Biology subjects

Sow, D.

Publications and source records attributed to Sow, D..

6 recordsLinked to original sources

bioRxiv2025

Phenotypic assessment and genetic validation of Plasmodium falciparum molecular markers associated with malaria chemoprevention in Senegal

Drug resistance in Plasmodium falciparum threatens to undermine malaria control and elimination efforts. Senegal is a malaria-endemic country that has implemented successive antimalarial and chemopreventive drug-based strategies for two decades. Sulfadoxine-pyrimethamine (SP) is used for chemoprevention in Senegal for intermittent preventive treatment in pregnancy (since 2004) and SP plus amodiaquine (AQ) is used for seasonal malaria chemoprevention (SMC, since 2013). Using whole genome sequence (WGS) data from malaria patient samples from health facilities across Senegal (2006 - 2022), we observed near fixation of Pfdhfr triple mutant and fluctuation in Pfdhps and Pfcrt mutation frequencies over time. It is unclear how these mutations influence drug resistance and fitness phenotypes in natural isolates; therefore, we evaluated natural parasite isolates with different Pfcrt, Pfmdr1, Pfdhps, and Pfdhfr haplotypes. Parasites were culture-adapted and phenotyped for antimalarial drug susceptibility and competitive growth (fitness). Pfcrt CVIET + A220S + Q271E + N326S + R371I and Pfcrt CVIET + A220S + Q271E + I356T + R371I mutants were significantly more resistant to monodesethyl-amodiaquine (md-AQ) compared to Pfcrt wild-type (WT) and Pfcrt CVIET + A220S + Q271E + R371I mutants. Pfdhfr triple mutants were significantly more pyrimethamine (PYR) resistant than Pfdhfr WT and revealed a range of phenotypes, but this was not explained by Pfgch1 copy-number. Pfdhps A437G parasites were significantly more sulfadoxine (SDX) resistant compared to Pfdhps wild-type and Pfdhps S436A mutants, suggesting that A437G is a key mutation for SDX resistance. Competitive growth assays between Pfdhfr-Pfdhps mutants revealed that Pfdhps mutations do not always result in fitness costs. Ongoing phenotypic assessment and genetic validation of these mutations in a Senegalese background is necessary to assess the impact of drug pressure, identify evolving genetic determinants of drug resistance, and provide molecular markers for ongoing surveillance to monitor and guide the use of drug-based interventions. AUTHOR SUMMARYDrug resistance is a major concern for both preventing and treating malaria, especially in Africa where most malaria cases and deaths occur. Since 2013, Senegal has been giving children under 10 years old a combination of sulfadoxine-pyrimethamine plus amodiaquine to prevent malaria during the transmission season, called Seasonal Malaria Chemoprevention (SMC), and plans to continue expanding its use. However, there is evidence from genetic surveillance that drug resistance mutations are present in Senegal which could render this antimalarial drug combination ineffective. Here we use natural P. falciparum isolates obtained from Senegalese patients that represent the extant parasite population to evaluate the consequences of evolving mutations on antimalarial drug resistance and fitness phenotypes. This study is one of the first to use natural parasites to assess the impact of naturally derived mutations on drug resistance and fitness phenotypes. Our results provide evidence that certain combinations of drug resistance mutations impact both parasite drug resistance and fitness, and therefore need to be closely monitored and can inform optimal antimalarial combinations for the prevention or treatment of malaria. This work informs the ongoing evolution of resistance and fitness phenotypes in malaria endemic settings that are introducing new multi first line therapies (MFTs) and SMC interventions that have been used for decades in Senegal. Our approach creates a framework for using genetic surveillance data to form a hypothesis, which can then be phenotypically tested by measuring the resistance and fitness levels of genetically diverse natural parasite isolates.

molecular biology↗

bioRxiv2025

Robust and accurate diagnosis of infectious skin diseases from histopathology images by integrating deep learning and explainable AI

Accurate diagnosis of infectious skin diseases remains a major challenge, particularly for neglected tropical diseases such as mycetoma, where precise pathogen identification is crucial for effective treatment. Histopathology imaging is the diagnostic gold standard, involving examination of tissue biopsies to identify characteristic inflammatory patterns, cellular changes, or microbial pathogens. However, its analysis is often limited by variability in tissue sampling and staining, subjective interpretation, inter-observer differences, and the absence of visible microbial grains in early disease stages. To elevate these challenges, we develop the Skin INfectious Diseases Intelligent (SINDI) framework, an integrated machine learning pipeline combining shallow learning, deep learning, stain normalization, and explainable AI to automate and enhance diagnostic accuracy from histopathology images. The SINDI framework is designed to systematically tackle increasingly complex tasks in diagnostics, including (1) disease phenotype classification and pathogen species identification, (2) understanding the importance of disease-specific regions (grains) and classification of grain-free images lacking visible microbial structures, (3) semantic segmentation of pathological features, and (4) explainable AI-driven interpretable decision support. Leveraging a comprehensive dataset of 1,324 histopathology images representing four predominant mycetoma pathogens that are curated by expert pathologists, alongside 7,000 healthy skin tissue images, SINDI demonstrated near-perfect accuracy in binary and multi-class classification tasks, particularly when employing Macenko stain normalization and domain-specific features. Remarkably, SINDI achieved high accuracy on images with masked grain regions and even on grain-free images, which are considered diagnostically intractable by human experts. Semantic segmentation models accurately delineated phenotype-related regions, while explainable AI methods provided transparent and clinically relevant interpretability of model decisions. Our results indicate that diagnostically relevant information is distributed beyond visible lesion areas, challenging traditional pathology paradigms. The SINDI framework thus represents a significant advance in automated infectious skin disease diagnostics, offering robust, interpretable, and scalable decision-support tools adaptable to diverse clinical settings.

