Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Karlsen ST

PROVIDER: S-EPMC7959382 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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

Karlsen Signe Tang ST Vesth Tammi Camilla TC Oregaard Gunnar G Poulsen Vera Kuzina VK Lund Ole O Henderson Gemma G Bælum Jacob J

PloS one 20210315 3

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 represe ...[more]

PMID: 33720959

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Project description:The development of transcriptomic tools has allowed exhaustive description of stress responses. These responses always superimpose a general response associated to growth rate decrease and a specific one corresponding to the stress. The exclusive growth rate response can be achieved through chemostat cultivation, enabling all parameters to remain constant except the growth rate. We analysed metabolic and transcriptomic responses of Lactococcus lactis in continuous cultures at different growth rates ranging from 0.09 to 0.47 h-1. Growth rate was conditioned by isoleucine supply. Although the metabolism was constant and homolactic, a widespread transcriptomic response involving 30 % of the genome was observed. The expression of genes encoding physiological functions associated with biogenesis increased with growth rate (transcription, translation, fatty acid and phospholipids metabolism). Many phages, prophages and transposons related genes were down regulated by growth rate suggesting genome plasticity to be involved in the adaptation to slow growth. The growth rate response was compared to carbon and amino-acid starvation transcriptomic responses, revealing constant and significant involvement of growth rate regulations in these two stressful conditions (overlap 26%). Although stringent response mechanism is considered as the one governing growth deceleration in bacteria, the rigorous comparison of the two transcriptomic responses clearly indicated the mechanisms are distinct. Moreover it was established that genes positively regulated by growth rate are preferentially located in the vicinity of replication origin while those negatively regulated are mainly encountered at the opposite. This result demonstrates the often neglected relationship between genes expression and their location on chromosome. Keywords: growth rate impact, continuous cultures Continuous cultivation of Lactococcus lactis IL1403 were carried out on a chemically defined medium and under controlled conditions (30 Â°C, pH 6.6, nitrogen atmosphere). Cell samples were harvested at steady state. Total RNA was extracted from these samples and radiolabelled cDNA were prepared and hybridized on nylon arrays. 1948 amplicons specific of Lactococcus lactis IL1403 genes were spotted twice on the array. Samples corresponding to various growth rates were analyzed simultaneously and 3 independent repetitions were performed.

Dataset Information

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

Publications

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

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