Andres-Martin, L. | Search bioRxiv

bioRxiv2023

The Dietary Supplement Taurine Suppresses Ovarian Cancer Growth

Taurine, a non-proteogenic amino acid, and commonly used nutritional supplement can protect various tissues from degeneration associated with the action of the DNA-damaging chemotherapeutic agent cisplatin. Whether and how taurine protects human ovarian cancer (OC) cells from DNA damage caused by cisplatin is not well understood. We have found that OC ascites-derived cells contained significantly more intracellular taurine than cell cultures modeling OC. In culture, elevation of intracellular taurine concentration to OC ascites-cells-associated levels suppressed proliferation of various OC cell lines and patient-derived organoids, reduced glycolysis, and induced cell protection from cisplatin. Taurine cell protection was associated with decreased DNA damage in response to cisplatin. A combination of RNA sequencing, reverse phase protein arrays, live-cell microscopy, flow cytometry, and biochemical validation experiments provided evidence for taurine-mediated induction of mutant- or wild-type p53 binding to DNA, and activation of p53 effectors involved in negative regulation of the cell cycle (p21), and glycolysis (TIGAR). Paradoxically, taurines suppression of cell proliferation was associated with activation of pro-mitogenic signal transduction including ERK, mTOR, and increased mRNA expression of major DNA damage sensing molecules such as DNAPK, ATM and ATR. While inhibition of ERK or p53 did not interfere with taurines ability to protect cells from cisplatin, suppression of mTOR with Torin2, a clinically relevant inhibitor that also targets DNAPK and ATM/ATR, broke taurines cell protection. Our studies implicate that elevation of intracellular taurine could suppress cell growth, metabolism, and activate cell protective mechanisms involving mTOR and DNA damage sensing signal transduction.

cancer biology↗

bioRxiv2020

Deep learning and automated Cell Painting reveal Parkinson's disease-specific signatures in primary patient fibroblasts

Drug discovery for diseases such as Parkinsons disease are impeded by the lack of screenable cellular phenotypes. We present an unbiased phenotypic profiling platform that combines automated cell culture, high-content imaging, Cell Painting, and deep learning. We applied this platform to primary fibroblasts from 91 Parkinsons disease patients and matched healthy controls, creating the largest publicly available Cell Painting image dataset to date at 48 terabytes. We use fixed weights from a convolutional deep neural network trained on ImageNet to generate deep embeddings from each image and train machine learning models to detect morphological disease phenotypes. Our platforms robustness and sensitivity allow the detection of individual-specific variation with high fidelity across batches and plate layouts. Lastly, our models confidently separate LRRK2 and sporadic Parkinsons disease lines from healthy controls (receiver operating characteristic area under curve 0.79 (0.08 standard deviation)), supporting the capacity of this platform for complex disease modeling and drug screening applications.

cell biology↗