The cloning and expression of the full-length tissue inhibitor of metalloproteinase 2 (TIMP-2), delta 187-194TIMP-2, and delta 128-194TIMP-2 and the purification of these inhibitors and a cleaved version of TIMP-2 lacking nine C-terminal amino acids (delta 186-194TIMP-2) are described. The mechanism of inhibition of gelatinase A by the TIMPs was investigated by comparing the kinetics of association of TIMP-1, TIMP-2, the C-terminal deletions, and the mutants of both TIMPs which consisted of the N-terminal domain only. The full-length TIMPs inhibited gelatinase A rapidly with association constants of 3.2 x 10(6) M-1 s-1 for TIMP-1 and 2.1 x 10(7) M-1 s-1 for TIMP-2 at I = 0.2. The C-terminal peptide of TIMP-2 is proposed to exist as an exposed "tail" responsible for binding to progelatinase A and for increasing the rate of inhibition of active gelatinase A through electrostatic interactions with the C-terminal domain of the enzyme. The C-terminal domains of both TIMP-1 and TIMP-2 participate in low-affinity interactions with the C-terminal domain of gelatinase A which increase the rate of association by a factor of about 100 in both cases.

BackgroundDisease activity measurement is a key component of rheumatoid arthritis (RA) management. Biomarkers that capture the complex and heterogeneous biology of RA have the potential to complement clinical disease activity assessment.ObjectivesTo develop a multi-biomarker disease activity (MBDA) test for rheumatoid arthritis.MethodsCandidate serum protein biomarkers were selected from extensive literature screens, bioinformatics databases, mRNA expression and protein microarray data. Quantitative assays were identified and optimized for measuring candidate biomarkers in RA patient sera. Biomarkers with qualifying assays were prioritized in a series of studies based on their correlations to RA clinical disease activity (e.g. the Disease Activity Score 28-C-Reactive Protein [DAS28-CRP], a validated metric commonly used in clinical trials) and their contributions to multivariate models. Prioritized biomarkers were used to train an algorithm to measure disease activity, assessed by correlation to DAS and area under the receiver operating characteristic curve for classification of low vs. moderate/high disease activity. The effect of comorbidities on the MBDA score was evaluated using linear models with adjustment for multiple hypothesis testing.Results130 candidate biomarkers were tested in feasibility studies and 25 were selected for algorithm training. Multi-biomarker statistical models outperformed individual biomarkers at estimating disease activity. Biomarker-based scores were significantly correlated with DAS28-CRP and could discriminate patients with low vs. moderate/high clinical disease activity. Such scores were also able to track changes in DAS28-CRP and were significantly associated with both joint inflammation measured by ultrasound and damage progression measured by radiography. The final MBDA algorithm uses 12 biomarkers to generate an MBDA score between 1 and 100. No significant effects on the MBDA score were found for common comorbidities.ConclusionWe followed a stepwise approach to develop a quantitative serum-based measure of RA disease activity, based on 12-biomarkers, which was consistently associated with clinical disease activity levels.

Glioblastoma multiforme is the most aggressive malignant tumor of the central nervous system. Due to the absence of effective pharmacological and surgical treatments, the identification of early diagnostic and prognostic biomarkers is of key importance to improve the survival rate of patients and to develop new personalized treatments. On these bases, the aim of this review article is to summarize the current knowledge regarding the application of molecular biology and proteomics techniques for the identification of novel biomarkers through the analysis of different biological samples obtained from glioblastoma patients, including DNA, microRNAs, proteins, small molecules, circulating tumor cells, extracellular vesicles, etc. Both benefits and pitfalls of molecular biology and proteomics analyses are discussed, including the different mass spectrometry-based analytical techniques, highlighting how these investigation strategies are powerful tools to study the biology of glioblastoma, as well as to develop advanced methods for the management of this pathology.

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