Anterograde trafficking of KCa3.1 in polarized epithelia is Rab1- And Rab8-Dependent and recycling endosome-independent

The intermediate conductance, Ca2+-activated K+ channel (KCa3.1) targets to the basolateral (BL) membrane in polarized epithelia where it plays a key role in transepithelial ion transport. However, there are no studies defining the anterograde and retrograde trafficking of KCa3.1 in polarized epithelia. Herein, we utilize Biotin Ligase Acceptor Peptide (BLAP)-tagged KCa3.1 to address these trafficking steps in polarized epithelia, using MDCK, Caco-2 and FRT cells. We demonstrate that KCa3.1 is exclusively targeted to the BL membrane in these cells when grown on filter supports. Following endocytosis, KCa3.1 degradation is prevented by inhibition of lysosomal/proteosomal pathways. Further, the ubiquitylation of KCa3.1 is increased following endocytosis from the BL membrane and PR-619, a deubiquitylase inhibitor, prevents degradation, indicating KCa3.1 is targeted for degradation by ubiquitylation. We demonstrate that KCa3.1 is targeted to the BL membrane in polarized LLC-PK1 cells which lack the m1B subunit of the AP-1 complex, indicating BL targeting of KCa3.1 is independent of μ1B. As Rabs 1, 2, 6 and 8 play roles in ER/Golgi exit and trafficking of proteins to the BL membrane, we evaluated the role of these Rabs in the trafficking of KCa3.1. In the presence of dominant negative Rab1 or Rab8, KCa3.1 cell surface expression was significantly reduced, whereas Rabs 2 and 6 had no effect. We also co-immunoprecipitated KCa3.1 with both Rab1 and Rab8. These results suggest these Rabs are necessary for the anterograde trafficking of KCa3.1. Finally, we determined whether KCa3.1 traffics directly to the BL membrane or through recycling endosomes in MDCK cells. For these studies, we used either recycling endosome ablation or dominant negative RME-1 constructs and determined that KCa3.1 is trafficked directly to the BL membrane rather than via recycling endosomes. These results are the first to describe the anterograde and retrograde trafficking of KCa3.1 in polarized epithelia cells. © 2014 Bertuccio et al

Fit between humanitarian professionals and project requirements: hybrid group decision procedure to reduce uncertainty in decision-making

Choosing the right professional that has to meet indeterminate requirements is a critical aspect in humanitarian development and implementation projects. This paper proposes a hybrid evaluation methodology for some non-governmental organizations enabling them to select the most competent expert who can properly and adequately develop and implement humanitarian projects. This methodology accommodates various stakeholders’ perspectives in satisfying the unique requirements of humanitarian projects that are capable of handling a range of uncertain issues from both stakeholders and project requirements. The criteria weights are calculated using a two-step multi-criteria decision-making method: (1) Fuzzy Analytical Hierarchy Process for the evaluation of the decision maker weights coupled with (2) Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank the alternatives which provide the ability to take into account both quantitative and qualitative evaluations. Sensitivity analysis have been developed and discussed by means of a real case of expert selection problem for a non-profit organisation. The results show that the approach allows a decrease in the uncertainty associated with decision-making, which proves that the approach provides robust solutions in terms of sensitivity analysis

Reconstruction of primary vertices at the ATLAS experiment in Run 1 proton–proton collisions at the LHC

This paper presents the method and performance of primary vertex reconstruction in proton–proton collision data recorded by the ATLAS experiment during Run 1 of the LHC. The studies presented focus on data taken during 2012 at a centre-of-mass energy of √s=8 TeV. The performance has been measured as a function of the number of interactions per bunch crossing over a wide range, from one to seventy. The measurement of the position and size of the luminous region and its use as a constraint to improve the primary vertex resolution are discussed. A longitudinal vertex position resolution of about 30μm is achieved for events with high multiplicity of reconstructed tracks. The transverse position resolution is better than 20μm and is dominated by the precision on the size of the luminous region. An analytical model is proposed to describe the primary vertex reconstruction efficiency as a function of the number of interactions per bunch crossing and of the longitudinal size of the luminous region. Agreement between the data and the predictions of this model is better than 3% up to seventy interactions per bunch crossing

Mouse antibody of IgM class is prone to non-enzymatic cleavage between CH1 and CH2 domains

