Draft Genome Sequences of Three Lactobacillus paracasei Strains, Members of the Nonstarter Microbiota of Mature Cheddar Cheese

peer-reviewedLactobacillus paracasei strains are common members of the nonstarter microbiota present in various types of cheeses. The draft genome sequences of three strains isolated from mature cheddar cheeses are reported here

Advances in the genomics and metabolomics of dairy lactobacilli: A review

The Lactobacillus genus represents the largest and most diverse genera of all the lactic acid bacteria (LAB), encompassing species with applications in industrial, biotechnological and medical fields. The increasing number of available Lactobacillus genome sequences has allowed understanding of genetic and metabolic potential of this LAB group. Pangenome and core genome studies are available for numerous species, demonstrating the plasticity of the Lactobacillus genomes and providing the evidence of niche adaptability. Advancements in the application of lactobacilli in the dairy industry lie in exploring the genetic background of their commercially important characteristics, such as flavour development potential or resistance to the phage attack. The integration of available genomic and metabolomic data through the generation of genome scale metabolic models has enabled the development of computational models that predict the behaviour of organisms under specific conditions and present a route to metabolic engineering. Lactobacilli are recognised as potential cell factories, confirmed by the successful production of many compounds. In this review, we discuss the current knowledge of genomics, metabolomics and metabolic engineering of the prevalent Lactobacillus species associated with the production of fermented dairy foods. In-depth understanding of their characteristics opens the possibilities for their future knowledge-based applications

Genetic, enzymatic and metabolite profiling of the Lactobacillus casei group reveals strain biodiversity and potential applications for flavour diversification

Aims: The Lactobacillus casei group represents a widely explored group of lactic acid bacteria, characterized by a high level of biodiversity. In this study, the genetic and phenotypic diversity of a collection of more than 300 isolates of the Lact. casei group and their potential to produce volatile metabolites important for flavour development in dairy products, was examined. Methods and Results: Following confirmation of species by 16S rRNA PCR, the diversity of the isolates was determined by pulsed-field gel electrophoresis. The activities of enzymes involved in the proteolytic cascade were assessed and significant differences among the strains were observed. Ten strains were chosen based on the results of their enzymes activities and they were analysed for their ability to produce volatiles in media with increased concentrations of a representative aromatic, branched chain and sulphur amino acid. Volatiles were assessed using gas chromatography coupled with mass spectrometry. Strain-dependent differences in the range and type of volatiles produced were evident. Conclusions: Strains of the Lact. casei group are characterized by genetic and metabolic diversity which supports variability in volatile production. Significance and Impact of the Study: This study provides a screening approach for the knowledge-based selection of strains potentially enabling flavour diversification in fermented dairy products

Strains of the Lactobacillus casei group show diverse abilities for the production of flavor compounds in 2 model systems

peer-reviewedCheese flavor development is directly connected to the metabolic activity of microorganisms used during its manufacture, and the selection of metabolically diverse strains represents a potential tool for the production of cheese with novel and distinct flavor characteristics. Strains of Lactobacillus have been proven to promote the development of important cheese flavor compounds. As cheese production and ripening are long-lasting and expensive, model systems have been developed with the purpose of rapidly screening lactic acid bacteria for their flavor potential. The biodiversity of 10 strains of the Lactobacillus casei group was evaluated in 2 model systems and their volatile profiles were determined by gas chromatography-mass spectrometry. In model system 1, which represented a mixture of free AA, inoculated cells did not grow. In total, 66 compounds considered as flavor contributors were successfully identified, most of which were aldehydes, acids, and alcohols produced via AA metabolism by selected strains. Three strains (DPC2071, DPC3990, and DPC4206) had the most diverse metabolic capacities in model system 1. In model system 2, which was based on processed cheese curd, inoculated cells increased in numbers over incubation time. A total of 47 compounds were identified, and they originated not only from proteolysis, but also from glycolytic and lipolytic processes. Tested strains produced ketones, acids, and esters. Although strains produced different abundances of volatiles, diversity was less evident in model system 2, and only one strain (DPC4206) was distinguished from the others. Strains identified as the most dissimilar in both of the model systems could be more useful for cheese flavor diversification

Use of multiple covariates in assessing treatment-effect modifiers: A methodological review of individual participant data meta-analyses

