517 research outputs found

    Fusaric acid-evoked oxidative stress affects plant defence system by inducing biochemical changes at subcellular level

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    Fusaric acid (FA) is one of the most harmful phytotoxins produced in various plant‚Äďpathogen interactions. Fusarium species produce FA as a secondary metabolite, which can infect many agronomic crops at all stages of development from seed to fruit, and FA production can further compromise plant survival because of its phytotoxic effects. FA exposure in plant species adversely affects plant growth, development and crop yield. FA exposure in plants leads to the generation of reactive oxygen species (ROS), which cause cellular damage and ultimately cell death. Therefore, FA-induced ROS accumulation in plants has been a topic of interest for many researchers to understand the plant‚Äďpathogen interactions and plant defence responses. In this study, we reviewed the FA-mediated oxidative stress and ROS-induced defence responses of antioxidants, as well as hormonal signalling in plants. The effects of FA phytotoxicity on lipid peroxidation, physiological changes and ultrastructural changes at cellular and subcellular levels were reported. Additionally, DNA damage, cell death and adverse effects on photosynthesis have been explained. Some possible approaches to overcome the harmful effects of FA in plants were also discussed. It is concluded that FA-induced ROS affect the enzymatic and non-enzymatic antioxidant system regulated by phytohormones. The effects of FA are also associated with other photosynthetic, ultrastructural and genotoxic modifications in plants

    Fusarium biocontrol: antagonism and mycotoxin elimination by lactic acid bacteria

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    Mycotoxins produced by Fusarium species are secondary metabolites with low molecular weight formed by filamentous fungi generally resistant to different environmental factors and, therefore, undergo slow degradation. Contamination by Fusarium mycotoxins in cereals and millets is the foremost quality challenge the food and feed industry faces across the globe. Several types of chemical preservatives are employed in the mitigation process of these mycotoxins, and they help in long-term storage; however, chemical preservatives can be used only to some extent, so the complete elimination of toxins from foods is still a herculean task. The growing demand for green-labeled food drives to evade the use of chemicals in the production processes is getting much demand. Thus, the biocontrol of food toxins is important in the developing food sector. Fusarium mycotoxins are world-spread contaminants naturally occurring in commodities, food, and feed. The major mycotoxins Fusarium species produce are deoxynivalenol, fumonisins, zearalenone, and T2/HT2 toxins. Lactic acid bacteria (LAB), generally regarded as safe (GRAS), is a well-explored bacterial community in food preparations and preservation for ages. Recent research suggests that LAB are the best choice for extenuating Fusarium mycotoxins. Apart from Fusarium mycotoxins, this review focuses on the latest studies on the mechanisms of how LAB effectively detoxify and remove these mycotoxins through their various bioactive molecules and background information of these molecules

    Determination of Mycotoxins in Plant-Based Meat Alternatives (PBMAs) and Ingredients after Microwave Cooking

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    In this study, we investigate the role of microwave cooking in reducing mycotoxin contamination in plant-based food matrices, with a focus on veggie burgers (purchased and home-made) and their ingredients (soybean, potatoes, zucchini, carrots). Two different conditions were studied (Max‚ÄďMin) that were 800 W for 60 s and 800 W for 90 s, respectively. The degradation patterns of aflatoxins (AFB1, AFB2, AFG1, AFG2), fumonisins (FB1, FB2, FB3), trichothecenes (T2, HT2, ZEA), and ochratoxin A (OTA) were studied. The extraction procedures were conducted with the QuEChERS extraction, and the analyses were conducted with liquid chromatography‚Äďtandem mass spectrometry (LC-MS/MS). Principal component analysis (PCA) showed that degradation under microwave cooking varies considerably across different food matrices and cooking conditions. This study provides valuable insights into the degradation of mycotoxins during microwave cooking and underscores the need for more research in this area to ensure food safety

    Dynamic geospatial modeling of mycotoxin contamination of corn in Illinois: unveiling critical factors and predictive insights with machine learning