bioinformatics↗

bioRxiv2025

Continental-scale genomic surveillance of Plasmodium falciparum malaria with rapid nanopore sequencing

In sub-Saharan Africa, continental-scale genomic surveillance of Plasmodium falciparum malaria is needed to track the spread of antimalarial drug resistance and diagnostic test evasion, as well as to monitor parasite evolutionary responses to vaccine rollout. Yet implementation of malaria genomic surveillance at a continental-scale is hindered by resource constraints, the vastness of the continent, and the lack of sequencing protocols suitable for most local laboratories. To address this, we developed an approach to enable a decentralized scale-up of P. falciparum genomic surveillance and established it in six African countries in one year, locally sequencing 1,065 samples. The approach includes a rapid ([~] 5 hours) and cost-efficient (<$25 USD/sample) nanopore sequencing protocol that provides surveillance of drug resistance-associated genes, hrp2/3 deletions, the vaccine target csp, and the polymorphic gene ama1. We coupled this to a bioinformatics dashboard that runs offline on a laptop and displays mapping and variant calling results in real-time. We demonstrate robust sequencing coverage across parasitemia levels and laboratories, accurate identification of antimalarial resistance markers and hrp2/3 deletions; and, with a novel variant caller, sensitive detection of mutations carried by minor clones. Our approach will accelerate genomic surveillance of P. falciparum malaria across sub-Saharan Africa at a time of urgent need.

genomics↗

bioRxiv2025

Rapid identification of Biomphalaria spp. and diagnosis of Schistosoma mansoni infestation using MALDI-TOF mass spectrometry

This study explores the use of Matrix-Assisted Laser Desorption/Ionization Time-of-Flight mass spectrometry (MALDI-TOF MS) to identify and differentiate Biomphalaria snails infected with the parasite S. mansoni, which causes schistosomiasis. The study was conducted on two snail species, Biomphalaria pfeifferi (collected in the field in Senegal) and Biomphalaria glabrata (a laboratory strain). The snails were infected in the laboratory with S. mansoni miracidia, and their infection was confirmed by cercariae emission tests and quantitative PCR. MALDI-TOF MS was then used to analyse proteins from infected and uninfected snails to identify spectral differences. Based on protein profiles, the results of MALDI-TOF mass spectrometry made it possible to accurately differentiate between S. mansoni-infected snails and uninfected snails. An increase in the number of peaks detected and their intensity was observed for the spectra of S. mansoni-infected snails compared to uninfected snails. The application of principal component analysis to these mass spectrometry profiles confirmed the discrimination between the two groups according to their infection status. In addition, specific discriminating peaks were identified for each snail species, allowing for the distinction of infected from uninfected snails. The present study revealed, for the first time, that MALDI-TOF MS appears to be a rapid, reliable, and specific tool for the diagnosis of schistosomiasis in snails, offering promising prospects for the surveillance and control of this disease in endemic areas. However, further work is needed to establish a MALDI-TOF MS reference spectra database specific to Schistosoma parasites and to standardise sample collection, storage, and preparation in order to apply this technique in the field.

zoology↗

bioRxiv2022

Identification of Bulinus forskalii as a potential intermediate host of Schistosoma haematobium in Senegal

Understanding the transmission of Schistosoma haematobium in the Senegal River Delta requires knowledge of the snails serving as intermediate hosts. Accurate identification of both the snails and the infecting Schistosoma species is therefore essential. Cercarial emission tests and multi-locus (COX1 and ITS) genetic analysis were performed on Bulinus forskalii snails to confirm their susceptibility to S. haematobium infection. A total of 55 B. forskalii, adequately identified by MALDI-TOF mass spectrometry, were assessed. Cercarial shedding and RT-PCR assays detected13 (23.6%) and 17 (31.0%), respectively, B. forskalii snails parasitised by S. haematobium complex fluke. Nucleotide sequence analysis identified 6 (11.0%), using COX1, and 3 (5.5%), using ITS2, S. haematobium, and 3 (5.5%) S. bovis. This result is the first report of infection of B. forskalii by S. haematobium complex parasites.

microbiology↗

bioRxiv2020

Disease Network Delineates the Disease Progression Profile of Cardiovascular Diseases

As Electronic Health Records (EHR) data accumulated explosively in recent years, the tremendous amount of patient clinical data provided opportunities to discover real world evidence. In this study, a graphical disease network, named progressive cardiovascular disease network (progCDN), was built based on EHR data from 14.3 million patients 1 to delineate the progression profiles of cardiovascular diseases (CVD). The network depicted the dominant diseases in CVD development, such as the heart failure and coronary arteriosclerosis. Novel progression relationships were also discovered, such as the progression path from long QT syndrome to major depression. In addition, three age-group progCDNs identified a series of age-associated disease progression paths and important successor diseases with age bias. Furthermore, we extracted a list of salient features to build a series of disease risk models based on the progression pairs in the disease network. The progCDN network can be further used to validate or explore novel disease relationships in real world data. Features with sufficient abundance and high correlation can be widely applied to train disease risk models when using EHR data.

bioinformatics↗