Large-scale phenotypic analyses have proved to be useful strategies in providing functional clues about the uncharacterized yeast genes. We used here a chemogenomic profiling of yeast deletion collections to identify the core of cellular processes challenged by treatment with the p-aminobenzoate/folate antimetabolite sulfanilamide. In addition to sulfanilamide-hypersensitive mutants whose deleted genes can be categorized into a number of groups, including one-carbon related metabolism, vacuole biogenesis and vesicular transport, DNA metabolic and cell cycle processes, and lipid and amino acid metabolism, two uncharacterized open reading frames (YHI9 and YMR289w) were also identified. A detailed characterization of YMR289w revealed that this gene was required for growth in media lacking p-aminobenzoic or folic acid and encoded a 4-amino-4-deoxychorismate lyase, which is the last of the three enzymatic activities required for p-aminobenzoic acid biosynthesis. In light of these results, YMR289w was designated ABZ2, in accordance with the accepted nomenclature. ABZ2 was able to rescue the p-aminobenzoate auxotrophy of an Escherichia coli pabC mutant, thus demonstrating that ABZ2 and pabC are functional homologues. Phylogenetic analyses revealed that Abz2p is the founder member of a new group of fungal 4-amino-4-deoxychorismate lyases that have no significant homology to its bacterial or plant counterparts. Abz2p appeared to form homodimers and dimerization was indispensable for its catalytic activity.
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http://dx.doi.org/10.1128/EC.00266-07 | DOI Listing |
Nucleic Acids Res
January 2025
Centre de Recherche en Infectiologie, Axe des Maladies Infectieuses et Immunitaires du CHU de Québec and Département de Microbiologie, Infectiologie et Immunologie, Faculté de Médecine, Université Laval, 2707 Bd Laurier, Québec, QC G1V 4G2, Canada.
DNA transformation is key for phenotypic diversity and adaptation of Streptococcus pneumoniae including in the emergence of multidrug resistance (MDR). Under laboratory conditions, DNA transformation is facilitated by the artificial triggering of competence by the competence stimulating peptide (CSP). In ongoing DNA transformation work, we observed that exogenous CSP was dispensable depending on the combination of strains and culture media.
View Article and Find Full Text PDFAnticancer Drugs
January 2025
Department of Microbiology, Immunology and Genetics, University of North Texas Health Science Center, Fort Worth, TX.
Triple-negative breast cancer (TNBC) is a highly invasive breast cancer subtype that is challenging to treat due to inherent heterogeneity and absence of estrogen, progesterone, and human epidermal growth factor 2 receptors. Kinase signaling networks drive cancer growth and development, and kinase inhibitors are promising anti-cancer strategies in diverse cancer subtypes. Kinase inhibitor screens are an efficient, valuable means of identifying compounds that suppress cancer cell growth in vitro , facilitating the identification of kinase vulnerabilities to target therapeutically.
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HepaPredict AB, Stockholm, 17165, Sweden.
Metabolic dysfunction-associated steatohepatitis (MASH) is a leading cause of chronic liver disease with few therapeutic options. To narrow the translational gap in the development of pharmacological MASH treatments, a 3D liver model from primary human hepatocytes and non-parenchymal cells derived from patients with histologically confirmed MASH was established. The model closely mirrors disease-relevant endpoints, such as steatosis, inflammation and fibrosis, and multi-omics analyses show excellent alignment with biopsy data from 306 MASH patients and 77 controls.
View Article and Find Full Text PDFBMC Bioinformatics
November 2024
Shanxi Key Lab for Modernization of TCVM, College of Basic Sciences, Shanxi Agricultural University, Taigu, 030801, China.
Background: Identification of drug-target interactions is an indispensable part of drug discovery. While conventional shallow machine learning and recent deep learning methods based on chemogenomic properties of drugs and target proteins have pushed this prediction performance improvement to a new level, these methods are still difficult to adapt to novel structures. Alternatively, large-scale biological and pharmacological data provide new ways to accelerate drug-target interaction prediction.
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