Biology subjects

Gerlovina, I.

Publications and source records attributed to Gerlovina, I..

5 recordsLinked to original sources

bioRxiv2025

Classification of outcomes in antimalarial therapeutic efficacy studies with Aster

Reliable assessment of antimalarial drug efficacy is crucial for effective response to emerging drug resistance, and therapeutic efficacy studies (TES) are the primary means of estimating in vivo efficacy. Accuracy of such estimates rests on correctly classifying recurrent infections developed during follow-up as recrudescences or new infections. Genotyping is used to guide classification, but polyclonal infections and alleles matching by chance make classification challenging, especially in high transmission settings. Match-counting algorithms currently recommended by World Health Organization are unreliable and produce biased results, necessitating development of principled statistical approaches. Modern genotyping methods such as multiplexed amplicon sequencing hold great potential for resolving recurrences and motivate the need for corresponding statistical methods able to utilize the rich data they provide. We propose an Adaptive Statistical framework for Therapeutic Efficacy and Recrudescence (Aster) that delivers accurate and consistent results by explicitly incorporating complexity of infection (COI), population allele frequencies, and imperfect detection of alleles in minority strains. Using an identity by descent approach, Aster accounts for alleles matching by chance and for a background infection relatedness structure that can otherwise lead to misclassification. The flexible framework can also use external information, such as parasite density and performance characteristics of a genotyping panel. Using simulations, we show that Aster dramatically outperforms match-counting algorithms in a wide variety of transmission settings and demonstrates consistently balanced performance that improves with more informative genotyping panels. Aster is implemented in a fast, fully scalable, and user-friendly R software package asterTES and provides accurate estimates of treatment failure for TES with any type of genotyping data, facilitating reliable evaluation of drug efficacy and effective management of malaria.

genetics↗

bioRxiv2024

Sensitive and modular amplicon sequencing of Plasmodium falciparum diversity and resistance for research and public health

BackgroundTargeted amplicon sequencing is a powerful and efficient tool for interrogating the Plasmodium falciparum genome, generating actionable data from infections to complement traditional malaria epidemiology. For maximum impact, genomic tools should be multi-purpose, robust, sensitive, and reproducible. MethodsWe developed, characterized, and implemented MAD4HatTeR, an amplicon sequencing panel based on Multiplex Amplicons for Drug, Diagnostic, Diversity, and Differentiation Haplotypes using Targeted Resequencing, along with a bioinformatic pipeline for data analysis. Additionally, we introduce an analytical approach to detect gene duplications and deletions from amplicon sequencing data. Laboratory control and field samples were used to demonstrate the panels high sensitivity and robustness. ResultsMAD4HatTeR targets 165 highly diverse loci, focusing on multiallelic microhaplotypes, key markers for drug and diagnostic resistance (including duplications and deletions), and csp and potential vaccine targets. The panel can also detect non-falciparum Plasmodium species. MAD4HatTeR successfully generated data from low-parasite-density dried blood spot and mosquito midgut samples, and detected minor alleles at within-sample allele frequencies as low as 1% with high specificity in high-parasite-density dried blood spot samples. Gene deletions and duplications were reliably detected in mono- and polyclonal controls. Data generated by MAD4HatTeR were highly reproducible across multiple laboratories. ConclusionsThe successful implementation of MAD4HatTeR in five laboratories, including three in malaria-endemic African countries, showcases its feasibility and reproducibility in diverse settings. MAD4HatTeR is thus a powerful tool for research and a robust resource for malaria public health surveillance and control.

genomics↗

bioRxiv2023

Plasmodium falciparum transmission in the highlands of Ethiopia is driven by closely related and clonal parasites