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    Mycotoxin contamination of corn is a pervasive problem that negatively impacts human and animal health and causes economic losses to the agricultural industry worldwide. Historical aflatoxin (AFL) and fumonisin (FUM) mycotoxin contamination data of corn, daily weather data, satellite data, dynamic geospatial soil properties, and land usage parameters were modeled to identify factors significantly contributing to the outbreaks of mycotoxin contamination of corn grown in Illinois (IL), AFL >20 ppb, and FUM >5 ppm. Two methods were used: a gradient boosting machine (GBM) and a neural network (NN). Both the GBM and NN models were dynamic at a state-county geospatial level because they used GPS coordinates of the counties linked to soil properties. GBM identified temperature and precipitation prior to sowing as significant influential factors contributing to high AFL and FUM contamination. AFL-GBM showed that a higher aflatoxin risk index (ARI) in January, March, July, and November led to higher AFL contamination in the southern regions of IL. Higher values of corn-specific normalized difference vegetation index (NDVI) in July led to lower AFL contamination in Central and Southern IL, while higher wheat-specific NDVI values in February led to higher AFL. FUM-GBM showed that temperature in July and October, precipitation in February, and NDVI values in March are positively correlated with high contamination throughout IL. Furthermore, the dynamic geospatial models showed that soil characteristics were correlated with AFL and FUM contamination. Greater calcium carbonate content in soil was negatively correlated with AFL contamination, which was noticeable in Southern IL. Greater soil moisture and available water-holding capacity throughout Southern IL were positively correlated with high FUM contamination. The higher clay percentage in the northeastern areas of IL negatively correlated with FUM contamination. NN models showed high class-specific performance for 1-year predictive validation for AFL (73%) and FUM (85%), highlighting their accuracy for annual mycotoxin prediction. Our models revealed that soil, NDVI, year-specific weekly average precipitation, and temperature were the most important factors that correlated with mycotoxin contamination. These findings serve as reliable guidelines for future modeling efforts to identify novel data inputs for the prediction of AFL and FUM outbreaks and potential farm-level management practices

