Proteomic profiling of Burkholderia cenocepacia clonal isolates with different virulence potential retrieved from a cystic fibrosis patient during chronic lung infection

Respiratory infections with Burkholderia cepacia complex (Bcc) bacteria in cystic fibrosis (CF) are associated with a worse prognosis and increased risk of death. In this work, we assessed the virulence potential of three B. cenocepacia clonal isolates obtained from a CF patient between the onset of infection (isolate IST439) and before death with cepacia syndrome 3.5 years later (isolate IST4113 followed by IST4134), based on their ability to invade epithelial cells and compromise epithelial monolayer integrity. The two clonal isolates retrieved during late-stage disease were significantly more virulent than IST439. Proteomic profiling by 2-D DIGE of the last isolate recovered before the patient's death, IST4134, and clonal isolate IST439, was performed and compared with a prior analysis of IST4113 vs. IST439. The cytoplasmic and membrane-associated enriched fractions were examined and 52 proteins were found to be similarly altered in the two last isolates compared with IST439. These proteins are involved in metabolic functions, nucleotide synthesis, translation and protein folding, cell envelope biogenesis and iron homeostasis. Results are suggestive of the important role played by metabolic reprogramming in the virulence potential and persistence of B. cenocepacia, in particular regarding bacterial adaptation to microaerophilic conditions. Also, the content of the virulence determinant AidA was higher in the last 2 isolates. Significant levels of siderophores were found to be secreted by the three clonal isolates in an iron-depleted environment, but the two late isolates were more tolerant to low iron concentrations than IST439, consistent with the relative abundance of proteins involved in iron uptake.This work was supported by FEDER and FCT – Fundação para a Ciência e a Tecnologia (contract PEst-OE/EQB/LA0023/2011_ research line: Systems and Synthetic Biology; PhD grant to A.M. – SFRH/BD/37012/2007, and PD grants to S.S. – SFRH/BPD/75483/2010 and C.C. – SFRH/BPD/ 81220/2011. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Human pancreatic islet transplantation: an update and description of the establishment of a pancreatic islet isolation laboratory

Type 1 diabetes mellitus (T1DM) is associated with chronic complications that lead to high morbidity and mortality rates in young adults of productive age. Intensive insulin therapy has been able to reduce the likelihood of the development of chronic diabetes complications. However, this treatment is still associated with an increased incidence of hypoglycemia. In patients with "brittle T1DM", who have severe hypoglycemia without adrenergic symptoms (hypoglycemia unawareness), islet transplantation may be a therapeutic option to restore both insulin secretion and hypoglycemic perception. The Edmonton group demonstrated that most patients who received islet infusions from more than one donor and were treated with steroid-free immunosuppressive drugs displayed a considerable decline in the initial insulin independence rates at eight years following the transplantation, but showed permanent C-peptide secretion, which facilitated glycemic control and protected patients against hypoglycemic episodes. Recently, data published by the Collaborative Islet Transplant Registry (CITR) has revealed that approximately 50% of the patients who undergo islet transplantation are insulin independent after a 3-year follow-up. Therefore, islet transplantation is able to successfully decrease plasma glucose and HbA1c levels, the occurrence of severe hypoglycemia, and improve patient quality of life. The goal of this paper was to review the human islet isolation and transplantation processes, and to describe the establishment of a human islet isolation laboratory at the Endocrine Division of the Hospital de Clínicas de Porto Alegre - Rio Grande do Sul, Brazil

Marked isotopic variability within and between the Amazon River and marine dissolved black carbon pools

Riverine dissolved organic carbon (DOC) contains charcoal byproducts, termed black carbon (BC). To determine the significance of BC as a sink of atmospheric CO2 and reconcile budgets, the sources and fate of this large, slow-cycling and elusive carbon pool must be constrained. The Amazon River is a significant part of global BC cycling because it exports an order of magnitude more DOC, and thus dissolved BC (DBC), than any other river. We report spatially resolved DBC quantity and radiocarbon (Δ14C) measurements, paired with molecular-level characterization of dissolved organic matter from the Amazon River and tributaries during low discharge. The proportion of BC-like polycyclic aromatic structures decreases downstream, but marked spatial variability in abundance and Δ14C values of DBC molecular markers imply dynamic sources and cycling in a manner that is incongruent with bulk DOC. We estimate a flux from the Amazon River of 1.9–2.7 Tg DBC yr−1 that is composed of predominately young DBC, suggesting that loss processes of modern DBC are important

An extensive experimental evaluation of automated machine learning methods for recommending classification algorithms

This paper presents an experimental comparison among four automated machine learning (AutoML) methods for recommending the best classification algorithm for a given input dataset. Three of these methods are based on evolutionary algorithms (EAs), and the other is Auto-WEKA, a well-known AutoML method based on the combined algorithm selection and hyper-parameter optimisation (CASH) approach. The EA-based methods build classification algorithms from a single machine learning paradigm: either decision-tree induction, rule induction, or Bayesian network classification. Auto-WEKA combines algorithm selection and hyper-parameter optimisation to recommend classification algorithms from multiple paradigms. We performed controlled experiments where these four AutoML methods were given the same runtime limit for different values of this limit. In general, the difference in predictive accuracy of the three best AutoML methods was not statistically significant. However, the EA evolving decision-tree induction algorithms has the advantage of producing algorithms that generate interpretable classification models and that are more scalable to large datasets, by comparison with many algorithms from other learning paradigms that can be recommended by Auto-WEKA. We also observed that Auto-WEKA has shown meta-overfitting, a form of overfitting at the meta-learning level, rather than at the base-learning level