Novel methods of microbiome analysis in the food industry

Tracing the origin and authenticity of Spanish PDO honey using metagenomics and machine learning

The Protected Designation of Origin (PDO) indication for foods intends to guarantee the conditions of production and the geographical origin of regional products within the European Union. Honey products are widely consumed due to their health-promoting properties and there is a general interest in tracing their authenticity. In this regard, metagenomics sequencing and machine learning (ML) have been proposed as complementary technologies to improve the traceability methods of foods. Therefore, the aim of this study was to analyze the metagenomic profiles of Spanish honeys from three different PDOs (Granada, Tenerife and Villuercas-Ibores), and compare them with non-PDO honeys using ML models (PLS, RF, LOGITBOOST, and NNET). According to the results obtained, non-PDO honeys and Granada PDO showed higher beta diversity values than Tenerife and Villuercas-Ibores PDOs. ML classification of honey products allowed the identification of different microbial biomarkers of the geographical origin of honeys: Lactobacillus kunkeei, Parasaccharibacter apium and Lactobacillus helsingborgensis for PDO honeys and Paenibacillus larvae, Lactobacillus apinorum and Klebsiella pneumoniae for non-PDO honeys. In addition, potential microbial biomarkers of some honey varieties including L. kunkeei for Albaida and Retama del Teide varieties, and P. apium for Tajinaste variety, were identified. ML models were validated on an independent set of samples leading to high accuracy rates (above 90 %). This work demonstrates the potential of ML to differentiate different types of honey using metagenome-based methods, leading to high performance metrics. In addition, ML models discriminate both the geographical origin and variety of products corresponding to different PDOs and non-PDO products. Results here presented may contribute to develop enhanced traceability and authenticity methods that could be applied to a wide range of foods.

Early Detection of Food Safety and Spoilage Incidents Based on Live Microbiome Profiling and PMA-qPCR Monitoring of Indicators

The early detection of spoilage microorganisms and food pathogens is of paramount importance in food production systems. We propose a novel strategy for the early detection of food production defects, harnessing the product microbiome. We hypothesize that by establishing microbiome datasets of proper and defective batches, indicator bacteria signaling production errors can be identified and targeted for rapid quantification as part of routine practice. Using the production process of pastrami as a model, we characterized its live microbiome profiles throughout the production stages and in the final product, using propidium monoazide treatment followed by 16S rDNA sequencing. Pastrami demonstrated product-specific and consistent microbiome profiles predominated by Serratia and Vibrionimonas, with distinct microbial signatures across the production stages. Based on the established microbiome dataset, we were able to detect shifts in the microbiome profile of a defective batch produced under lactate deficiency. The most substantial changes were observed as increased relative abundances of Vibrio and Lactobacillus, which were subsequently defined as potential lactate-deficiency indicators. PMA-qPCR efficiently detected increased levels of these species, thus proving useful in rapidly pinpointing the production defect. This approach offers the possibility of the in-house detection of defective production events with same-day results, promoting safer food production systems.

Deciphering microbial communities of three Savoyard raw milk cheeses along ripening and regarding the cheese process

Different Savoyard cheeses are granted with PDO (Protected Designation or Origin) and PGI (Protected Geographical Indication) which guarantees consumers compliance with strict specifications. The use of raw milk is known to be crucial for specific flavor development. To unravel the factors influencing microbial ecosystems across cheese making steps, according to the seasonality (winter and summer) and the mode of production (farmhouse and dairy factory ones), gene targeting on bacteria and fungus was used to have a full picture of 3 cheese making technologies, from the raw milk to the end of the ripening. Our results revealed that Savoyard raw milks are a plenteous source of biodiversity together with the brines used during the process, that may support the development of specific features for each cheese. It was shown that rinds and curds have very contrasted ecosystem diversity, composition, and evolution. Ripening stage was selective for some bacterial species, whereas fungus were mainly ubiquitous in dairy samples. All ripening stages are impacted by the type of cheese technologies, with a higher impact on bacterial communities, except for fungal rind communities, for which the technology is the more discriminant. The specific microorganism's abundance for each technology allow to see a real bar-code, with more or less differences regarding bacterial or fungal communities. Bacterial structuration is shaped mainly by matrices, differently regarding technologies while the influence of technology is higher for fungi. Production types showed 10 differential bacterial species, farmhouses showed more ripening taxa, while dairy factory products showing more lactic acid bacteria. Meanwhile, seasonality looks to be a minor element for the comprehension of both microbial ecosystems, but the uniqueness of each dairy plant is a key explicative feature, more for bacteria than for fungus communities.

