Recapitulating native tissue organization is a central challenge in regenerative medicine as it is critical for generating functional tissues. One strategy to generate engineered tissues with predictable and appropriate organization is to mimic the gene expression patterning process that organizes tissues in the developing embryo. In a developing embryo, correct organization is accomplished by tissue patterning via the generation of temporal and spatial patterns of gene expression coupled with, and leading to, extensive cellular re-organization. Methods to pattern gene expression in vitro could therefore provide both better models for understanding the cellular and molecular events taking place during tissue morphogenesis and novel strategies for engineering tissues with more realistic and complex architectures. While a few attempts have been made to genetically pattern tissues in vitro, these do not produce sharp predictable patterning. In both the embryo and an in vitro tissue, patterning often occurs during extensive cell re-organization but how the dynamics of gene induction and cell re-distribution interact to impact the final outcome of patterning and ultimately tissue organization is not known. Understanding this relationship and the system parameters that dictate robust pattern formation is critical for engineering genetic patterning in vitro to organize artificial tissues. We set out to identify key requirements for pattern formation by patterning gene expression in vitro in sheets of re-distributing cells using a drug-inducible gene expression system and patterned drug delivery to mimic morphogen gene induction. Based on our experimental observations, we develop a mathematical model that allows us to identify and experimentally verify the conditions under which generation of sharp gene expression patterns is possible in vitro. Our results highlight the importance of coordinating gene induction dynamics and cellular movement in order to achieve robust pattern formation.
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http://dx.doi.org/10.1039/c3ib20274g | DOI Listing |
Cancer Rep (Hoboken)
January 2025
Department of Medical Biotechnology, School of Advanced Technologies, Shahrekord University of Medical Sciences, Shahrekord, Iran.
Background: Bioinformatics analysis of hepatocellular carcinoma (HCC) expression profiles can aid in understanding its molecular mechanisms and identifying new targets for diagnosis and treatment.
Aim: In this study, we analyzed expression profile datasets and miRNA expression profiles related to HCC from the GEO using R software to detect differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRs).
Methods And Results: Common DEGs were identified, and a PPI network was constructed using the STRING database and Cytoscape software to identify hub genes.
Postgrad Med J
January 2025
Department of Pediatric Metabolic Diseases, University of Health Sciences, Ankara Etlik City Hospital, Ankara 06170, Turkey.
Metabolism is the name given to all of the chemical reactions in the cell involving thousands of proteins, including enzymes, receptors, and transporters. Inborn errors of metabolism (IEM) are caused by defects in the production and breakdown of proteins, fats, and carbohydrates. Micro ribonucleic acids (miRNAs) are short non-coding RNA molecules, ⁓19-25 nucleotides long, hairpin-shaped, produced from DNA.
View Article and Find Full Text PDFCNS Neurosci Ther
January 2025
Department of Neurology, School of Medicine, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China.
Objective: This study aims to investigate how the E3 ubiquitin ligase LITAF influences mitochondrial autophagy by modulating MCL-1 ubiquitination, and its role in the development of epilepsy.
Methods: Employing single-cell RNA sequencing (scRNA-seq) to analyze brain tissue from epilepsy patients, along with high-throughput transcriptomics, we identified changes in gene expression. This was complemented by in vivo and in vitro experiments, including protein-protein interaction (PPI) network analysis, western blotting, and behavioral assessments in mouse models.
Brief Bioinform
November 2024
Center for Genomics and Biotechnology, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, No. 15 Shangxiadian Road, Cangshan District, Fuzhou 350002, China.
Spatial transcriptomics (ST) technologies enable dissecting the tissue architecture in spatial context. To perceive the global contextual information of gene expression patterns in tissue, the spatial dependence of cells must be fully considered by integrating both local and non-local features by means of spatial-context-aware. However, the current ST integration algorithm ignores for ST dropouts, which impedes the spatial-aware of ST features, resulting in challenges in the accuracy and robustness of microenvironmental heterogeneity detecting, spatial domain clustering, and batch-effects correction.
View Article and Find Full Text PDFPest Manag Sci
January 2025
Key Laboratory of Plant Protection Resources and Pest Management of the Ministry of Education, Key Laboratory of Integrated Pest Management on the Loess Plateau of Ministry of Agriculture and Rural Affairs, College of Plant Protection, Northwest A&F University, Yangling, China.
Background: The function of some testis-specific genes (TSGs) in model insects have been studied, but their function in non-model insects remains largely unexplored. In the present study, we identified several TSGs in the fall armyworm (FAW), a significant agricultural pest, through comparative transcriptomic analysis. A testis-specific gene cluster (TSGC) comprising multiple functional genes and long non-coding RNAs was found.
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