A phenotypic array method, developed for quantifying cell growth, was applied to the haploid and homozygous diploid yeast deletion strain sets. A growth index was developed to screen for non-additive interacting effects between gene deletion and induced perturbations. From a genome screen for hydroxyurea (HU) chemical-genetic interactions, 298 haploid deletion strains were selected for further analysis. The strength of interactions was quantified using a wide range of HU concentrations affecting reference strain growth. The selectivity of interaction was determined by comparison with drugs targeting other cellular processes. Bio-modules were defined as gene clusters with shared strength and selectivity of interaction profiles. The functions and connectivity of modules involved in processes such as DNA repair, protein secretion and metabolic control were inferred from their respective gene composition. The work provides an example of, and a general experimental framework for, quantitative analysis of gene interaction networks that buffer cell growth.
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http://dx.doi.org/10.1186/gb-2004-5-7-r49 | DOI Listing |
Heliyon
January 2025
Guangdong Provincial Biotechnology Research Institute (Guangdong Provincial Laboratory Animals Monitoring Center), Guangzhou, Guangdong, 510663, China.
Spondyloarthritis is a prevalent and persistent condition that significantly impacts the quality of life. Its intricate pathological mechanisms have led to a scarcity of animal models capable of replicating the disease progression in humans, making it a prominent area of research interest in the field. To delve into the pathological and physiological traits of spontaneous non-human primate spondyloarthritis, this study meticulously examined the disease features of this natural disease model through an array of techniques including X-ray imaging, MRI imaging, blood biochemistry, markers of bone metabolism, transcriptomics, proteomics, and metabolomics.
View Article and Find Full Text PDFJ Transl Med
January 2025
Department of Gastrointestinal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China.
Background: Tumor microenvironment (TME), particularly immune cell infiltration, programmed cell death (PCD) and stress, has increasingly become a focal point in colorectal cancer (CRC) treatment. Uncovering the intricate crosstalk between these factors can enhance our understanding of CRC, guide therapeutic strategies, and improve patient prognosis.
Methods: We constructed an immune-related cell death and stress (ICDS) prognostic model utilizing machine learning methodologies.
Innovation (Camb)
January 2025
AIM Center, College of Life Sciences and Technology, Beijing University of Chemical Technology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.
Predicting free energy changes (ΔΔG) is essential for enhancing our understanding of protein evolution and plays a pivotal role in protein engineering and pharmaceutical development. While traditional methods offer valuable insights, they are often constrained by computational speed and reliance on biased training datasets. These constraints become particularly evident when aiming for accurate ΔΔG predictions across a diverse array of protein sequences.
View Article and Find Full Text PDFGenome
January 2025
ICAR - National Bureau of Animal Genetic Resources, Karnal, Haryana, India;
India harbours a substantial population of 9.43 million dogs, showcasing diverse phenotypes and utility. Initiatives focusing on awareness, conservation and informed breeding can greatly enhance the recognition and welfare of the unique Indian canine heritage.
View Article and Find Full Text PDFJ Anim Breed Genet
January 2025
Departamento de Mejora Genética Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), CSIC, Madrid, Spain.
The advancement of epigenetics has highlighted DNA methylation as an intermediate-omic influencing gene regulation and phenotypic expression. With emerging technologies enabling the large-scale and affordable capture of methylation data, there is growing interest in integrating this information into genetic evaluation models for animal breeding. This study used methylome information from six dairy cows to simulate the methylation profile of 13,183 genotyped animals.
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