Biology subjects

Nagy, S. A.

Publications and source records attributed to Nagy, S. A..

10 recordsLinked to original sources

bioRxiv2024

Impact of Mycotoxins and Glyphosate residue on the Gut Microbiome and Resistome of European Fallow Deer

Mycotoxins and herbicides residue significant threats to animal health. This study investigated the impact of these toxins on the gut microbiome of the fallow deer, a valuable game species. We analyzed the intestinal contents of deer exposed to varying levels of zearalenone (ZEA) and other mycotoxins and toxic compounds such as aflatoxins, deoxynivalenol, fumonisin B1 and glyphosate residues. Metagenomic analysis revealed significant alterations in the bacterial community composition, particularly in the abundance of specific taxa. Higher ZEA levels were associated with decreased alpha diversity, whereas higher aflatoxin levels had the opposite effect. We also observed changes in the abundance of antibiotic resistance genes (ARGs), suggesting a potential link between mycotoxin exposure and antimicrobial resistance. Furthermore, five complete bacterial genomes were assembled from the metagenomic data. These findings highlight the complex interplay between environmental toxins, the gut microbiota, and animal health. Understanding these interactions is crucial for developing strategies to mitigate the negative effects of toxin exposure on wildlife populations.

genomics↗

bioRxiv2024

Metagenomic peek into a corn mummy

Numerous studies have shown that metagenomics has opened a dimension in reading the contents of archaeological remains as time capsules. Corn mummies are ritual objects from ancient Egypt, created by forming human-shaped figures from cereal grains grown in a mixture of water and earth. The aim of our study was to determine whether ancient DNA could be preserved in the mummy, and if so, which organisms it might have originated from. To find answers, we performed metagenomic analyses on samples taken from a corn mummy dating to the second half of the third century BC. Alongside a number of clearly modern contaminants, we identified organisms that cannot be excluded as being of historical origin. Besides considerable amounts of bacterial sequences belonging to the genus Bacillus, Mesobacillus, Metabacillus, Neobacillus, Niallia, Peribacillus and Paenibacillus, we also found traces of plants, animals, and humans. Sequences assigned to the genus Triticum showed the highest similarity to ancient T. turgidum ssp. dicoccum specimens from Egypt and the southern Levant. The fragments identified as of Lepidopteran origin showed the greatest similarity to Sphingidae genomes. Analysis of the human-derived sequences revealed L3 (mtDNA), E, and J (Y chromosome) haplotypes, which are common lineages in Africa today.

bioinformatics↗

bioRxiv2024

Clinical considerations on antimicrobial resistance potential of complex microbiological samples

Antimicrobial resistance (AMR) is one of our greatest public health challenges. Targeted use of antibiotics (AB) can reduce the occurrence and spread of AMR and boost the effectiveness of treatment. This requires knowledge of the antibiotic susceptibility (AS) of the pathogens involved in the disease. Therapeutic recommendations based on classical antibiotic susceptibility testing (AST) are based on the analysis of only a fraction of the bacteria present in the disease process. Next and third generation sequencing technologies allow the identification of antimicrobial resistance genes (ARGs) present in a bacterial community. Using this genomic approach, we can map the antimicrobial resistance potential (AMRP) of a complex, multi-bacterial microbial sample. The same approach can be used to identify antibiotics without any ARGs in the sample that interfere with their activity. Our paper summarises the clinical interpretation opportunities of genomic analysis results from 574 Escherichia coli strains and a complex microbiological sample from canine external otitis. In clinical metagenomics, AMRP may be an important approach to make AB therapy more targeted and effective. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/594174v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@d84733org.highwire.dtl.DTLVardef@12aa447org.highwire.dtl.DTLVardef@873811org.highwire.dtl.DTLVardef@1721aba_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2023

Shotgun metagenomic analysis of the skin mucus bacteriome of the common carp (Cyprinus carpio)