Individual participant data (IPD) meta-analyses of randomised trials are considered a reliable way to assess participant-level treatment effect modifiers but may not make the best use of the available data. Traditionally, effect modifiers are explored one covariate at a time, which gives rise to the possibility that evidence of treatment-covariate interaction may be due to confounding from a different, related covariate. We aimed to evaluate current practice when estimating treatment-covariate interactions in IPD meta-analysis, specifically focusing on involvement of additional covariates in the models. We reviewed 100 IPD meta-analyses of randomised trials, published between 2015 and 2020, that assessed at least one treatment-covariate interaction. We identified four approaches to handling additional covariates: (1) Single interaction model (unadjusted): No additional covariates included (57/100 IPD meta-analyses); (2) Single interaction model (adjusted): Adjustment for the main effect of at least one additional covariate (35/100); (3) Multiple interactions model: Adjustment for at least one two-way interaction between treatment and an additional covariate (3/100); and (4) Three-way interaction model: Three-way interaction formed between treatment, the additional covariate and the potential effect modifier (5/100). IPD is not being utilised to its fullest extent. In an exemplar dataset, we demonstrate how these approaches lead to different conclusions. Researchers should adjust for additional covariates when estimating interactions in IPD meta-analysis providing they adjust their main effects, which is already widely recommended. Further, they should consider whether more complex approaches could provide better information on who might benefit most from treatments, improving patient choice and treatment policy and practice

Model-based prediction of human hair color using DNA variants

Predicting complex human phenotypes from genotypes is the central concept of widely advocated personalized medicine, but so far has rarely led to high accuracies limiting practical applications. One notable exception, although less relevant for medical but important for forensic purposes, is human eye color, for which it has been recently demonstrated that highly accurate prediction is feasible from a small number of DNA variants. Here, we demonstrate that human hair color is predictable from DNA variants with similarly high accuracies. We analyzed in Polish Europeans with single-observer hair color grading 45 single nucleotide polymorphisms (SNPs) from 12 genes previously associated with human hair color variation. We found that a model based on a subset of 13 single or compound genetic markers from 11 genes predicted red hair color with over 0.9, black hair color with almost 0.9, as well as blond, and brown hair color with over 0.8 prevalence-adjusted accuracy expressed by the area under the receiver characteristic operating curves (AUC). The identified genetic predictors also differentiate reasonably well between similar hair colors, such as between red and blond-red, as well as between blond and dark-blond, highlighting the value of the identified DNA variants for accurate hair color prediction

Atomic and electronic structure of graphene oxide/Cu interface

The results of X-ray photoemission (XPS) and valence bands spectroscopy, optically stimulated electron emission (OSEE) measurements and density functional theory based modeling of graphene oxide (GO) placed on Cu via an electrophoretic deposition (EPD) are reported. The comparison of XPS spectra of EPD prepared GO/Cu composites with those of as prepared GO, strongly reduced GO, pure and oxidized copper demonstrate the partial (until C/O ratio about two) removal of oxygen-containing functional groups from GO simultaneously with the formation of copper oxide-like layers over the metallic substrate. OSEE measurements evidence the presence of copper oxide phase in the systems simultaneously with the absence of contributions from GO with corresponding energy gap. All measurements demonstrate the similarity of the results for different thickness of GO cover of the copper surface. Theoretical modeling demonstrates favorability of migration of oxygen-containing functional groups from GO to the copper substrate only for the case of C/O ratio below two and formation of Cu-O-C bonds between substrate and GO simultaneously with the vanishing of the energy gap in GO layer. Basing on results of experimental measurements and theoretical calculations we suggest the model of atomic structure for Cu/GO interface as Cu/CuO/GO with C/O ratio in gapless GO about two.Comment: 22 pages, 14 figures, accepted to Thin Solid Films journa

