Biology subjects

Leekitcharoenphon, P.

Publications and source records attributed to Leekitcharoenphon, P..

6 recordsLinked to original sources

bioRxiv2026

Syndromic cholera diagnosis masks diverse causes of diarrhoeal disease in Burundi revealed by portable metagenomics

BackgroundCholera outbreaks remain a major public-health challenge in sub-Saharan Africa, where diagnostic capacity is limited and clinical case definitions are non-specific and reply heavily on syndromic diagnosis. Rapid identification of Vibrio cholerae is critical, yet cholera-suspected diarrhoea can have multiple infectious causes not captured by targeted diagnostics. MethodsWe evaluated a mobile, culture-independent metagenomic sequencing workflow for on-site detection of gastrointestinal pathogens directly from faecal samples in Burundi. The offline workflow combined long-read ONT sequencing with rapid, laptop-based taxonomic and antimicrobial resistance (AMR) screening and was deployed across a health centre, a district hospital, and a refugee transit camp. The frontline and real-time results were verified using both conventional culturing and in-depth bioinformatic analyses. ResultsV. cholerae signals were only detected in a subset of suspected cholera cases, while many samples were dominated by alternative bacterial taxa, most frequently Escherichia coli. V. cholerae abundance correlated strongly with detection of the cholera toxin phage CTX{varphi}, supporting differentiation between toxigenic signal and background exposure. AMR genes were detected across samples, providing early situational insight into resistance determinants among gastrointestinal bacteria. ConclusionsMobile, offline metagenomic sequencing enables rapid frontline characterization of gastrointestinal disease, especially cholera-suspected, in resource-limited settings and complements existing diagnostics by improving etiological resolution and outbreak response. Author SummaryCholera remains a major cause of severe diarrhoeal disease in many low-resource settings, where diagnosis often relies on symptoms and limited laboratory testing. However, patients suspected of cholera can be infected by a wide range of other pathogens that are not detected by standard diagnostics. In this study, we evaluated a portable, sequencing-based approach that allows direct identification of pathogens from stool samples at the point of care, without the need for laboratory infrastructure, internet access, or culture. Using this approach in multiple settings in Burundi, including a health centre, hospital, and refugee camp, we found a subset of suspected cholera cases were associated with Vibrio cholerae. Other cases were also dominated by other bacteria, particularly Escherichia coli. We also detected antimicrobial resistance genes across samples, providing additional information relevant for treatment and surveillance. Our findings demonstrate that mobile metagenomic sequencing can improve the identification of disease causes directly in outbreak settings and help distinguish true cholera cases from other gastrointestinal infections. This approach has the potential to strengthen outbreak response, improve patient management, and support more accurate disease surveillance in resource-limited environments.

microbiology↗

bioRxiv2025

Ecological dynamics of the Atlantic salmon gut microbiota across developmental phases and geographic regions

The gut microbiota is vital to host health, yet the relative influence of host traits and environmental factors on fish gut microbiota dynamics remains underexplored. We investigated the ecological dynamics of Atlantic salmon (Salmo salar) gut microbiota, by analysing 847 samples from wild and farmed salmon across diverse geographic regions, developmental phases, and associated diet and environmental microbiota. Farmed salmon exhibits reduced microbial diversity and distinct community composition with increased Firmicutes and reduced Proteobacteria compared to wild salmon. Microbial diversity declined with advancing developmental phases notably due to reduced Proteobacteria and expanded Mycoplasma. Diet was the primary contributor ([~]23%) to farmed salmon microbiota, with environmental inputs varying by region and phase. These findings highlight the importance of aquaculture practices guided by microbiota insights, while emphasize the need to preserve microbial diversity in wild populations to enhance resilience against environmental pressures, contributing to both sustainable farming and conservation strategies.

microbiology↗

bioRxiv2025

Acinetobacter enrichment shapes composition and function of the bacterial microbiota of field-grown tomato plants

