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Integration of a miniaturized DMMB assay with high-throughput screening for identifying regulators of proteoglycan metabolism.

ABSTRACT: Defective biosynthesis or function of proteoglycans causes pathological conditions in a variety of tissue systems. Osteoarthritis (OA) is a prevalent degenerative joint disorder characterized by progressive cartilage destruction caused by imbalanced proteoglycan synthesis and degradation. Identifying agents that regulate proteoglycan metabolism may benefit the development of OA-modifying therapeutics. High-throughput screening (HTS) of chemical libraries has paved the way for achieving this goal. However, the implementation and adaptation of HTS assays based on proteoglycan measurement remain underexploited. Using primary porcine chondrocytes as a model, we report a miniaturized dimethyl-methylene blue (DMMB) assay, which is commonly used to quantitatively evaluate sulfated glycosaminoglycan (GAG) content, with an optimized detection range and reproducibility and its integration with HTS. Treatment with TGF-β1 and IL1-α, known as positive and negative proteoglycan regulators, respectively, supported the assay specificity. A pre-test of chemical screening of 960 compounds identified both stimulators (4.48%) and inhibitors (6.04%) of GAG production. Fluorophore-assisted carbohydrate electrophoresis validated the activity of selected hits on chondroitin sulfate expression in an alginate culture system. Our findings support the implementation of this simple colorimetric assay in HTS to discover modifiers of OA or other diseases related to dysregulated proteoglycan metabolism.

SUBMITTER: Sun Y

PROVIDER: S-EPMC8776954 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Quantitative high throughput screening (qHTS) assays use cells or tissues to screen thousands of compounds in a short period of time. Data generated from qHTS assays are then evaluated using nonlinear regression models, such as the Hill model, and decisions regarding toxicity are made using the estimates of the parameters of the model. For any given compound, the variability in the observed response may either be constant across dose groups (homoscedasticity) or vary with dose (heteroscedasticity). Since thousands of compounds are simultaneously evaluated in a qHTS assay, it is not practically feasible for an investigator to perform residual analysis to determine the variance structure before performing statistical inferences on each compound. Since it is well-known that the variance structure plays an important role in the analysis of linear and nonlinear regression models it is therefore important to have practically useful and easy to interpret methodology which is robust to the variance structure. Furthermore, given the number of chemicals that are investigated in the qHTS assay, outliers and influential observations are not uncommon. In this article we describe preliminary test estimation (PTE) based methodology which is robust to the variance structure as well as any potential outliers and influential observations. Performance of the proposed methodology is evaluated in terms of false discovery rate (FDR) and power using a simulation study mimicking a real qHTS data. Of the two methods currently in use, our simulations studies suggest that one is extremely conservative with very small power in comparison to the proposed PTE based method whereas the other method is very liberal. In contrast, the proposed PTE based methodology achieves a better control of FDR while maintaining good power. The proposed methodology is illustrated using a data set obtained from the National Toxicology Program (NTP). Additional information, simulation results, data and computer code are available online as supplementary materials.

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Integration of a miniaturized DMMB assay with high-throughput screening for identifying regulators of proteoglycan metabolism.

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