Background: Gene expression profiling has the potential to produce new insights into complex biologic systems. To test the value of complement DNA arrays in identifying pathways involved in organ transplant rejection, we examined the gene expression profiles of rat heart allografts from recipients treated with or without immunosuppression to prevent acute allograft rejection.
Methods: Heterotopic heart transplantation was performed using ACI or Lewis donors and Lewis recipients. Recipients were treated with tacrolimus (Tac) or cyclosporine (CsA) at the equivalent effective doses, and graft hearts were harvested on days 3, 5, and 7. A commercial microarray was used to measure gene expression levels of 588 genes in day 5 grafts. Selected genes were analyzed by reverse transcriptase-polymerase chain reaction.
Results: The expression levels of 118 genes were perturbed in the untreated allograft in comparison with the isograft control, of which 77 genes were categorized as candidate genes for Tac- or CsA-mediated immunosuppression or both, and 41 as genes associated with other pathways. Among the 77 candidate genes, 55 genes shared the same response to suppression by both drugs, including inducible nitric oxide synthase, interferon-gamma, and interferon regulatory factor 1. Drug-specific effects were observed in 22 genes: Fourteen genes were exclusively reversed by Tac and eight by CsA.
Conclusions: Gene expression profiling reveals a large variety of genes affected during acute rejection, indicating that multiple metabolic pathways, including immune and nonimmune responses, are involved in the local graft rejection events. The differences and similarities of the gene expression profiles relative to the two immunosuppressants may provide more detailed therapeutic approaches for optimal immunosuppression.
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http://dx.doi.org/10.1097/01.TP.0000081398.65568.1B | DOI Listing |
Brief Bioinform
November 2024
Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC 27101, United States.
Pathway analysis plays a critical role in bioinformatics, enabling researchers to identify biological pathways associated with various conditions by analyzing gene expression data. However, the rise of large, multi-center datasets has highlighted limitations in traditional methods like Over-Representation Analysis (ORA) and Functional Class Scoring (FCS), which struggle with low signal-to-noise ratios (SNR) and large sample sizes. To tackle these challenges, we use a deep learning-based classification method, Gene PointNet, and a novel $P$-value computation approach leveraging the confusion matrix to address pathway analysis tasks.
View Article and Find Full Text PDFClin Cancer Res
January 2025
Stanford University, Palo Alto, CA, United States.
Purpose: After failing primary and secondary hormonal therapy, castration-resistant and neuroendocrine prostate cancer metastatic to the bone is invariably lethal, although treatment with docetaxel and carboplatin can modestly improve survival. Therefore, agents targeting biologically relevant pathways in PCa and potentially synergizing with docetaxel and carboplatin in inhibiting bone metastasis growth are urgently needed.
Experimental Design: Phosphorylated (activated) AXL expression in human prostate cancer bone metastases was assessed by immunohistochemical staining.
STAR Protoc
January 2025
Department of Statistics, University of Georgia, 310 Herty Drive, Athens, GA 30602, USA. Electronic address:
Spatial transcriptomics enhances our understanding of cellular organization by mapping gene expression data to precise tissue locations. Here, we present a protocol for using weighted ensemble method for spatial transcriptomics (WEST), which uses ensemble techniques to boost the robustness and accuracy of existing algorithms. We describe steps for preprocessing data, obtaining embeddings from individual algorithms, and ensemble integrating all embeddings as a similarity matrix.
View Article and Find Full Text PDFSci Transl Med
January 2025
Department of Cell Biology and Physiology, Washington University School of Medicine, Saint Louis, MO 63110, USA.
Sci Transl Med
January 2025
Graduate Program in Human Genetics, University of Miami Miller School of Medicine, 1501 NW 10th Avenue (M-860), Miami, FL 33136, USA.
Primary mitochondrial disorders are most often caused by deleterious mutations in the mitochondrial DNA (mtDNA). Here, we used a mitochondrial DddA-derived cytosine base editor (DdCBE) to introduce a compensatory edit in a mouse model that carries the pathological mutation in the mitochondrial transfer RNA (tRNA) alanine (mt-tRNA) gene. Because the original m.
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