Highly accurate detection of ovarian cancer using CA125 but limited improvement with serum matrix-assisted laser desorption/ionization time-of-flight mass spectrometry profiling

Objectives: Our objective was to test the performance of CA125 in classifying serum samples from a cohort of malignant and benign ovarian cancers and age-matched healthy controls and to assess whether combining information from matrix-assisted laser desorption/ionization (MALDI) time-of-flight profiling could improve diagnostic performance. Materials and Methods: Serum samples from women with ovarian neoplasms and healthy volunteers were subjected to CA125 assay and MALDI time-of-flight mass spectrometry (MS) profiling. Models were built from training data sets using discriminatory MALDI MS peaks in combination with CA125 values and tested their ability to classify blinded test samples. These were compared with models using CA125 threshold levels from 193 patients with ovarian cancer, 290 with benign neoplasm, and 2236 postmenopausal healthy controls. Results: Using a CA125 cutoff of 30 U/mL, an overall sensitivity of 94.8% (96.6% specificity) was obtained when comparing malignancies versus healthy postmenopausal controls, whereas a cutoff of 65 U/mL provided a sensitivity of 83.9% (99.6% specificity). High classification accuracies were obtained for early-stage cancers (93.5% sensitivity). Reasons for high accuracies include recruitment bias, restriction to postmenopausal women, and inclusion of only primary invasive epithelial ovarian cancer cases. The combination of MS profiling information with CA125 did not significantly improve the specificity/accuracy compared with classifications on the basis of CA125 alone. Conclusions: We report unexpectedly good performance of serum CA125 using threshold classification in discriminating healthy controls and women with benign masses from those with invasive ovarian cancer. This highlights the dependence of diagnostic tests on the characteristics of the study population and the crucial need for authors to provide sufficient relevant details to allow comparison. Our study also shows that MS profiling information adds little to diagnostic accuracy. This finding is in contrast with other reports and shows the limitations of serum MS profiling for biomarker discovery and as a diagnostic too

High resolution crystal structure of the Endo-N-acetyl-beta-D-glucosaminidase responsible for the deglycosylation of hypocrea jecorina cellulases

Endo-N-acetyl-beta-D-glucosaminidases (ENGases) hydrolyze the glycosidic linkage between the two N-acetylglucosamine units that make up the chitobiose core of N-glycans. The endo-N-acetyl-beta-D-glucosaminidases classified into glycoside hydrolase family 18 are small, bacterial proteins with different substrate specificities. Recently two eukaryotic family 18 deglycosylating enzymes have been identified. Here, the expression, purification and the 1.3 angstrom resolution structure of the ENGase ( Endo T) from the mesophilic fungus Hypocrea jecorina (anamorph Trichoderma reesei) are reported. Although the mature protein is C-terminally processed with removal of a 46 amino acid peptide, the protein has a complete (beta/alpha) 8 TIM-barrel topology. In the active site, the proton donor (E131) and the residue stabilizing the transition state (D129) in the substrate assisted catalysis mechanism are found in almost identical positions as in the bacterial GH18 ENGases: Endo H, Endo F1, Endo F3, and Endo BT. However, the loops defining the substrate-binding cleft vary greatly from the previously known ENGase structures, and the structures also differ in some of the alpha-helices forming the barrel. This could reflect the variation in substrate specificity between the five enzymes. This is the first three-dimensional structure of a eukaryotic endo-N-acetyl-beta-D-glucosaminidase from glycoside hydrolase family 18. A glycosylation analysis of the cellulases secreted by a Hypocrea jecorina Endo T knock-out strain shows the in vivo function of the protein. A homology search and phylogenetic analysis show that the two known enzymes and their homologues form a large but separate cluster in subgroup B of the fungal chitinases. Therefore the future use of a uniform nomenclature is proposed

The use of urinary proteomics in the assessment of suitability of mouse models for ageing

Ageing is a complex process characterised by a systemic and progressive deterioration of biological functions. As ageing is associated with an increased prevalence of age-related chronic disorders, understanding its underlying molecular mechanisms can pave the way for therapeutic interventions and managing complications. Animal models such as mice are commonly used in ageing research as they have a shorter lifespan in comparison to humans and are also genetically close to humans. To assess the translatability of mouse ageing to human ageing, the urinary proteome in 89 wild-type (C57BL/6) mice aged between 8–96 weeks was investigated using capillary electrophoresis coupled to mass spectrometry (CE-MS). Using age as a continuous variable, 295 peptides significantly correlated with age in mice were identified. To investigate the relevance of using mouse models in human ageing studies, a comparison was performed with a previous correlation analysis using 1227 healthy subjects. In mice and humans, a decrease in urinary excretion of fibrillar collagens and an increase of uromodulin fragments was observed with advanced age. Of the 295 peptides correlating with age, 49 had a strong homology to the respective human age-related peptides. These ortholog peptides including several collagen (N = 44) and uromodulin (N = 5) fragments were used to generate an ageing classifier that was able to discriminate the age among both wild-type mice and healthy subjects. Additionally, the ageing classifier depicted that telomerase knock-out mice were older than their chronological age. Hence, with a focus on ortholog urinary peptides mouse ageing can be translated to human ageing

Structural insights on TRPV5 gating by endogenous modulators.

TRPV5 is a transient receptor potential channel involved in calcium reabsorption. Here we investigate the interaction of two endogenous modulators with TRPV5. Both phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2) and calmodulin (CaM) have been shown to directly bind to TRPV5 and activate or inactivate the channel, respectively. Using cryo-electron microscopy (cryo-EM), we determined TRPV5 structures in the presence of dioctanoyl PI(4,5)P2 and CaM. The PI(4,5)P2 structure reveals a binding site between the N-linker, S4-S5 linker and S6 helix of TRPV5. These interactions with PI(4,5)P2 induce conformational rearrangements in the lower gate, opening the channel. The CaM structure reveals two TRPV5 C-terminal peptides anchoring a single CaM molecule and that calcium inhibition is mediated through a cation-π interaction between Lys116 on the C-lobe of calcium-activated CaM and Trp583 at the intracellular gate of TRPV5. Overall, this investigation provides insight into the endogenous modulation of TRPV5, which has the potential to guide drug discovery

Computational Models for Transplant Biomarker Discovery.

Translational medicine offers a rich promise for improved diagnostics and drug discovery for biomedical research in the field of transplantation, where continued unmet diagnostic and therapeutic needs persist. Current advent of genomics and proteomics profiling called "omics" provides new resources to develop novel biomarkers for clinical routine. Establishing such a marker system heavily depends on appropriate applications of computational algorithms and software, which are basically based on mathematical theories and models. Understanding these theories would help to apply appropriate algorithms to ensure biomarker systems successful. Here, we review the key advances in theories and mathematical models relevant to transplant biomarker developments. Advantages and limitations inherent inside these models are discussed. The principles of key -computational approaches for selecting efficiently the best subset of biomarkers from high--dimensional omics data are highlighted. Prediction models are also introduced, and the integration of multi-microarray data is also discussed. Appreciating these key advances would help to accelerate the development of clinically reliable biomarker systems