The skin mucus bacteriome of fish plays an important role in the health of their hosts. Despite the economic importance of the common carp (Cyprinus carpio), research on its skin bacteriome composition is still missing. To date, most studies on the composition of fish skin bacteriome have used amplicon sequencing, despite the limitations associated with this method. In our study, a shotgun metagenomic approach was applied to characterize the external mucus bacteriome of 8 carp specimens from two different ponds on a fish farm in Hungary. Besides the carp samples, water was also sequenced from the two corresponding ponds. Each carp skin sample was dominated by the phylum Proteobacteria, followed by Actinobacteria, Bacteroidota, Firmicutes, Cyanobacteria and Planctomycetota. Additionally, we have found strong concordance between the water and carp skin mucus samples, despite most studies describing an opposite relationship. Furthermore, shotgun metagenomics allowed us to apply functional annotation to the metagenomes, which revealed several metabolic functions. We present, to our knowledge, the first description of the common carp (Cyprinus carpio) skin mucus bacteriome. Even though our results showed a high level of host genome contamination, we could still provide valuable insight into the external bacterial community of this species. The presented data can provide a basis for future metagenome studies of carp or other fish species.

microbiology↗

bioRxiv2023

Bacterial colony size growth estimation by deep learning

The bacterial growth rate is important for pathogenicity and food safety. Therefore, the study of bacterial growth rate over time can provide important data from a medical and veterinary point of view. We trained convolutional neural networks (CNNs) on manually annotated solid medium cultures to detect bacterial colonies as accurately as possible. Predictions of bacterial colony size and growth rate were estimated from image sequences of independent Staphylococcus aureus cultures using trained CNNs. A simple linear model for control cultures with less than 150 colonies estimated that the mean growth rate was 60.3 m/h for the first 24 h. Analyzing with a mixed effect model that also takes into account the effect of culture, smaller values of change in colony size were obtained (control: 51.0 m/h, rifampicin pretreated: 36.5m/h). An increase in the number of neighboring colonies clearly reduces the colony growth rate in the control group but less typically in the rifampicin-pretreated group. Based on our results, CNN-based bacterial colony detection and the subsequent analysis of bacterial colony growth dynamics might become an accurate and efficient tool for bacteriological work and research.

bioinformatics↗

bioRxiv2022

Antimicrobial resistance gene lack in tick-borne pathogenic bacteria

Tick-borne infections, including those of bacterial origin, are significant public health issues. Antimicrobial resistance (AMR), which is one of the most pressing health challenges of our time, is driven by specific genetic determinants, primarily by the antimicrobial resistance genes (ARGs) of bacteria. In our work, we investigated the occurrence of ARGs in the genomes of tick-borne bacterial species that can cause human infections. For this purpose, we processed short/long reads of 1550 bacterial isolates of the genera Anaplasma (n=20), Bartonella (n=131), Borrelia (n=311), Coxiella (n=73), Ehrlichia (n=13), Francisella (n=959) and Rickettsia (n=43) generated by second/third generation sequencing that have been freely accessible at the NCBI SRA repository. From Francisella tularensis, 98.9% of the samples contained the FTU-1 gene, and 16.3% contained additional ARGs. Only 2.2% of isolates from other genera (Bartonella: 2, Coxiella: 8, Ehrlichia: 1, Rickettsia: 2) contained any ARG. We found that the odds of ARG occurrence in Coxiella samples were significantly higher in isolates related to farm animals than from other sources. Our results describe a lack in ARGs in these bacteria and suggest that antibiotic susceptibility testing might be considered before the treatment of tick-borne infections in farm animals.

genomics↗

bioRxiv2022

Impact evaluation of score classes and annotation regions in deep learning-based dairy cow body condition prediction

Body condition scoring is a simple method to estimate the energy supply of dairy cattle. Our study aimed to investigate the accuracy with which supervised machine learning, a deep convolutional neural network, can be used to retrieve body condition score (BCS) classes estimated by an expert. Using a simple action camera, we recorded images of animals rumps in three large-scale farms. The images were annotated with three different-sized boxes by an expert. A Faster-RCNN pre-trained model was trained on 12 and 3 BCS classes. Training in 12 classes, with a 0 error range, the Cohens kappa value yielded minimal agreement. Allowing an error range of 0.25, we obtained a minimum or week agreement. With an error range of 0.5, we had strong or almost perfect agreements. The kappa values of the approach trained on 3 classes show that we can classify all animals into BCS categories with at least moderate agreement. Furthermore, CNNs trained in 3 BCS classes show a remarkably higher proportion of strong agreement than those trained in 12 classes. The prediction precision based on training with various annotation regions showed no meaningful differences.

