Two decades of molecular surveillance in Senegal reveal rapid changes in known drug resistance mutations over time.

BACKGROUND: Drug resistance in Plasmodium falciparum is a major threat to malaria control efforts. Pathogen genomic surveillance could be invaluable for monitoring current and emerging parasite drug resistance. METHODS: Data from two decades (2000-2020) of continuous molecular surveillance of P. falciparum parasites from Senegal were retrospectively examined to assess historical changes in malaria drug resistance mutations. Several known drug resistance markers and their surrounding haplotypes were profiled using a combination of single nucleotide polymorphism (SNP) molecular surveillance and whole genome sequence based population genomics. RESULTS: This dataset was used to track temporal changes in drug resistance markers whose timing correspond to historically significant events such as the withdrawal of chloroquine (CQ) and the introduction of sulfadoxine-pyrimethamine (SP) in 2003. Changes in the mutation frequency at Pfcrt K76T and Pfdhps A437G coinciding with the 2014 introduction of seasonal malaria chemoprevention (SMC) in Senegal were observed. In 2014, the frequency of Pfcrt K76T increased while the frequency of Pfdhps A437G declined. Haplotype-based analyses of Pfcrt K76T showed that this rapid increase was due to a recent selective sweep that started after 2014. DISCUSSION (CONCLUSION): The rapid increase in Pfcrt K76T is troubling and could be a sign of emerging amodiaquine (AQ) resistance in Senegal. Emerging AQ resistance may threaten the future clinical efficacy of artesunate-amodiaquine (ASAQ) and AQ-dependent SMC chemoprevention. These results highlight the potential of molecular surveillance for detecting rapid changes in parasite populations and stress the need to monitor the effectiveness of AQ as a partner drug for artemisinin-based combination therapy (ACT) and for chemoprevention.

Two mosquito salivary antigens demonstrate promise as biomarkers of recent exposure to P. falciparum infected mosquito bites.

Background: Measuring malaria transmission intensity using the traditional entomological inoculation rate is difficult. Antibody responses to mosquito salivary proteins such as SG6 have previously been used as biomarkers of exposure to Anopheles mosquito bites. Here, we investigate four mosquito salivary proteins as potential biomarkers of human exposure to mosquitoes infected with P. falciparum: mosGILT, SAMSP1, AgSAP, and AgTRIO. Methods: We tested population-level human immune responses in longitudinal and cross-sectional plasma samples from individuals with known P. falciparum infection from low and moderate transmission areas in Senegal using a multiplexed magnetic bead-based assay. Results: AgSAP and AgTRIO were the best indicators of recent exposure to infected mosquitoes. Antibody responses to AgSAP, in a moderate endemic area, and to AgTRIO in both low and moderate endemic areas, were significantly higher than responses in a healthy non-endemic control cohort (p-values = 0.0245, 0.0064, and <0.0001 respectively). No antibody responses significantly differed between the low and moderate transmission area, or between equivalent groups during and outside the malaria transmission seasons. For AgSAP and AgTRIO, reactivity peaked 2-4 weeks after clinical P. falciparum infection and declined 3 months after infection. Discussion: Reactivity to both AgSAP and AgTRIO peaked after infection and did not differ seasonally nor between areas of low and moderate transmission, suggesting reactivity is likely reflective of exposure to infectious mosquitos or recent biting rather than general mosquito exposure. Kinetics suggest reactivity is relatively short-lived. AgSAP and AgTRIO are promising candidates to incorporate into multiplexed assays for serosurveillance of population-level changes in P. falciparum-infected mosquito exposure.

Quality assessment of malaria microscopic diagnosis at the Aristide Le Dantec University Hospital of Dakar, Senegal, in 2020.

BACKGROUND: Following WHO guidelines, microscopy is the gold standard for malaria diagnosis in endemic countries. The Parasitology-Mycology laboratory (LPM) is the National Reference Laboratory and is currently undergoing ISO 15189 accreditation. In this context, we assessed the performance of the laboratory by confirming the reliability and the accuracy of results obtained in accordance with the requirements of the ISO 15189 standards. This study aimed to verify the method of microscopic diagnosis of malaria at the LPM, in the Aristide Le Dantec hospital (HALD) in Dakar, Senegal. METHODS: This is a validation/verification study conducted from June to August 2020. Twenty (20) microscopic slides of thick/thin blood smear with known parasite densities (PD) selected from the Cheick Anta Diop University malaria slide bank in Dakar were used for this assessment. Six (6) were used to assess microscopists' ability to determine PD and fourteen (14) slides were used for detection (positive vs negative) and identification of parasites. Four (4) LPM-HALD microscopists read and recorded their results on prepared sheets. Data analysis was done with Microsoft Excel 2010 software. RESULTS: A minimum threshold of 50% concordance was used for comparison. Of the twenty (20) slides read, 100% concordance was obtained on eight (8) detection (positive vs negative) slides. Four (4) out of the six (6) parasite density evaluation slides obtained a concordance of less than 50%. Thirteen (13) out of the fourteen (14) identification slides obtained a concordance greater than 50%. Only one (1) identification slide obtained zero agreement from the microscopists. For species identification a concordance greater than 80% was noted and the microscopists obtained scores between 0.20 and 0.4 on a scale of 0 to 1 for parasite density reading. The microscopists obtained 100% precision, sensitivity, specificity and both negative and positive predictive values. CONCLUSION: This work demonstrated that the microscopic method of malaria diagnosis used in the LPM/HALD is in accordance with the requirements of WHO and ISO 15189. Further training of microscopists may be needed to maintain competency.

