Biology subjects

Wesolowski, A.

Publications and source records attributed to Wesolowski, A..

9 recordsLinked to original sources

bioRxiv2025

The Portable Microhaplotype Object and Tools

Structured AbstractO_ST_ABSMotivationC_ST_ABSThe rapid increase in the generation of targeted sequencing data offers immense potential for research, medicine, and public health, however the lack of an established standard for these data has led to disparate solutions for data storage. A widely accepted standard is essential for data sharing, reuse, and the coordinated development of interoperable analysis tools. ResultsWe propose the Portable Microhaplotype Object (PMO), a standardized format for efficiently and losslessly storing phased targeted sequencing data (microhaplotypes). The PMO format is JSON-based, allowing efficient, relational storage of genetic data together with relevant metadata to minimize orphaned data. The format includes required fields and a curated set of optional fields leveraging established ontologies. To facilitate ease of use, we developed pmotools-python, an open-source package for creating, manipulating, and exporting PMO data into common formats. Additionally, we provide a simple web-based app to quickly create PMO files from tabular inputs, making the format accessible to a wide variety of users. Example datasets from Plasmodium, Anopheles, Escherichia coli, and Staphylococcus aureus demonstrate the broad applicability of the approach. PMO will streamline data sharing, foster interoperability, and accelerate the development of harmonized analysis tools. Availability and implementationThe Portable Microhaplotype Object (PMO) project, including the ontology specification, software tools, example datasets, and tutorials, is freely available athttps://plasmogenepi.github.io/PMO_Docs/. Key software components and datasets have archived releases with DOIs to ensure permanence, detailed in the Supplementary Text 1-5. Contactkathrynmmurie@gmail.com or nickjhathaway@gmail.com

bioinformatics↗

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↗

bioRxiv2022

Superinfection plays an important role in the acquisition of complex Plasmodium falciparum infections among female Anopheles mosquitoes

Studies of human malaria infections with multiple, genetically distinct parasites have illuminated mechanisms of malaria transmission. However, few studies have used the genetic diversity in mosquito infections to understand how transmission is sustained. We identified likely human sources of mosquito infections from a longitudinal cohort in Western Kenya based on genetic similarity between parasites and the timing of infections. We found that several human infections were required to reconstitute each mosquito infection and that multiple parasite clones were likely transmitted from infected humans to mosquitoes in each bite, suggesting that superinfection and co-transmission occur simultaneously and are important mechanisms of transmission. We further investigated this using an individual human and mosquito simulation model and found that co-transmission alone was unlikely to reproduce the high complexity of mosquito infections. We concluded that the superinfection of mosquitoes likely plays an important, but under studied, role in sustaining moderate to high malaria transmission.

systems biology↗

bioRxiv2022

A Bayesian Hierarchical Model for Signal Extraction from Protein Microarrays

SO_SCPLOWUMMARYC_SCPLOWProtein microarrays are a promising technology that measure protein levels in serum or plasma samples. Due to the high technical variability of these assays and high variation in protein levels across serum samples in any population, directly answering biological questions of interest using protein microarray measurements is challenging. Using within-array ranks of protein levels for analysis can mitigate the impact of between-sample variation on downstream analysis. Although ranks are sensitive to pre-processing steps, ranking methods that accommodate uncertainty provide robust and loss-function optimal ranks. Such ranking methods require Bayesian modeling that produces full posterior distributions for parameters of interest. Bayesian models that produce such outputs have been developed for other assays, for example DNA microarrays, but those modeling assumptions are not appropriate for protein microarrays. We develop and evaluate a Bayesian model to extract a full posterior distribution of normalized fluorescent signals and associated ranks for protein microarrays, and show that it fits well to data from two studies that use protein microrarrays from different manufacturing processes. We validate the model via simulation and demonstrate the downstream impact of using estimates from this model to obtain optimal ranks.

