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Krahn, J.

Publications and source records attributed to Krahn, J..

2 recordsLinked to original sources

Heparan sulfate selectively inhibits the collagenase activity of cathepsin K

Cathepsin K (CtsK) is a cysteine protease with potent collagenase activity. CtsK is highly expressed by bone-resorbing osteoclasts and plays an essential role in bone remodeling. Although CtsK is known to bind heparan sulfate (HS), the structural details of the interaction, and how HS ultimately regulates the biological functions of CtsK, remains largely unknown. In this report, we determined that CtsK preferably binds to larger HS oligosaccharides, such as dodecasaccharides (12mer), and that the12mer can induce monomeric CtsK to form a stable dimer in solution. Interestingly, while HS has no effect on the peptidase activity of CtsK, it greatly inhibits the collagenase activity of CtsK in a manner dependent on sulfation level. By forming a complex with CtsK, HS was able to preserve the full peptidase activity of CtsK for prolonged periods, likely by stabilizing its active conformation. Crystal structures of Ctsk with a bound 12mer, alone and in the presence of the endogenous inhibitor cystatin-C reveal the location of HS binding is remote from the active site. Mutagenesis based on these complex structures identified 6 basic residues of Ctsk that play essential roles in mediating HS-binding. At last, we show that HS 12mers can effectively block osteoclast resorption of bone in vitro. Combined, we have shown that HS can function as a multifaceted regulator of CtsK and that HS-based oligosaccharide might be explored as a new class of selective CtsK inhibitor in many diseases that involve exaggerated bone resorption.

biochemistry↗

Predicting Tumor Response to Drugs based on Gene-Expression Biomarkers of Sensitivity Learned from Cancer Cell Lines

SUMMARYHuman cancer cell line profiling and drug sensitivity studies provide valuable information about the therapeutic potential of drugs and their possible mechanisms of action. The goal of those studies is to translate the findings from in vitro studies of cancer cell lines into in vivo therapeutic relevance and, eventually, patients’ care. Tremendous progress has been made. In this work, we built predictive models for 453 drugs using data on gene expression and drug sensitivity (IC50) from cancer cell lines. We identified many known drug-gene interactions and uncovered several potentially novel drug-gene associations. Importantly, we further applied these predictive models to ∼17,000 bulk RNA-seq samples from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database to predict drug sensitivity for both normal and tumor tissues. We created a web site for users to visualize and download our predicted data (https://edelgene.niehs.nih.gov/cancerRxTissue). Using trametinib as an example, we showed that our approach can faithfully recapitulate the known tumor specificity of the drug. We further demonstrated that our approach can predict drugs that 1) are tumor-type specific; 2) elicit higher sensitivity from tumor compared to corresponding normal tissue; 3) elicit differential sensitivity across breast cancer subtypes. If validated, our predictions could have clinical relevance for patients’ care.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioinformatics↗