Biology subjects

Cooper, D. G.

Publications and source records attributed to Cooper, D. G..

5 recordsLinked to original sources

bioRxiv2026

Transcriptomic shift in ethanol and amino acid metabolic genes regulated by Med15 during alcoholic fermentation

Organisms that thrive in extreme environments provide natural experiments in evolution, revealing the genetic regulators that orchestrate complex phenotypic change. Wine yeast are specialized strains that are adapted to survive in the wine making environment while producing high concentrations of ethanol. In addition to large genomic changes that differentiate wine yeast from yeast used in other industries, single nucleotide and polyglutamine tract polymorphisms in the transcriptional regulator Med15 are associated with the fermentation efficiency and stress response phenotypes of wine yeast. In this study we investigated the transcriptional differences during wine fermentation in transgenic lab strain yeast having integrated wine yeast MED15 alleles. Compared to the unmodified lab strain (LAB or MED15LAB), the same strain in which the MED15 locus was replaced with a MED15 allele from yeast isolated from palm wine, the fermented sap of palm (oil, date, coconut) trees, (WY23, or MED15WY23) exhibited enhanced expression of glycolytic, fermentation, and amino acid biosynthesis genes. Our experimental data confirms the importance of arginine biosynthetic genes during the fermentation process and suggests that the improvement in fermentation efficiency in strains with MED15 alleles from some wine yeast strains may be related to the role of Med15 in expression of the genes of the arginine biosynthetic pathway. The global benefit conferred by polymorphisms in a single transcriptional regulator, makes Med15 a prime target for engineering of strains devoted to various types of alcohol production.

genomics↗

bioRxiv2024

The role of Med15 sequence features in transcription factor interactions

Med15 is a general transcriptional regulator and subunit within the tail module of the RNA Pol II Mediator complex. The S. cerevisiae Med15 protein has a well-structured N-terminal KIX domain, three Activator Binding Domains (ABDs), several naturally variable polyglutamine (poly-Q) tracts (Q1, Q2, Q3) embedded in an intrinsically disordered central region, and a C-terminal Mediator Association Domain (MAD). We investigated how the presence of ABDs and changes in length and composition of poly-Q tracts influences Med15 activity and function using phenotypic, gene expression, transcription factor interaction and phase separation assays of truncation, deletion, and synthetic alleles. We found that individual Med15 activities were influenced by the number of activator binding domains (ABDs) and adjacent polyglutamine tract composition. Robust Med15 activity required at least the Q1 tract and the length of that tract modulated activity in a context-dependent manner. We found that loss of Msn2-dependent transcriptional activation due to Med15 Q1 tract variation correlated well with a reduction in Msn2:Med15 interaction strength, but that interaction strength did not always mirror the propensity for phase separation. We also observed that distant glutamine tracts and Med15 phosphorylation affected the activities of the KIX domain, suggesting that intramolecular interactions may affect some Med15-transcription factor interactions. Further, two-hybrid based interaction studies revealed intramolecular interactions between the N-terminal KIX domain and the Q1R domain of Med15. Author SummaryGlutamine tracts are relatively uncommon, but are a feature of many transcriptional regulators including the Med15 subunit of the Mediator Complex which is a large protein complex that plays an important role in gene expression in eukaryotic organisms including yeast and animals. Strains lacking Med15 are compromised in their ability to grow on many kinds of media, under stress conditions, and in fermentation, reflecting its importance in gene expression. Naturally occurring yeast strains specialized for growth in specific environments (e.g., wine, beer, clinical) vary in their glutamine tract lengths, suggesting that the length of glutamine tracts may influence Med15 function in a manner that is adaptive for a specific environment. In this study, we intentionally manipulated the length of the glutamine tracts in Med15 and found that these changes have subtle effects on Med15 interactions with transcription factors, target gene expression and growth. Taken together, our data suggests that glutamine tracts do not themselves mediate critical interactions with partner proteins, but instead may influence the shape of the Med15 protein, thus indirectly affecting the nature of these interactions.

molecular biology↗

bioRxiv2024

Gene expression signatures of response to fluoxetine treatment: systematic review and meta-analyses

