The Dimerization Domain in DapE Enzymes Is Required for Catalysis

The emergence of antibiotic-resistant bacterial strains underscores the importance of identifying new drug targets and developing new antimicrobial compounds. Lysine and meso-diaminopimelic acid are essential for protein production and bacterial peptidoglycan cell wall remodeling and are synthesized in bacteria by enzymes encoded within dap operon. Therefore dap enzymes may serve as excellent targets for developing a new class of antimicrobial agents. The dapE-encoded N-succinyl-L,L-diaminopimelic acid desuccinylase (DapE) converts N-succinyl-L,L-diaminopimelic acid to L,Ldiaminopimelic acid and succinate. The enzyme is composed of catalytic and dimerization domains, and belongs to the M20 peptidase family. To understand the specific role of each domain of the enzyme we engineered dimerization domain deletion mutants of DapEs from Haemophilus influenzae and Vibrio cholerae, and characterized these proteins structurally and biochemically. No activity was observed for all deletion mutants. Structural comparisons of wild-type, inactive monomeric DapE enzymes with other M20 peptidases suggest that the dimerization domain is essential for DapE enzymatic activity. Structural analysis and molecular dynamics simulations indicate that removal of the dimerization domain increased the flexibility of a conserved active site loop that may provide critical interactions with the substrate

Reagrupación familiar de menores en Aragón. Propuestas de acompañamiento en el proceso migratorio

La reagrupación familiar es una de las vías de migración legal más frecuentes, también entre los menores de edad, recayendo sobre las administraciones locales la responsabilidad de ofrecer políticas de integración de estos niños y adolescentes. Esta investigación propone la transferencia de programas exitosos en esta tarea. Para ello, aborda el diagnóstico de este proceso migratorio en un territorio, a través de dos vías: el análisis cuantitativo de los registros públicos y la categorización de las necesidades detectadas en los servicios sociales, mediante entrevistas y análisis de expedientes. Los resultados señalan la existencia de tensiones derivadas de factores asociados, por un lado, al proceso migratorio y a la reconfiguración de la estructura familiar; y, por otro, a la organización de los servicios públicos que atienden a las familias. Concluimos que, con el fin de promover un itinerario de integración en la sociedad de acogida, son necesarias estrategias preventivas, articuladas como programas de acompañamiento. Family reunification is one of the most frequent legal migration routes, also among minors. The responsibility of offering integration policies for these children and adolescents falls on local authorities. This research proposes the transfer of successful programs in this task. For this, it addresses the diagnosis of this migratory process in a territory, through two ways: a quantitative analysis of public data and the categorization of needs detected in social services, through interviews and file analysis. The results indicate the existence of tensions derived from factors related, on the one hand, to the migratory process and to the reconfiguration of the family structure; and, on the other, to the organization of public services that serve families. We conclude that, in order to promote an integration itinerary in the host society, preventive strategies are necessary, articulated as accompaniment programs

Pilot study of an online intervention for young people with a first psychotic episode: Thinkapp

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A study on multi-scale kernel optimisation via centered kernel-target alignment

Kernel mapping is one of the most widespread approaches to intrinsically deriving nonlinear classifiers. With the aim of better suiting a given dataset, different kernels have been proposed and different bounds and methodologies have been studied to optimise them. We focus on the optimisation of a multi-scale kernel, where a different width is chosen for each feature. This idea has been barely studied in the literature, although it has been shown to achieve better performance in the presence of heterogeneous attributes. The large number of parameters in multi-scale kernels makes it computationally unaffordable to optimise them by applying traditional cross-validation. Instead, an analytical measure known as centered kernel-target alignment (CKTA) can be used to align the kernel to the so-called ideal kernel matrix. This paper analyses and compares this and other alternatives, providing a review of the literature in kernel optimisation and some insights into the usefulness of multi-scale kernel optimisation via CKTA. When applied to the binary support vector machine paradigm (SVM), the results using 24 datasets show that CKTA with a multi-scale kernel leads to the construction of a well-defined feature space and simpler SVM models, provides an implicit filtering of non-informative features and achieves robust and comparable performance to other methods even when using random initialisations. Finally, we derive some considerations about when a multi-scale approach could be, in general, useful and propose a distance-based initialisation technique for the gradient-ascent method, which shows promising results

