Harman, R. | Search bioRxiv

bioRxiv2024

Heat stress and recovery induce transcriptomic changes in lactogenic bovine mammary epithelial (MAC-T) cells

Heat stress (HS) in cattle significantly challenges the dairy industry by reducing milk production. However, the molecular mechanism behind mammary gland responses to HS and recovery remains poorly understood. This study aimed to determine the transcriptomic changes in lactogenic bovine mammary epithelial (MAC-T) cells after HS and post-HS recovery. Six culture conditions were analyzed: MAC-T cells cultured in basal medium, cells in lactogenic medium to induce differentiation, differentiated cells at standard temperature (37) or HS (42) for 1 hour. HS cells were collected after incubation at 37 for either 2 or 6 hours to examine the extent of recovery. A total of 1,668 differentially expressed genes (DEGs) were identified. Differentiated cells expressed genes associated with milk lipid synthesis, indicating lactogenic potential. HS suppressed genes involved in cellular differentiation and activated heat shock protein genes. Several transcription factors were identified as potential regulators of HS response. During recovery, chaperon-mediated protein folding genes remained elevated. Apoptosis regulation genes were induced at 2 hours, and cellular homeostasis regulation genes were enriched at 6 hours. Overall, these findings provide a foundation for the molecular mechanisms involved in HS and recovery in cattle.

genomics↗

bioRxiv2018

A mixed quantum chemistry/machine learning approach for the fast and accurate prediction of biochemical redox potentials and its large-scale application to 315,000 redox reactions

A quantitative understanding of the thermodynamics of biochemical reactions is essential for accurately modeling metabolism. The group contribution method (GCM) is one of the most widely used approaches to estimating standard Gibbs energies and redox potentials of reactions for which no experimental measurements exist. Previous work has shown that quantum chemical predictions of biochemical thermodynamics are a promising approach to overcome the limitations of GCM. However, the quantum chemistry approach is significantly more expensive. Here we use a combination of quantum chemistry and machine learning to obtain a fast and accurate method for predicting the thermodynamics of biochemical redox reactions. We focus on predicting the redox potentials of carbonyl functional group reductions to alcohols and amines, two of the most ubiquitous carbon redox transformations in biology. Our method relies on semi-empirical quantum chemistry calculations calibrated with Gaussian Process (GP) regression against available experimental data. Our approach results in higher predictive power than the GCM at a low computational cost. We design and implement a network expansion algorithm that iteratively reduces and oxidizes a set of natural seed metabolites, and demonstrate the high-throughput applicability of our method by predicting the standard potentials of more than 315,000 redox reactions involving approximately 70,000 compounds. Additionally, we developed a novel fingerprint-based framework for detecting molecular environment motifs that are enriched or depleted across different regions of the redox potential landscape. We provide open access to all source code and data generated.

biophysics↗