Shotgun metagenomic sequencing of bulk tank milk filters reveals the role of Moraxellaceae and Enterobacteriaceae as carriers of antimicrobial resistance genes

Dental plaque microbiota sequence counts for microbial profiling and resistance genes detection

Shotgun metagenomics sequencing experiments are finding a wide range of applications. Nonetheless, there are still limited guidelines regarding the number of sequences needed to acquire meaningful information for taxonomic profiling and antimicrobial resistance gene (ARG) identification. In this study, we explored this issue in the context of oral microbiota by sequencing with a very high number of sequences (~ 100 million), four human plaque samples, and one microbial community standard and by evaluating the performance of microbial identification and ARGs detection through a downsampling procedure. When investigating the impact of a decreasing number of sequences on quantitative taxonomic profiling in the microbial community standard datasets, we found some discrepancies in the identified microbial species and their abundances when compared to the expected ones. Such differences were consistent throughout downsampling, suggesting their link to taxonomic profiling methods limitations. Overall, results showed that the number of sequences has a great impact on metagenomic samples at the qualitative (i.e., presence/absence) level in terms of loss of information, especially in experiments having less than 40 million reads, whereas abundance estimation was minimally affected, with only slight variations observed in low-abundance species. The presence of ARGs was also assessed: a total of 133 ARGs were identified. Notably, 23% of them inconsistently resulted as present or absent across downsampling datasets of the same sample. Moreover, over half of ARGs were lost in datasets having less than 20 million reads. This study highlights the importance of carefully considering sequencing aspects and suggests some guidelines for designing shotgun metagenomics experiments with the final goal of maximizing oral microbiome analyses. Our findings suggest varying optimized sequence numbers according to different study aims: 40 million for microbiota profiling, 50 million for low-abundance species detection, and 20 million for ARG identification. KEY POINTS: • Forty million sequences are a cost-efficient solution for microbiota profiling • Fifty million sequences allow low-abundance species detection • Twenty million sequences are recommended for ARG identification.

Rapid detection of major enterotoxin genes and antibiotic resistance of Staphylococcus aureus isolated from raw milk in the Yazd province, Iran

INTRODUCTION

Raw milk is a nutrient-rich food, but it may harbour harmful bacteria, such as enterotoxigenic Staphylococcus aureus (S. aureus), which can cause staphylococcal food poisoning. Antibiotic resistance of S. aureus in raw milk can increase the risk of such infections, particularly among susceptible individuals.

OBJECTIVE

This study aimed to investigate the prevalence of enterotoxin genes a, d, g, i and j and the antibiotic resistance of S. aureus isolated from raw milk samples.

METHODS

During a 6-month sampling period, 60 raw milk specimens were obtained from diverse locations in Yazd province, Iran. Antibiogram profiling was conducted via the disc diffusion method. In addition, staphylococcal enterotoxin (SE) genes a, d, g, i, and j were detected through real-time PCR analysis.

RESULTS

Bacteriological assays confirmed the presence of S. aureus in 11 samples (18.3%). All isolates demonstrated 100% resistance to penicillin G but exhibited sensitivity to vancomycin, while resistance to other antibiotics ranged from 36.4% to 45.5%. The prevalence of enterotoxin genes in these strains showed variable distribution, with sea being the predominant SE (45.5%), followed by sed (36.4%), seg (18.2), sej and sei (9.1% each).

CONCLUSIONS

This study discovered the presence of multiple enterotoxins in S. aureus strains obtained from raw milk samples. These strains also demonstrated resistance to a variety of antibiotics. Since enterotoxigenic S. aureus is known to cause human food poisoning, monitoring food hygiene practices, especially during raw milk production, is critical.

The detailed analysis of the microbiome and resistome of artisanal blue-veined cheeses provides evidence on sources and patterns of succession linked with quality and safety traits

BACKGROUND

Artisanal cheeses usually contain a highly diverse microbial community which can significantly impact their quality and safety. Here, we describe a detailed longitudinal study assessing the impact of ripening in three natural caves on the microbiome and resistome succession across three different producers of Cabrales blue-veined cheese.