    Comparative genomics of recent adaptation in Candida pathogens

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    [eng] Fungal infections pose a serious health threat, affecting >1,000 million people and causing ~1.5 million deaths each year. The problem is growing due to insufficient diagnostic and therapeutic options, increased number of susceptible patients, expansion of pathogens partly linked to climate change and the rise of antifungal drug resistance. Among other fungal pathogens, Candida species are a major cause of severe hospital-acquired infections, with high mortality in immunocompromised patients. Various Candida pathogens constitute a public health issue, which require further efforts to develop new drugs, optimize currently available treatments and improve diagnostics. Given the high dynamism of Candida genomes, a promising strategy to improve current therapies and diagnostics is to understand the evolutionary mechanisms of adaptation to antifungal drugs and to the human host. Previous work using in vitro evolution, population genomics, selection inferences and Genome Wide Association Studies (GWAS) have partially clarified such recent adaptation, but various open questions remain. In the three research articles that conform this PhD thesis we addressed some of these gaps from the perspective of comparative genomics. First, we addressed methodological issues regarding the analysis of Candida genomes. Studying recent adaptation in these pathogens requires adequate bioinformatic tools for variant calling, filtering and functional annotation. Among other reasons, current methods are suboptimal due to limited accuracy to identify structural variants from short read sequencing data. In addition, there is a need for easy-to-use, reproducible variant calling pipelines. To address these gaps we developed the ‚Äúpersonalized Structural Variation detection‚ÄĚ pipeline (perSVade), a framework to call, filter and annotate several variant types, including structural variants, directly from reads. PerSVade enables accurate identification of structural variants in any species of interest, such as Candida pathogens. In addition, our tool automatically predicts the structural variant calling accuracy on simulated genomes, which informs about the reliability of the calling process. Furthermore, perSVade can be used to analyze single nucleotide polymorphisms and copy number-variants, so that it facilitates multi-variant, reproducible genomic studies. This tool will likely boost variant analyses in Candida pathogens and beyond. Second, we addressed open questions about recent adaptation in Candida, using perSVade for variant identification. On the one hand, we investigated the evolutionary mechanisms of drug resistance in Candida glabrata. For this, we used a large-scale in vitro evolution experiment to study adaptation to two commonly-used antifungals: fluconazole and anidulafungin. Our results show rapid adaptation to one or both drugs, with moderate fitness costs and through few mutations in a narrow set of genes. In addition, we characterize a novel role of ERG3 mutations in cross-resistance towards fluconazole in anidulafungin-adapted strains. These findings illuminate the mutational paths leading to drug resistance and cross-resistance in Candida pathogens. On the other hand, we reanalyzed ~2,000 public genomes and phenotypes to understand the signs of recent selection and drug resistance in six major Candida species: C. auris, C. glabrata, C. albicans, C. tropicalis, C. parapsilosis and C. orthopsilosis. We found hundreds of genes under recent selection, suggesting that clinical adaptation is diverse and complex. These involve species-specific but also convergently affected processes, such as cell adhesion, which could underlie conserved adaptive mechanisms. In addition, using GWAS we predicted known drivers of antifungal resistance alongside potentially novel players. Furthermore, our analyses reveal an important role of generally-overlooked structural variants, and suggest an unexpected involvement of (para)sexual recombination in the spread of resistance. Taken together, our findings provide novel insights on how Candida pathogens adapt to human-related environments and suggest candidate genes that deserve future attention. In summary, the results of this thesis improve our knowledge about the mechanisms of recent adaptation in Candida pathogens, which may enable improved therapeutic and diagnostic applications.[cat] Les infeccions f√ļngiques representen una greu amena√ßa per a la salut, afectant a m√©s de 1.000 milions de persones i causant aproximadament 1,5 milions de morts cada any. El problema est√† augmentant a causa d‚Äôunes opcions terap√®utiques i diagn√≤stiques insuficients, l'increment del nombre de pacients susceptibles, l'expansi√≥ dels pat√≤gens parcialment vinculada al canvi clim√†tic i l'augment de la resist√®ncia als f√†rmacs antif√ļngics. D‚Äôentre diversos fongs pat√≤gens, els llevats del g√®nere Candida s√≥n una causa important d'infeccions nosocomials, amb una alta mortalitat en pacients immunodeprimits. Diverses esp√®cies de Candida constitueixen un problema de salut p√ļblica, cosa que requereix m√©s esfor√ßos per a desenvolupar nous medicaments, optimitzar els tractaments disponibles i millorar els diagn√≤stics. Tenint en compte el dinamisme gen√≤mic d‚Äôaquests pat√≤gens, una estrat√®gia prometedora per millorar les ter√†pies i diagn√≤stics actuals √©s comprendre els mecanismes evolutius d'adaptaci√≥ als f√†rmacs antif√ļngics i a l‚Äôhoste hum√†. Treballs anteriors utilitzant l'evoluci√≥ in vitro, la gen√≤mica de poblacions, les infer√®ncies de selecci√≥ i els estudis d'associaci√≥ de genoma complet (GWAS, per les sigles en angl√®s) han aclarit parcialment aquesta adaptaci√≥ recent, per√≤ encara hi ha diverses preguntes obertes. En els tres articles que conformen aquesta tesi doctoral, hem abordat algunes d'aquestes preguntes des de la perspectiva de la gen√≤mica comparativa. En primer lloc, hem abordat q√ľestions metodol√≤giques relatives a l'an√†lisi dels genomes de les esp√®cies Candida. L'estudi de l'adaptaci√≥ recent en aquests pat√≤gens requereix eines bioinform√†tiques adequades per a la detecci√≥, filtratge i anotaci√≥ funcional de variants gen√®tiques. Entre altres raons, els m√®todes actuals s√≥n sub√≤ptims a causa de la limitada precisi√≥ per identificar variants estructurals a partir de dades de seq√ľenciaci√≥ amb lectures curtes. A m√©s, hi ha una necessitat d‚Äôeines computacionals per a la detecci√≥ de variants que siguin senzilles d'utilitzar i reproduibles. Per abordar aquestes mancances, hem desenvolupat el m√®tode bioinform√†tic "personalized Structural Variation detection" (perSVade), una eina que permet la detecci√≥, filtratge i anotaci√≥ de diversos tipus de variants, incloent-hi les variants estructurals, directament des de les lectures. PerSVade permet la identificaci√≥ precisa de les variants estructurals en qualsevol esp√®cie d'inter√®s, com ara els pat√≤gens Candida. A m√©s, la nostra eina prediu autom√†ticament la precisi√≥ de la detecci√≥ d‚Äôaquestes variants en genomes simulats, la qual cosa informa sobre la fiabilitat del proc√©s. Finalment, perSVade es pot utilitzar per analitzar altres tipus de variants, com els polimorfismes de nucle√≤tid √ļnic o els canvis en el nombre de c√≤pies, facilitant aix√≠ estudis gen√≤mics integrals i reproduibles. Aquesta eina probablement impulsar√† les an√†lisis gen√≤miques en els pat√≤gens Candida i tamb√© en altres esp√®cies. En segon lloc, hem abordat algunes de les preguntes obertes sobre l'adaptaci√≥ recent en els llevats Candida, utilitzant perSVade per a la identificaci√≥ de variants. D'una banda, hem investigat els mecanismes evolutius de resist√®ncia als f√†rmacs antif√ļngics en Candida glabrata. Per a aix√≤, hem utilitzat un experiment d'evoluci√≥ in vitro a gran escala per estudiar l'adaptaci√≥ a dos antif√ļngics comuns: el fluconazol i l‚Äôanidulafungina. Els nostres resultats mostren una adaptaci√≥ r√†pida a un o ambd√≥s f√†rmacs, amb un cost per al creixement moderat i a trav√©s de poques mutacions en un nombre redu√Įt de gens. A m√©s, hem caracteritzat un paper nou de les mutacions en ERG3 en la resist√®ncia creuada al fluconazol en soques adaptades a anidulafungina. Aquests descobriments aclareixen els processos mutacionals que condueixen a la resist√®ncia als f√†rmacs i a la resist√®ncia creuada en els pat√≤gens Candida. D'altra banda, hem re-analitzat aproximadament 2.000 genomes i fenotips disponibles en repositoris p√ļblics per a comprendre els senyals gen√≤mics de selecci√≥ recent i de resist√®ncia a f√†rmacs antif√ļngics, en sis esp√®cies rellevants de Candida: C. auris, C. glabrata, C. albicans, C. tropicalis, C. parapsilosis i C. orthopsilosis. Hem trobat centenars de gens sota selecci√≥ recent, suggerint que l'adaptaci√≥ cl√≠nica √©s diversa i complexa. Aquests gens estan relacionats amb funcions espec√≠fiques de cada esp√®cie, per√≤ tamb√© trobem processos alterats de manera similar en diferents pat√≤gens, com per exemple l‚Äôadhesi√≥ cel¬∑lular, cosa que indica fen√≤mens d‚Äôadaptaci√≥ conservats. A part, utilitzant GWAS hem predit mecanismes esperats de resist√®ncia a antif√ļngics i tamb√© possibles nous factors. A m√©s, les nostres an√†lisis revelen un paper important de les variants estructurals, generalment poc estudiades, i suggereixen una implicaci√≥ inesperada de la recombinaci√≥ (para)sexual en la propagaci√≥ de la resist√®ncia. En conjunt, els nostres descobriments proporcionen noves perspectives sobre com els pat√≤gens Candida s'adapten als entorns humans, i suggereixen gens candidats que mereixen investigacions futures. En resum, els resultats d‚Äôaquesta tesi milloren el nostre coneixement sobre els mecanismes d'adaptaci√≥ recent en els pat√≤gens Candida, cosa que pot permetre el disseny de noves ter√†pies i diagn√≤stics

