes (M64)’, ‘myeloid cell enriched receptors and transporters (M4.3)’, and ‘enriched in monocytes (II) (M11.0)’.An analogous analysis, using the same downselected BTMs, was performed on comparator recipients. For all seven BTMs whose levels in vehicle-stimulated PBMC associated either directly or inversely with risk in RTS,S/AS01 recipients , signifi+ T-cell polyfunctionality score is higher at month 3 vs. baseline in RTS,S/AS01 vaccine recipients (+ T-cell expressing IL-2 or TNF or CD154) at month 3 vs. baseline in RTS,S/AS01 vaccine recipients . Due to differences in sampling schedules, and the presence of the CHMI challenge (which would complicate results interpretation), we could not compare the exact same month 3 timepoint across studies. We chose instead to compare 21 days post-third dose in MAL068 and in MAL071, that is of day of challenge, and 14 days post-third dose in WRAIR 1032, that is just before or on day of challenge. We refer to these slightly different postvaccination timepoints as ‘month 3’ for simplicity. The month 3 cross-study correlates analysis included BTMs whose month 3 levels (in vehicle-stimulated PBMC) associated with clinical malaria risk in MAL067 RTS,S/AS01E recipients  and is sWe next performed the baseline correlates analysis of MAL067 ; of these, 12 were also associated with risk in MAL068 RRR. BTMs related to dendritic cells and to monocytes were most consistently associated with risk across these three studies .CCNF, MK167, KIF18A, NPL, RBM47, CFD, MAFB, IL13RA1, and CCR1) also had significant association with nonprotection in one of the CHMI studies. Although no individual gene was significantly associated with risk across >two studies, many showed consistent effect (direction and magnitude) across three studies. This further supports our choice to focus on modules instead of individual genes as GSEA increases power to detect more subtle but coordinated changes in gene expression data that would be missed otherwise. For this same reason, GSEA has been shown to enhance cross-study comparisons  was found to positively associate with malaria risk , a transmembrane glycoprotein scavenger receptor that links extracellular signals to intracellular vesicle trafficking pathways  ratio and clinical malaria risk and/or severity , and a hria risk , 2 and 6pathways . Stabilitivation . Thus, oO-desacyl-monophosphoryl lipid A (MPL) and the saponin QS-21 . We hypothesize that the AMA1 stimulation activated T cells that had been previously primed by prior exposure to P. falciparum and that RTS,S/AS01 recipients had fewer primed T cells due to decreased P. falciparum infection , providing a potential explanation for the transcriptional differences in AMA1-stimulated PBMC between RTS,S/AS01 vs. comparator recipients.It is perhaps counterintuitive – considering that the RTS,S/AS01 vaccine does not contain AMA1 – that we observed a small number of BTMs associated with the response to RTS,S/AS01 vaccination and with clinical malaria risk when analyzing AMA1-stimulated PBMC. To explain this result, we refer the reader to our previous work that showed that RTS,S/AS01 vaccination alters antibody responses to antigens not contained in the RTS,S/AS01 vaccine . RTS,S/ACompared to the 45 BTMs whose baseline levels significantly associated with clinical malaria risk in RTS,S/AS01-vaccinated African children, fewer BTMs (seven) had levels at 1-month postfinal RTS,S/AS01 dose that significantly associated with clinical malaria risk. Moreover, if a more stringent FDR cutoff had been used , six of these seven BTMs would not have been identified. Thus, it is entirely possible that, at 1-month postfinal RTS,S/AS01 dose, there is no circulating immune transcriptomic signature predictive of risk. Such a conclusion would not be surprising, given that in malaria-naive adults, the transcriptional response to the third RTS,S/AS01 dose has been shown to peak at day 1 postinjection, with some decline by day 6 and approximately 90% of the response having waned by day 21 . TherefoAdditional limitations of our study include the following: first, PBMCs were stimulated on site and then frozen. As each site performed the procedure separately, this renders our data susceptible to batch effects. However, a standardized SOP and shared reagents were used, decreasing the possibility of such effects. Moreover, an advantage of onsite stimulation of fresh PBMC is that it avoids the decrease in cell viability, and potential loss of detection of Ag-specific cells, that may have occurred if PBMC had been frozen, thawed, and then stimulated at a central location. Second, there was confounding between age and location. As all infants were from Manhiça and the majority of children were from Bagamoyo, it was not possible to examine the impact of age or clinical trial site on RTS,S/AS01 transcriptional response. Third, we do not know whether the controls were truly protected or whether they were never exposed to malaria in the first place. This limitation highlights the importance of our cross-study analysis, where all participants are exposed. Fourth, despite the relatively large size of the study, our statistical power was limited by the number of malaria cases with available samples; sampling additional controls would not have increased our statistical power. Fifth, as only patrolling cell subsets are present in PBMC, we were unable to detect potential signals from T cells, B cells, NK cells, and macrophages localized to an infection site including skin and liver or the immune memory compartment localized in secondary lymphoid organs. Finally, while it is not uncommon to use an FDR cutoff of 20% in high-dimensional immune correlates studies e.g., , our resP. falciparum, as occurs in endemic areas, influences naturally acquired immunity, which in turn interacts with immunity conferred by RTS,S vaccination  and to identify molecular correlates of RTS,S/AS01-mediated protection against clinical malaria after CHMI in malaria-naive adults , in our cination . RelatedWhile it will be necessary to perform follow-up studies at more sites and with larger sample sizes to validate the baseline transcriptional signature associated with malaria risk identified here, our study suggests that innate immune cells may shape responses to RTS,S/AS01 and raises hypotheses for future testing related to monocytes and RTS,S/AS01-mediated protection. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Betty Diamond as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Wiebke Nahrendorf (Reviewer #1).Thank you for submitting your article "A baseline transcriptional signature associates with clinical malaria risk in RTS,S/AS01-vaccinated African children" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Reviewer #1 (Recommendations for the authors):The authors use a systems vaccinology approach to find a circulating immune signature that can predict RTS,S vaccine efficacy. It is perfectly possible that such a signature simply does not exist (the null hypothesis). I would recommend throughout the paper for the authors to check if their results  support or reject the null hypothesis – both of which are equally valid outcomes!The MAL067 trial set up is fantastic , but unfortunately isolating PBMCs 1-month after final vaccination to assess recall responses did not really work: whilst unstimulated PBMCs are transcriptional different between RTS,S and comparator, no signifiant signature of RTS,S vaccination after CSP and HBS stimulation was observed . It is biologically hard to explain why differences were not maintained after stimulation. Together with the inconsistencies (opposite correlations) when analysing AMA-1 stimulated PBMCs or matched pre- and post RTS,S vaccination samples , and allowing for 20% false discoveries it raises questions about the experimental and statistical robustness of this approach. Since antigen-stimulation of PBMCs is not informative the authors could focus their analysis on unstimulated PBMCs from Figure 3 onwards, which would massively streamline their message.Figure 4: include Figure S2 as a panels as they convey crucial information: explicitly draw the conclusion that the production of multiple cytokines after 1-month after final RTS,S vaccination does not predict protection from clinical malaria (reflect in relevant results subheading and figure legend title).Figure 5: this Figure is too dense which makes it hard to find the interesting information – it could be simplified by only including unstimulated PBMCs. It is surprising that antibodies do not correlate with B cell modules – causing some doubt about this correlation approach .Figure 6: A the five post vaccination modules identified in Figure 3 do not associate with risk in RTS,S CHMI studies – why might this be? young African children in malaria endemic environment vs malaria-naive US adults? or because these modules were not very strongly associated with risk in the first place? B – this panel is what the title and main conclusions of the paper are based on – it is interesting to speculate that the innate responses before vaccination may shape vaccine efficacy. Drilling down into the genes in the modules  would be useful to get a glimpse into how this might work and to guide future work where e.g monocytes could be directly isolated. As it stands the conclusion that "inflammatory monocytes may inhibit protective RTS,S/AS01-induced responses" (line 58/59) is not well supported – especially since the authors published a paper last year which finds an inverse association . The explanations offered in the discussion (621 – 633) are technical rather than biological again raising some questions about the robustness of the here presented approach.General remarksThe decision to use blood transcriptional module analysis to reduce data dimensionality is a good one, but once candidate modules are identified the authors should drill down into the nitty gritty of which genes are up and downregulated and infer cell function to provide testible hypothesis for future functional and mechanistic studies.Use figure legend headings/ results subheadings to summarise results.Include n in Figure legends."month 3" could be called "post vaccination" for a more intuitive read.Unstimulated PBMCs: sometimes called vehicle, sometimes DMSO – standardise.SpecificsLine 319 "tended to correlate in opposite directions with risk" : no talk about malaria risk yet.Line 466 – 475 move trial details from Results section to methods.Supplementary methods are 3 lines long – include in text.Reviewer #3 (Recommendations for the authors):I thank the authors for presenting and interesting and well-written manuscript. In addition to my comments above, I have the following suggestions which I believe would improve the clarity and transparency of the paper1. It would be helpful to provide an explanation of how the sample sizes were derived. Since the main objective was to identify BTMs associated with developing malaria after RTS,S/AS01 vaccine, it would be useful to report the malaria incidence in the main Phase 3 trial for the age groups studied at each site in the follow-up period of this study in the RTS,S and comparator vaccine arms. This will help to address the question of whether receipt of RTS,S vaccine is the main reason for the difference between cases and controls, or whether it is more related to exposure to infectious bites. If the EIR is low, then one might expect that the vaccine explains rather little of the difference between cases and controls, and therefore a large sample size would be needed to detect any significant association between gene expression and vaccine efficacy.2. It would be helpful to include more data in table 1 to show how well the matching process for cases and controls worked. Table 1 could be reformatted to be easier to read, with separate columns for each site (rather than the current presentation of this information in parentheses) and could include rows for age, sex, time of vaccination and duration of follow-up.3. In view of the perplexing result mentioned in the previous review section for DMSO stimulation, I think further detail about the PBMC stimulation is needed. I cannot find the DMSO concentration used for vehicle or antigen stimulation in either this paper or the "previously described" reference (24). The authors do not describe that they have subtracted or adjusted for the vehicle-induced gene expression in assessing antigen induced gene expression, but this might be one explanation for this odd result. If this is not the explanation, I worry that different concentrations of DMSO may have been used in the vehicle and antigen stimulation conditions. As this results appears anomalous, it requires further explanation of the methods or discussion as to why this result may have occurred.4. The choice of an FDR significance threshold of 0.2 has not been justified. As mentioned in the previous section of the review this is extremely liberal, and diminishes confidence in the significance of the findings. In most analyses there are BTMs with FDRs below 0.05, so I think it is essential that the authors explain why they chose to use an FDR of 0.2 throughout the paper, and that they discuss the implications of selecting such a liberal threshold.5. The authors have described quite a lot of different statistical approaches in the methods (line 227-256) but it is currently difficult for the reader to understand where each of these has been applied to the data that are presented (particularly in the figures). I think it is essential that figure legends include the number of subjects from each group included in analyses and the type of analysis which has been performed and the criteria for statistical significance. There are several elements in the current description of these analyses which did not fully make sense or which I could not see used in the results that have been presented:a. Line 229-231 state that Spearman's rank correlation was used and then in the next sentence that Pearson correlation was used – please clarify as only one or the other should be used for each analysis.b. Line 235-239 states that adjustment was made for clinical and experimental covariates – these covariates do not appear to be reported anywhere and it is unlcear in the Results section which analyses (if any) included this adjustment.c. Line 247-250: the results of this logistic regression are not obviously presented  and the stratification variables do not appear to have been reported anywhere.6. The authors have decided to include combine rabies and meningococcal C vaccine arms into a single "comparator" vaccine group. One might expect these to elicit quite different effects on the immune system and I think it is important to present data to justify the decision to include them together. For example, a series of PCA plots of the DMSO-stimulated transcriptional responses, with subjects coloured by vaccine, by age group, and by site could be very informative for interpretation of the rest of the data in the paper.7. In all figures the use of a log10 FDR colour scale in the heatmaps makes it very difficult to identify the FDR values. I appreciate that these are included in the supplementary tables, but is it possible to add the FDR value in each cell of the heatmap for the significant values, or if not, perhaps to adopt a categorical approach to colouring them ? This would enhance interpretation. Reviewer #1 (Recommendations for the authors):The authors use a systems vaccinology approach to find a circulating immune signature that can predict RTS,S vaccine efficacy. It is perfectly possible that such a signature simply does not exist (the null hypothesis). I would recommend throughout the paper for the authors to check if their results  support or reject the null hypothesis – both of which are equally valid outcomes!The MAL067 trial set up is fantastic , but unfortunately isolating PBMCs 1-month after final vaccination to assess recall responses did not really work: whilst unstimulated PBMCs are transcriptional different between RTS,S and comparator, no signifiant signature of RTS,S vaccination after CSP and HBS stimulation was observed . It is biologically hard to explain why differences were not maintained after stimulation.+ T cells was very low among CD4+ T cells . This low frequency of CSP-specific T cells is consistent with other RTS,S studies . Moreover, CD4+ T cells themselves comprise approximately 45-57% of all PBMCs (2). Thus, finding an expression signal between the RTS,S vs. comparator group would require the signal to be high enough to be detected in only 0.002% of all PBMCs [0.004%  x 51%  = 0.002%]. Thus, lack of detectable recall response does not mean lack of recall response. Moreover, as suggested below, we opted not to focus the rest of the manuscript on the Ag-stimulation results.The fact that bulk transcriptional profiling of Ag-stimulated PBMCs  did not identify large significant differences in BTM expression between the RTS,S vs. comparator group could be due to several factors. First of all, the frequency of antigen-specific CD4Second of all, the PBMCs were stimulated on site for 12h and then cryopreserved. This stimulation time was chosen based on the kinetics of IFN-g and IL-2 mRNA response (3), but other responses may have had different kinetics and thus have already resolved or have not yet occurred by the 12-h cryopreservation. We have added text in the manuscript to discuss these caveats  and then cryopreserved. Thus, we were unable to detect earlier transient responses that had already resolved by 12 hours, as well as more delayed response that had not yet initiated by 12 hours, if such responses occurred.; lines 599-605).It should be noted that in all our analyses, the stimulated results were adjusted for DMSO to focus on the antigen-specific response only. This would explain why we detect signal in the DMSO samples but not in response to stimulation. We have realized that this was not very well described in the figure captions and the Methods section and have added more details, including the model description in Methods section. As such, we do not believe that these results impact all downstream conclusions. We believe that the unstimulated results provide significant new insights into the immune and molecular mechanisms of RTS,S vaccine efficacy, not necessarily directly related to the RTS,S-specific acquired immune response. Finally, we would like to highlight the fact that we have improved our model specification to directly account for the pairing of some of the samples using a random effect using the limma package. This has slightly increased statistical