Inhibition of protein disulfide isomerase in glioblastoma causes marked downregulation of DNA repair and DNA damage response genes.

Aberrant overexpression of endoplasmic reticulum (ER)-resident oxidoreductase protein disulfide isomerase (PDI) plays an important role in cancer progression. In this study, we demonstrate that PDI promotes glioblastoma (GBM) cell growth and describe a class of allosteric PDI inhibitors that are selective for PDI over other PDI family members. Methods: We performed a phenotypic screening triage campaign of over 20,000 diverse compounds to identify PDI inhibitors cytotoxic to cancer cells. From this screen, BAP2 emerged as a lead compound, and we assessed BAP2-PDI interactions with gel filtration, thiol-competition assays, and site-directed mutagenesis studies. To assess selectivity, we compared BAP2 activity across several PDI family members in the PDI reductase assay. Finally, we performed in vivo studies with a mouse xenograft model of GBM combining BAP2 and the standard of care (temozolomide and radiation), and identified affected gene pathways with nascent RNA sequencing (Bru-seq). Results: BAP2 and related analogs are novel PDI inhibitors that selectively inhibit PDIA1 and PDIp. Though BAP2 contains a weak Michael acceptor, interaction with PDI relies on Histidine 256 in the b' domain of PDI, suggesting allosteric binding. Furthermore, both in vitro and in vivo, BAP2 reduces cell and tumor growth. BAP2 alters the transcription of genes involved in the unfolded protein response, ER stress, apoptosis and DNA repair response. Conclusion: These results indicate that BAP2 has anti-tumor activity and the suppressive effect on DNA repair gene expression warrants combination with DNA damaging agents to treat GBM.

A metabonomics investigation of multiple sclerosis by nuclear magnetic resonance.

Multiple sclerosis (MS) is a nervous system disease that affects the fatty myelin sheaths around the axons of the brain and spinal cord, leading to demyelination and a broad range of signs and symptoms. MS can be difficult to diagnose because its signs and symptoms may be similar to other medical problems. To find out which metabolites in serum are effective for the diagnosis of MS, we utilized metabolic profiling using proton nuclear magnetic resonance spectroscopy ((1)H-NMR). Random forest (RF) was used to classify the MS patients and healthy subjects. Atomic absorption spectroscopy was used to measure the serum levels of selenium. The results showed that the levels of selenium were lower in the MS group, when compared with the control group. RF was used to identify the metabolites that caused selenium changes in people with MS by building a correlation model between these metabolites and serum levels of selenium. For the external test set, the obtained classification model showed a 93% correct classification of MS and healthy subjects. The regression model of levels of selenium and metabolites showed the correlation (R(2)) value of 0.88 for the external test set. The results indicate the suitability of NMR as a screen for identifying MS patients and healthy subjects. A novel model with good prediction outcomes was constructed between serum levels of selenium and NMR data.