Dissecting the Genetic Basis of the Technological, Functional, and Safety Characteristics of Lacticaseibacillus paracasei SRX10

Nonstarter lactic acid bacteria (NSLAB) are major contributors to the unique characteristics (e.g., aroma, flavor, texture) of dairy and nondairy fermented products. Lc. paracasei SRX10 is an NSLAB strain originally isolated from a traditional Greek cheese and previously shown to exhibit favorable biotechnological characteristics. More specifically, the strain showed tolerance to simulated gastrointestinal conditions, exopolysaccharide (EPS) biosynthetic capacity, and lack of hemolytic activity and was used in the production of yoghurt and feta cheese with distinct organoleptic characteristics. The aim of the present study was to investigate these traits at the genome level through whole-genome sequencing (WGS), annotation, and comparative genomics. Functional annotation of the genome revealed that Lc. paracasei SRX10 can utilize different carbon sources, leading to the generation of flavor compounds, including lactic acid, acetate, ethanol, and acetoin. Similarly, full clusters for fatty acid biosynthesis, protein and peptide degradation, as well as genes related to survival under extreme temperatures, osmotic shock, and oxidative stress were annotated. Importantly, no transferable antibiotic resistance genes or virulence factors were identified. Finally, strain-specific primers based on genome-wide polymorphisms were designed for the efficient and rapid identification of Lc. paracasei SRX10 via multiplex PCR in fermented products.

Next-Generation Sequencing for the Detection of Microbial Agents in Avian Clinical Samples

Direct-targeted next-generation sequencing (tNGS), with its undoubtedly superior diagnostic capacity over real-time PCR (RT-PCR), and direct-non-targeted NGS (ntNGS), with its higher capacity to identify and characterize multiple agents, are both likely to become diagnostic methods of choice in the future. tNGS is a rapid and sensitive method for precise characterization of suspected agents. ntNGS, also known as agnostic diagnosis, does not require a hypothesis and has been used to identify unsuspected infections in clinical samples. Implemented in the form of multiplexed total DNA metagenomics or as total RNA sequencing, the approach produces comprehensive and actionable reports that allow semi-quantitative identification of most of the agents present in respiratory, cloacal, and tissue samples. The diagnostic benefits of the use of direct tNGS and ntNGS are high specificity, compatibility with different types of clinical samples (fresh, frozen, FTA cards, and paraffin-embedded), production of nearly complete infection profiles (viruses, bacteria, fungus, and parasites), production of "semi-quantitative" information, direct agent genotyping, and infectious agent mutational information. The achievements of NGS in terms of diagnosing poultry problems are described here, along with future applications. Multiplexing, development of standard operating procedures, robotics, sequencing kits, automated bioinformatics, cloud computing, and artificial intelligence (AI) are disciplines converging toward the use of this technology for active surveillance in poultry farms. Other advances in human and veterinary NGS sequencing are likely to be adaptable to avian species in the future.

Characterization of Microbial Diversity of Two Tomato Cultivars through Targeted Next-Generation Sequencing 16S rRNA and ITS Techniques

Even though the nutritional and economic values of Solanum lycopersicum (tomato) are substantially impacted by microbial spoilage, the available data on its microbial community, particularly during spoilage, are limited and have primarily been characterized using conventional culture-dependent methods. This study employed a targeted high-throughput next-generation sequencing method to longitudinally characterize the microbial diversity of two South African tomato cultivars (jam and round) at varied storage intervals (1, 6, and 12 days). Throughout the storage period, the bacterial communities of the two cultivars were more diverse than the fungal communities. The microbial diversity of both bacteria and fungi was greater and comparable between the cultivars on day 1, but becomes distinct as the storage period increases, with round tomatoes being more diverse than jam tomato, though, on day 12, jam tomato develops greater diversity than round tomato. Overall, the most abundant phyla (though Proteobacteria was most dominant) were Proteobacteria, Firmicutes, and Bacteriodota in the bacterial communities, while Ascomycota and Basidiomycota formed most fungal communities with Ascomycota being dominant. At the genus level, Pantoea and Klebsiella (bacteria), Hanseniaspora, Stemphylium, and Alternaria (fungi) were prevalent. Taken together, this study casts light on a broad microbial diversity profile thus, confirms the cultivars' diversity and abundance differences.

Food systems microbiome-related educational needs

Within the European-funded Coordination and Support Action MicrobiomeSupport (https://www.microbiomesupport.eu/), the Workshop 'Education in Food Systems Microbiome Related Sciences: Needs for Universities, Industry and Public Health Systems' brought together over 70 researchers, public health and industry partners from all over the world to work on elaborating microbiome-related educational needs in food systems. This publication provides a summary of discussions held during and after the workshop and the resulting recommendations.

Metagenomics: An Effective Approach for Exploring Microbial Diversity and Functions

Various fields have been identified in the "omics" era, such as genomics, proteomics, transcriptomics, metabolomics, phenomics, and metagenomics. Among these, metagenomics has enabled a significant increase in discoveries related to the microbial world. Newly discovered microbiomes in different ecologies provide meaningful information on the diversity and functions of microorganisms on the Earth. Therefore, the results of metagenomic studies have enabled new microbe-based applications in human health, agriculture, and the food industry, among others. This review summarizes the fundamental procedures on recent advances in bioinformatic tools. It also explores up-to-date applications of metagenomics in human health, food study, plant research, environmental sciences, and other fields. Finally, metagenomics is a powerful tool for studying the microbial world, and it still has numerous applications that are currently hidden and awaiting discovery. Therefore, this review also discusses the future perspectives of metagenomics.