BackgroundSelecting the best antidepressant for a patient with major depressive disorder (MDD) remains a challenge, and some have turned to genomic (and other omic) data to identify an optimal therapy. In this work, we synthesized gene expression data for fluoxetine treatment in both human patients and rodent models, to better understand biological pathways affected by treatment, as well as those that may distinguish clinical or behavioral response. MethodsFollowing the PRISMA guidelines, we searched the Gene Expression Omnibus (GEO) for studies profiling humans or rodent models with treatment of the antidepressant fluoxetine, excluding those not done in the context of depression or anxiety, in an irrelevant tissue type, or with fewer than three samples per group. Included studies were systematically reanalyzed by differential expression analysis and Gene Set Enrichment Analysis (GSEA). Individual pathway and gene statistics were synthesized across studies by three p-value combination methods, and then corrected for false discovery. ResultsOf the 74 data sets that were screened, 20 were included: 18 in rodents, and two in tissue from human patients. Studies were highly heterogeneous in the comparisons of both treated vs. control samples and responders vs. non-responders, with 737 and 356 pathways, respectively, identified as significantly different between groups in at least one study. However, 19 pathways were identified as consistently different in responders vs. non-responders, including toll-like receptor (TLR) and other immune pathways. Signal transduction pathways were identified as consistently affected by fluoxetine treatment in depressed patients and rodent models. DiscussionThese meta-analyses confirm known pathways and provide new hints toward antidepressant resistance, but more work is needed. Most included studies involved rodent models, and both patient studies had small cohorts. Additional large-cohort studies applying additional omics technologies are necessary to understand the intricacies and heterogeneity of antidepressant response.

genomics↗

bioRxiv2022

The Role of Polyglutamine in Inter- and Intra-molecular Interactions in Med15-dependent Regulation

Med15 is a general transcriptional regulator and member of the tail module of the RNA Pol II Mediator complex. The S. cerevisiae Med15 protein has a well-structured N-terminal KIX domain, three Activator Binding Domains (ABDs) and several naturally variable polyglutamine (poly-Q) tracts (Q1, Q2, Q3) embedded in an intrinsically disordered central region, and a C-terminal Mediator Association Domain (MAD). We investigated how the presence of ABDs and changes in length and composition of poly-Q tracts influences Med15 activity and function using phenotypic, gene expression, and transcription factor interaction assays of truncation, deletion, and synthetic alleles. We found that individual Med15 activities were influenced by the number of activator binding domains (ABDs) and adjacent polyglutamine composition. We also observed that distant glutamine tracts and Med15 phosphorylation affected the activities of the KIX domain, suggesting that intramolecular interactions may be required for KIX domain interactions with transcription factors. We conclude that robust Med15 activity required at least the Q1 tract and that the length of that tract modulates activity in a context-dependent manner. We speculate that the glutamine tract provides a degree of intramolecular flexibility that is needed for Med15 function. Finally, we found that loss of Msn2-dependent transcriptional activation in Med15 Q1 tract variants correlates well with a reduction in Msn2:Med15 interaction strength.

molecular biology↗

bioRxiv2020

CASTLE: Cell Adhesion with Supervised Training and Learning Environment

Different types of microscopy are used to uncover signatures of cell adhesion and mechanics. Automating the identification and analysis often involve sacrificial routines of cell manipulation such as in vitro staining. Phase-contrast microscopy (PCM) is rarely used in automation due to the difficulties with poor quality images. However, it is the least intrusive method to provide insights into the dynamics of cells, where other types of microscopy are too destructive to monitor. In this study, we propose an efficient workflow to automate cell counting and morphology in PCM images. We introduce Cell Adhesion with Supervised Training and Learning Environment (CASTLE), available as a series of additional plugins to ImageJ. CASTLE combines effective techniques for phase-contrast image processing with statistical analysis and machine learning algorithms to interpret the results. The proposed workflow was validated by comparing the results to a manual count and manual segmentation of cells in images investigating different adherent cell types, including monocytes, neutrophils and platelets. In addition, the effect of different molecules on cell adhesion was characterised using CASTLE. For example, we demonstate that Galectin-9 leads to differences in adhesion of leukocytes. CASTLE also provides information using machine learning techniques, namely principal component analysis and k-means clustering, to distinguish morphology currently inaccessible with manual methods. All scripts and documentation is open-source and available at the corresponding GitLab project.

immunology↗