    Understanding host-microbe interactions in maize kernel and sweetpotato leaf metagenomic profiles.

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    Functional and quantitative metagenomic profiling remains challenging and limits our understanding of host-microbe interactions. This body of work aims to mediate these challenges by using a novel quantitative reduced representation sequencing strategy (OmeSeq-qRRS), development of a fully automated software for quantitative metagenomic/microbiome profiling (Qmatey: quantitative metagenomic alignment and taxonomic identification using exact-matching) and implementing these tools for understanding plant-microbe-pathogen interactions in maize and sweetpotato. The next generation sequencing-based OmeSeq-qRRS leverages the strengths of shotgun whole genome sequencing and costs lower that the more affordable amplicon sequencing method. The novel FASTQ data compression/indexing and enhanced-multithreading of the MegaBLAST in Qmatey allows for computational speeds several thousand-folds faster than typical runs. Regardless of sample number, the analytical pipeline can be completed within days for genome-wide sequence data and provides broad-spectrum taxonomic profiling (virus to eukaryotes). As a proof of concept, these protocols and novel analytical pipelines were implemented to characterize the viruses within the leaf microbiome of a sweetpotato population that represents the global genetic diversity and the kernel microbiomes of genetically modified (GMO) and nonGMO maize hybrids. The metagenome profiles and high-density SNP data were integrated to identify host genetic factors (disease resistance and intracellular transport candidate genes) that underpin sweetpotato-virus interactions Additionally, microbial community dynamics were observed in the presence of pathogens, leading to the identification of multipartite interactions that modulate disease severity through co-infection and species competition. This study highlights a low-cost, quantitative and strain/species-level metagenomic profiling approach, new tools that complement the assay’s novel features and provide fast computation, and the potential for advancing functional metagenomic studies

    Artisanal food productions of animal origin: exploring food safety in the age of Whole Genome Sequencing

