Serum SmD autoantibody proteomes are clonally restricted and share variable-region peptides

This article is under embargo for 12 months from the date of publication [Publication date: 7 Jan 2015] in accordance with publisher copyright policy.Recent advances in mass spectrometry-based proteomic methods have allowed variable (V)- region peptide signatures to be derived from human autoantibodies present in complex serum mixtures. Here, we analysed the clonality and V-region composition of immunoglobulin (Ig) proteomes specific for the immunodominant SmD protein subunit of the lupus-specific Sm autoantigen. Precipitating SmD-specific IgGs were eluted from native SmD-coated ELISA plates preincubated with sera from six patients with systemic lupus erythematosus (SLE) positive for anti-Sm/RNP. Heavy (H)- and light (L)-chain clonality and V-region sequences were analysed by 2-dimensional gel electrophoresis and combined de novo database mass spectrometric sequencing. SmD autoantibody proteomes from all six patients with SLE expressed IgG1 kappa restricted clonotypes specified by IGHV3-7 and IGHV1-69 H-chains and IGKV3-20 and IGKV2-28 L-chains, with shared and individual V-region amino acid replacement mutations. Clonotypic sharing and restricted V-region diversity of systemic autoimmunity can now be extended from the Ro/La cluster to Sm autoantigen and implies a common pathway of anti-Sm autoantibody production in unrelated patients with SLE

Normative perspectives in manufacturing

This research proposes a normative approach in two infrastructural manufacturing decisions: quality and product development. Quality, as employed in the research, is defined as satisfying customer needs given a market segment. Product defects lead to customer dissatisfaction and have a negative economic impact. These defects can be due to a manufacturing process variation or an ill-advised product design. The former cause of defects is eliminated using Six Sigma. Six Sigma has spread widely due to its successful implementation by GE, Motorola, and other companies. However, there is no notion of using a normative approach in Six Sigma. The second source of product defects is errors in product design that can be reduced by conducting early testing in the product development phase. The normative approach in this research consists of three decision bases: (1) alternatives, (2) preferences, and (3) information. This research raises the questions: when is improving quality of the product worthwhile, and is ???process perfection??? a wise economic objective? This research builds a decision analytic model for the decision to incorporate a Six Sigma quality process. The objective is to evaluate the economic impact of Six Sigma based on a rigorous approach, given that previous research revolved around best practice. The model employs quality as a binary outcome (good or bad) in order to examine the effects of some key elements regarding the Six Sigma decision. These elements include implementation cost, firm size, defect costs, and defects??? opportunities (number of production or service stages). The optimal solution reveals several managerial insights regarding the impact of the various factors related to the Six Sigma decision. Naturally, implementation cost makes Six Sigma less attractive, while attitude toward risk plays a role in determining the optimal sigma (quality) level. In addition, the best decision alternative for small- and middle-sized firms is not necessarily to have the highest quality standard or the highest sigma level. In some instances it may be more important to consider other profit-generating alternatives before taking the Six Sigma route. The market characteristic (demand ???price relation) and competitors??? performance and price provide significant impact with regard to the economical value of Six Sigma. The firm values its process perfection more when the competitor is inferior in price and performance. Moreover, it is not necessary to improve every manufacturing process to achieve economical benefits. Timing and frequency of reliability tests during the product development phase is essential for eliminating design errors. Testing is modeled as an activity that generates information about technical errors and problems related to customer needs that will require redesign. Optimal testing strategies (number and timing of tests) must balance the tradeoff among several variables, including the cost of a test, the increasing cost of redesign when discovered at a later stage, and the relationship between sequential tests. This research investigated two testing environments: deterministic and stochastic. Optimal strategies in both domains are obtained. The strategy reveals several managerial insights. The design team should run more tests when the mathematical relationship between the redesign cost and the accumulated error is linear when compared to concave or convex relationships. Attitude toward risk has a major role regarding the optimal number of tests done within a stochastic domain. The nature of redesign cost has the most effect on risk aversion when compared to neutral or risk seeking behaviors. Moreover, the team ought to run more tests when the redesign cost is a function of time elapsed between tests, compared solely to clock time. Learning by doing increases the optimal number of tests

Malformin-A1 (MA1) Sensitizes Chemoresistant Ovarian Cancer Cells to Cisplatin-Induced Apoptosis

High-grade epithelial ovarian cancer is a fatal disease in women frequently associated with drug resistance and poor outcomes. We previously demonstrated that a marine-derived compound MalforminA1 (MA1) was cytotoxic for the breast cancer cell line MCF-7. In this study, we aimed to examine the effect of MA1 on human ovarian cancer cells. The potential cytotoxicity of MA1was tested on cisplatin-sensitive (A2780S) and cisplatin-resistant (A2780CP) ovarian cancer cell lines using AlamarBlue assay, Hoechst dye, flow cytometry, Western blot, and RT-qPCR. MA1 had higher cytotoxic activity on A2780S (IC50 = 0.23 µM) and A2780CP (IC50 = 0.34 µM) cell lines when compared to cisplatin (IC50 = 31.4 µM and 76.9 µM, respectively). Flow cytometry analysis confirmed the cytotoxic effect of MA1. The synergistic effect of the two drugs was obvious, since only 13% of A2780S and 7% of A2780CP cells remained alive after 24 h of treatment with both MA1 and cisplatin. Moreover, we examined the expression of bcl2, p53, caspase3/9 genes at RNA and protein levels using RT-qPCR and Western blot, respectively, to figure out the cell death mechanism induced by MA1. A significant down-regulation in bcl2 and p53 genes was observed in treated cells compared to non-treated cells (p < 0.05), suggesting that MA1 may not follow the canonical pathway to induce apoptosis in ovarian cancer cell lines. MalforminA1 showed promising anticancer activity by inducing cytotoxicity in cisplatin-sensitive and cisplatin-resistant cancer cell lines. Interestingly, a synergistic effect was observed when MA1 was combined with cisplatin, leading to it overcoming its resistance to cisplatin