Browsing by Author "Knowlton NS"

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    Accessing a New Dimension in TP53 Biology: Multiplex Long Amplicon Digital PCR to Specifically Detect and Quantitate Individual TP53 Transcripts
    (MDPI (Basel, Switzerland), 2020-03-24) Lasham A; Tsai P; Fitzgerald SJ; Mehta SY; Knowlton NS; Braithwaite AW; Print CG
    TP53, the most commonly-mutated gene in cancer, undergoes complex alternative splicing. Different TP53 transcripts play different biological roles, both in normal function and in the progression of diseases such as cancer. The study of TP53's alternative RNA splice forms and their use as clinical biomarkers has been hampered by limited specificity and quantitative accuracy of current methods. TP53 RNA splice variants differ at both 5' and 3' ends, but because they have a common central region of 618 bp, the individual TP53 transcripts are impossible to specifically detect and precisely quantitate using standard PCR-based methods or short-read RNA sequencing. Therefore, we devised multiplex probe-based long amplicon droplet digital PCR (ddPCR) assays, which for the first time allow precise end-to-end quantitation of the seven major TP53 transcripts, with amplicons ranging from 0.85 to 1.85 kb. Multiple modifications to standard ddPCR assay procedures were required to enable specific co-amplification of these long transcripts and to overcome issues with secondary structure. Using these assays, we show that several TP53 transcripts are co-expressed in breast cancers, and illustrate the potential for this method to identify novel TP53 transcripts in tumour cells. This capability will facilitate a new level of biological and clinical understanding of the alternatively-spliced TP53 isoforms.
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    Identification of novel biomarkers for the prediction of subclinical coronary artery atherosclerosis in patients with rheumatoid arthritis: an exploratory analysis
    (BioMed Central Ltd, 2023-12) Bathon JM; Centola M; Liu X; Jin Z; Ji W; Knowlton NS; Ferraz-Amaro I; Fu Q; Giles JT; Wasko MC; Stein CM; Van Eyk JE
    Background: Cardiovascular (CV) risk estimation calculators for the general population underperform in patients with rheumatoid arthritis (RA). The purpose of this study was to identify relevant protein biomarkers that could be added to traditional CV risk calculators to improve the capacity of coronary artery calcification (CAC) prediction in individuals with RA. In a second step, we quantify the improvement of this prediction of CAC when these circulating biomarkers are added to standard risk scores. Methods: A panel of 141 serum and plasma proteins, which represent a broad base of both CV and RA biology, were evaluated and prioritized as candidate biomarkers. Of these, 39 proteins were selected and measured by commercial ELISA or quantitative mass spectroscopy in 561 individuals with RA in whom a measure of CAC and frozen sera were available. The patients were randomly split 50:50 into a training/validation cohort. Discrimination (using area under the receiver operator characteristic curves) and re-classification (through net reclassification improvement and integrated discrimination improvement calculation) analyses were performed first in the training cohort and replicated in the validation cohort, to estimate the increase in prediction accuracy for CAC using the ACA/AHA (American College of Cardiology and the American Heart Association) score with, compared to without, addition of these circulating biomarkers. Results: The model containing ACC/AHA score plus cytokines (osteopontin, cartilage glycoprotein-39, cystatin C, and chemokine (C–C motif) ligand 18) and plus quantitative mass spectroscopy biomarkers (serpin D1, paraoxonase, and clusterin) had a statistically significant positive net reclassifications index and integrated discrimination improvement for the prediction of CAC, using ACC/AHA score without any biomarkers as the reference category. These results were confirmed in the validation cohort. Conclusion: In this exploratory analysis, the addition of several circulating CV and RA biomarkers to a standard CV risk calculator yielded significant improvements in discrimination and reclassification for the presence of CAC in individuals with RA.