bioinformatics↗

bioRxiv2022

A Random Forest Classifier Uses Antibody Responses to Plasmodium Antigens to Reveal Candidate Biomarkers of the Intensity and Timing of Past Exposure to Plasmodium falciparum

1Important goals of malaria surveillance efforts include accurately quantifying the burden of malaria over time, which can be useful to target and evaluate interventions. The majority of malaria surveillance methods capture active or recent infections which poses several challenges to achieving malaria surveillance goals. In high transmission settings, asymptomatic infections are common and therefore accurate measurement of malaria burden often demands active surveillance; in low transmission regions where infections are rare accurate surveillance requires sampling a large subset of the population; and in any context monitoring malaria burden over time necessitates serial sampling. Antibody responses to Plasmodium falciparum parasites persist after infection and therefore measuring antibodies has the potential to overcome several of the current difficulties associated with malaria surveillance. However, identifying which antibody responses are markers of the timing and intensity of past exposure to P. falciparum is challenging, particularly among adults who tend to be re-exposed multiple times over the course of their lifetime and therefore have similarly high antibody responses to many P. falciparum antigens. A previous analysis of 479 serum samples from individuals in three regions in southern Africa with different historical levels of P. falciparum malaria transmission (high, intermediate, and low) revealed regional differences in antibody responses to P. falciparum antigens among children under 5 years of age. Using a novel bioinformatic pipeline optimized for protein microarrays that minimizes between-sample technical variation, we used antibody responses to P. falciparum and P. vivax antigens as predictors in random forest models to classify adult samples into these three regions of differing historical malaria transmission with high accuracy. Many of the antigens that were most important for classification in these models do not overlap with previously published results and are therefore novel candidate markers for the timing and intensity of past exposure to P. falciparum. Measuring antibody responses to these antigens could potentially lead to improved malaria serosurveillance that captures the timing and intensity of past exposure.

immunology↗

bioRxiv2021

A Pre-Processing Pipeline to Quantify, Visualize andReduce Technical Variation in Protein Microarray

Technical variation, or variation from non-biological sources, is present in most laboratory assays. Correcting for this variation enables analysts to extract a biological signal that informs questions of interest. However, each assay has different sources and levels of technical variation and the choice of correction methods can impact downstream analyses. Compared to similar assays such as DNA microarrays, relatively few methods have been developed and evaluated for protein microarrays, a versatile tool for measuring levels of various proteins in serum samples. Here, we propose a pre-processing pipeline to correct for some common sources of technical variation in protein microarrays. The pipeline builds upon an existing normalization method by using controls to reduce technical variation. We evaluate our method using data from two protein microarray studies, and by simulation. We demonstrate that pre-processing choices impact the fluorescent-intensity based ranks of proteins, which in turn, impact downstream analysis. 1 Impact StatementProtein microarrays are in wide use in cancer research, infectious disease diagnostics and biomarker identification. To inform research and practice in these and other fields, technical variation must be corrected using normalization and pre-processing. Current protein microarray studies use a variety of normalization methods, many of which were developed for DNA microarrays, and therefore are based on assumptions and data that are not ideal for protein microarrays. To address this issue, we develop, evaluate, and implement a pre-processing pipeline that corrects for technical variation in protein microarrays. We show that pre-processing and normalization directly impact the validity of downstream analysis, and protein-specific approaches are essential.

bioinformatics↗

bioRxiv2018

The geography of malaria elimination in Bangladesh: combining data layers to estimate the spatial spread of parasites

