Biology subjects

Watson, C. A.

Publications and source records attributed to Watson, C. A..

3 recordsLinked to original sources

bioRxiv2023

LCP1 Regulates Cell Motility in Chondrosarcoma and Correlates with Metastatic Potential and Poor Patient Outcomes

Chondrosarcoma (CSA) is the second most common primary malignancy of bone, whose aggressive potential and chemo-resistant nature result in extremely poor outcomes in patients with advanced disease. Grading and prognostication of these tumors remain a significant challenge for pathologists, and medical oncologists have no effective therapies to prevent or treat metastatic disease. In the present study, we sought to explore the pathogenesis and aggressive progression of chondrosarcoma by comparing gene expression differences from metastasizing and non-metastasizing CSA tumors. We hypothesized that metastasizing tumors have inherent differences that may be attributed to the dysregulation of specific oncogenic cell signaling pathways. RNA-Seq analysis from patient-derived low-passage cell lines of metastasizing and non-metastasizing tumors pinpointed the gene LCP1 (lymphocyte cytosolic protein 1) as upregulated in CSA cells from metastasizing tumors. Analysis of a large clinical cohort of CSA demonstrated that increased expression of LCP1 correlated with poor patient survival. In vitro analyses confirmed the ability of LCP1 to promote migration and invasion of CSA cells. These data support the key role of LCP1 on metastatic potential and poor prognosis in CSA. In conclusion, we confirm the ability of LCP1 to drive metastatic behavior and correlate with poor outcomes in patients.

cancer biology↗

bioRxiv2022

CCS Predictor 2.0: An Open-Source Jupyter Notebook Tool for Filtering Out False Positives in Metabolomics

Metabolite annotation continues to be the widely accepted bottleneck in non-targeted metabolomics workflows. Annotation of metabolites typically relies on a combination of high resolution mass spectrometry (MS) with parent and tandem measurements, isotope cluster evaluations, and Kendrick mass defect (KMD) analysis. Chromatographic retention time matching with standards is often used at the later stages of the process, which can also be followed by metabolite isolation and structure confirmation utilizing nuclear magnetic resonance (NMR) spectroscopy. The measurement of gas phase collision cross section (CCS) values by ion mobility (IM) spectrometry also adds an important dimension to this workflow by generating an additional molecular parameter that can be used for filtering unlikely structures. The millisecond timescale of IM spectrometry allows the rapid measurement of CCS values and allows easy pairing with existing MS workflows. Here, we report on a highly accurate machine learning algorithm (CCSP 2.0) in an open-source Jupyter Notebook format to predict CCS values based on linear support vector regression models. This tool allows customization of the training set to the needs of the user, enabling the production of models for new adducts or previously unexplored molecular classes. CCSP produces predictions with accuracy equal to or greater than existing machine learning approaches such as CCSbase, DeepCCS and AllCCS, while being better aligned with FAIR (Findable, Accessible, Interoperable and Reusable) data principles. Another unique aspect of CCSP 2.0 its inclusion of a large library of 1613 molecular descriptors via the Mordred Python package, further encoding the fine aspects of isomeric molecular structures. CCS prediction accuracy was tested using CCS values in the McLean CCS Compendium with median relative errors of 1.25, 1.73 and 1.87% for the 170 [M-H]-, 155 [M+H]+ and 138 [M+Na]+ adducts tested. For class-matched data sets, CCS predictions via CCSP allowed filtering of 36.1% of incorrect structures while retaining a total of 100% of the correct annotations using a {Delta}CCS threshold of 2.8% and a mass error of 10 ppm.

biochemistry↗

bioRxiv2017

Rapid degradation and 3D CLEM of condensin reveal chromatin compaction uncoupled from chromosome architecture in mitosis

The requirement for condensin in chromosome formation in somatic cells remains unclear as imperfectly condensed chromosomes do form in conventional condensindepleted cells. Here we have dissected the role of condensin at different stages of vertebrate mitosis by combining auxin-mediated rapid depletion of condensin subunit SMC2 with chemical genetics to obtain near-synchronous mitotic entry of chicken DT40 cells. We analysed the outcomes by live and fixed-cell microscopy methods, including 3D correlative light and serial block face scanning electron microscopy. Following rapid depletion of condensin, chromosomal defects were obvious. The chromatin was compacted normally, but formed a single mass of mitotic chromosomes clustered at one side of a bent mitotic spindle. Cultures arrest at prometaphase, eventually exiting mitosis without segregating chromosomes. Experiments titrating the auxin concentration suggest a previously unsuspected dual role of condensin, as different condensin levels are required for anaphase chromosome segregation and formation of a normal chromosome architecture.

cell biology↗