Abstract IgM is a multivalent antibody which evolved as a first line defense of adaptive immunity. It consists of heavy and light chains assembled into a complex oligomer. In mouse serum there are two forms of IgM, a full-length and a truncated one. The latter contains μ’ chain, which lacks a variable region. Although μ’ chain was discovered many years ago, its origin has not yet been elucidated. Our results indicate that μ’ chain is generated from a full-length heavy chain by non-enzymatic cleavage of the protein backbone. The cleavage occurred specifically after Asn209 and is prevented by mutating this residue into any other amino acid. The process requires the presence of other proteins, preferentially with an acidic isoelectric point, and is facilitated by neutral or alkaline pH. This unique characteristic of the investigated phenomenon distinguishes it from other, already described, Asn-dependent protein reactions. A single IgM molecule is able to bind up to 12 epitopes via its antigen binding fragments (Fabs). The cleavage at Asn209 generates truncated IgM molecules and free Fabs, resulting in a reduced IgM valence and probably affecting IgM functionality in vivo

Serum S100A6 Concentration Predicts Peritoneal Tumor Burden in Mice with Epithelial Ovarian Cancer and Is Associated with Advanced Stage in Patients

BACKGROUND:Ovarian cancer is the 5th leading cause of cancer related deaths in women. Five-year survival rates for early stage disease are greater than 94%, however most women are diagnosed in advanced stage with 5 year survival less than 28%. Improved means for early detection and reliable patient monitoring are needed to increase survival. METHODOLOGY AND PRINCIPAL FINDINGS:Applying mass spectrometry-based proteomics, we sought to elucidate an unanswered biomarker research question regarding ability to determine tumor burden detectable by an ovarian cancer biomarker protein emanating directly from the tumor cells. Since aggressive serous epithelial ovarian cancers account for most mortality, a xenograft model using human SKOV-3 serous ovarian cancer cells was established to model progression to disseminated carcinomatosis. Using a method for low molecular weight protein enrichment, followed by liquid chromatography and mass spectrometry analysis, a human-specific peptide sequence of S100A6 was identified in sera from mice with advanced-stage experimental ovarian carcinoma. S100A6 expression was documented in cancer xenografts as well as from ovarian cancer patient tissues. Longitudinal study revealed that serum S100A6 concentration is directly related to tumor burden predictions from an inverse regression calibration analysis of data obtained from a detergent-supplemented antigen capture immunoassay and whole-animal bioluminescent optical imaging. The result from the animal model was confirmed in human clinical material as S100A6 was found to be significantly elevated in the sera from women with advanced stage ovarian cancer compared to those with early stage disease. CONCLUSIONS:S100A6 is expressed in ovarian and other cancer tissues, but has not been documented previously in ovarian cancer disease sera. S100A6 is found in serum in concentrations that correlate with experimental tumor burden and with clinical disease stage. The data signify that S100A6 may prove useful in detecting and/or monitoring ovarian cancer, when used in concert with other biomarkers

Comparison of predictive modeling approaches for 30-day all-cause non-elective readmission risk

Abstract Background This paper explores the importance of electronic medical records (EMR) for predicting 30-day all-cause non-elective readmission risk of patients and presents a comparison of prediction performance of commonly used methods. Methods The data are extracted from eight Advocate Health Care hospitals. Index admissions are excluded from the cohort if they are observation, inpatient admissions for psychiatry, skilled nursing, hospice, rehabilitation, maternal and newborn visits, or if the patient expires during the index admission. Data are randomly and repeatedly divided into fitting and validating sets for cross validations. Approaches including LACE, STEPWISE logistic, LASSO logistic, and AdaBoost, are compared with sample sizes varying from 2,500 to 80,000. Results Our results confirm that LACE has moderate discrimination power with the area under receiver operating characteristic curve (AUC) around 0.65-0.66, which can be improved to 0.73-0.74 when additional variables from EMR are considered. These variables include Inpatient in the last six months, Number of emergency room visits or inpatients in the last year, Braden score, Polypharmacy, Employment status, Discharge disposition, Albumin level, and medical condition variables such as Leukemia, Malignancy, Renal failure with hemodialysis, History of alcohol substance abuse, Dementia and Trauma. When sample size is small (≤5000), LASSO is the best; when sample size is large (≥20,000), the predictive performance is similar. The STEPWISE method has a slightly lower AUC (0.734) comparing to LASSO (0.737) and AdaBoost (0.737). More than one half of the selected predictors can be false positives when using a single method and a single division of fitting/validating data. Conclusions True predictors can be identified by repeatedly dividing data into fitting/validating subsets and referring the final model based on summarizing results. LASSO is a better alternative to the STEPWISE logistic regression, especially when sample size is not large. The evidence for adequate sample size can be explored by fitting models on gradually reduced samples. Our model comparison strategy is not only good for 30-day all-cause non-elective readmission risk predictions, but also applicable to other types of predictive models in clinical studies

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe