Identification and quantification of meat product ingredients by whole-genome metagenomics (All-Food-Seq)

Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome

Complex microbiomes are part of the food we eat and influence our own microbiome, but their diversity remains largely unexplored. Here, we generated the open access curatedFoodMetagenomicData (cFMD) resource by integrating 1,950 newly sequenced and 583 public food metagenomes. We produced 10,899 metagenome-assembled genomes spanning 1,036 prokaryotic and 108 eukaryotic species-level genome bins (SGBs), including 320 previously undescribed taxa. Food SGBs displayed significant microbial diversity within and between food categories. Extension to >20,000 human metagenomes revealed that food SGBs accounted on average for 3% of the adult gut microbiome. Strain-level analysis highlighted potential instances of food-to-gut transmission and intestinal colonization (e.g., Lacticaseibacillus paracasei) as well as SGBs with divergent genomic structures in food and humans (e.g., Streptococcus gallolyticus and Limosilactobabillus mucosae). The cFMD expands our knowledge on food microbiomes, their role in shaping the human microbiome, and supports future uses of metagenomics for food quality, safety, and authentication.

Specific metagenomic asset drives the spontaneous fermentation of Italian sausages

Metagenomics is a powerful tool to study and understand the microbial dynamics that occur during food fermentation and allows to close the link between microbial diversity and final sensory characteristics. Each food matrix can be colonized by different microbes, but also by different strains of the same species. In this study, using an innovative integrated approach combining culture-dependent method with a shotgun sequencing, we were able to show how strain-level biodiversity could influence the quality characteristics of the final product. The attention was placed on a model food fermentation process: Salame Piemonte, a Protected Geographical Indication (PGI) Italian fermented sausage. Three independent batches produced in February, March and May 2018 were analysed. The sausages were manufactured, following the production specification, in a local meat factory in the area of Turin (Italy) without the use of starter cultures. A pangenomic approach was applied in order to identify and evaluate the lactic acid bacteria (LAB) population driving the fermentation process. It was observed that all batches were characterized by the presence of few LAB species, namely Pediococcus pentosaceus, Latilactobacillus curvatus and Latilactobacillus sakei. Sausages from the different batches were different when the volatilome was taken into consideration, and a strong association between quality attributes and strains present was determined. In particular, different strains of L. sakei, showing heterogeneity at genomic level, colonized the meat at the beginning of each production and deeply influenced the fermentation process by distinctive metabolic pathways that affected the fermentation process and the final sensory aspects.

Big Data in metagenomics: Apache Spark vs MPI

The progress of next-generation sequencing has lead to the availability of massive data sets used by a wide range of applications in biology and medicine. This has sparked significant interest in using modern Big Data technologies to process this large amount of information in distributed memory clusters of commodity hardware. Several approaches based on solutions such as Apache Hadoop or Apache Spark, have been proposed. These solutions allow developers to focus on the problem while the need to deal with low level details, such as data distribution schemes or communication patterns among processing nodes, can be ignored. However, performance and scalability are also of high importance when dealing with increasing problems sizes, making in this way the usage of High Performance Computing (HPC) technologies such as the message passing interface (MPI) a promising alternative. Recently, MetaCacheSpark, an Apache Spark based software for detection and quantification of species composition in food samples has been proposed. This tool can be used to analyze high throughput sequencing data sets of metagenomic DNA and allows for dealing with large-scale collections of complex eukaryotic and bacterial reference genome. In this work, we propose MetaCache-MPI, a fast and memory efficient solution for computing clusters which is based on MPI instead of Apache Spark. In order to evaluate its performance a comparison is performed between the original single CPU version of MetaCache, the Spark version and the MPI version we are introducing. Results show that for 32 processes, MetaCache-MPI is 1.65× faster while consuming 48.12% of the RAM memory used by Spark for building a metagenomics database. For querying this database, also with 32 processes, the MPI version is 3.11× faster, while using 55.56% of the memory used by Spark. We conclude that the new MetaCache-MPI version is faster in both building and querying the database and uses less RAM memory, when compared with MetaCacheSpark, while keeping the accuracy of the original implementation.