Malaria control programs face difficult resource allocation decisions. Of particular concern for countries aiming for malaria elimination, the regular movement of individuals to and from endemic areas undermines local interventions by reintroducing infections and sustaining local transmission. Quantifying this movement of malaria parasites around a country has become a priority for national control programs, but remains methodologically challenging, particularly in areas with highly mobile populations. Here, we combined multiple data sources to measure the geographical spread of malaria parasites, including epidemiological surveillance data, travel surveys, parasite genetic data, and anonymized mobile phone data. We collected parasite genetic barcodes and travel surveys from 2,090 patients residing in 176 unions in southeast Bangladesh. We developed a genetic mixing index to quantify the likelihood of samples being local or imported. We then inferred the direction and intensity of parasite flow between locations using an epidemiological model, and estimated the proportion of imported cases assuming mobility patterns parameterized using the travel survey and mobile phone calling data. Our results show that each data source provided related but different information about the patterns of geographic spread of parasites. We identify a consistent north/south separation of the Chittagong Hill Tracts region in Bangladesh, and found that in addition to imported infections from forested regions, frequent mixing also occurs in low transmission but highly populated areas in the southwest. Thus, unlike risk maps generated from incidence alone, our maps provide evidence that elimination programs must address ongoing movement of parasites around the lower transmission areas in the southwest.

epidemiology↗

bioRxiv2018

Mapping malaria by combining parasite genomic and epidemiologic data

Recent global progress in scaling up malaria control interventions has revived the goal of complete elimination in many countries. Decreasing transmission intensity generally leads to increasingly patchy spatial patterns of malaria transmission, however, and control programs must accurately identify remaining foci in order to target interventions efficiently. In particular, mosquito control interventions like bed nets and insecticide spraying are best targeted to transmission hotspots, and the role of connectivity between different pockets of local transmission becomes increasingly important since humans are able to move parasites beyond the limits of mosquito dispersal and re-introduce parasites to previously malaria-free regions. Quantifying the connectivity between regions due to human travel, measuring malaria transmission intensity in different areas, and monitoring parasite spatial spread are therefore key issues for policy-makers because they underpin the feasibility of elimination and inform the path to its attainment. To this end, recent efforts have been made to develop new approaches to incorporating human mobility into spatial epidemiological models, for example using mobile phone data, and there has been a surge of interest in collecting spatially informative parasite samples to measure the genomic signatures of parasite connectivity. Due to their complicated life-cycles, Plasmodium parasites pose unique challenges to researchers in this respect and new methods that move beyond traditional phylogenetic and population genetic tools must be developed to harness genetic information effectively. Here, we discuss the spatial epidemiology of malaria in the context of transmission-reduction interventions, and the challenges and promising directions for the development of integrated mapping, modeling, and genomic approaches that leverage disparate data sets to measure both connectivity and transmission.

epidemiology↗

bioRxiv2017

Non-annual seasonality of influenza-like illness in a tropical urban setting

In temperate countries, influenza and other viral respiratory diseases often have distinct seasonal peaks occurring during colder, wintertime months. However, little is known about the dynamics of influenza and viral respiratory disease dynamics in the tropics, despite high morbidity and a clear epidemiological link between tropical and temperate countries. In temperate countries, the dynamics of influenza and other respiratory diseases are often analyzed using syndromic surveillance data describing influenza-like illness (ILI) as ILI is highly correlated with virological surveillance for influenza. To obtain a detailed picture of respiratory disease incidence patterns in a large tropical city, we established an mHealth study in community outpatient clinics in Ho Chi Minh City, Vietnam (11N latitude). From August 2009 through December 2015, clinics reported daily case numbers of ILI using standard mobile-phone SMS messaging. A subset of these clinics performed molecular diagnostics for influenza A and B viruses. Unlike the annual patterns seen in temperate countries, ILI activity in Ho Chi Minh City exhibited strong non-annual periodicity and was not correlated with PCR-confirmed influenza. The dominant periodicity in the data was approximately 200 days. This was confirmed by a time series decomposition, a step-wise regression analysis on annual and non-annual covariates, and a forecasting exercise showing that forecasting was 30% to 40% more accurate when a 200-day non-annual cycle was included in the forecast. This suggests, for the first-time, that a non-annual cycle may be an essential driver of ILI dynamics in the tropics. This raises new questions about the seasonality and drivers of respiratory disease transmission in tropical countries.

epidemiology↗