Sequence-based GWAS and post-GWAS analyses reveal a key role of SLC37A1, ANKH, and regulatory regions on bovine milk mineral content.

The mineral composition of bovine milk plays an important role in determining its nutritional and cheese-making value. Concentrations of the main minerals predicted from mid-infrared spectra produced during milk recording, combined with cow genotypes, provide a unique opportunity to decipher the genetic determinism of these traits. The present study included 1 million test-day predictions of Ca, Mg, P, K, Na, and citrate content from 126,876 Montbéliarde cows, of which 19,586 had genotype data available. All investigated traits were highly heritable (0.50-0.58), with the exception of Na (0.32). A sequence-based genome-wide association study (GWAS) detected 50 QTL (18 affecting two to five traits) and positional candidate genes and variants, mostly located in non-coding sequences. In silico post-GWAS analyses highlighted 877 variants that could be regulatory SNPs altering transcription factor (TF) binding sites or located in non-coding RNA (mainly lncRNA). Furthermore, we found 47 positional candidate genes and 45 TFs highly expressed in mammary gland compared to 90 other bovine tissues. Among the mammary-specific genes, SLC37A1 and ANKH, encoding proteins involved in ion transport were located in the most significant QTL. This study therefore highlights a comprehensive set of functional candidate genes and variants that affect milk mineral content.

Sequence-based GWAS, network and pathway analyses reveal genes co-associated with milk cheese-making properties and milk composition in Montbéliarde cows.

BACKGROUND: Milk quality in dairy cattle is routinely assessed via analysis of mid-infrared (MIR) spectra; this approach can also be used to predict the milk's cheese-making properties (CMP) and composition. When this method of high-throughput phenotyping is combined with efficient imputations of whole-genome sequence data from cows' genotyping data, it provides a unique and powerful framework with which to carry out genomic analyses. The goal of this study was to use this approach to identify genes and gene networks associated with milk CMP and composition in the Montbéliarde breed. RESULTS: Milk cheese yields, coagulation traits, milk pH and contents of proteins, fatty acids, minerals, citrate, and lactose were predicted from MIR spectra. Thirty-six phenotypes from primiparous Montbéliarde cows (1,442,371 test-day records from 189,817 cows) were adjusted for non-genetic effects and averaged per cow. 50 K genotypes, which were available for a subset of 19,586 cows, were imputed at the sequence level using Run6 of the 1000 Bull Genomes Project (comprising 2333 animals). The individual effects of 8.5 million variants were evaluated in a genome-wide association study (GWAS) which led to the detection of 59 QTL regions, most of which had highly significant effects on CMP and milk composition. The results of the GWAS were further subjected to an association weight matrix and the partial correlation and information theory approach and we identified a set of 736 co-associated genes. Among these, the well-known caseins, PAEP and DGAT1, together with dozens of other genes such as SLC37A1, ALPL, MGST1, SEL1L3, GPT, BRI3BP, SCD, GPAT4, FASN, and ANKH, explained from 12 to 30% of the phenotypic variance of CMP traits. We were further able to identify metabolic pathways (e.g., phosphate and phospholipid metabolism and inorganic anion transport) and key regulator genes, such as PPARA, ASXL3, and bta-mir-200c that are functionally linked to milk composition. CONCLUSIONS: By using an approach that integrated GWAS with network and pathway analyses at the whole-genome sequence level, we propose candidate variants that explain a substantial proportion of the phenotypic variance of CMP traits and could thus be included in genomic evaluation models to improve milk CMP in Montbéliarde cows.

Following pathogen development and gene expression in a food ecosystem: the case of a Staphylococcus aureus isolate in cheese.

Human intoxication or infection due to bacterial food contamination constitutes an economic challenge and a public health problem. Information on the in situ distribution and expression of pathogens responsible for this risk is to date lacking, largely because of technical bottlenecks in detecting signals from minority bacterial populations within a complex microbial and physicochemical ecosystem. We simulated the contamination of a real high-risk cheese with a natural food isolate of Staphylococcus aureus, an enterotoxin-producing pathogen responsible for food poisoning. To overcome the problem of a detection limit in a solid matrix, we chose to work with a fluorescent reporter (superfolder green fluorescent protein) that would allow spatiotemporal monitoring of S. aureus populations and targeted gene expression. The combination of complementary techniques revealed that S. aureus localizes preferentially on the cheese surface during ripening. Immunochemistry and confocal laser scanning microscopy enabled us to visualize, in a single image, dairy bacteria and pathogen populations, virulence gene expression, and the toxin produced. This procedure is readily applicable to other genes of interest, other bacteria, and different types of food matrices.

Tool for quantification of staphylococcal enterotoxin gene expression in cheese.

