We examined the gene expression profiles in arthroscopic biopsies retrieved from 10 rheumatoid arthritis patients before and after anti-TNF treatment with infliximab to investigate whether such profiles can be used to predict responses to the therapy, and to study effects of the therapy on the profiles. Responses to treatment were assessed using European League Against Rheumatism response criteria. Three patients were found to be good responders, five patients to be moderate responders and two patients to be nonresponders. The TNF-alpha status of the biopsies from each of the patients before treatment was also investigated immunohistochemically, and it was detected in biopsies from four of the patients, including all three of the good responders. The gene expression data demonstrate that all patients had unique gene expression signatures, with low intrapatient variability between biopsies. The data also revealed significant differences between the good responding and nonresponding patients (279 differentially expressed genes were detected, with a false discovery rate < 0.025). Among the identified genes we found that MMP-3 was significantly upregulated in good responders (log2 fold change, 2.95) compared with nonresponders, providing further support for the potential of MMP-3 as a marker for good responses to therapy. An even more extensive list of 685 significantly differentially expressed genes was found between patients in whom TNF-alpha was found and nonresponders, indicating that TNF-alpha could be an important biomarker for successful infliximab treatment. Significant differences were also observed between biopsies taken before and after anti-TNF treatment, including 115 differentially expressed genes in the good responding group. Interestingly, the effect was even stronger in the group in which TNF-alpha was immunohistochemically detected before therapy. Here, 1,058 genes were differentially expressed, including many that were novel in this context (for example, CXCL3 and CXCL14). Subsequent Gene Ontology analysis revealed that several 'themes' were significantly over-represented that are known to be affected by anti-TNF treatment in inflammatory tissue; for example, immune response (GO:0006955), cell communication (GO:0007154), signal transduction (GO:0007165) and chemotaxis (GO:0006935). No genes reached statistical significance in the moderately responding or nonresponding groups. In conclusion, this pilot study suggests that further investigation is warranted on the usefulness of gene expression profiling of synovial tissue to predict and monitor the outcome of rheumatoid arthritis therapies.
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http://dx.doi.org/10.1186/ar2090 | 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.
Primary ciliary dyskinesia is a rare monogenic syndrome that is associated with chronic respiratory disease, infertility, and laterality defects. Although more than 50 genes causative of primary ciliary dyskinesia have been identified, variants in the genes encoding coiled-coil domain-containing 39 (CCDC39) and CCDC40 in particular cause severe disease that is not explained by loss of ciliary motility alone. Here, we sought to understand the consequences of these variants on cellular functions beyond impaired motility.
View Article and Find Full Text PDFSci 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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