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Teratogenesis testing can be challenging due to the limitations of both in vitro and in vivo models. Test-systems, based especially on human embryonic cells, have been helping to overcome the difficulties when allied to omics strategies, such as transcriptomics. In these test-systems, cells exposed to different compounds are then analyzed in microarray or RNA-seq platforms regarding the impacts of the potential teratogens in the gene expression. Nevertheless, microarray and RNA-seq dataset processing requires computational resources and bioinformatics knowledge. Here, a pipeline for microarray and RNA-seq processing is presented, aiming to help researchers from any field to interpret the main transcriptome results, such as differential gene expression, enrichment analysis, and statistical interpretation. This chapter also discusses the main difficulties that can be encountered in a transcriptome analysis and the better alternatives to overcome these issues, describing both programming codes and user-friendly tools. Finally, specific issues in the teratogenesis field, such as time-course analysis, are also described, demonstrating how the pipeline can be applied in these studies.
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http://dx.doi.org/10.1007/978-1-0716-3625-1_20 | DOI Listing |
Funct Integr Genomics
September 2025
Department of Plastic Surgery, the First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, China.
Keloid scarring and Metabolic Syndrome (MS) are distinct conditions marked by chronic inflammation and tissue dysregulation, suggesting shared pathogenic mechanisms. Identifying common regulatory genes could unveil novel therapeutic targets. Methods.
View Article and Find Full Text PDFOncol Res
September 2025
Hospital for Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences & Peking Union Medical College, Nanjing, 210042, China.
Introduction: Acral melanoma (AM) is the predominant subtype of cutaneous melanoma in Asian populations, characterized by more aggressive clinical features and limited neoadjuvant therapy response. Centrosomal protein 55 kDa (CEP55) has been implicated in the pathogenesis of various malignancies, but its role in AM remains undefined.
Methods: CEP55 expression in melanoma tissues and cell lines was analyzed by RT-qPCR, Western blotting, and immunohistochemistry (IHC).
Biochem Biophys Rep
December 2025
Département de Sciences Biologiques, Université de Montréal, Montréal, Québec, Canada.
• This is a preliminary study exploring the effect of the mitochondrial alternative peptide MTALTND4 on gene expression in two different culture media using microarrays, RNA-seq and RT-qPCR. • Microarrays in MiR05 medium suggest that exogenous treatment with MTALTND4 may alter gene expression and that responsive genes are mostly related to cell metabolism. • Conversely, RNA-seq in DMEM low glucose suggests that MTALTND4 has a negligible impact on gene expression.
View Article and Find Full Text PDFAnn Rheum Dis
September 2025
Immunogenetics Lab, Fundación Pública Galega de Medicina Xenómica, SERGAS, Grupo de Medicina Xenómica-USC, Instituto de investigación Sanitaria de Santiago de Compostela, Santiago de Compostela, Spain; Faculty of Health Sciences, Universidad Autónoma de Chile, Talca, Chile. Electronic address:
Objectives: This study aims to identify and validate a transcriptomic signature capable of predicting the response to tumour necrosis factor inhibitors (TNFi) therapy in patients with rheumatoid arthritis (RA) before treatment initiation.
Methods: We performed a retrospective transcriptomic analysis using 2 public datasets, RNA-seq data from peripheral blood mononuclear cells (GSE138746) and microarray data from whole blood (GSE33377), to define a small-scale gene signature predictive of the response to TNFi treatment. Three external validations were then conducted, resulting in a total of 279 individuals, 169 responders, and 110 nonresponders.
Nature
September 2025
Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Transcription factors (TFs) regulate gene expression by interacting with DNA in a sequence-specific manner. High-throughput in vitro technologies, such as protein-binding microarrays and HT-SELEX (high-throughput systematic evolution of ligands by exponential enrichment), have revealed the DNA-binding specificities of hundreds of TFs. However, they have limited ability to reliably identify lower-affinity DNA binding sites, which are increasingly recognized as important for precise spatiotemporal control of gene expression.
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