Cheese is a complex and dynamic microbial ecosystem characterized by the presence of a large variety of bacteria, yeasts, and molds. Some microorganisms, including species of lactobacilli or lactococci, are known to contribute to the organoleptic quality of cheeses, whereas the presence of other microorganisms may lead to spoilage or constitute a health risk. Staphylococcus aureus is recognized worldwide as an important food-borne pathogen, owing to the production of enterotoxins in food matrices. In order to study enterotoxin gene expression during cheese manufacture, we developed an efficient procedure to recover total RNA from cheese and applied a robust strategy to study gene expression by reverse transcription-quantitative PCR (RT-qPCR). This method yielded pure preparations of undegraded RNA suitable for RT-qPCR. To normalize RT-qPCR data, expression of 10 potential reference genes was investigated during S. aureus growth in milk and in cheese. The three most stably expressed reference genes during cheese manufacture were ftsZ, pta, and gyrB, and these were used as internal controls for RT-qPCR of the genes sea and sed, encoding staphylococcal enterotoxins A and D, respectively. Expression of these staphylococcal enterotoxin genes was monitored during the first 72 h of the cheese-making process, and mRNA data were correlated with enterotoxin production.

Biodiversity of bacterial ecosystems in traditional Egyptian Domiati cheese.

Bacterial biodiversity occurring in traditional Egyptian soft Domiati cheese was studied by PCR-temporal temperature gel electrophoresis (TTGE) and PCR-denaturing gradient gel electrophoresis (DGGE). Bands were identified using a reference species database (J.-C. Ogier et al., Appl. Environ. Microbiol. 70:5628-5643, 2004); de novo bands having nonidentified migration patterns were identified by DNA sequencing. Results reveal a novel bacterial profile and extensive bacterial biodiversity in Domiati cheeses, as reflected by the numerous bands present in TTGE and DGGE patterns. The dominant lactic acid bacteria (LAB) identified were as follows: Leuconostoc mesenteroides, Lactococcus garvieae, Aerococcus viridans, Lactobacillus versmoldensis, Pediococcus inopinatus, and Lactococcus lactis. Frequent non-LAB species included numerous coagulase-negative staphylococci, Vibrio spp., Kocuria rhizophila, Kocuria kristinae, Kocuria halotolerans, Arthrobacter spp./Brachybacterium tyrofermentans. This is the first time that the majority of these species has been identified in Domiati cheese. Nearly all the dominant and frequent bacterial species are salt tolerant, and several correspond to known marine bacteria. As Domiati cheese contains 5.4 to 9.5% NaCl, we suggest that these bacteria are likely to have an important role in the ripening process. This first systematic study of the microbial composition of Domiati cheeses reveals great biodiversity and evokes a role for marine bacteria in determining cheese type.

Raw cow milk bacterial population shifts attributable to refrigeration.

We monitored the dynamic changes in the bacterial population in milk associated with refrigeration. Direct analyses of DNA by using temporal temperature gel electrophoresis (TTGE) and denaturing gradient gel electrophoresis (DGGE) allowed us to make accurate species assignments for bacteria with low-GC-content (low-GC%) (<55%) and medium- or high-GC% (>55%) genomes, respectively. We examined raw milk samples before and after 24-h conservation at 4 degrees C. Bacterial identification was facilitated by comparison with an extensive bacterial reference database ( approximately 150 species) that we established with DNA fragments of pure bacterial strains. Cloning and sequencing of fragments missing from the database were used to achieve complete species identification. Considerable evolution of bacterial populations occurred during conservation at 4 degrees C. TTGE and DGGE are shown to be a powerful tool for identifying the main bacterial species of the raw milk samples and for monitoring changes in bacterial populations during conservation at 4 degrees C. The emergence of psychrotrophic bacteria such as Listeria spp. or Aeromonas hydrophila is demonstrated.

Identification of the bacterial microflora in dairy products by temporal temperature gradient gel electrophoresis.

Numerous microorganisms, including bacteria, yeasts, and molds, are present in cheeses, forming a complex ecosystem. Among these organisms, bacteria are responsible for most of the physicochemical and aromatic transformations that are intrinsic to the cheesemaking process. Identification of the bacteria that constitute the cheese ecosystem is essential for understanding their individual contributions to cheese production. We used temporal temperature gradient gel electrophoresis (TTGE) to identify different bacterial species present in several dairy products, including members of the genera Lactobacillus, Lactococcus, Leuconostoc, Enterococcus, Pediococcus, Streptococcus, and Staphylococcus. The TTGE technique is based on electrophoretic separation of 16S ribosomal DNA (rDNA) fragments by using a temperature gradient. It was optimized to reveal differences in the 16S rDNA V3 regions of bacteria with low-G+C-content genomes. Using multiple control strains, we first set up a species database in which each species (or group of species) was characterized by a specific TTGE fingerprint. TTGE was then applied to controlled dairy ecosystems with defined compositions, including liquid (starter), semisolid (home-made fermented milk), and solid (miniature cheese models) matrices. Finally, the potential of TTGE to describe the bacterial microflora of unknown ecosystems was tested with various commercial dairy products. Subspecies, species, or groups of species of lactic acid bacteria were distinguished in dairy samples. In conclusion, TTGE was shown to distinguish bacterial species in vitro, as well as in both liquid and solid dairy products.