Biology subjects

Ndiaye, M.

Publications and source records attributed to Ndiaye, M..

7 recordsLinked to original sources

bioRxiv2026

Competition between co-localized gut symbionts underlies inter-individual variation in the honeybee gut microbiota

Microbes in the animal gut compete to colonize spatially restricted host niches. However, whether competition for space drives inter-individual variation among hosts, and which factors determine the outcome of such competition, remain poorly understood. Here, we investigated competitive interactions of the western honeybee gut symbiont Frischella perrara with other members of the bee gut microbiota. Shotgun metagenomics analysis of individual bees revealed that F. perrara is negatively correlated with a specific species of the genus Gilliamella. Co-colonization of microbiota-depleted bees with these two bacteria resulted in their competitive exclusion. The outcome of this competition depended on the relative number of bacteria each bee received and benefited Gilliamella when one of the two type VI secretion systems of F. perrara was mutated. Using fluorescently tagged strains, high-resolution microscopy, and gut region-specific quantification, we show that both bacteria localize to the same host niche in the ileum of mono-colonized bees, indicating competition in a spatially restricted host niche. Moreover, both microbes protected against infection, promoting bee health. This competition provides an explanatory mechanism underlying variation in the occurrence of F. perrara across honeybee colonies and highlights the importance of gut spatial structure and microbial competition in shaping microbiome composition and inter-individual variability.

microbiology↗

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

Effect of pharmacokinetically-relevant ivermectin concentrations on survivorship and fecundity of Anopheles coluzzii and Aedes aegypti in Burkina Faso: a laboratory experimental study

BackgroundThe control of vector-borne diseases is increasingly challenged by widespread insecticide resistance. Therefore, innovative vector control strategies with alternative modes of action are urgently needed. Ivermectin (IVM), an endectocide, has demonstrated toxicity to mosquito species such as Anopheles and Aedes when they feed on treated humans or livestock. In this study, we conducted a laboratory experiment to assess the effect of IVM, at concentrations equivalent to human plasma levels following mass drug administration (MDA), on the survival and fecundity of Anopheles coluzzii and Aedes aegypti in Burkina Faso. MethodsTwo laboratory experiments were conducted using 3-5-day old wild-derived female An. coluzzii and Aedes aegypti. Each experiment included four replicates per IVM concentration and was performed on separate dates. Mosquitoes were fed via membrane feeding on rabbit blood treated with five concentrations of IVM (C=112 ng/ml, C2=29 ng/ml, C3=15 ng/ml, C4=6.5 ng/ml, C5=2.5ng/ml), corresponding to the mean human plasma levels at 2, 4, 7, 14, and 28 days post-MDA with IVM at a dose of 300 g/kg. A negative control (C6=0.0 ng/ml) was also included. Mosquito mortalities were recorded daily for 7 days. Fecundity was measured by counting both laid eggs and developed eggs (via ovary dissection). ResultsIVM significantly reduced the survival of An. coluzzii compared to the control group (p<0.001), with the risk of death increasing from 4.2-fold at the lowest concentration (2.5 ng/ml) to 64.2-fold at the highest (112 ng/ml). In contrast, IVM had no significant effect on Aedes aegypti (p>0.05). Additionally, in An. coluzzii, IVM significantly reduced both egg laying and egg development (p<0.0001 and p<0.001, respectively), whereas no significant impact on fecundity was observed in Ae. aegypti (all p>0.80). ConclusionIvermectin concentrations typically achieved in human plasma during mass drug administration campaigns were sufficient to significantly reduce both survival and fecundity of wild type An. coluzzii, but had no measurable effect on recently colonized Ae. aegypti. These findings highlight the species-specific efficacy of ivermectin and support its potential role in integrated vector control strategies targeting malaria vectors in Africa.

zoology↗

bioRxiv2025

How fast can PPRV spread? Analyzing the results of an experimental PPRV infection of goats equipped with Ultra-WideBand sensors