Tomato is a staple crop and an excellent model to study host-microbiota interactions in the plant food chain. In this study we describe a lab-in-the-field approach to investigate the microbiota of field-grown tomato plants. High-throughput amplicon sequencing revealed a three-microhabitat partition, phyllosphere, rhizosphere and root interior, differentiating host-associated communities from the environmental microbiota. An individual bacterium, classified as Acinetobacter sp., emerged as a dominant member of the microbiota at the plant-soil continuum. To gain insights into the functional significance of this enrichment, we subjected rhizosphere specimens to shotgun metagenomics. Similar to the amplicon sequencing survey, a microhabitat effect defined by a set of rhizosphere-enriched functions was identified. Mobilisation of mineral nutrients, as well as adaptation to salinity and polymicrobial communities, including antimicrobial resistance genes (ARGs), emerged as a functional requirement sustaining metagenomic diversification. A metagenome-assembled genome (MAG) representative of Acinetobacter calcoaceticus was retrieved and metagenomic reads associated to this species identified a functional specialisation for plant-growth promotion traits (PGPTs), such as phosphate solubilization, siderophores production and reactive oxygen species detoxification, which were similarly represented in across tomato genotype-independent fashion. Our results revealed that the enrichment of a beneficial bacterium capable of alleviating plants abiotic stresses appears decoupled from ARGs facilitating microbiota persistence at the root-soil interface. IMPORTANCETomatoes are at centre-stage in global food security due to their high nutritional value, widespread cultivation, and versatility. Tomatoes provide essential vitamins and minerals, contribute to diverse diets, and support farmer livelihoods, making them a cornerstone of sustainable food systems. Beyond direct dietary benefits, the intricate relationship between tomatoes, their associated microbiota, and ARG is increasingly recognised. Tomato plants host diverse microbial communities in association with their organs, which influence plant health and productivity. Crop management impacts on composition and function of these communities, contributing to the prevalence of ARG in the soil and on the plants themselves. These genes can potentially transfer to human pathogens, posing a food safety and public health risk. Understanding these complex interactions is critical for developing sustainable agricultural practices capable of mitigating the impact of climatic modifications and the global threat of antimicrobial resistance.

plant biology↗

bioRxiv2024

ListPred: A predictive ML tool for virulence potential and disinfectant tolerance in Listeria monocytogenes

Despite current surveillance and sanitation strategies, foodborne pathogens continue to threaten the food industry and public health. Whole genome sequencing (WGS) has reached an unprecedented resolution to analyse and compare pathogenic bacterial isolates. The increased resolution significantly enhances the possibility of tracing transmission routes and contamination sources of foodborne pathogens. In addition, machine learning (ML) on WGS data has shown promising applications for predicting important microbial traits such as virulence, growth potential, and resistance to antimicrobials. Many regulatory agencies have already adapted WGS and ML methods. However, the food industry hasnt followed a similarly enthusiastic implementation. Some possible reasons for this might be the lack of computational resources and limited expertise to analyse WGS and ML data and interpret the results. Here, we present ListPred, a ML tool to analyse WGS data of Listeria monocytogenes, a very concerning foodborne pathogen. ListPred is able to predict two important bacterial traits, namely virulence potential and disinfectant tolerance, and only requires limited computational resources and practically no bioinformatic expertise, which is essential for a broad application in the food industry. AUTHOR SUMMARYThe contamination of food products with microbes such as pathogenic bacteria is a big concern for the food industry. The rapid detection, characterisation and eradication of microbial contaminants are of utmost importance to ensure safe food products. Fortunately, strict food safety regulations and stringent cleaning protocols prevent the transmission of harmful bacteria to humans. Individual bacteria of the same species can have varying abilities to resist cleaning agents or infect a host, meaning that some pathogen isolates might be more dangerous than others. Novel techniques such as genome sequencing and machine learning can help to determine such differences in individual bacteria. Unfortunately, these techniques require a lot of computational power and expertise that is limited in the food industry. This is why we developed an easy-to-use software tool called ListPred that can be used with few computational requirements and little expertise. ListPred helps food companies to answer two essential questions: how dangerous are Listeria monocytogenes pathogens, and how to get rid of them most efficiently?

bioinformatics↗

bioRxiv2023

Quantitative prediction of disinfectant tolerance in Listeria monocytogenes using whole genome sequencing and machine learning

