Search for: All records

Extremophilic yeasts have favorable metabolic and tolerance traits for biomanufacturing‐ like lipid biosynthesis, flavinogenesis, and halotolerance – yet the connection between these favorable phenotypes and strain genotype is not well understood. To this end, this study compares the phenotypes and gene expression patterns of biotechnologically relevant yeasts Yarrowia lipolytica, Debaryomyces hansenii, and Debaryomyces subglobosus grown under nitrogen starvation, iron starvation, and salt stress. To analyze the large data set across species and conditions, two approaches were used: a “network‐first” approach where a generalized metabolic network serves as a scaffold for mapping genes and a “cluster‐first” approach where unsupervised machine learning co‐expression analysis clusters genes. Both approaches provide insight into strain behavior. The network‐first approach corroborates that Yarrowia upregulates lipid biosynthesis during nitrogen starvation and provides new evidence that riboflavin overproduction in Debaryomyces yeasts is overflow metabolism that is routed to flavin cofactor production under salt stress. The cluster‐first approach does not rely on annotation; therefore, the coexpression analysis can identify known and novel genes involved in stress responses, mainly transcription factors and transporters. Therefore, this work links the genotype to the phenotype of biotechnologically relevant yeasts and demonstrates the utility of complementary computational approaches to gain insight from transcriptomics data across species and conditions. 

Abstract Self‐assembled networks of bottlebrush copolymers are promising materials for biomedical applications due to a unique combination of ultra‐softness and strain‐adaptive stiffening, characteristic of soft biological tissues. Transitioning from ABA linear‐brush‐linear triblock copolymers to A‐g‐B bottlebrush graft copolymer architectures allows significant increasing the mechanical strength of thermoplastic elastomers. Using real‐time synchrotron small‐angle X‐ray scattering, it is shown that annealing of A‐g‐B elastomers in a selective solvent for the linear A blocks allows for substantial network reconfiguration, resulting in an increase of both the A domain size and the distance between the domains. The corresponding increases in the aggregation number and extension of bottlebrush strands lead to a significant increase of the strain‐stiffening parameter up to 0.7, approaching values characteristic of the brain and skin tissues. Network reconfiguration without disassembly is an efficient approach to adjusting the mechanical performance of tissue‐mimetic materials to meet the needs of diverse biomedical applications. 

Protein content variation in milk can impact the quality and consistency of dairy products, necessitating access to in-line real time monitoring. Here, we present a chemometric approach for the qualitative and quantitative monitoring of β-lactoglobulin and α-lactalbumin, using mid-infrared spectroscopy (MIR). In this study, we employed Hotelling T2 and Q-residual for outlier detection, automated preprocessing using nippy, conducted wavenumber selection with genetic algorithms, and evaluated four chemometric models, including partial least squares, support vector regression (SVR), ridge, and logistic regression to accurately predict the concentrations of β-lactoglobulin and α-lactalbumin in milk. For the quantitative analysis of these two whey proteins, SVR performed the best to interpret protein concentration from 197 MIR spectra originating from 42 Cornell University samples of preserved pasteurized modified milk. The R2 values obtained for β-lactoglobulin and α-lactalbumin using leave one out cross-validation (LOOCV) are 92.8% and 92.7%, respectively, which is the highest correlation reported to date. Our approach introduced a combination of preprocessing automation, genetic algorithm-based wavenumber selection, and used Optuna to optimize the framework for tuning hyperparameters of the chemometric models, resulting in the best chemometric analysis of MIR data to quantitate β-lactoglobulin and α-lactalbumin to date. 

Abstract Probiotic yeasts are emerging as preventative and therapeutic solutions for disease. Often ingested via cultured foods and beverages, they can survive the harsh conditions of the gastrointestinal tract and adhere to it, where they provide nutrients and inhibit pathogens like Candida albicans. Yet, little is known of the genomic determinants of these beneficial traits. To this end, we have sequenced 2 food-derived probiotic yeast isolates that mitigate fungal infections. We find that the first strain, KTP, is a strain of Saccharomyces cerevisiae within a small clade that lacks any apparent ancestry from common European/wine S. cerevisiae strains. Significantly, we show that S. cerevisiae KTP genes involved in general stress, pH tolerance, and adherence are markedly different from S. cerevisiae S288C but are similar to the commercial probiotic yeast species S. boulardii. This suggests that even though S. cerevisiae KTP and S. boulardii are from different clades, they may achieve probiotic effect through similar genetic mechanisms. We find that the second strain, ApC, is a strain of Issatchenkia occidentalis, one of the few of this family of yeasts to be sequenced. Because of the dissimilarity of its genome structure and gene organization, we infer that I. occidentalis ApC likely achieves a probiotic effect through a different mechanism than the Saccharomyces strains. Therefore, this work establishes a strong genetic link among probiotic Saccharomycetes, advances the genomics of Issatchenkia yeasts, and indicates that probiotic activity is not monophyletic and complimentary mixtures of probiotics could enhance health benefits beyond a single species. 

Cell migration initiates by traction generation through reciprocal actomyosin tension and focal adhesion reinforcement, but continued motility requires adaptive cytoskeletal remodeling and adhesion release. Here, we asked whether de novo gene expression contributes to this cytoskeletal feedback. We found that global inhibition of transcription or translation does not impair initial cell polarization or migration initiation, but causes eventual migratory arrest through excessive cytoskeletal tension and over-maturation of focal adhesions, tethering cells to their matrix. The transcriptional coactivators YAP and TAZ mediate this feedback response, modulating cell mechanics by limiting cytoskeletal and focal adhesion maturation to enable persistent cell motility and 3D vasculogenesis. Motile arrest after YAP/TAZ ablation was partially rescued by depletion of the YAP/TAZ-dependent myosin phosphatase regulator, NUAK2, or by inhibition of Rho-ROCK-myosin II. Together, these data establish a transcriptional feedback axis necessary to maintain a responsive cytoskeletal equilibrium and persistent migration.