Investigating the etiologies of non-malarial febrile illness in Senegal using metagenomic sequencing.

The worldwide decline in malaria incidence is revealing the extensive burden of non-malarial febrile illness (NMFI), which remains poorly understood and difficult to diagnose. To characterize NMFI in Senegal, we collected venous blood and clinical metadata in a cross-sectional study of febrile patients and healthy controls in a low malaria burden area. Using 16S and untargeted sequencing, we detected viral, bacterial, or eukaryotic pathogens in 23% (38/163) of NMFI cases. Bacteria were the most common, with relapsing fever Borrelia and spotted fever Rickettsia found in 15.5% and 3.8% of cases, respectively. Four viral pathogens were found in a total of 7 febrile cases (3.5%). Sequencing also detected undiagnosed Plasmodium, including one putative P. ovale infection. We developed a logistic regression model that can distinguish Borrelia from NMFIs with similar presentation based on symptoms and vital signs (F1 score: 0.823). These results highlight the challenge and importance of improved diagnostics, especially for Borrelia, to support diagnosis and surveillance.

Improving diagnosis of non-malarial fevers in Senegal: Borrelia and the contribution of tick-borne bacteria.

The worldwide decline in malaria incidence is revealing the extensive burden of non-malarial febrile illness (NMFI), which remains poorly understood and difficult to diagnose. To characterize NMFI in Senegal, we collected venous blood and clinical metadata from febrile patients and healthy controls in a low malaria burden area. Using 16S and unbiased sequencing, we detected viral, bacterial, or eukaryotic pathogens in 29% of NMFI cases. Bacteria were the most common, with relapsing fever Borrelia and spotted fever Rickettsia found in 15% and 3.7% of cases, respectively. Four viral pathogens were found in a total of 7 febrile cases (3.5%). Sequencing also detected undiagnosed Plasmodium, including one putative P. ovale infection. We developed a logistic regression model to distinguish Borrelia from NMFIs with similar presentation based on symptoms and vital signs. These results highlight the challenge and importance of improved diagnostics, especially for Borrelia, to support diagnosis and surveillance.

Plasmodium infection is associated with cross-reactive antibodies to carbohydrate epitopes on the SARS-CoV-2 Spike protein.

Sero-surveillance can monitor and project disease burden and risk. However, SARS-CoV-2 antibody test results can produce false positive results, limiting their efficacy as a sero-surveillance tool. False positive SARS-CoV-2 antibody results are associated with malaria exposure, and understanding this association is essential to interpret sero-surveillance results from malaria-endemic countries. Here, pre-pandemic samples from eight malaria endemic and non-endemic countries and four continents were tested by ELISA to measure SARS-CoV-2 Spike S1 subunit reactivity. Individuals with acute malaria infection generated substantial SARS-CoV-2 reactivity. Cross-reactivity was not associated with reactivity to other human coronaviruses or other SARS-CoV-2 proteins, as measured by peptide and protein arrays. ELISAs with deglycosylated and desialated Spike S1 subunits revealed that cross-reactive antibodies target sialic acid on N-linked glycans of the Spike protein. The functional activity of cross-reactive antibodies measured by neutralization assays showed that cross-reactive antibodies did not neutralize SARS-CoV-2 in vitro. Since routine use of glycosylated or sialated assays could result in false positive SARS-CoV-2 antibody results in malaria endemic regions, which could overestimate exposure and population-level immunity, we explored methods to increase specificity by reducing cross-reactivity. Overestimating population-level exposure to SARS-CoV-2 could lead to underestimates of risk of continued COVID-19 transmission in sub-Saharan Africa.

R H: a genetic metric for measuring intrahost Plasmodium falciparum relatedness and distinguishing cotransmission from superinfection.