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    The artisanal food chain is enriched by a wide diversity of local food productions with delightful organoleptic characteristics and valuable nutritional properties. Despite their increasing worldwide popularity and appeal, several food safety challenges are addressed in artisanal facilities context suffering from less standardized processing conditions. In such scenario, recent advances in molecular typing and genomic surveillance (e.g., Whole Genome Sequencing [WGS]) represent an unprecedent solution capable of inferring sources of contamination as well as contributing to food safety along the artisanal food continuum. The overall objective of this PhD thesis was to explore potential microbial hazards among different artisanal food productions of animal origins (dairy and meat-derived) typical of the food culture and heritage landscape belonging to Mediterranean countries. Three different studies were then carried out, specifically focussing on: 1) compare the seasonal variability of microbiological quality and potential occurrence of microbial hazards in two batches of Italian artisanal fermented dairy and meat productions; 2) Investigate genetic relationships as well as virulome and resistome of foodborne pathogens isolated within dairy and meat-derived productions located in Italy, Spain, Portugal and Morocco; 3) investigate the population structure, virulome, resistome and mobilome of Klebsiella spp. isolates collected from study 1, including an extended range of public sequences

    Artificial Intelligence for detection and prevention of mold contamination in tomato processing

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    openIl presente elaborato si propone di analizzare l'uso dell'intelligenza artificiale attraverso il riconoscimento di immagini per rilevare la presenza di muffa nei pomodori durante il processo di essiccazione. La muffa nei pomodori rappresenta un rischio sia per la salute umana sia per l'industria alimentare, comportando, anche, una serie di problemi che vanno oltre l'aspetto estetico. Essa √® causata principalmente da funghi che si diffondono rapidamente sulla superficie dei pomodori. Tale processo compromette cos√¨ la qualit√† con la conseguente produzione di tossine che possono influire sulla salute umana. L'obiettivo sperimentale di questo lavoro √® il problema dello spreco e della perdita di prodotto nell'industria alimentare. Quando i pomodori sono colpiti da muffe, infatti, diventano inadatti al consumo, con conseguente perdita di cibo. Lo spreco di pomodori a causa delle muffe rappresenta anche la perdita di preziose risorse, utili alla produzione, come terra, acqua, energia e tempo. Il proposito √® testare, anche nella fase iniziale, la capacit√† di un algoritmo di rilevamento degli oggetti per identificare la muffa, e adottare misure preventive. L'analisi sperimentale ha previsto l'addestramento dell'algoritmo con un'ampia serie di foto, tra cui pomodori sani e rovinati di diversi tipi, forme e consistenze. Per etichettare le immagini e creare le epoche di addestramento √® stato quindi utilizzato YOLOv7, l'algoritmo di rilevamento degli oggetti scelto, basato su reti neurali. Per valutare le prestazioni sono state utilizzate metriche di valutazione, tra cui ‚ÄúPrecision‚ÄĚ e ‚ÄúRecall‚ÄĚ. L'ipotesi di applicazione dell'intelligenza artificiale in futuro sar√† un grande potenziale per migliorare i processi di produzione alimentare, facilitando, cos√¨, l'identificazione delle muffe. Il rilevamento rapido delle muffe faciliterebbe la separazione tempestiva dei prodotti contaminati, riducendo cos√¨ il rischio di diffusione delle tossine e preservando la qualit√† degli alimenti non contaminati. Questo approccio contribuirebbe a ridurre al minimo gli sprechi alimentari e le inefficienze delle risorse associate allo scarto di grandi quantit√† di prodotto. Inoltre, l'integrazione della computer vision nel contesto dell'HACCP (Hazard Analysis Critical Control Points) potrebbe migliorare i protocolli di sicurezza alimentare grazie a un rilevamento accurato e tempestivo. Questa tecnologia potr√† offrire, dando priorit√† alla prevenzione, una promettente opportunit√† per migliorare la qualit√†, l'efficienza e la sostenibilit√† dei futuri processi di produzione alimentare.This study investigates the use of computer vision couples with artificial intelligence to detect mold in tomatoes during the drying process. Mold presence in tomatoes poses threats to human health and the food industry as it leads to several issues beyond appearance. It is primarily caused by fungi that spread rapidly over the tomato surface, compromising their quality, and potentially producing toxins that can harm human health. The experimental aim of this work focused on the issue of wastage and loss within the food industry. When tomatoes succumb to mold, they become unsuitable for consumption, resulting in a loss of food and resources. Considering that tomato production requires resources such as land, water, energy, and time, wasting tomatoes due to mold also represents a waste of these valuable resources. The goal was to evaluate the mold detection capabilities of an object detection algorithm, particularly in its early stages, to facilitate preventative measures. This experimental analysis entailed training the algorithm with an extensive array of images, encompassing a variety of healthy and spoiled tomatoes of different shapes, types, textures and drying stages. The chosen object detection algorithm, YOLOv7, is convolutional neural network-based and was utilized for image labeling and training epochs. Evaluation metrics, including precision and recall, were utilized to assess the algorithm's performance. The implementation of artificial intelligence in the future has significant potential for enhancing food production processes by streamlining mold identification. Prompt mold detection would expedite segregation of contaminated products, thus reducing the risk of toxin dissemination and preserving the quality of uncontaminated food. This approach could minimize food waste and resource inefficiencies linked to discarding significant product amounts. Furthermore, integrating computer vision in the HACCP (Hazard Analysis Critical Control Points) context could enhance food safety protocols via accurate and prompt detection. By prioritizing prevention, this technology offers a promising chance to optimize quality, efficiency, and sustainability of future food production processes

