Dataset Information

Combinatorial Analysis of Secretory Immunoglobulin A (sIgA) Expression in Plants.

ABSTRACT: Delivery of secretory immunoglobulin A (sIgA) to mucosal surfaces as a passive immunotherapy agent is a promising strategy to prevent infectious diseases. Recombinant sIgA production in plants requires the co-expression of four transcriptional units encoding the light chain (LC), heavy chain (HC), joining chain (JC) and secretory component (SC). As a way to optimize sIgA production in plants, we tested the combinatorial expression of 16 versions of a human sIgA against the VP8* rotavirus antigen in Nicotiana benthamiana, using the recently developed GoldenBraid multigene assembly system. Each sIgA version was obtained by combining one of the two types of HC (?1 and ?2) with one of the two LC types (k and ?) and linking or not a KDEL peptide to the HC and/or SC. From the analysis of the anti-VP8* activity, it was concluded that those sIgA versions carrying HC?1 and LC? provided the highest yields. Moreover, ER retention significantly increased antibody production, particularly when the KDEL signal was linked to the SC. Maximum expression levels of 32.5 ?g IgA/g fresh weight (FW) were obtained in the best performing combination, with an estimated 33% of it in the form of a secretory complex.

SUBMITTER: Juarez P

PROVIDER: S-EPMC3634489 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Combinatorial Analysis of Secretory Immunoglobulin A (sIgA) Expression in Plants.

Juarez Paloma P Huet-Trujillo Estefania E Sarrion-Perdigones Alejandro A Falconi Erica Elvira EE Granell Antonio A Orzaez Diego D

International journal of molecular sciences 20130318 3

Delivery of secretory immunoglobulin A (sIgA) to mucosal surfaces as a passive immunotherapy agent is a promising strategy to prevent infectious diseases. Recombinant sIgA production in plants requires the co-expression of four transcriptional units encoding the light chain (LC), heavy chain (HC), joining chain (JC) and secretory component (SC). As a way to optimize sIgA production in plants, we tested the combinatorial expression of 16 versions of a human sIgA against the VP8* rotavirus antigen ...[more]

PMID: 23507755

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Project description:Background:With the continuous improvement of the modernization of the Chinese military and the major adjustments made by the state to the recruitment policy, the newly recruited military undergone multiple pressures such as targeted high-intensity military training and environmental changes. The mental health of military has become a crucial factor of improving the fighting capacity effectiveness of the troops. Objectives:To explore occupational stress of young recruits in the Xinjiang plateau environment during their basic military training period and analyze the relationship between occupational stress and secretory immunoglobulin A (sIgA) levels. Methods:Using multistage stratified cluster random sampling, 625 recruits stationed at Xinjiang plateau command in 2014 were enrolled as subjects. Occupational stress was assessed by the Occupational Stress Inventory Revised Edition (OSI-R). sIgA in saliva was quantified by enzyme-linked immunosorbent assay. The resulting data were analyzed using descriptive statistics, nonparametric tests, and correlation analysis. Results:Based on demographic characteristics, occupational stress was higher in the urban group than the rural group, coping ability for stress was greater in individuals who were students before joining the army than nonstudents, occupational stress was higher in smokers than nonsmokers, and coping ability for stress was higher in nonsmokers than in smokers (all P < 0.05). Being an only child, educational level and age were not significantly related to occupational stress scores (P > 0.05). Salivary sIgA level was higher in the high occupational stress group than in the low stress group (P < 0.01). Salivary sIgA was positively correlated with scores on the occupational role and personal strain questionnaires (r s = 0.229, r s = 0.268, P < 0.01). Conclusion:Demographic characteristics influenced occupational stress among young recruits in cold and high-altitude area. Further, there were some relationships between occupational stress and salivary sIgA in young military recruits.

Project description:The potential of applying data analysis tools to microarray data for diagnosis and prognosis is illustrated on the recent breast cancer dataset of van 't Veer and coworkers. We re-examine that dataset using the novel technique of logical analysis of data (LAD), with the double objective of discovering patterns characteristic for cases with good or poor outcome, using them for accurate and justifiable predictions; and deriving novel information about the role of genes, the existence of special classes of cases, and other factors.Data were analyzed using the combinatorics and optimization-based method of LAD, recently shown to provide highly accurate diagnostic and prognostic systems in cardiology, cancer proteomics, hematology, pulmonology, and other disciplines.LAD identified a subset of 17 of the 25,000 genes, capable of fully distinguishing between patients with poor, respectively good prognoses. An extensive list of 'patterns' or 'combinatorial biomarkers' (that is, combinations of genes and limitations on their expression levels) was generated, and 40 patterns were used to create a prognostic system, shown to have 100% and 92.9% weighted accuracy on the training and test sets, respectively. The prognostic system uses fewer genes than other methods, and has similar or better accuracy than those reported in other studies. Out of the 17 genes identified by LAD, three (respectively, five) were shown to play a significant role in determining poor (respectively, good) prognosis. Two new classes of patients (described by similar sets of covering patterns, gene expression ranges, and clinical features) were discovered. As a by-product of the study, it is shown that the training and the test sets of van 't Veer have differing characteristics.The study shows that LAD provides an accurate and fully explanatory prognostic system for breast cancer using genomic data (that is, a system that, in addition to predicting good or poor prognosis, provides an individualized explanation of the reasons for that prognosis for each patient). Moreover, the LAD model provides valuable insights into the roles of individual and combinatorial biomarkers, allows the discovery of new classes of patients, and generates a vast library of biomedical research hypotheses.

Dataset Information

Combinatorial Analysis of Secretory Immunoglobulin A (sIgA) Expression in Plants.

Publications

Combinatorial Analysis of Secretory Immunoglobulin A (sIgA) Expression in Plants.

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