Vomocytosis of live pathogens from macrophages is regulated by the atypical MAP kinase ERK5

Vomocytosis, or non-lytic extrusion, is a poorly understood process through which macrophages release live pathogens that they have failed to kill back into the extracellular environment. Vomocytosis is conserved across vertebrates and occurs with a diverse range of pathogens, but to date the host signaling events that underpin expulsion remain entirely unknown. Here we use a targeted inhibitor screen to identify the MAP-kinase ERK5 as a critical suppressor of vomocytosis. Pharmacological inhibition or genetic manipulation of ERK5 activity significantly raises vomocytosis rates in human macrophages whilst stimulation of the ERK5 signaling pathway inhibits vomocytosis. Lastly, using a zebrafish model of cryptococcal disease, we show that reducing ERK5 activity in vivo stimulates vomocytosis and results in reduced dissemination of infection. ERK5 therefore represents the first host regulator of vomocytosis to be identified and a potential target for the future development of vomocytosis-modulating therapies

A comparison of machine learning techniques for survival prediction in breast cancer

<p>Abstract</p> <p>Background</p> <p>The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many "gene expression signatures" have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established <it>70-gene signature</it>.</p> <p>Results</p> <p>We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptrons and Random Forests in classifying patients from the NKI breast cancer dataset, and comparably to the scoring-based method originally proposed by the authors of the 70-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection.</p> <p>Conclusions</p> <p>Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data.</p

Bacterial size matters:Multiple mechanisms controlling septum cleavage and diplococcus formation are critical for the virulence of the opportunistic pathogen Enterococcus faecalis

Enterococcus faecalis is an opportunistic pathogen frequently isolated in clinical settings. This organism is intrinsically resistant to several clinically relevant antibiotics and can transfer resistance to other pathogens. Although E. faecalis has emerged as a major nosocomial pathogen, the mechanisms underlying the virulence of this organism remain elusive. We studied the regulation of daughter cell separation during growth and explored the impact of this process on pathogenesis. We demonstrate that the activity of the AtlA peptidoglycan hydrolase, an enzyme dedicated to septum cleavage, is controlled by several mechanisms, including glycosylation and recognition of the peptidoglycan substrate. We show that the long cell chains of E. faecalis mutants are more susceptible to phagocytosis and are no longer able to cause lethality in the zebrafish model of infection. Altogether, this work indicates that control of cell separation during division underpins the pathogenesis of E. faecalis infections and represents a novel enterococcal virulence factor. We propose that inhibition of septum cleavage during division represents an attractive therapeutic strategy to control infections

Genetic programming for knowledge discovery in chest pain diagnosis

This work aims at discovering classification rules for diagnosing certain pathologies. These rules are capable of discriminating among 12 different pathologies, whose main symptom is chest pain. In order to discover these rules we have used genetic programming as well as some concepts of data mining, with emphasis on the discovery of comprehensible knowledge. The fitness function used combines a measure of rule comprehensibility with two usual indicators in medical domain: sensitivity and specificity. Results regarding the predictive accuracy of the discovered rule set as a whole and the predictive accuracy of individual rules are presented and compared to other approaches

A constrained-syntax genetic programming system for discovering classification rules: application to medical data sets

This paper proposes a new constrained-syntax genetic programming (GP) algorithm for discovering classification rules in medical data sets. The proposed GP contains several syntactic constraints to be enforced by the system using a disjunctive normal form representation, so that individuals represent valid rule sets that are easy to interpret. The GP is compared with C4.5, a well-known decision-tree-building algorithm, and with another GP that uses Boolean inputs (BGP), in five medical data sets: chest pain, Ljubljana breast cancer, dermatology, Wisconsin breast cancer, and pediatric adrenocortical tumor. For this last data set a new preprocessing step was devised for survival prediction. Computational experiments show that, overall, the GP algorithm obtained good results with respect to predictive accuracy and rule comprehensibility, by comparison with C4.5 and BGP

Data Mining with Constrained-syntax Genetic Programming: Applications in Medical Data Sets

This work is intended to discover classification rules for diagnosing certain pathologies. In order to discover these rules we have developed a new constrained-syntax genetic programming algorithm based on some concepts of data mining, particularly with emphasis on the discovery of comprehensible knowledge. We compare the performance of the proposed GP algorithm with a genetic algorithm and with the very well-known decision-tree algorithm C4.5. Keywords: Genetic Programming, Data Mining, Classification, Constrained-Syntax Genetic Programmin