    Obesity promotes fumonisin B1 hepatotoxicity

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    Obesity, which is a worldwide public health issue, is associated with chronic inflammation that contribute to long-term complications, including insulin resistance, type 2 diabetes and non-alcoholic fatty liver disease. We hypothesized that obesity may also influence the sensitivity to food contaminants, such as fumonisin B1 (FB1), a mycotoxin produced mainly by the Fusarium verticillioides. FB1, a common contaminant of corn, is the most abundant and best characterized member of the fumonisins family. We investigated whether diet-induced obesity could modulate the sensitivity to oral FB1 exposure, with emphasis on gut health and hepatotoxicity. Thus, metabolic effects of FB1 were assessed in obese and non-obese male C57BL/6J mice. Mice received a high-fat diet (HFD) or normal chow diet (CHOW) for 15¬†weeks. Then, during the last three weeks, mice were exposed to these diets in combination or not with FB1 (10¬†mg/kg body weight/day) through drinking water. As expected, HFD feeding induced significant body weight gain, increased fasting glycemia, and hepatic steatosis. Combined exposure to HFD and FB1 resulted in body weight loss and a decrease in fasting blood glucose level. This co-exposition also induces gut dysbiosis, an increase in plasma FB1 level, a decrease in liver weight and hepatic steatosis. Moreover, plasma transaminase levels were significantly increased and associated with liver inflammation in HFD/FB1-treated mice. Liver gene expression analysis revealed that the combined exposure to HFD and FB1 was associated with reduced expression of genes involved in lipogenesis and increased expression of immune response and cell cycle-associated genes. These results suggest that, in the context of obesity, FB1 exposure promotes gut dysbiosis and severe liver inflammation. To our knowledge, this study provides the first example of obesity-induced hepatitis in response to a food contaminant.L.D. PhD was supported by the INRAE Animal Health department. This work was also supported by grants from the French National Research Agency (ANR) Fumolip (ANR-16-CE21-0003) and the Hepatomics FEDER program of R√©gion Occitanie. We thank Prof Wentzel C. Gelderblom for generously providing the FB1 and for his interest and support in our project. B.C. laboratory is supported by a Starting Grant from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No. ERC-2018-StG- 804135), a Chaire d'Excellence from IdEx Universit√© de Paris - ANR-18-IDEX-0001, an Innovator Award from the Kenneth Rainin Foundation, an ANR grant EMULBIONT ANR-21-CE15-0042-01 and the national program ‚ÄúMicrobiote‚ÄĚ from INSERM. We thank Anexplo (Genotoul, Toulouse) for their excellent work on plasma biochemistry. Neutral Lipids MS and NMR experiments were performed with instruments in the Metatoul-AXIOM platform. Sphingolipid MS analysis were performed with instruments in the RUBAM platform. The FB1 plasma levels were determined using an UPLC-MS/MS instrument part of the Ghent University MSsmall expertise centre for advanced mass spectrometry analysis of small organic molecules. We thank Elodie Rousseau-Bacqui√© and all members of the EZOP staff for their assistance in the animal facility. We are very grateful to Talal al Saati for histology analyses and review, and we thank all members of the US006/CREFRE staff at the histology facility and the Genom'IC platforms (INSERM U1016, Paris, France) for their expertise.Peer reviewe
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