Versatile Lactococcus lactis strains improve texture in both fermented milk and soybean matrices

Lactic acid bacteria (LAB) have long been used to extend the shelf life and improve the taste and texture of fermented milk. In this study, we investigated the texturing potential of LAB in plant-based fermentation by high-throughput screening of 1232 Lactococcus lactis strains for texture in milk and liquid soybean matrices. We found that most strains with texturing abilities in fermented milk were also capable of enhancing the texture in fermented soybean, despite the large differences in composition of the two matrices. Exocellular polysaccharide production is believed to contribute positively to fermented milk and plant-base texture. It appeared as if it was the properties of the polysaccharides rather than their protein interaction partners that were responsible for the enhanced texture in both matrices. We mined whole genome sequences of texturing strains for polysaccharide biosynthesis (eps) gene clusters. The comparative genomics approach revealed 10 texturing strains with novel eps gene clusters. Currently, the relationship between the novel genes and their functionality in milk and plant matrices is unknown.

Measurement of water-holding capacity in fermented milk using near-infrared spectroscopy combined with chemometric methods

We investigated the use of near-infrared spectroscopy (NIR) for measuring water-holding capacity (WHC) in fermented milk. Increased WHC ensures improved texture and decreased syneresis in fermented dairy products and also improves cheese yield. NIR combined with partial least-squares-discriminant analysis (PLS-DA) was found to be a promising rapid and non-invasive method with no pretreatment of the samples for prediction of WHC in fermented milk samples. Analysis of the chemical bonds in the region 10 700-4500 cm-1 (935-2200 nm) of the electromagnetic spectrum was able to distinguish between samples with high vs. low WHC. This technique was successfully used to screen different strains of lactic acid bacteria for their ability to provide fermented milk with increased WHC, which is of great importance for use in various dairy products.

Machine learning predicts and provides insights into milk acidification rates of Lactococcus lactis

Lactococcus lactis strains are important components in industrial starter cultures for cheese manufacturing. They have many strain-dependent properties, which affect the final product. Here, we explored the use of machine learning to create systematic, high-throughput screening methods for these properties. Fast acidification of milk is such a strain-dependent property. To predict the maximum hourly acidification rate (Vmax), we trained Random Forest (RF) models on four different genomic representations: Presence/absence of gene families, counts of Pfam domains, the 8 nucleotide long subsequences of their DNA (8-mers), and the 9 nucleotide long subsequences of their DNA (9-mers). Vmax was measured at different temperatures, volumes, and in the presence or absence of yeast extract. These conditions were added as features in each RF model. The four models were trained on 257 strains, and the correlation between the measured Vmax and the predicted Vmax was evaluated with Pearson Correlation Coefficients (PC) on a separate dataset of 85 strains. The models all had high PC scores: 0.83 (gene presence/absence model), 0.84 (Pfam domain model), 0.76 (8-mer model), and 0.85 (9-mer model). The models all based their predictions on relevant genetic features and showed consensus on systems for lactose metabolism, degradation of casein, and pH stress response. Each model also predicted a set of features not found by the other models.

Screening for texturing Leuconostoc and genomics behind polysaccharide production

Synthesis of polysaccharides by Leuconostoc can result in improved texture of fermented products. A total of 249 Leuconostoc strains were screened for homo-polysaccharide production and for texturing capabilities in milk. A total of six Ln. mesenteroides strains with superior texturing properties had the genetic blueprint for both homo- (HoPS) and hetero-polysaccharide (HePS) synthesis. Only one strain produced texture in milk without added sucrose, suggesting HePS synthesis via the Wzy dependent pathway. In milk acidification experiments with added sucrose, all six strains depleted the sucrose and released fructose. Thus, they can be used for both texture and possibly also for sweetness enhancement.

High-throughput screening for texturing Lactococcus strains

In the food industry, lactic acid bacteria (LAB) are used in dairy fermentations, extending the shelf life by lowering the pH and also affecting taste and texture of the fermented milk. The texture of fermented milk is an important quality parameter, affecting consumer acceptance. Finding LAB providing desired texture of a product is time consuming and laborious when using standard methods for measuring texture, e.g. rheology measurements. Screening of 986 Lactococcus lactis strains resulted in few strains with the ability to enhance texture, demonstrating the necessity of implementation of high-throughput screening methods. A high-throughput screening assay was developed, combining small-scale 96-well microtiter plates and pressure measurements during liquid handling, e.g. aspiration, to find strains that give good texture in fermented milk. Only about 1% of the strains were found to enhance milk texture. Two of the texturing strains belong to L. lactis subsp. lactis, which are the first texturing strains from this subsp. reported. Mining for eps gene clusters responsible for exocellular polysaccharide production was performed, as polysaccharide production can contribute positively to fermented milk texture. Comparative genomics approach revealed four types of texturing L. lactis strains with diverse eps gene clusters.