power, and as such the number of significantly differentially expressed BTMs in response to stimulation is a bit higher (but still much less than that for the DMSO). Originally, we had decided against the use of a random effect due to the computational cost of estimating the random effect.In addition, one clarification, we investigated whether a circulating immune signature associates with individual-level clinical malaria case/control status, not RTS,S vaccine efficacy.Together with the inconsistencies (opposite correlations) when analysing AMA-1 stimulated PBMCs or matched pre- and post RTS,S vaccination samples , and allowing for 20% false discoveries it raises questions about the experimental and statistical robustness of this approach.Our response explains the opposite correlations. Since AMA1 is actually looking at the effect of AMA1-DMSO.As for the FDR rate, it is not uncommon to use a threshold of 20% for immune correlates studies [e.g. (6-11)]. We agree with you that it is important to clearly state the chosen FDR rate and to discuss conclusions in the context of the FDR rate used. We see we could improve our manuscript in this respect. We have added the following:Results: “Compared to the 45 BTMs whose baseline levels significantly associated with clinical malaria risk in RTS,S/AS01-vaccinated African children, fewer BTMs (seven) had levels at one month post-final RTS,S/AS01 dose that significantly associated with clinical malaria risk. Moreover, if a more stringent FDR cutoff had been used (i.e. 5%), six of these seven BTMs would not have been identified. Thus it is entirely possible that, at one month post-final RTS,S/AS01 dose, there is no circulating immune transcriptomic signature predictive of risk…” (lines 617-628)Discussion: “Finally, while it is not uncommon to use an FDR cutoff of 20% in high-dimensional immune correlates studies [e.g. (65-70)], our results should be interpreted with the requisite level of caution. However, we do note that many of our significant modules in the baseline risk analysis would have survived even lower FDR cutoffs (in many cases even a 1% cutoff), giving us a fair degree of confidence in our results. For example, of the seven monocyte-related BTMs whose baseline levels associated with risk, all would have survived a 5% FDR cut-off, and three even a 1% cut-off; likewise, of the four dendritic cell-related BTMs whose baseline levels associated with risk, all would have survived a 5% FDR cut-off, and three even a 1% cut-off.” (lines 644-651)Moreover, we have revised Figures 2, 3, and 6 so that it is easy to discern whether a specific BTM correlation would also pass more stringent FDR cutoffs, through the addition of 1, 2, or 3 asterisks where appropriate: “|FDR| < 0.2 (*), < 0.05 (**), < 0.01 (***).” Note that, most central to the key message of the paper, many of the monocyte-related, DC-related, and cell cycle-related BTMs would have passed more stringent FDR cutoffs, with many even passing a 1% FDR cutoff (as discussed above).Since antigen-stimulation of PBMCs is not informative the authors could focus their analysis on unstimulated PBMCs from Figure 3 onwards, which would massively streamline their message.We believe that the stimulation results should be reported in Figure 3 but agree that the manuscript could be streamlined by removing subsequent antigen stimulation results. We have made the following revisions to the manuscript:– Removed all data from Ag-stimulated PBMC from Figure 5– Removed related discussion of Ag-stimulation results– Removed all data from Ag-stimulated PBMC from Figure S2– Removed all data from Ag-stimulated PBMC from Figure S11Figure 4: include Figure S2 as a panels as they convey crucial information: explicitly draw the conclusion that the production of multiple cytokines after 1-month after final RTS,S vaccination does not predict protection from clinical malaria (reflect in relevant results subheading and figure legend title).We have modified Figure 4 as suggested and made the requested edits to the results subheading and figure title.Figure 5: this Figure is too dense which makes it hard to find the interesting information – it could be simplified by only including unstimulated PBMCs. It is surprising that antibodies do not correlate with B cell modules – causing some doubt about this correlation approach .Thank you for this good suggestion. We have modified Figure 5 as requested.The fact that expression levels of B-cell-related modules in PBMC circulating at month 3 do not correlate with antibodies circulating at the same time is not surprising given that antibodies are secreted by antigen-specific B cells that constitute a very small percentage of total B cells .Moreover, the samples were collected 1 month post vaccination, which is past the plasma cell response. Typically, plasmablast response and B cell activation occurs about one week post-vaccination [e.g. (12-15)]; the effect on circulating antibodies is seen later, e.g. Nakaya et al. showed that, in influenza vaccine recipients, the frequency of influenza-specific IgG secreting plasmablasts at day 7 correlates significantly with the influenza antibody response at day 28 (12). In the RTS,S study in naïve adults (4), activation was detected after 1 and 2 days after the administration of RTS,S vaccine doses. In that study, antigen-specific plasmablasts were detected after 6 days after vaccination but frequencies of these cells did not correlate with the antibody response.Figure 6: A the five post vaccination modules identified in Figure 3 do not associate with risk in RTS,S CHMI studies – why might this be? young African children in malaria endemic environment vs malaria-naive US adults? or because these modules were not very strongly associated with risk in the first place?It is true that in panel A there is no clear pattern for the whole 7 modules across all the RTS,S CHMI studies . In the text related to Figure 6A we state:“No BTM was consistently associated with malaria risk (or non-protection) across all four studies. The most consistent result was for the monocyte-related BTM “enriched in monocytes (II) (M11.0),” whose month 3 expression was significantly associated with risk in two of the three CHMI studies .” (lines 480-483).rd dose, whereas samples in the other studies were collected at different times (21 or 14 days post-vaccination), which could result also in some differences between studies. Finally, there were differences in the formulation of the RTS,S vaccine (AS01E adjuvant vs AS02A or AS01B adjuvants). All these differences are already explained in the manuscript.As to the differences between study populations and whether this may explain the lack of a clear pattern for the whole 7 modules in Figure 6A across all the CHMI studies, the study populations clearly differ with respect to a variety of factors including but not limited to age, genetics, malaria exposure/malaria history, and other infection history. Thus, yes, we believe that these differences are one of the main reasons for the observed differences between studies. In addition, samples from the Phase 3 trial were collected a month after the 3As to the question of whether these seven modules were “not very strongly associated with risk in the first place”, this is also possible. As discussed above, only one of these seven modules would have passed a more stringent FDR cutoff of 5%. We now state in the Discussion:“Compared to the 45 BTMs whose baseline levels significantly associated with clinical malaria risk in RTS,S/AS01-vaccinated African children, fewer BTMs (seven) had levels at one month post-final RTS,S/AS01 dose that significantly associated with clinical malaria risk. Moreover, if a more stringent FDR cutoff had been used (i.e. 5%), six of these seven BTMs would not have been identified. Thus it is entirely possible that, at one month post-final RTS,S/AS01 dose, there is no circulating immune transcriptomic signature predictive of risk. Such a conclusion would not be surprising, given that in malaria-naïve adults, the transcriptional response to the third RTS,S/AS01 dose has been shown to peak at Day 1 post-injection, with some decline by Day 6 and approximately 90% of the response having waned by Day 21 (17). Therefore, it is likely that the sampling scheme in this study  misses the majority of the transcriptional response to RTS,S/AS01. Future studies with dense PBMC sampling during the transcriptional peak of the vaccine-induced response could be useful for further investigating RTS,S transcriptional immune correlates.” (lines 617-628)B – this panel is what the title and main conclusions of the paper are based on – it is interesting to speculate that the innate responses before vaccination may shape vaccine efficacy. Drilling down into the genes in the modules  would be useful to get a glimpse into how this might work and to guide future work where e.g monocytes could be directly isolated. As it stands the conclusion that "inflammatory monocytes may inhibit protective RTS,S/AS01-induced responses" (line 58/59) is not well supported – especially since the authors published a paper last year which finds an inverse association . The explanations offered in the discussion (621 – 633) are technical rather than biological again raising some questions about the robustness of the here presented