Global skin colour prediction from DNA

Human skin colour is highly heritable and externally visible with relevance in medical, forensic, and anthropological genetics. Although eye and hair colour can already be predicted with high accuracies from small sets of carefully selected DNA markers, knowledge about the genetic predictability of skin colour is limited. Here, we investigate the skin colour predictive value of 77 single-nucleotide polymorphisms (SNPs) from 37 genetic loci previously associated with human pigmentation using 2025 individuals from 31 global populations. We identified a minimal set of 36 highly informative skin colour predictive SNPs and developed a statistical prediction model capable of skin colour prediction on a global scale. Average cross-validated prediction accuracies expressed as area under the receiver-operating characteristic curve (AUC) ± standard deviation were 0.97 ± 0.02 for Light, 0.83 ± 0.11 for Dark, and 0.96 ± 0.03 for Dark-Black. When using a 5-category, this resulted in 0.74 ± 0.05 for Very Pale, 0.72 ± 0.03 for Pale, 0.73 ± 0.03 for Intermediate, 0.87±0.1 for Dark, and 0.97 ± 0.03 for Dark-Black. A comparative analysis in 194 independent samples from 17 populations demonstrated that our model outperformed a previously proposed 10-SNP-classifier approach with AUCs rising from 0.79 to 0.82 for White, comparable at the intermediate level of 0.63 and 0.62, respectively, and a large increase from 0.64 to 0.92 for Black. Overall, this study demonstrates that the chosen DNA markers and prediction model, particularly the 5-category level; allow skin colour predictions within and between continental regions for the first time, which will serve as a valuable resource for future applications in forensic and anthropologic genetics

Solution structure of the SGTA dimerisation domain and investigation of its interactions with the ubiquitin-like domains of BAG6 and UBL4A

BACKGROUND: The BAG6 complex resides in the cytosol and acts as a sorting point to target diverse hydrophobic protein substrates along their appropriate paths, including proteasomal degradation and ER membrane insertion. Composed of a trimeric complex of BAG6, TRC35 and UBL4A, the BAG6 complex is closely associated with SGTA, a co-chaperone from which it can obtain hydrophobic substrates. METHODOLOGY AND PRINCIPAL FINDINGS: SGTA consists of an N-terminal dimerisation domain (SGTA_NT), a central tetratricopeptide repeat (TPR) domain, and a glutamine rich region towards the C-terminus. Here we solve a solution structure of the SGTA dimerisation domain and use biophysical techniques to investigate its interaction with two different UBL domains from the BAG6 complex. The SGTA_NT structure is a dimer with a tight hydrophobic interface connecting two sets of four alpha helices. Using a combination of NMR chemical shift perturbation, isothermal titration calorimetry (ITC) and microscale thermophoresis (MST) experiments we have biochemically characterised the interactions of SGTA with components of the BAG6 complex, the ubiquitin-like domain (UBL) containing proteins UBL4A and BAG6. We demonstrate that the UBL domains from UBL4A and BAG6 directly compete for binding to SGTA at the same site. Using a combination of structural and interaction data we have implemented the HADDOCK protein-protein interaction docking tool to generate models of the SGTA-UBL complexes. SIGNIFICANCE: This atomic level information contributes to our understanding of the way in which hydrophobic proteins have their fate decided by the collaboration between SGTA and the BAG6 complex

A mechanism for the inhibition of DNA-PK-mediated DNA sensing by a virus

The innate immune system is critical in the response to infection by pathogens and it is activated by pattern recognition receptors (PRRs) binding to pathogen associated molecular patterns (PAMPs). During viral infection, the direct recognition of the viral nucleic acids, such as the genomes of DNA viruses, is very important for activation of innate immunity. Recently, DNA-dependent protein kinase (DNA-PK), a heterotrimeric complex consisting of the Ku70/Ku80 heterodimer and the catalytic subunit DNA-PKcs was identified as a cytoplasmic PRR for DNA that is important for the innate immune response to intracellular DNA and DNA virus infection. Here we show that vaccinia virus (VACV) has evolved to inhibit this function of DNA-PK by expression of a highly conserved protein called C16, which was known to contribute to virulence but by an unknown mechanism. Data presented show that C16 binds directly to the Ku heterodimer and thereby inhibits the innate immune response to DNA in fibroblasts, characterised by the decreased production of cytokines and chemokines. Mechanistically, C16 acts by blocking DNA-PK binding to DNA, which correlates with reduced DNA-PK-dependent DNA sensing. The C-terminal region of C16 is sufficient for binding Ku and this activity is conserved in the variola virus (VARV) orthologue of C16. In contrast, deletion of 5 amino acids in this domain is enough to knockout this function from the attenuated vaccine strain modified vaccinia virus Ankara (MVA). In vivo a VACV mutant lacking C16 induced higher levels of cytokines and chemokines early after infection compared to control viruses, confirming the role of this virulence factor in attenuating the innate immune response. Overall this study describes the inhibition of DNA-PK-dependent DNA sensing by a poxvirus protein, adding to the evidence that DNA-PK is a critical component of innate immunity to DNA viruses