Direct medical costs of severe asthma in two colombian reference centers

Objectives: Severe asthma, although infrequent, generates an important clinical and economic burden in both patients and healthcare system. We aimed to describe demographic and clinical characteristics, exacerbations, healthcare resource utilization (HRU), and annual direct medical costs in a severe asthma patient cohort in Colombia. Methods: Cost ofillness study from payer perspective. Patients with clinicianconfirmed severe asthma diagnosis (GINA criteria) from two specialized reference centers between January 2014 and August 2018 were included. The last year within this period under GINA step 4/5 therapy was observed for each patient. Clinical information was extracted from medical records, and HRU from hospital invoices and public price lists. Results: 147 patients were included, 59% female. Mean (6SD) age and time with asthma diagnosis was 46615 and 21617 years, respectively. Most frequent comorbidities were allergic rhinitis (70%), conjunctivitis (27%) and hypertension (19%). Most common sensitization cause was house dust mite (61%). Median baseline blood eosinophil count was 260 cells/ml (range 10-4,040), mean total IgE serum level was 69761,893 IU/ml. The mean annual frequency of HRU was 5.064.0 for laboratory tests, 4.161.2 for medical visits, 1.061.5 for emergency visits, 0.360.7 for hospitalizations, and 0.160.3 for ICU. Omalizumab was prescribed in 42.2% of patients, with a mean among users of 30.2620.3 vials per year. Mean annual direct cost for outpatient care was 4,743.666,331.1 USD (range 256.7-31,286.1) (1 USD=2,956.4 COP); medications were responsible for 98% of costs. Data from 55 hospitalizations was obtained, 4 in ICU. Mean stay and cost per episode were 6.564.9 days and 1,010.561,379.9 USD in general ward, and 14.164.1 days and 3768.963748.2 USD in ICU. Conclusions: Severe asthma is a costly disease for the Colombian health system. Most of the direct outpatient medical costs in this cohort were caused by pharmacological therapy, particularly omalizumab. Funding: GSK (PRJ2813

In vitro antileishmanial activity and iron superoxide dismutase inhibition of arylamine Mannich base derivatives

Leishmaniasis is one of the world’s most neglected diseases, and it has a worldwide prevalence of 12 million. There are no effective human vaccines for its prevention, and treatment is hampered by outdated drugs. Therefore, research aiming at the development of new therapeutic tools to fight Leishmaniasis remains a crucial goal today. With this purpose in mind, we present twenty arylaminoketone derivatives with a very interesting in vitro and in vivo efficacy against Trypanosoma cruzi that have now been studied against promastigote and amastigote forms of L. infantum, L. donovani and L. braziliensis strains. Six out of the twenty Mannich base-type derivatives showed Selectivity Index between 39 and 2337 times higher in the amastigote form than the reference drug glucantime. These six derivatives affected the parasite infectivity rates; the result was lower parasite infectivity rates than glucantime tested at a IC25 dose. In addition, these derivatives were substantially more active against the three Leishmania species tested than glucantime. The mechanism of action of these compounds has been studied, showing a greater alteration in glucose catabolism and leading to greater levels of Fe-SOD (iron superoxide dismutase) inhibition. These molecules could be potential candidates for Leishmaniasis chemotherapy

Transfer of extracellular vesicle-microRNA controls germinal center reaction and antibody production

Intercellular communication orchestrates effective immune responses against disease-causing agents. Extracellular vesicles (EVs) are potent mediators of cell-cell communication. EVs carry bioactive molecules, including microRNAs, which modulate gene expression and function in the recipient cell. Here, we show that formation of cognate primary T-B lymphocyte immune contacts promotes transfer of a very restricted set of T-cell EV-microRNAs (mmu-miR20-a-5p, mmu-miR-25-3p, and mmu-miR-155-3p) to the B cell. Transferred EV-microRNAs target key genes that control B-cell function, including pro-apoptotic BIM and the cell cycle regulator PTEN. EV-microRNAs transferred during T-B cognate interactions also promote survival, proliferation, and antibody class switching. Using mouse chimeras with Rab27KO EV-deficient T cells, we demonstrate that the transfer of small EVs is required for germinal center reaction and antibody production in vivo, revealing a mechanism that controls B-cell responses via the transfer of EV-microRNAs of T-cell origin. These findings also provide mechanistic insight into the Griscelli syndrome, associated with a mutation in the Rab27a gene, and might explain antibody defects observed in this pathogenesis and other immune-related and inflammatory disorders.This manuscript was funded by grants SAF2017-82886-R (FS-M) from the Spanish Ministry of Economy and Competitiveness; CAM (S2017/BMD-3671-INFLAMUNE-CM) from the Comunidad de Madrid (FS-M); CIBERCV (CB16/11/00272), BIOIMID PIE13/041 from the Instituto de Salud Carlos III and from the Fundación La MaratóTV3(grant122/C/2015). The current research has received funding from “la Caixa” Foundation under the project code HR17-00016. VGY is supported by the AECC foundation. A.R.R. is supported by CNIC funding. This project was funded by the Spanish Ministerio de Ciencia, Innovacion y Universidades SAF2016-75511-R, and La Caixa Health Research Program HR17-00247 grant to A.R.R. Grants from Ramón Areces Foundation “Ciencias de la Vida y de la Salud” (XIX Concurso-2018) and from Ayuda Fundación BBVA y Equipo de Investigación Científica (BIOMEDICINA-2018) (to FSM). The CNIC is supported by the Ministerio de Ciencia, Innovacion y Universidades and the Pro-CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505).S

