Biology subjects

Thornton, C.

Publications and source records attributed to Thornton, C..

3 recordsLinked to original sources

bioRxiv2024

The Splice Index as a prognostic biomarker of strength and function in myotonic dystrophy type 1

Myotonic dystrophy type 1 (DM1) is a slowly progressive, multisystemic disorder caused by a CTG repeat expansion in the DMPK 3UTR that leads to global dysregulation of alternative splicing. Here, we employed a composite RNA splicing biomarker called the Myotonic Dystrophy Splice Index (SI), which incorporates 22 disease-specific splice events that sensitively and robustly assesses transcriptomic dysregulation across the disease spectrum. Targeted RNA sequencing was used to derive the SI in 95 muscle biopsies of the tibialis anterior collected from DM1 individuals with baseline (n = 52) and 3-months (n = 37) outcomes. The SI had significant associations with timepoint matched measures of muscle strength and ambulation, including ankle dorsiflexion strength (ADF) and 10-meter run/fast walk speed (Pearson r = -0.719 and -0.680, respectively). Linear regression modeling showed that the combination of baseline ADF and SI was predictive of strength at 3-months (adjusted R2 = 0.830) in our cohort. These results indicate the SI can reliably capture the association of disease-specific RNA mis-splicing to physical strength and mobility and may be predictive of future function.

molecular biology↗

bioRxiv2021

Multiple ETS Factors Participate in the Transcriptional Control of TERT Mutant Promoter in Thyroid Cancers

Hotspot mutations in the TERT (telomerase reverse transcriptase) gene are key determinants of thyroid cancer progression. TERT promoter mutations (TPM) create de novo consensus binding sites for the ETS ("E26 transforming sequence") family of transcription factors. In this study, we systematically knocked down each of the 20 ETS factors expressed in thyroid tumors and screened their effects on TERT expression in seven thyroid cancer cell lines with defined TPM status. We observed that, unlike in other TPM-carrying cancers such as glioblastomas, ETS factor GABPA does not unambiguously regulate transcription from the TERT mutant promoter in thyroid specimens. In fact, multiple members of the ETS family impact TERT expression, and they typically do so in a mutation-independent manner. In addition, we observe that partial inhibition of MAPK, a central pathway in thyroid cancer transformation, is more effective at suppressing TERT transcription in the absence of TPMs. Taken together, our results show a more complex scenario of TERT regulation in thyroid cancers compared to other lineages, suggest that compensatory mechanisms by ETS and other regulators likely exist and advocate for the need of a more comprehensive understanding of the mechanisms of TERT deregulation in thyroid tumors before eventually exploring TPM-specific therapeutic strategies. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=157 HEIGHT=200 SRC="FIGDIR/small/472152v1_ufig1.gif" ALT="Figure 1"> View larger version (54K): org.highwire.dtl.DTLVardef@63d70corg.highwire.dtl.DTLVardef@bb5d3corg.highwire.dtl.DTLVardef@e613a5org.highwire.dtl.DTLVardef@113bf54_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2019

Towards development of a statistical framework to evaluate myotonic dystrophy type 1 mRNA biomarkers in the context of a clinical trial

Myotonic dystrophy type 1 (DM1) is a rare genetic disorder, characterised by muscular dystrophy, myotonia, and other symptoms. DM1 is caused by the expansion of a CTG repeat in the 3-untranslated region of DMPK. Longer CTG expansions are associated with greater symptom severity and earlier age at onset. The primary mechanism of pathogenesis is thought to be mediated by a gain of function of the CUG-containing RNA, that leads to trans-dysregulation of RNA metabolism of many other genes. Specifically, the alternative splicing (AS) and alternative polyadenylation (APA) of many genes is known to be disrupted. In the context of clinical trials of emerging DM1 treatments, it is important to be able to objectively quantify treatment efficacy at the level of molecular biomarkers. We show how previously described candidate mRNA biomarkers can be used to model an effective reduction in CTG length, using modern high-dimensional statistics (machine learning), and a blood and muscle mRNA microarray dataset. We show how this model could be used to detect treatment effects in the context of a clinical trial.

bioinformatics↗