approach.Thank you for this excellent suggestion. We have discussed above the gene-level analyses we have done and the new supplementary figures and supplementary file that have been added to the manuscript.Please note that the hypothesis that inflammatory monocytes specifically inhibit RTS,S vaccine efficacy was originally put forth by Warimwe et al. in 2013 “It is plausible that RTS,S vaccine efficacy is specifically inhibited by inflammatory monocytes, thus confounding induction of an effective adaptive response, but further studies in both animal models and humans will be needed to confirm this.” (16) We mention this in our Discussion:“Based on previous evidence in various mouse models of viral infection that inflammatory monocytes inhibit T cell proliferation , T cell activation (55), and B cell responses (56), Warimwe et al. proposed that inflammatory monocytes may inhibit RTS,S-induced protective adaptive responses.” (lines 564-567).Prior to performing the gene-level analyses, the fact that we did observe upregulation of many monocyte-related BTMs at baseline in cases vs controls , as well as many inflammatory-related BTMs at baseline in cases vs controls , did seem to support this hypothesis.high immunosuppressive monocytes , this would support an opposite hypothesis that immunosuppressive monocytes may inhibit protective RTS,S/AS01-induced responses.We agree that our previous hypothesis that “inflammatory monocytes may inhibit protective RTS,S/AS01-induced responses” should be revised in light of our stabilin-1 (clever-1) findings, discussed above. In fact, if the increased expression of STAB1 at baseline in PBMC in cases vs controls  is reflective of an increase in circulating stabilin-1We have made the following edits to remove the mentions of “inflammatory monocytes”:Abstract (lines 71-72): “suggesting that inflammatory monocytes may inhibit protective RTS,S/AS01-induced responses” γ edited to “suggests that certain monocyte subsets may inhibit protective RTS,S/AS01-induced responses”..We have also removed the discussion of Mitchell et al. showing a potential association of inflammatory monocytes and decreased vaccine immunity, as well as removing the suggestion of potentially modulating monocyte populations via chemokine receptor antagonists to help boost RTS,S efficacyWe have added the following to the Discussion (lines 567-578):STAB1  was found to positively associate with clinical malaria risk. STAB1 encodes stabilin-1 , a transmembrane glycoprotein scavenger receptor that links extracellular signals to intracellular vesicle trafficking pathways (17). Interestingly, stabilin-1high monocytes show downregulation of proinflammatory genes, and T cells co-cultured with stabilin-1high monocytes showed decreased antigen recall, suggesting that monocyte stabilin-1 suppresses T cell activation (18). Thus one possibility is that stabilin-1high immunosuppressive monocytes circulating at baseline could decrease protective RTS,S-induced T-cell responses, or inhibit another aspect of adaptive immunity. Single-cell transcriptomic profiling of PBMC in future RTS,S trials in African children in malaria-endemic areas could help test this hypothesis.”“Our gene-level correlates analyses suggest an alternative hypothesis, however. With the caveat that the gene-level analyses were performed post hoc, high baseline expression of Please note that we stress that this is a “possibility” and a “hypothesis” rather than a definitive conclusion of the work; as we state at the beginning of the Discussion:“Our main finding is the identification of a baseline blood transcriptional module (BTM) signature that associates with clinical malaria risk in RTS,S/AS01-vaccinated African children. In a cross-study comparison, much of this baseline risk signature – specifically, dendritic cell- and monocyte-related BTMs – was also recapitulated in two of the three CHMI studies in healthy, malaria-naïve adults.” (lines 543-546)This conclusion is well-supported by our data.Regarding how the results of the present manuscript relate to the results of Moncunill et al. 2020 STM: We see how the two sets of results may seem discrepant; however, when we examine the specific details of our previous and current analyses, we see that they are in fact compatible.We have added the following text to our Discussion (lines 584-605):“We have also previously reported that interferon, NF-κB, TLR, and monocyte-related BTMs were associated with protection in children and infants in the RTS,S/AS01 phase 3 trial (64). […] Thus, we were unable to detect earlier transient responses that had already resolved by 12 hours, as well as more delayed response that had not yet initiated by 12 hours, if such responses occurred.”General remarksThe decision to use blood transcriptional module analysis to reduce data dimensionality is a good one, but once candidate modules are identified the authors should drill down into the nitty gritty of which genes are up and downregulated and infer cell function to provide testible hypothesis for future functional and mechanistic studies.We have added new supplementary figures  looking at individual genes. These analyses did yield interesting results, especially with respect to STAB1 . We have significantly revised our Discussion in the light of these new results.Use figure legend headings/ results subheadings to summarise results+ T-cell responses elicited that do not correlate with malaria risk”, “Month 3 levels of RTS,S/AS01 signature BTMs tend to correlate directly with month 3 IgM antibody responses and inversely with month 3 IgG responses”, and “Cross-study immune correlates analysis reveals a mostly consistent association in RTS,S/AS01-vaccinees between baseline expression of DC- and monocyte-related BTMs and risk”.Currently, our results subheadings are written in conclusion form e.g. “RTS,S/AS01 vaccination is associated with month 3 downregulation of B cell- and monocyte-related BTMs, along with upregulation of T cell-related BTMs”, “Monocyte-related RTS,S/AS01 signature BTMs associate with clinical malaria risk”, “RTS,S/AS01 vaccination elicits polyfunctional CSP-specific CD4We prefer to keep our figure titles descriptive for two reasons: (1) avoiding redundancy with the results subheadings, and (2) we think it is most straightforward to describe what analysis is shown in the figure, and leave the interpretation/result from that figure to the “Results” text.Include n in Figure legends.We have included the “n”s in the legends of Figures 2, 3, 4, 5 and 6.Reviewer #3 (Recommendations for the authors):I thank the authors for presenting and interesting and well-written manuscript. In addition to my comments above, I have the following suggestions which I believe would improve the clarity and transparency of the paper1. It would be helpful to provide an explanation of how the sample sizes were derived. Since the main objective was to identify BTMs associated with developing malaria after RTS,S/AS01 vaccine, it would be useful to report the malaria incidence in the main Phase 3 trial for the age groups studied at each site in the follow-up period of this study in the RTS,S and comparator vaccine arms. This will help to address the question of whether receipt of RTS,S vaccine is the main reason for the difference between cases and controls, or whether it is more related to exposure to infectious bites. If the EIR is low, then one might expect that the vaccine explains rather little of the difference between cases and controls, and therefore a large sample size would be needed to detect any significant association between gene expression and vaccine efficacy.Malaria transmission intensity of each site in 2007 (2 years before the start of the study) and the malaria incidence during the Phase 3 Clinical Trial can be found in the final Phase 3 Clinical trial paper (21). Malaria transmission intensity in Bagamoyo and Manhiça sites was low/moderate. Therefore, we used a case-control design for the study instead of a cohort design. Sample sizes were based on availability of samples and malaria cases. We used all samples available from malaria cases and selected 2 to 4 matched controls for each case for RTS,S vaccinees and 2 controls for comparators. Higher numbers of controls were used to consider the heterogeneity and the lack of specificity of the controls as we do not know if these were truly protected children or they had not been exposed. Adding more controls would not increase statistical power.We have made the following revisions to the text in this light:Materials and methods: “As malaria transmission intensity at the Bagamoyo and Manhiça sites was low/moderate (11), we used a case-control design for the study instead of a cohort design. Sample sizes were based on availability of samples and malaria cases. We used all samples available from malaria cases and selected 2 to 4 matched controls for each case for RTS,S vaccinees and 2 controls for comparators. Higher numbers of controls were used to consider the heterogeneity and the lack of specificity of the controls as we do not know if these were truly protected children . In selecting controls, we prioritized participants who had samples at both month 0 and month 3 and in whom the complete set of antigen stimulations was conducted.” (lines 166-170).” (lines 637-641)Discussion (of limitations): “Third, we do not know whether the controls were truly protected or whether they were never exposed to malaria in the first place. This limitation highlights the importance of our cross-study analysis, where all participants are known to be exposed2. It would be helpful to include more data in table 1 to show how well the matching process for cases and controls worked. Table 1 could be reformatted to be easier to read, with separate columns for each site (rather than the current presentation of this information in parentheses) and could include rows for age, sex, time of vaccination and duration of follow-up.Thank you for the good suggestion to reformat Table 1. We have redone Table 1 to include separate columns for each site and agree that readability has improved substantially. In parallel, we have also reformatted Supplementary File 2 for improved readability.Moreover, we now also provide the new Supplementary File 1, which provides complete information on participant match ID, site, age cohort, sex assigned at birth, and time of vaccination.3. In view of the perplexing result mentioned in the previous review section for DMSO stimulation, I think further detail about the PBMC stimulation is needed. I cannot find the DMSO concentration used for vehicle or antigen stimulation in either this paper or the "previously described" reference (24). The authors do not describe that they have subtracted or adjusted for the vehicle-induced gene expression in assessing antigen induced gene expression, but this might be one explanation for this odd result. If this is not the explanation, I worry that different concentrations of DMSO may have been used in the vehicle and antigen stimulation conditions. As this results appears anomalous, it requires further explanation of the methods or discussion as to why this result may have occurred.DMSO  was used at a final dilution of 1/322, the same concentration of DMSO as used for CSP peptide pool. We have now added this information to the manuscript. (lines 184-1485) As discussed above, we have also added details on how the stimulation data were analyzed (i.e. correcting for DMSO).4. The choice of an FDR significance threshold of 0.2 has not been justified. As mentioned in the previous section of the review this is extremely liberal, and diminishes confidence in the significance of the findings. In most analyses there are BTMs with FDRs below 0.05, so I think it is essential that the authors explain why they chose to use an FDR of 0.2 throughout the paper, and that they discuss the implications of selecting such a liberal threshold.While it is not uncommon to use a threshold of 20% for immune correlates studies [e.g. (6-11)], we agree with you that it is important to clearly state the chosen FDR rate and to discuss conclusions in the context of the FDR rate used. We see we could improve our manuscript in this respect. We have added the following:Results: “Compared to the 45 BTMs whose baseline levels significantly associated with clinical malaria risk in RTS,S/AS01-vaccinated African children, fewer BTMs (seven) had levels at one month post-final RTS,S/AS01 dose that significantly associated with clinical malaria risk. Moreover, if a more stringent FDR cutoff had been used (i.e. 5%), six of these seven BTMs would not have been identified. Thus it is entirely possible that, at one month post-final RTS,S/AS01 dose, there is no circulating immune transcriptomic signature predictive of risk…” (lines 617-628)Discussion: “Finally, while it is not uncommon to use an FDR cutoff of 20% in high-dimensional immune correlates studies [e.g. (65-70)], our results should be interpreted with the requisite level of caution. However, we do note that many of our significant modules in the baseline risk analysis would have survived even lower FDR cutoffs (in many cases even a 1% cutoff), giving us a fair degree of confidence in our results. For example, of the seven monocyte-related BTMs whose baseline levels associated with risk, all would have survived a 5% FDR cut-off, and three even a 1% cut-off; likewise, of the four dendritic cell-related BTMs whose baseline levels associated with risk, all would have survived a 5% FDR cut-off, and three even a 1% cut-off.” (lines 644-651)Moreover, we have revised Figures 2, 3, and 6 so that it is easy to discern whether a specific BTM correlation would also pass more stringent FDR cutoffs, through the addition of 1, 2, or 3 asterisks where appropriate: “|FDR| < 0.2 (*), < 0.05 (**), < 0.01 (***).” Note that, most central to the key message of the paper, many of the monocyte-related, DC-related, and cell cycle-related BTMs would have passed more stringent FDR cutoffs, with many even passing a 1% FDR cutoff (as discussed above).5. The authors have described quite a lot of different statistical approaches in the methods (line 227-256) but it is currently difficult for the reader to understand where each of these has been applied to the data that are presented (particularly in the figures). I think it is essential that figure legends include the number of subjects from each group included in analyses and the type of analysis which has been performed and the criteria for statistical significance. There are several elements in the current description of these analyses which did not fully make sense or which I could not see used in the results that have been presented:a. Line 229-231 state that Spearman's rank correlation was used and then in the next sentence that Pearson correlation was used – please clarify as only one or the other should be used for each analysis.b. Line 235-239 states that adjustment was made for clinical and experimental covariates – these covariates do not appear to be reported anywhere and it is unlcear in the Results section which analyses (if any) included this adjustment.c. Line 247-250: the results of this logistic regression are not obviously presented  and the stratification variables do not appear to have been reported anywhere.We have significantly improved our method description both in the results and methods section, as detailed below:Figure legends: Numbers of participants included in each analysis have been included. Methods: Full equations for all analyses have been provided (“plain English” versions of these are now in the figure legends). Criteria for statistical significance are already present in the figure legends.a) “Pearson” changed to “Spearman”b) Edited to “All analyses controlled for plate, total reads, and age.” In addition, the full equations have been provided in Methods.c) We have removed the results of this analysis and the related Methods text. Instead, for testing for significant differences in controls vs cases for each of the monocyte-related variables in Figure 6—figure supplement 10, we have added the following text to the legend of Figure 6—figure supplement 10:“The p values at the bottom of each panel are from testing for a significant difference in controls vs cases within each panel, and were modeled using a mixed-effects model (using lmer) with match id as a random effect.”Each panel also contains a p value underneath for the significance of the difference in cases vs controls.6. The authors have decided to include combine rabies and meningococcal C vaccine arms into a single "comparator" vaccine group. One might expect these to elicit quite different effects on the immune system and I think it is important to present data to justify the decision to include them together. For example, a series of PCA plots of the DMSO-stimulated transcriptional responses, with subjects coloured by vaccine, by age group, and by site could be very informative for interpretation of the rest of the data in the paper.While PCA plots could be generated as the reviewer suggests, there is a complete overlap of vaccine in comparators and age cohort . Thus, we are not sure how informative such plots would be as they would be confounded by age group.The impact of combining both vaccines in comparator recipients on the study results and conclusions is minimal since the main results are based on baseline gene expression and its association with malaria risk within RTS,S vaccinees. Correlates of malaria risk in comparators are done separately. Comparator vaccination may be a confounding factor for age cohort, but we are not analyzing the effect of age cohort on the transcriptional profile. Comparators are only included in the analysis of RTS,S immunogenicity at post-vaccination ) and we have adjusted analyses by age cohort and hence by comparator vaccine. The fact that the comparators received different control vaccines only stresses that the BTMs found to be associated with RTS,S vaccination are specific to the RTS,S vaccine.Moreover, as an alternative way to identify RTS,S-specific transcriptional responses, we also include Comparison (2), which compares Month 3 to Month 0 transcription levels within RTS,S vaccinees.7. In all figures the use of a log10 FDR colour scale in the heatmaps makes it very difficult to identify the FDR values. I appreciate that these are included in the supplementary tables, but is it possible to add the FDR value in each cell of the heatmap for the significant values, or if not, perhaps to adopt a categorical approach to colouring them ? This would enhance interpretation.Yes, Figures 2, 3, 3—figure supplement 1, and 6 now have asterisks to denote three different FDR cutoffs: |FDR| < 0.2 (*), < 0.05 (**), < 0.01 (***).References:1. Moncunill G, De Rosa SC, Ayestaran A, Nhabomba AJ, Mpina M, Cohen KW, Jairoce C, Rutishauser T, Campo JJ, Harezlak J, Sanz H, Diez-Padrisa N, Williams NA, Morris D, Aponte JJ, Valim C, Daubenberger C, Dobano C, McElrath MJ. RTS,S/AS01E Malaria Vaccine Induces Memory and Polyfunctional T Cell Responses in a Pediatric African Phase III Trial. Front Immunol. 