Partial order label decomposition approaches for melanoma diagnosis

Melanoma is a type of cancer that develops from the pigment-containing cells known as melanocytes. Usually occurring on the skin, early detection and diagnosis is strongly related to survival rates. Melanoma recognition is a challenging task that nowadays is performed by well trained dermatologists who may produce varying diagnosis due to the task complexity. This motivates the development of automated diagnosis tools, in spite of the inherent difficulties (intra-class variation, visual similarity between melanoma and non-melanoma lesions, among others). In the present work, we propose a system combining image analysis and machine learning to detect melanoma presence and severity. The severity is assessed in terms of melanoma thickness, which is measured by the Breslow index. Previous works mainly focus on the binary problem of detecting the presence of the melanoma. However, the system proposed in this paper goes a step further by also considering the stage of the lesion in the classification task. To do so, we extract 100 features that consider the shape, colour, pigment network and texture of the benign and malignant lesions. The problem is tackled as a five-class classification problem, where the first class represents benign lesions, and the remaining four classes represent the different stages of the melanoma (via the Breslow index). Based on the problem definition, we identify the learning setting as a partial order problem, in which the patterns belonging to the different melanoma stages present an order relationship, but where there is no order arrangement with respect to the benign lesions. Under this assumption about the class topology, we design several proposals to exploit this structure and improve data preprocessing. In this sense, we experimentally demonstrate that those proposals exploiting the partial order assumption achieve better performance than 12 baseline nominal and ordinal classifiers (including a deep learning model) which do not consider this partial order. To deal with class imbalance, we additionally propose specific over-sampling techniques that consider the structure of the problem for the creation of synthetic patterns. The experimental study is carried out with clinician-curated images from the Interactive Atlas of Dermoscopy, which eases reproducibility of experiments. Concerning the results obtained, in spite of having augmented the complexity of the classification problem with more classes, the performance of our proposals in the binary problem is similar to the one reported in the literature

Library of Seleno-Compounds as Novel Agents against Leishmania Species

The in vitro leishmanicidal activities of a series of 48 recently synthesized selenium derivatives against Leishmania infantum and Leishmania braziliensis parasites were tested using promastigotes and intracellular amastigote forms. The cytotoxicity of the tested compounds for J774.2 macrophage cells was also measured in order to establish their selectivity. Six of the tested compounds (compounds 8, 10, 11, 15, 45, and 48) showed selectivity indexes higher than those of the reference drug, meglumine antimonate (Glucantime), for both Leishmania species; in the case of L. braziliensis, compound 20 was also remarkably selective. Moreover, data on infection rates and amastigote numbers per macrophage showed that compounds 8, 10, 11, 15, 45, and 48 were the most active against both Leishmania species studied. The observed changes in the excretion product profile of parasites treated with these six compounds were also consistent with substantial cytoplasmic alterations. On the other hand, the most active compounds were potent inhibitors of Fe superoxide dismutase (Fe-SOD) in the two parasite species considered, whereas their impact on human CuZn-SOD was low. The high activity, low toxicity, stability, low cost of the starting materials, and straightforward synthesis make these compounds appropriate molecules for the development of affordable antileishmanicidal agents

Enhancing a de novo enzyme activity by computationally-focused ultra-low-throughput screening

Directed evolution has revolutionized protein engineering. Still, enzyme optimization by random library screening remains sluggish, in large part due to futile probing of mutations that are catalytically neutral and/or impair stability and folding. FuncLib is a novel approach which uses phylogenetic analysis and Rosetta design to rank enzyme variants with multiple mutations, on the basis of predicted stability. Here, we use it to target the active site region of a minimalist-designed, de novo Kemp eliminase. The similarity between the Michaelis complex and transition state for the enzymatic reaction makes this system particularly challenging to optimize. Yet, experimental screening of a small number of active-site variants at the top of the predicted stability ranking leads to catalytic efficiencies and turnover numbers ( 2 104 M 1 s 1 and 102 s 1) for this anthropogenic reaction that compare favorably to those of modern natural enzymes. This result illustrates the promise of FuncLib as a powerful tool with which to speed up directed evolution, even on scaffolds that were not originally evolved for those functions, by guiding screening to regions of the sequence space that encode stable and catalytically diverse enzymes. Empirical valence bond calculations reproduce the experimental activation energies for the optimized eliminases to within 2 kcal mol 1 and indicate that the enhanced activity is linked to better geometric preorganization of the active site. This raises the possibility of further enhancing the stabilityguidance of FuncLib by computational predictions of catalytic activity, as a generalized approach for computational enzyme designKnut and Alice Wallenberg Foundation (Wallenberg Academy Fellowship) 2018.0140Human Frontier Science Program RGP0041/2017FEDER Funds/Spanish Ministry of Science, Innovation and Universities BIO2015-66426-R RTI2018-097142-B-100FEDER/Junta de Andalucia - Consejeria de Economia y Conocimiento E.FQM.113.UGR18Swedish National Infrastructure for computing (SNAC) 2018/2-3 2019/2-