bioinformatics↗

bioRxiv2022

A survey on antimicrobial resistance genes of frequently used bacteria in kefir and yoghurt

Antimicrobial resistance (AMR) is one of the foremost threats facing the treatment of infectious diseases worldwide. Recent studies have highlighted the potential for antimicrobial resistance genes (ARGs) in fermented foods to contribute to AMR via horizontal gene transfer (HGT). The focus of our study was investigating the ARG content (resistome) and mobility potential of the ARGs (mobilome) of bacterial strains commonly used in probiotic products, namely yoghurt and kefir. We performed metagenomic analyses on freely available data sets (n=584) originating from various kefir and yoghurt strains using next generation sequencing (NGS) in order to gain an insight into the ARG diversity, frequency and mobility. Our study shows that kefir and yoghurt products carry diverse and significant amounts of ARGs and that these genes may often be associated with iMGEs or plasmids, conferring mobility. Certain bacteria species such as Bifidobacterium animalis and Streptococcus thermophilus were found to have higher ARG content. Overall, our results support the hypothesis that ARGs are present in fermented foods, namely yoghurt and kefir, and have the potential to contribute to AMR.

genomics↗

bioRxiv2021

Antimicrobial resistance determinants in silage

Animal products may play a role in developing and spreading antimicrobial resistance in several ways. On the one hand, residues of antibiotics not adequately used in animal farming can enter the human body via food. But resistant bacteria may also be present in animal products, which can transfer the antimicrobial resistance genes (ARG) to the bacteria in the consumers body by horizontal gene transfer. As previous studies have shown that fermented foods have a meaningful ARG content, it is indicated that such genes may also be present in silage used as mass feed in the cattle sector. In our study, we aspired to answer what ARGs occur in silage and what mobility characteristics they have? For this purpose, we have analyzed bioinformatically 52 freely available deep sequenced silage samples from shotgun metagenome next-generation sequencing. A total of 17 perfect matched ARGs occurred 55 times in the samples. More than half of these ARGs are mobile because they can be linked to integrative mobile genetic elements, prophages or plasmids. Our results point to a neglected but substantial ARG source in the food chain.

genomics↗

bioRxiv2020

A glimpse of antimicrobial resistance gene diversity in kefir and yoghurt

Antimicrobial resistance (AMR) is a global threat gaining more and more practical significance every year. The protection of bacteria against antimicrobials based on antimicrobial resistance genes (ARGs) developed in evolution. One of the essential clinical questions is the origin of ARGs of pathogen bacteria. Since the bacteria can share genetic components by horizontal gene transfer (HGT), all even non-pathogen bacteria may provide ARG to any pathogens when they became close physically. The bacteria of the human gut may make contact with bacteria entered into the body by food. The fermented food contains bacteria in high amount by its nature. Here we studied the diversity of ARG content by a unified metagenomic approach in various kefir and yoghurt products, in grain and isolated bacterial strains. We found numerous ARGs of commonly used fermenting bacteria with diversity characteristics in kefir and yoghurt samples. Even with the strictest filter restrictions we identified ARGs undermining the efficacy of aminocoumarin, aminoglycoside, carbapenem, cephalosporin, cephamycin, diaminopyrimidine, elfamycin, fluoroquinolone, fosfomycin, glycylcycline, lincosamides, macrolide, monobactam, nitrofuran, nitroimidazole, penam, penem, peptide, phenicol, rifamycin, tetracycline and triclosan. In the case of gene lmrD, we detected genetic environment providing mobility of this ARG. Our findings support that theory during the fermentation process the food ARG content can grow by the bacteria multiplication. Results presented suggest that starting culture strains of fermented food should be monitored and selected to decrease the ARG amount intake by nutrition.

genomics↗