2017;8:1008.2. Kleiveland CR. Peripheral Blood Mononuclear Cells. In: Verhoeckx K, Cotter P, López-Expósito I, Kleiveland C, Lea T, Mackie A, et al., editors. 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Plasmodium infection patterns in two low-endemic districts of Meghalaya was undertaken to better understand the epidemiology of malaria in northeast India.A detailed analysis of household and individual level Plasmodium falciparum and/or Plasmodium vivax infection using rapid diagnostic test, microscopy and PCR. Plasma samples from a subset of participants were analysed for antibodies against thirteen P. falciparum and four P. vivax antigens. Associations between household and individual level risk factors, and Plasmodium infections were evaluated using multilevel logistic regression models.Socio-demographic and behavioural information from residents (aged 1–69 years) of households were collected through pre-tested, questionnaire conducted in 2018 and 2019. Blood samples collected from participants were tested for P. falciparum in 2018 and none were positive for P. vivax. In 2019, no PCR-positive individuals were detected. All, but one, infections were asymptomatic; all 33 infections were sub-microscopic. Reported history of malaria in the past 12 months (OR = 8.84) and history of travel in the past 14 days (OR = 10.06) were significantly associated with Plasmodium infection. A significant trend of increased seropositivity with age was noted for all 17 antigens. Although adults (≥ 18 years) consistently had the highest seropositivity rates, a sizeable proportion of under-five children were also found to be seropositive. Almost all individuals (99.4%) reported sleeping under an insecticide-treated bed-net, and household indoor residual spray coverage in the 12 months preceding the survey was low (23%). Most participants correctly identified common signs and symptoms of malaria, i.e., fever (96.4%), headache (71.2%), chills (83.2%) and body-ache (61.8%). Almost all participants (94.3%) used government-provided services for treatment of malaria.A total of 2753 individuals from 827 households were enrolled in 2018, and 834 individuals from 222 households were enrolled in 2019. Of them, 33 (1.2%) were positive by PCR for Plasmodium transmission persists in this region. Implications of the study findings for malaria elimination efforts in low-transmission settings are discussed.This study explored the epidemiology of malaria in two communities in Meghalaya, India, in the context of declining transmission. The presence of widespread asymptomatic infections and seropositivity among under-five children suggest that low-level  Malaria continues to be a major public health problem globally with an estimated 229 million cases reported from 87 endemic countries . India hIn 2016, India launched the National Framework for Malaria Elimination with the ambitious goals of eliminating malaria from the country by 2030, maintaining malaria free status, and preventing reintroduction of infection in areas where transmission interruption has been achieved . To achiPlasmodium falciparum cases and 22% of the malaria deaths reported in 2019 [Even though the northeast region (NER) comprises about 4% of India’s population, it accounted for around 15% of the country's  in 2019 . This is in 2019 , 11. ThePlasmodium infection during 2018–2019 in 21 villages, summarised village-level risk factors for infection, and identified 13 Anopheles mosquito species as potential vectors in a subset of villages [Plasmodium infection patterns in the same two low-endemic communities in 2018–2019, evaluated social and behavioural risk factors for infection, and explored patterns of Plasmodium antigen-specific antibodies in the population.Information on malaria patterns in Meghalaya is limited to unpublished government reports and a recent cross-sectional survey in two districts . The latvillages . To furtThis study was conducted in two districts of Meghalaya: West Khasi Hills (KH) and West Jaintia Hills (JH). Meghalaya is a hilly and mountainous state in northeast India, located between Assam and Bangladesh Fig. , with a Plasmodium infections , representation across primary health centres (PHC) and health sub-centres in the study area and location. A random sample of households from each village (selected based on probability proportion to size technique) were consented and enrolled. A household survey was administered to one resident of each participating household, usually the ‘head of household’ that included questions on family size, source of water supply, building materials for roofs and walls, presence of electricity, toilets, animals and malaria prevention methods . In addition, the household members were administered individual surveys during which they were asked mostly close-ended questions in their local language  to obtain information on age, gender, education, occupation, knowledge of malaria, health history , travel history (past two weeks) and malaria prevention methods. Malaria risk behaviours and practices were also surveyed. Parents or caretakers responded on behalf of their children.Socio-demographic and behavioural information was collected from residents (aged 1–69 years) of households in the JH and KH from April to November 2018 and again from May to September 2019. Details of the sampling design and methods have been described elsewhere . BrieflyPlasmodium detection methods have been described elsewhere [P. falciparum and/or P. vivax infections was determined by a bivalent rapid diagnostic test  and an ultra-sensitive RDT (Abbott Alere). Blood smears were also collected, fixed in methanol and Giemsa-stained prior to qualitative and quantitative evaluation by light microscopy. From the small blood volume, blood components were separated by centrifugation into plasma and a red blood cell (RBC) pellet and stored at -80 °C until assayed.Details of the blood sample collection and lsewhere . BrieflyPlasmodium infections were detected by PCR amplification of concentrated DNA extracted from microvette RBC pellets using a single-step PCR targeting Pfr364  and Pvr47 (for P. vivax) genomes, respectively [Plasmodium infection was indicated based upon RDT results and/or PCR results; no infections were detected by microscopy.Species-specific ectively , as prevectively . A positP. falciparum and P. vivax proteins and/or peptides were coupled to unique Luminex Magplex magnetic microspheres and used in a multiplexed, bead-based assay to quantify host IgG antibodies as previously described [E. coli extract. All samples were assayed singularly with positive controls, naïve sample pools, and blanks run in duplicate or triplicate according to standardized procedures [Seventeen recombinant escribed . Plasma ocedures . Plates Plasmodium infection, along with the 95% confidence interval (CI), was calculated using the Taylor linearized method that accounts for the clustered data-structure [Plasmodium infections were evaluated using multilevel logistic regression models with exchangeable correlation matrix, considering village and household as grouping variables and reported as odds ratios (OR) with 95% CIs.Data were collected and managed using REDCap electronic data capture tools , 19 hosttructure . AssociaAg = raw MFIAg − background MFIAg) where background MFIAg is the mean MFI of a given antigen in the blank wells was calculated for each antigen in each sample assayed. The seropositive threshold for each antigen was defined as mean net MFInegative pool plus three standard deviations. The number and proportion of individuals seropositive for each antigen was tabulated for three age categories: children (1–7 years), adolescents (8–17 years), and adults (≥ 18 years). Differences in the proportion of seropositive individuals across the three age categories was determined by using the chi-square test for trend. The net MFIAg values from seropositive individuals were normalised with respect to the corresponding seropositive threshold value to generate a relative MFI value for comparison of response magnitudes . The median (IQR) relative MFI values were determined for each antigen across all age groups, and differences in the relative magnitude of response across age groups was tested using a nonparametric test for trend across ordered groups [Net mean fluorescence intensity (MFI) . Assent was obtained for participants aged 7–17 years, in addition to the parental consent.From 21 villages surveyed in JH (N = 9) and KH (N = 12) that represented 9306 residents from 1688 households, a total of 3017 (32.4%) individuals were approached for participation; 2753 individuals (29.6%) living in 820 households (48.6%) were enrolled in the study during 2018. In 2019, 222 households (13.2%) and 834 people (9%) from these villages were enrolled. The age and