Non-covalent inhibitors of thioredoxin glutathione reductase with schistosomicidal activity in vivo.

Only praziquantel is available for treating schistosomiasis, a disease affecting more than 200 million people. Praziquantel-resistant worms have been selected for in the lab and low cure rates from mass drug administration programs suggest that resistance is evolving in the field. Thioredoxin glutathione reductase (TGR) is essential for schistosome survival and a validated drug target. TGR inhibitors identified to date are irreversible and/or covalent inhibitors with unacceptable off-target effects. In this work, we identify noncovalent TGR inhibitors with efficacy against schistosome infections in mice, meeting the criteria for lead progression indicated by WHO. Comparisons with previous in vivo studies with praziquantel suggests that these inhibitors outperform the drug of choice for schistosomiasis against juvenile worms.

Detection of Ovarian Cancer Using Samples Sourced from the Vaginal Microenvironment.

Mass spectrometry (MS) offers high levels of specificity and sensitivity in clinical applications, and we have previously been able to demonstrate that matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS is capable of distinguishing two-component cell mixtures at low limits of detection. Ovarian cancer is notoriously difficult to detect due to the lack of diagnostic techniques available to the medical community. By sampling a local microenvironment, such as the vaginal canal and cervix, a MS based method is presented for monitoring disease progression from proximal samples to the diseased tissue. A murine xenograft model of high grade serous ovarian carcinoma (HGSOC) was used for this study, and vaginal lavages were obtained from mice on a weekly basis throughout disease progression and subjected to our MALDI-TOF MS workflow followed by statistical analyses. Proteins in the 4-20 kDa region of the mass spectrum yielded a fingerprint that we could consistently measure over time that correlated with disease progression. These fingerprints were found to be largely stable across all mice, with the protein fingerprint converging toward the end point of the study. MALDI-TOF MS serves as a unique analytical technique for measuring a sampled vaginal microenvironment in a specific and sensitive manner for the detection of HGSOC in a murine model.

Whole Cell MALDI Fingerprinting Is a Robust Tool for Differential Profiling of Two-Component Mammalian Cell Mixtures.

MALDI fingerprinting was first described two decades ago as a technique to identify microbial cell lines. Microbial fingerprinting has since evolved into an automated platform for microorganism identification and classification, which is now routinely used in clinical and environmental sectors. The extension of fingerprinting to mammalian cells has yet to progress partly due to compartmentalization of eukaryotic cells and overall higher cellular complexity. A number of publications on mammalian whole cell fingerprinting suggest that the method could be useful for classification of different cell types, cell states, and monitoring cell differentiation. We report the optimization of MALDI fingerprinting workflow parameters for mammalian cells and its application for differential profiling of mammalian cell lines and two-component cell line mixtures. Murine fallopian tube cells and high-grade ovarian carcinoma cell lines and their mixtures are used as model mammalian cell lines. Two-component cell mixtures serve to determine the method's feasibility for complex biological samples as the ability to detect cancer cells in a mixed cell population. The level of detection of cancer cells in the two-component mixture by principle component analysis (PCA) starts to deteriorate at 5% but with application of a different statistical approach, Wilcoxon rank sum test, the level of detection was determined to be 1%. The ability to differentiate heterogeneous cell mixtures will help further extend whole cell MALDI fingerprinting to complex biological systems. Graphical Abstract.