RESULTS

Both the producer and cave in which cheeses were ripened significantly influenced the cheese microbiome. Lactococcus and the former Lactobacillus genus, among other taxa, showed high abundance in cheeses at initial stages of ripening, either coming from the raw material, starter culture used, and/or the environment of processing plants. Along cheese ripening in caves, these taxa were displaced by other bacteria, such as Tetragenococcus, Corynebacterium, Brevibacterium, Yaniella, and Staphylococcus, predominantly originating from cave environments (mainly food contact surfaces), as demonstrated by source-tracking analysis, strain analysis at read level, and the characterization of 613 metagenome-assembled genomes. The high abundance of Tetragenococcus koreensis and Tetragenococcus halophilus detected in cheese has not been found previously in cheese metagenomes. Furthermore, Tetragenococcus showed a high level of horizontal gene transfer with other members of the cheese microbiome, mainly with Lactococcus and Staphylococcus, involving genes related to carbohydrate metabolism functions. The resistome analysis revealed that raw milk and the associated processing environments are a rich reservoir of antimicrobial resistance determinants, mainly associated with resistance to aminoglycosides, tetracyclines, and β-lactam antibiotics and harbored by aerobic gram-negative bacteria of high relevance from a safety point of view, such as Escherichia coli, Salmonella enterica, Acinetobacter, and Klebsiella pneumoniae, and that the displacement of most raw milk-associated taxa by cave-associated taxa during ripening gave rise to a significant decrease in the load of ARGs and, therefore, to a safer end product.

CONCLUSION

Overall, the cave environments represented an important source of non-starter microorganisms which may play a relevant role in the quality and safety of the end products. Among them, we have identified novel taxa and taxa not previously regarded as being dominant components of the cheese microbiome (Tetragenococcus spp.), providing very valuable information for the authentication of this protected designation of origin artisanal cheese. Video Abstract.

Modeling the limits of detection for antimicrobial resistance genes in agri-food samples: a comparative analysis of bioinformatics tools

BACKGROUND

Although the spread of antimicrobial resistance (AMR) through food and its production poses a significant concern, there is limited research on the prevalence of AMR bacteria in various agri-food products. Sequencing technologies are increasingly being used to track the spread of AMR genes (ARGs) in bacteria, and metagenomics has the potential to bypass some of the limitations of single isolate characterization by allowing simultaneous analysis of the agri-food product microbiome and associated resistome. However, metagenomics may still be hindered by methodological biases, presence of eukaryotic DNA, and difficulties in detecting low abundance targets within an attainable sequence coverage. The goal of this study was to assess whether limits of detection of ARGs in agri-food metagenomes were influenced by sample type and bioinformatic approaches.

RESULTS

We simulated metagenomes containing different proportions of AMR pathogens and analysed them for taxonomic composition and ARGs using several common bioinformatic tools. Kraken2/Bracken estimates of species abundance were closest to expected values. However, analysis by both Kraken2/Bracken indicated presence of organisms not included in the synthetic metagenomes. Metaphlan3/Metaphlan4 analysis of community composition was more specific but with lower sensitivity than the Kraken2/Bracken analysis. Accurate detection of ARGs dropped drastically below 5X isolate genome coverage. However, it was sometimes possible to detect ARGs and closely related alleles at lower coverage levels if using a lower ARG-target coverage cutoff (< 80%). While KMA and CARD-RGI only predicted presence of expected ARG-targets or closely related gene-alleles, SRST2 (which allows read to map to multiple targets) falsely reported presence of distantly related ARGs at all isolate genome coverage levels. The presence of background microbiota in metagenomes influenced the accuracy of ARG detection by KMA, resulting in mcr-1 detection at 0.1X isolate coverage in the lettuce but not in the beef metagenome.

CONCLUSIONS

This study demonstrates accurate detection of ARGs in synthetic metagenomes using various bioinformatic methods, provided that reads from the ARG-encoding organism exceed approximately 5X isolate coverage (i.e. 0.4% of a 40 million read metagenome). While lowering thresholds for target gene detection improved sensitivity, this led to the identification of alternative ARG-alleles, potentially confounding the identification of critical ARGs in the resistome. Further advancements in sequencing technologies providing increased coverage depth or extended read lengths may improve ARG detection in agri-food metagenomic samples, enabling use of this approach for tracking clinically important ARGs in agri-food samples.

High throughput qPCR analyses suggest that Enterobacterales of French sheep and cow cheese rarely carry genes conferring resistances to critically important antibiotics for human medicine

Bacteria present in raw milk can carry acquired or intrinsic antimicrobial resistance genes (ARGs) and mobile genetic elements (MGEs). However, only a few studies have evaluated raw milk cheese as a potential reservoir of ARGs. This study thus aimed at providing new data regarding resistance markers present in raw milk cheese. Sheep (n = 360) and cow (n = 360) cheese samples produced in France were incubated in buffered peptone water supplemented with acriflavin or novobiocin; as corroborated by 16S metabarcoding, samples were enriched in Gram-negative bacteria since Escherichia coli and Hafnia alvei respectively accounted for 40 % and 20 % of the samples' microbiota. Screening of the samples for the presence of 30 ARGs and 16 MGEs by high throughput qPCR array showed that nine ARGs conferring resistances to 1st-generation beta-lactams, aminoglycosides, trimethoprim/sulfonamides and tetracyclines occurred in >75 % of both sheep and cow samples. This is neither surprising nor alarming since these resistance genes are widely spread across the One Health human, animal and environmental sectors. Conversely, genes conferring resistances to last-generations cephalosporins were rarely identified, while those conferring resistances to carbapenems or amikacin, which are restricted to human use, were never detected. Multiple MGEs were detected, the most frequent ones being IncF plasmids, confirming the potential transmission of ARGs. Our results are in line with the few studies of the resistome of milk or milk cheese showing that genes conferring resistances to 1st-generation beta-lactams, aminoglycosides and tetracyclines families are widespread, while those conferring resistances to critically important antibiotics are rare or absent.

Effect of biochar on antibiotic resistance genes in the anaerobic digestion system of antibiotic mycelial dreg

To address the problem of antibiotic mycelial dreg (AMD) treatment and removal of antibiotic resistance genes (ARGs), this study adopted anaerobic digestion (AD) technology, and added biochar (BC) and biochar loaded with nanosized zero-valent iron (nZVI-BC) to promote the AD of AMD and enhance the removal of ARGs. Results showed that nZVI-BC was better than BC in promoting AD due to the hydrogen evolution corrosion and the synergistic effect of nZVI and BC. In addition, BC and nZVI-BC can enhance the oxidative stress response and reduce ammonia stress phenomenon, which significantly reduces the abundance of aadA, ant(2″)-Ⅰ, qacEdelta1 and sul1. In conclusion, the enhance effect of nZVI-BC is greater than BC. The removal efficiency rates of nZVI-BC on the above-mentioned four ARGs were improved by 33%, 9%, 24% and 11%.

Comparison Between Full-Length 16S rRNA Metabarcoding and Whole Metagenome Sequencing Suggests the Use of Either Is Suitable for Large-Scale Microbiome Studies

Since the number of studies of the microbial communities related to food and food-associated matrices almost completely reliant on next-generation sequencing techniques is rising, evaluations of these high-throughput methods are critical. Currently, the two most used sequencing methods to profile the microbiota of complex samples, including food and food-related matrices, are the 16S ribosomal RNA (rRNA) metabarcoding and the whole metagenome sequencing (WMS), both of which are powerful tools for the monitoring of foodborne pathogens and the investigation of the microbiome. Herein, the microbial profiles of 20 bulk tank milk filters from different dairy farms were investigated using both the full-length 16S (FL-16S) rRNA metabarcoding, a third-generation sequencing method whose application in food and food-related matrices is yet in its infancy, and the WMS, to evaluate the correlation and the reliability of these two methods to explore the microbiome of food-related matrices. Metabarcoding and metagenomic data were generated on a MinION platform (Oxford Nanopore Technologies) and on a Illumina NovaSeq 6000 platform, respectively. Our findings support the greater resolution of WMS in terms of both increased detection of bacterial taxa and enhanced detection of diversity; in contrast, FL-16S rRNA metabarcoding has proven to be a promising, less expensive, and more practical tool to profile most abundant taxa. The significant correlation of the two technologies both in terms of taxa diversity and richness, together with the similar profiles defined for both highly abundant taxa and core microbiomes, including Acinetobacter, Bacillus, and Escherichia genera, highlights the possible application of both methods for different purposes. This study allowed the first comparison of FL-16S rRNA sequencing and WMS to investigate the microbial composition of a food-related matrix, pointing out the advantageous use of FL-16S rRNA to identify dominant microorganisms and the superior power of WMS for the taxonomic detection of low abundant microorganisms and to perform functional analysis of the microbial communities.