gender distribution of the total population of the two districts and that of the study participants is presented in Fig. P. falciparum; none of the participants tested positive for P. vivax. One (3%) of the 33 PCR-positive samples was also positive by RDT . The prevalence of P. falciparum infection was similar in JH  and KH . In 2019, none of 834 blood samples (0%) were positive for either Plasmodium species, including samples from 18 PCR-positive individuals from the 2018 survey. For this reason, analysis of risk factors for P. falciparum was undertaken solely for 2018.Blood from microvette samples during 2018 was available for testing from 1463 of the 1467 (99.7%) participants in JH, and 1234 of the 1286 (98.3%) KH participants. In 2018, 33 of 2697 study participants  were positive by PCR for The household level characteristics, aggregated by village, have been described elsewhere . BrieflyThe mean (SD) age of participants was 20.8 (17.2) years. Slightly more females (56.5%) than males participated in the study. Of the adult participants (≥ 18 years), a majority (47.6%) had less than primary education . Most participants were either students (35.8%) or engaged in agriculture-related activity (27.5%). Malaria diagnosis within the past 12 months was reported by only 3.6% of the participants; 2% of participants reported a history of travel outside of the village in the last 14 days. Around 5% of the participants reported staying in the field for one or more nights, with a mean (SD) of 6.9 (3.0) days. A large majority (86.2%) reported using ITNs while staying in the field. Most participants had knowledge of the common signs and symptoms of malaria, i.e., fever (96.4%), headache (71.2%), chills (83.2%) and body-ache (61.8%). Almost everyone (94.3%) preferred to seek treatment from government healthcare facility or community health worker , if diagnosed with malaria.The individual level socio-demographic characteristics of the study participants are presented in Table Plasmodium infections were assessed separately for JH and KH. A total of 2697 individuals from 819 households for whom a PCR-test result for Plasmodium infection was available were included in the analysis .The household and individual level risk factors for Plasmodium infection in individual household members  was significantly associated with Plasmodium infection in JH and history of travel in the past 14 days  was significantly associated with Plasmodium infection in KH. None of the other factors were significantly associated with risk of Plasmodium infection in either of the two study districts.The individual level risk factors for ed Table . A reporThe number and proportion of seropositive individuals in each age category are listed by antigen/antibody in Table P. falciparum antigens assayed: PfAMA1 (P < 0.001), PfEBA175 (P < 0.001), PfEBA181 (P = 0.001), PfEtramp5.Ag1 (P = 0.001), PfGlurp.R2 (P = 0.007), PfMSP119 (P < 0.001), PfRh2 2030 (P < 0.001), and PfRh5 (P = 0.011) (Table 19 (5.45), PfAMA1 (4.83), Rh2 2030 (4.74) and PfGlurp.R2 (4.72).The relative magnitude of antibody detected was significantly different across age groups for eight of the 13 1) Table . The relP. vivax antigens, PvAMA1 (P = 0.020) and PvMSP10 (P = 0.002). Independent of statistical significance, the relative magnitude of antibody was greatest in the adult age category for all four P. vivax targets with the highest relative MFI observed for PvAMA1 (5.04).The relative magnitude of antibody detected was significantly different across age groups for two of the four Questions about presence of mosquitoes and methods for reducing household level human exposure indicated widespread mosquito abundance and extensive efforts to decrease contact Table . MosquitIndividual level anti-mosquito protection was also widespread and appropriate Table . VirtualPlasmodium infection in the context of ongoing intensive malaria elimination effort in two districts of Meghalaya that only a few years ago had relatively high transmission [Plasmodium infection in the two communities in 2018 was determined, with malaria prevalence declining to essentially undetectable levels in 2019. Ninety-seven percent of the infected individuals had asymptomatic, sub-microscopic infection, which could only be detected through molecular (PCR-based) methods. A high prevalence of sub-microscopic infections was previously reported from malaria-endemic areas in India [Plasmodium infection [This community-based cross-sectional study investigated the prevalence, and household and individual level risk factors of malaria to better understand the epidemiology of smission . A low pin India –24 and ein India –27. As snfection , the usenfection .P. falciparum and four P. vivax antigens, chosen based upon prior classification as indicators of protection from clinical disease [Plasmodium transmission intensity [Plasmodium antigens in children under five years of age, however, can be considered as a marker of relatively recent exposure [Plasma samples from a subset of the study participants were analysed for presence of antibodies against thirteen  disease  or marke disease . The prontensity –35. The exposure  and provPlasmodium infection in this study. With increasing movement of people , imported malaria from cross-border and regional human movement represents a major obstacle to malaria elimination [A history of travel outside of the village within the past 14 days was associated with a higher risk of mination , 38, espmination  that shaP. falciparum infection in this study. Recurrent episodes of malaria occurring in a small percentage of individuals have earlier been reported from endemic areas [A reported history of malaria in the past 12 months was associated with a higher risk of ic areas –41. In aic areas . Mathemaic areas , 44. Theic areas –47 and eic areas , 48.P. falciparum and P. vivax malaria, respectively [Virtually every participant in the present study reported using ITNs. In a recent meta-analysis, ITN use was associated with 45% and 39% reduction in incidence of uncomplicated episodes of ectively . In anotectively . Also, nectively .Indoor residual spray coverage in the study districts was low, with only 23% of the households reported having been sprayed in the preceding 12 months, much less than the World Health Organization target of > 85% IRS coverage for preventing malaria transmission . Low levThe lack of understanding of what causes malaria, the use of ineffective prevention methods, the belief that malaria cannot be prevented, and general reliance on traditional remedies have been cited as major barriers to malaria prevention and treatment . In MeghPlasmodium-positive individuals resulted in reduced statistical power to evaluate the risk factors for infection in this population, which is a limitation of this study. Future studies in low-transmission settings may consider including serological markers of recent infection as a tool to estimate the burden and transmission patterns of malaria in low-intensity settings [Plasmodium infection prevalence may have been underestimated in this study. Finally, detailed exposure history could not be obtained for participants with serology results, which limited the ability to differentiate between recent and long-term exposure to Plasmodium infection.Despite enrolling more than 2500 participants, the low prevalence of settings . As withsettings , 65, thePlasmodium infections and the presence of anti-Plasmodium antibodies detected in children under five years of age suggests “hidden” transmission in the region, although the relative contribution of asymptomatic and sub-microscopic individuals to parasite transmission and/or clinical malaria in low-transmission settings need to be explored further. The higher infection risk observed among those with a travel history in a region with extensive international borders, heterogeneous distribution of Plasmodium infections, abundant vector populations, and favourable environmental conditions [This study provides important insights into the epidemiology of malaria in a low-transmission setting in India. The high proportion of asymptomatic nditions , 11 altonditions , 66. Con