Multiple-strain (polygenomic) infections are a ubiquitous feature of Plasmodium falciparum parasite population genetics. Under simple assumptions of superinfection, polygenomic infections are hypothesized to be the result of multiple infectious bites. As a result, polygenomic infections have been used as evidence of repeat exposure and used to derive genetic metrics associated with high transmission intensity. However, not all polygenomic infections are the result of multiple infectious bites. Some result from the transmission of multiple, genetically related strains during a single infectious bite (cotransmission). Superinfection and cotransmission represent two distinct transmission processes, and distinguishing between the two could improve inferences regarding parasite transmission intensity. Here, we describe a new metric, R H, that utilizes the correlation in allelic state (heterozygosity) within polygenomic infections to estimate the likelihood that the observed complexity resulted from either superinfection or cotransmission. R H is flexible and can be applied to any type of genetic data. As a proof of concept, we used R H to quantify polygenomic relatedness and estimate cotransmission and superinfection rates from a set of 1,758 malaria infections genotyped with a 24 single nucleotide polymorphism (SNP) molecular barcode. Contrary to expectation, we found that cotransmission was responsible for a significant fraction of 43% to 53% of the polygenomic infections collected in three distinct epidemiological regions in Senegal. The prediction that polygenomic infections frequently result from cotransmission stresses the need to incorporate estimates of relatedness within polygenomic infections to ensure the accuracy of genomic epidemiology surveillance data for informing public health activities.

Plasmodium falciparum genomic surveillance reveals spatial and temporal trends, association of genetic and physical distance, and household clustering.

Molecular epidemiology using genomic data can help identify relationships between malaria parasite population structure, malaria transmission intensity, and ultimately help generate actionable data to assess the effectiveness of malaria control strategies. Genomic data, coupled with geographic information systems data, can further identify clusters or hotspots of malaria transmission, parasite genetic and spatial connectivity, and parasite movement by human or mosquito mobility over time and space. In this study, we performed longitudinal genomic surveillance in a cohort of 70 participants over four years from different neighborhoods and households in Thiès, Senegal-a region of exceptionally low malaria transmission (entomological inoculation rate less than 1). Genetic identity (identity by state, IBS) was established using a 24-single nucleotide polymorphism molecular barcode, identity by descent was calculated from whole genome sequence data, and a hierarchical Bayesian regression model was used to establish genetic and spatial relationships. Our results show clustering of genetically similar parasites within households and a decline in genetic similarity of parasites with increasing distance. One household showed extremely high diversity and warrants further investigation as to the source of these diverse genetic types. This study illustrates the utility of genomic data with traditional epidemiological approaches for surveillance and detection of trends and patterns in malaria transmission not only by neighborhood but also by household. This approach can be implemented regionally and countrywide to strengthen and support malaria control and elimination efforts.

The Origins and Future of Sentinel: An Early-Warning System for Pandemic Preemption and Response.

While investigating a signal of adaptive evolution in humans at the gene LARGE, we encountered an intriguing finding by Dr. Stefan Kunz that the gene plays a critical role in Lassa virus binding and entry. This led us to pursue field work to test our hypothesis that natural selection acting on LARGE-detected in the Yoruba population of Nigeria-conferred resistance to Lassa Fever in some West African populations. As we delved further, we conjectured that the "emerging" nature of recently discovered diseases like Lassa fever is related to a newfound capacity for detection, rather than a novel viral presence, and that humans have in fact been exposed to the viruses that cause such diseases for much longer than previously suspected. Dr. Stefan Kunz's critical efforts not only laid the groundwork for this discovery, but also inspired and catalyzed a series of events that birthed Sentinel, an ambitious and large-scale pandemic prevention effort in West Africa. Sentinel aims to detect and characterize deadly pathogens before they spread across the globe, through implementation of its three fundamental pillars: Detect, Connect, and Empower. More specifically, Sentinel is designed to detect known and novel infections rapidly, connect and share information in real time to identify emerging threats, and empower the public health community to improve pandemic preparedness and response anywhere in the world. We are proud to dedicate this work to Stefan Kunz, and eagerly invite new collaborators, experts, and others to join us in our efforts.

Plasmodium infection induces cross-reactive antibodies to carbohydrate epitopes on the SARS-CoV-2 Spike protein.

Individuals with acute malaria infection generated high levels of antibodies that cross-react with the SARS-CoV-2 Spike protein. Cross-reactive antibodies specifically recognized the sialic acid moiety on N-linked glycans of the Spike protein and do not neutralize in vitro SARS-CoV-2. Sero-surveillance is critical for monitoring and projecting disease burden and risk during the pandemic; however, routine use of Spike protein-based assays may overestimate SARS-CoV-2 exposure and population-level immunity in malaria-endemic countries.