Listeria monocytogenes is a potentially severe disease-causing bacteria mainly transmitted through food. This pathogen is of great concern for public health and the food industry in particular. Many countries have implemented thorough regulations, and some have even set zero-tolerance thresholds for particular food products to minimise the risk of L. monocytogenes outbreaks. This emphasises that proper sanitation of food processing plants is of utmost importance. Consequently in recent years, there has been an increased interest in L. monocytogenes tolerance to disinfectants used in the food industry. Even though many studies are focusing on laboratory quantification of L. monocytogenes tolerance, the possibility of predictive models remains poorly studied. Within this study, we explore the prediction of tolerance and minimum inhibitory concentrations (MIC) using whole genome sequencing (WGS) and machine learning (ML). We used WGS data and MIC values to quaternary ammonium compound (QAC) disinfectants from 1649 L. monocytogenes isolates to train different ML predictors. Our study shows promising results for predicting tolerance to QAC disinfectants using WGS and machine learning. We were able to train high-performing ML classifiers to predict tolerance with balanced accuracy scores up to 0.97{+/-}0.02. For the prediction of MIC values, we were able to train ML regressors with mean squared error as low as 0.07{+/-}0.02. We also identified several new genes related to cell wall anchor domains, plasmids, and phages, putatively associated with disinfectant tolerance in L. monocytogenes. The findings of this study are a first step towards prediction of L. monocytogenes tolerance to QAC disinfectants used in the food industry. In the future, predictive models might be used to monitor disinfectant tolerance in food production and might support the conceptualisation of more nuanced sanitation programs. AUTHOR SUMMARYMicrobial contamination challenges food safety by potentially transmitting harmful microbes such as bacteria to consumers. Listeria monocytogenes is an example of such a bacteria, which is primarily transmitted through food and can cause severe diseases in at-risk groups. Fortunately, strict food safety regulations and stringent cleaning protocols are in place to prevent the transmission of Listeria monocytogenes. However, in recent years, there has been an increase in tolerance towards disinfectants used in the food industry, which can reduce their effectiveness. In this study, we used genome sequencing and phenotypic data to train machine learning models that can accurately predict whether individual Listeria monocytogenes isolates are tolerant to selected disinfectants. We were able to train models that are not only able to distinguish sensitive/tolerant isolates but also can predict different degrees of tolerance to disinfectants. Further, we were able to report a set of genes that were important for the machine learning prediction and could give information about possible tolerance mechanisms. In the future, similar predictive models might be used to guide cleaning and disinfection protocols to facilitate maximum effectiveness.

bioinformatics↗

bioRxiv2023

Large-scale phenotypic and genomic characterization of Listeria monocytogenes susceptibility to quaternary ammonium compounds

Listeria monocytogenes is a significant concern for the food industry due to its ability to persist in the food processing environment. Decreased susceptibility to disinfectants is one of the factors that contribute to the persistence of L. monocytogenes. The objective of this study was to explore the diversity of L. monocytogenes susceptibility to quaternary ammonium compounds (QACs) using 1,671 L. monocytogenes isolates. This was used to determine the phenotype-genotype concordance and characterize genomes of the QAC sensitive and tolerant isolates for stress resistance, virulence and plasmid replicon genes. Distribution of QAC tolerance genes among 37,897 publicly available L. monocytogenes genomes were also examined. The minimum inhibitory concentration to QACs was determined by the broth microdilution method and non-sequenced isolates (n=1,244) were whole genome sequenced. Genotype-phenotype concordance was 99% for benzalkonium chloride, DDAC and a commercial QAC based sanitizer. Prevalence of QAC tolerance genes was 23% and 28% in our L. monocytogenes collection and in the global dataset, respectively. qacH was the most prevalent gene in our collection (61%), with 19% prevalence in the global dataset. Notably, bcrABC was most common (72%) globally, while 25% in our collection. Prevalence of emrC and emrE was comparable in both datasets, 7% and 2%, respectively. Replicon genes, indicative of plasmid harborage, were detected in 44% of the isolates and associated with the QAC tolerant phenotype. The presented analysis is based on the biggest L. monocytogenes collection in diversity and quantity for characterization of the L. monocytogenes QAC tolerance at both phenotypic and genomic levels. IMPORTANCEContamination of Listeria monocytogenes within the food processing environment is of concern to the food industry due to challenges in eradicating the pathogen once it becomes persistent in the environment. Genetic markers associated with increased tolerance to disinfectants have been identified, which alongside factors favor the persistence of L. monocytogenes in the production environment. By employing a comprehensive large-scale phenotypic testing and genomic analysis our study significantly enhances the understanding of the prevalence of quaternary ammonium compound (QAC) tolerant L. monocytogenes and the genetic determinants associated with the increased tolerance. Furthermore, we report on the prevalence of QAC tolerance genes among 37,897 publicly available L. monocytogenes sequences and their distribution within clonal complexes, isolation sources and geographical locations. As the propagation of QAC tolerance showed not be evenly distributed globally this highlights that understanding the development of L. monocytogenes disinfectant tolerance can be monitored using publicly available WGS data.

microbiology↗