Azithromycin has recently been shown to reduce all-cause childhood mortality in sub-Saharan Africa. One potential mechanism of this effect is via the anti-malarial effect of azithromycin, which may help treat or prevent malaria infection. This study evaluated short- and longer-term effects of azithromycin on malaria outcomes in children.Children aged 8 days to 59 months were randomized in a 1:1 fashion to a single oral dose of azithromycin (20 mg/kg) or matching placebo. Children were evaluated for malaria via thin and thick smear and rapid diagnostic test (for those with tympanic temperature ≥ 37.5 °C) at baseline and 14 days and 6 months after treatment. Malaria outcomes in children receiving azithromycin versus placebo were compared at each follow-up timepoint separately.P = 0.43) and 7.6% vs. 5.6% in the azithromycin compared to placebo arms at 6 months (P = 0.47).Of 450 children enrolled, 230 were randomized to azithromycin and 220 to placebo. Children were a median of 26 months and 51% were female, and 17% were positive for malaria parasitaemia at baseline. There was no evidence of a difference in malaria parasitaemia at 14 days or 6 months after treatment. In the azithromycin arm, 20% of children were positive for parasitaemia at 14 days compared to 17% in the placebo arm .The online version contains supplementary material available at 10.1186/s12936-021-03895-9. Biannual mass azithromycin distribution with a single oral 20 mg/kg dose has been shown to reduce all-cause childhood mortality in settings with high malaria transmission . In thisPlasmodium falciparum malaria.Prevention or treatment of existing malaria infection may be one mechanism of any effect of single dose oral azithromycin on all-cause childhood mortality, particularly where malaria is an important cause of child mortality. This individually randomized trial of single oral azithromycin compared to matching placebo was designed to elucidate potential mechanisms of any effect of azithromycin on mortality in Nouna, Burkina Faso. A pre-specified secondary analysis of the trial evaluated the effect of single dose azithromycin on malaria parasitaemia administered at the end of the high malaria transmission season in a region with predominantly The GAMIN study was an individually randomized 1:1 trial comparing a single oral dose of azithromycin (20 mg/kg) or matching placebo to better understand potential mechanisms for reducing child mortality in children aged 0–59 months in Burkina Faso. Assessments were performed at baseline, 14 days from enrollment, and 6 months from enrollment. The study was reviewed and approved by the Institutional Review Board at the University of California, San Francisco and the Comité National d’Ethique pour la Recherche  in Ouagadougou, Burkina Faso. Written informed consent was obtained from the caregiver of each enrolled participant.Participants were enrolled in Nouna Town, Burkina Faso. Nouna is the capital of Kossi Province and located in the northwest of Burkina Faso, with a population of approximately 20,000 residents. As with much of the Sahel, Nouna has highly seasonal malaria transmission, with transmission peaking during the rainy season from approximately July through October . ChildreNouna Town is located in the Nouna Health and Demographic Surveillance Site (HDSS) , which cAzithromycin and matching placebo were prepared as pediatric oral suspension . A single oral 20 mg/kg dose was offered to each participant or an equivalent volume of placebo. Dosing was weight-based for children who were too young to stand, and a height stick approximation was used for children over 12 months of age, as in trachoma control programmes and has been previously done in studies of azithromycin for prevention of childhood mortality  to assess parasite density, and presence of gametocytes. Discrepancies were adjudicated by a third masked microscopist. A positive smear was considered positive for malaria. The median of the two parasite density measurements was used for analysis. Tympanic temperature was measured in all children at all time points. Those with fever (defined as temperature ≥ 37.5 °C) were administered a rapid diagnostic test (RDT) for malaria. Children with a positive malaria RDT were referred for appropriate care.At each study visit , thin and thick smears were collected from each study participant for assessment of malaria parasitaemia. Blood smears were collected on glass slides, air dried, and stored at room temperature. Smears were stained with 3% Giemsa and read by two masked microscopists who determined the presence or absence of The sample size was determined based on the primary outcome of the trial, which was Shannon’s diversity index of the gut microbiome. A sample size of 225 children per arm  would provide at least 80% power for a detectable effect size of 1.02 standard deviations of Shannon’s diversity with no loss to follow-up. Under this fixed design, 225 children per arm would have approximately 80% power to detect an 11% absolute difference in malaria parasitaemia, assuming 18.5% prevalence in the placebo arm at 14 days.P < 0.05 was considered statistically significant. No adjustments were made for multiple comparisons. All analyses were conducted in Stata version 15.1 .Baseline demographic variables were summarized with medians and interquartile ranges (IQR) for continuous variables and proportions for categorical variables. The prevalence of parasitaemia, gametocytaemia, and parasitaemia plus fever were calculated at each time point with binomial 95% confidence intervals. Parasite density measurements were log transformed for analysis. Analyses of parasite density were restricted only to those who had positive malaria smears. Unadjusted logistic regression models to estimate odds ratios (OR) and 95% confidence intervals (CI) were used to estimate the effect of azithromycin versus placebo on each dichotomous malaria outcome, and an unadjusted linear regression model to estimate mean differences in parasite density in children receiving azithromycin compared to placebo. As a non-pre-specified subgroup analysis, 14-day malaria parasitaemia models were stratified by malaria parasitaemia positivity at baseline (presence or absence of parasites). Per the study’s pre-specified analysis plan, all analyses were intention-to-treat and children were analysed as per their random treatment assignment. As a sensitivity analysis, children who did not receive their random treatment assignment were excluded, following the same analyses outlined for the intention-to-treat analysis. For parasite density outcomes at 14 days, a sensitivity analysis adjusting for baseline parasite density was conducted. Of 450 children screened, all were eligible and enrolled in the trial, and 230 were randomized to azithromycin and 220 to placebo Fig. . Three . MalariaP = 0.43). Restricting models only to children who received their allocated study treatment did not affect results for any of the outcomes  and adjusted for baseline parasite density .No evidence of a difference between the azithromycin and placebo groups for any malaria outcomes were observed Table . MalariaP = 0.75) nor in children who were malaria parasitaemia positive at baseline .In stratified models, there was no evidence of a difference in malaria parasitaemia prevalence at 14 days in children who were negative for malaria parasitaemia at baseline (OR 1.11 for azithromycin vs. placebo, 95% CI 0.57 to 2.17, No evidence that a single dose of oral azithromycin reduces malaria infection was observed in this community-based sample of children in a setting in Burkina Faso with highly seasonal malaria transmission. A household randomized trial of azithromycin versus placebo given alongside seasonal malaria chemoprevention for 3 consecutive years of administration in children aged 2 to 59 months showed no additional effect of azithromycin on malaria parasitaemia in Burkina Faso and Mali . HoweverOne possible explanation for discordant results between this study, which found no effect of azithromycin at 14 days or 6 months, and previous studies showing short- and longer-term effects of malaria may be the proportion of the population receiving azithromycin. Mass drug administration may be more effective for control of infectious disease than individual-level treatment because it may disrupt transmission in communities. In the Niger study of biannual mass azithromycin distribution, all preschool children in an entire community were treated on the same day and may have introduced some herd-like protection of untreated individuals by reducing community transmission . The parThere was no evidence of a difference in 14-day malaria results in models stratified by baseline malaria status, although the sample size was small, especially for models restricted only to those with positive baseline smears. Azithromycin has weak anti-malarial properties and is not typically considered suitable as monotherapy for treatment of malaria . StudiesThe results of this study must be considered in the context of several limitations. This trial was designed around the primary endpoint of evaluating the paediatric microbiome, and thus was not specifically powered for malaria outcomes. This limited statistical power especially in subgroup analyses by baseline malaria status. Although there was no significant effect of azithromycin on any malaria outcome, point estimates for some models suggested that children receiving azithromycin had worse malaria outcomes than those receiving placebo. This may have been due to a chance imbalance in malaria parasitaemia at baseline. Children with a baseline infection may have been at higher risk of repeated infection, for example, due to environmental or behavioural factors such as improved housing materials or proximity to standing water, or children with positive smears at baseline may have still been positive at 14 days , 26. ModIn this individually randomized trial, there was no evidence of an effect of a single oral dose of azithromycin on malaria outcomes in children under 5 years of age. Individual-level treatment of children with azithromycin with a single dose may not be sufficient to affect malaria transmission and thus outcomes compared to community distributions.Additional file 1: Table S1. Malaria outcomes by treatment group among children who received their allocated study treatment.Additional file 2. Height stick for height-based dosing for children aged 12-59 months, Burkina Faso.