Peste des Petits Ruminants (PPR) is a highly contagious disease affecting goats and sheep. The speed and extent of its spread depend on contact patterns and the viruss intrinsic characteristics. To estimate propagation speed and the role of inter-individual patterns, we analyzed the results of a series of 12 experimental infections conducted in five different sessions, involving six or seven goats in a secured stable. In each experiment, one animal was inoculated with the PPR strain and placed in contact with the other naive animals. All the animals were equipped with Ultra-WideBand sensors to collect inter-individual distance data. The duration of this phase varied from 1 to 48 hours across the experiments. Afterwards, the animals were isolated and monitored for three to five weeks. Temperature, symptoms, nasal and ocular discharges, and blood samples were routinely collected to detect the presence of the virus. Using Bayesian statistical analysis, data on inter-individual distances, and health status were analyzed to estimate R0, the incubation period, and PPRV transmissibility. The latter was used to estimate the exposure period, i.e., the minimum amount of time for a naive animal to become infected. Of the 70 naive animals exposed to the virus, 18 were infected. R0 was estimated to be around 4.3 or 8.6, depending on the infectious period value. The incubation period was estimated to be around 16.3 days (95% CI: 12.6-23.3 days). The exposure time varies greatly depending on the density, ranging from nearly two days at low density to around four hours at high density. Large gatherings of animals, such as at livestock markets, could greatly facilitate the spread of PPRV. Furthermore, the long incubation period coupled with livestock mobility could favor the virus geographical dissemination on a large scale. Author summaryPPR is an infectious disease that is transmitted directly and affects goats and sheep. Since its discovery in the Ivory Coast in 1942, the PPR virus has spread worldwide, reaching China in 2010 and Europe in 2018. Despite the fact that PPR poses a huge threat to the lives of small ruminants and the livelihoods of smallholders, more research is needed into its epidemic potential and transmission speed. Here, we used a combined approach of experimental infections and contact tracing with UWB devices to estimate the probability of transmission of a specific PPRV strain, and to determine the minimum exposure time required for an animal to become infected. Our results indicate that reducing the average distance between animals by a factor of four could reduce exposure time tenfold, indicating a much higher risk for high-density herds compared to roaming ones. Coupled with new estimates of R0 and incubation time inferred from data analysis, our work sheds light on the danger posed by PPR. These informations are valuable to veterinarians and policymakers, to better assess the risk of introduction, spread, and impact of PPR, to implement timely and effective interventions.

animal behavior and cognition↗

bioRxiv2025

Strain-level and phenotypic stability contrasts with plasmid and phage variability in water kefir communities

Microbial communities can change in response to top-down factors, such as phages, and bottom-up factors, such as nutrient availability. Previous studies have successfully investigated bacterial species-level dynamics, but diversity and interactions beyond the species-level is usually lacking. Traditional fermented foods, such as water kefir, provide ideal systems to study ecological and evolutionary dynamics beyond the species-level, as they are simple and trackable systems that are cultivated in non-sterile, nutrient-rich environments which foster microbial growth and invasion. Despite the central role of only a few lactic acid bacteria for fermentation, little is known about the genomic diversity and dynamics of these community members over time. Within the framework of a graduate course, 35 students propagated water kefir across several generations under different nutrient conditions and in different households to study microbial responses over time. We found that water kefir communities were generally stable at the species-level, with only rare bacterial species replaced over long timescales (more than 2 years). While we observed little strain-level diversity with few strain replacements over long timescales, closely related strains exhibited variation in accessory gene content, often encoded on plasmids, particularly those involved in ecologically meaningful functions such as sugar utilization pathways and phage defense systems. We hypothesise that these genomic variations could reflect the adaptations of strains to different sugars and phages. Consistent with this, we observed a diverse array of phages, many likely originating from the unique household environments. By documenting the genomic landscape of microbial species, strains, plasmids, and phages, this study advances our understanding of the diversity and dynamics of microbial communities in fermented foods. Furthermore, our course material is publicly available and offers a blueprint for bridging the gap between teaching and research, inspiring the next generation of scientists to unravel the complexities of microbial ecosystems. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=137 SRC="FIGDIR/small/640646v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@56db6borg.highwire.dtl.DTLVardef@5f6dc6org.highwire.dtl.DTLVardef@11fff14org.highwire.dtl.DTLVardef@1a37a9f_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2024

Imputed DNA methylation outperforms measured loci associations with smoking and chronological age

Multi-locus signatures of blood-based DNA methylation are well-established biomarkers for lifestyle and health outcomes. Here, we focus on two CpGs that are strongly associated with age and smoking behaviour. Imputing these loci via epigenome-wide CpGs results in stronger associations with outcomes in external datasets compared to directly measured CpGs. If extended epigenome-wide, CpG imputation could augment historic arrays and recently-released, inexpensive but lower-content arrays, thereby yielding better-powered association studies.

genetics↗