Malaria cases are frequently recorded in the Ethiopian highlands even at altitudes above 2,000 m. The epidemiology of malaria in the Ethiopian highlands, and in particular the role of importation by human migration from the highly endemic lowlands is not well understood. We characterized the parasite population structure and genetic relatedness by sequencing 159 P. falciparum samples from Gondar and an additional 28 samples from Ziway using a highly multiplexed droplet digital PCR (ddPCR)-based amplicon deep sequencing method targeting 35 microhaplotypes and drug resistance loci. Diversity was moderate (mean HE: 0.54), and infection complexity was low (74.9% single clone infections). A significant percentage of infections shared genomic haplotypes, even across transmission seasons, indicating persistent local and focal transmission. Multiple clusters of clonal or near-clonal infections were identified, highlighting the overall high genetic relatedness. Frequently, infections from travelers were the earliest observed cases, suggesting that parasites may have been imported and then transmitted locally. We observed population structure between Gondar and Ziway, although some haplotypes were shared between sites. 31.1% of infections carried pfhrp2 deletions and 84.4% pfhrp3 deletions, and 28.7% pfhrp2/pfhrp3 double deletions. Parasites with pfhrp2/3 deletions and wild-type parasites were genetically distinct. Mutations associated with resistance to sulfadoxine-pyrimethamine and lumefantrine were observed at near-fixation, but no mutations in pfk13 were found. In conclusion, genomic data corroborates local transmission and the importance of intensified control in the Ethiopian highlands.

genomics↗

bioRxiv2023

Highly multiplexed ddPCR-amplicon sequencing reveals strong Plasmodium falciparum population structure and isolated populations amenable to local elimination efforts in Zanzibar

Over the past 15 years, Zanzibar has made great strides towards malaria elimination; yet progress has stalled. Parasite genetic data of Plasmodium falciparum may inform strategies for malaria elimination by helping to identify contributory factors to parasite persistence. Here we elucidate fine-scale parasite population structure and infer relatedness and connectivity of infections using an identity-by-descent (IBD) approach. We sequenced 518 P. falciparum samples from 5 districts covering both main islands using a novel, highly multiplexed droplet digital PCR (ddPCR)-based amplicon deep sequencing method targeting 35 microhaplotypes and drug-resistance loci. Despite high genetic diversity, we observe strong fine-scale spatial and temporal structure of local parasite populations, including isolated populations on Pemba Island and genetically admixed populations on Unguja Island, providing evidence of ongoing local transmission. We observe a high proportion of highly related parasites in individuals living closer together, including between clinical index cases and the mostly asymptomatic cases surrounding them, consistent with isolation-by-distance. We identify a substantial fraction (2.9%) of related parasite pairs between Zanzibar, and mainland Tanzania and Kenya, consistent with recent importation. We identify haplotypes known to confer resistance to known antimalarials in all districts, including multidrug-resistant parasites, but most parasites remain sensitive to current first-line treatments. Our study provides a high-resolution view of parasite genetic structure across the Zanzibar archipelago and reveals actionable patterns, including isolated parasite populations, which may be prioritized for malaria elimination.

genomics↗

bioRxiv2022

Dcifer: an IBD-based method to calculate genetic distance between polyclonal infections

An essential step toward reconstructing pathogen transmission and answering epidemiologically relevant questions from genomic data is obtaining pairwise genetic distance between infections. For recombining organisms such as malaria parasites, relatedness measures quantifying recent shared ancestry would provide a meaningful distance, suggesting methods based on identity by descent (IBD). While the concept of relatedness and consequently an IBD approach is fairly straightforward for individual parasites, the distance between polyclonal infections, which are prevalent in malaria, presents specific challenges and awaits a general solution that could be applied to infections of any clonality and accommodate multiallelic (e.g. microsatellite or microhaplotype) and biallelic (SNP) data. Filling this methodological gap, we present Dcifer (Distance for complex infections: fast estimation of relatedness), a method for calculating genetic distance between polyclonal infections, which is designed for unphased data, explicitly accounts for population allele frequencies and complexity of infection, and provides reliable inference. Dcifers IBD-based framework allows us to define model parameters that represent interhost relatedness and to propose corresponding estimators with attractive statistical properties. By using combinatorics to account for unobserved phased haplotypes, Dcifer is able to quickly process large datasets and estimate pairwise relatedness along with measures of uncertainty. We show that Dcifer delivers accurate and interpretable results and detects related infections with statistical power that is 2-4 times greater than that of approaches based on identity by state. Applications to real data indicate that relatedness structure aligns with geographic locations. Dcifer is implemented in a comprehensive publicly available software package.

genetics↗