Length of the artificial incubation in red-legged partridge (Alectoris rufa)

La incubación artificial de los huevos es una fase del manejo clave para la viabilidad de las granjas cinegéticas de perdiz roja (Alectoris rufa). Sin embargo, la duración de la incubación artificial y la dispersión de las eclosiones no han sido previamente cuantificadas en esta especie. Con este objetivo se analizaron cuatro ensayos de incubación artificial de huevos de perdiz roja procedentes de tres granjas cinegéticas del sur de España realizados incluyendo variabilidad de factores de manejo de los reproductores y de la incubación. La duración media de la incubación fue de 23,4 días, difiriendo entre ensayos (P = 0,004), con un valor modal de 23 días y finalizando la mayoría de las eclosiones (percentil 95) el día 24,5 de incubación. La eclosión mostró una distribución asimétrica positiva y leptocúrtica, como corresponde al patrón de eclosión de las especies precociales. Las eclosiones, que pueden comenzar el día 21,5 y finalizar el día 26 de incubación, se extendieron en promedio durante cuatro días, periodo mayor que el descrito en la literatura divulgativa probablemente porque en el presente estudio los huevos no estuvieron en contacto entre sí, lo que pudo limitar la sincronía en la eclosión. Los resultados de este estudio son útiles para conocer la distribución de la eclosión en las granjas cinegéticas de perdiz roja, posibilitando la mejora del manejo de los lotes de huevos en la nacedoraThe artificial incubation of the eggs is a key management phase for the feasibility of the red-legged partridge (Alectoris rufa) game farms. However, the length of the artificial incubation and the spreading pattern of the hatching have not been previously quantified in this species. To this end, four trials of artificial incubation of eggs from three red-legged partridge game farms located in southern Spain were analised. The trials included a wide range of variability with regard to management of breeders and incubation process. The average length of the incubation period was 23.4 days, with differences among trials (P = 0,004), showing a modal value of 23 days. Most of the chicks (percentile 95) hatched before 24.5 days of incubation. The distribution of the hatch was leptokurtic and showed positive asymmetry, fitting with the hatching pattern of the precocial species. The hatching, that can start on day 21.5 and finish on day 26 of incubation, were spread over four days on average. This period was longer than that described in the informative literature, probably because in the present study the eggs were not in contact with each other, which could have limited the hatching synchrony. The results of the present study are useful to understand the distribution of hatching in the red-legged game farms, enabling improved management of the batches of eggs in the hatchery

Perturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compounds

[Abstract] Sarcomas are a group of malignant neoplasms of connective tissue with a different etiology than carcinomas. The efforts to discover new drugs with antisarcoma activity have generated large datasets of multiple preclinical assays with different experimental conditions. For instance, the ChEMBL database contains outcomes of 37,919 different antisarcoma assays with 34,955 different chemical compounds. Furthermore, the experimental conditions reported in this dataset include 157 types of biological activity parameters, 36 drug targets, 43 cell lines, and 17 assay organisms. Considering this information, we propose combining perturbation theory (PT) principles with machine learning (ML) to develop a PTML model to predict antisarcoma compounds. PTML models use one function of reference that measures the probability of a drug being active under certain conditions (protein, cell line, organism, etc.). In this paper, we used a linear discriminant analysis and neural network to train and compare PT and non-PT models. All the explored models have an accuracy of 89.19–95.25% for training and 89.22–95.46% in validation sets. PTML-based strategies have similar accuracy but generate simplest models. Therefore, they may become a versatile tool for predicting antisarcoma compounds.Ministerio de Economía y Competitividad; CTQ2016-74881-PMinisterio de Economía y Competitividad; UNLC08-1E-002Ministerio de Economía y Competitividad; UNLC13-13-3503Xunta de Galicia; ED431C 2018/49Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/23Gobierno Vasco; IT1045-16Instituto de Salud Carlos III; PI17/0182

A Multi-Objective Approach for Anti-Osteosarcoma Cancer Agents Discovery through Drug Repurposing

[Abstract] Osteosarcoma is the most common type of primary malignant bone tumor. Although nowadays 5-year survival rates can reach up to 60–70%, acute complications and late effects of osteosarcoma therapy are two of the limiting factors in treatments. We developed a multi-objective algorithm for the repurposing of new anti-osteosarcoma drugs, based on the modeling of molecules with described activity for HOS, MG63, SAOS2, and U2OS cell lines in the ChEMBL database. Several predictive models were obtained for each cell line and those with accuracy greater than 0.8 were integrated into a desirability function for the final multi-objective model. An exhaustive exploration of model combinations was carried out to obtain the best multi-objective model in virtual screening. For the top 1% of the screened list, the final model showed a BEDROC = 0.562, EF = 27.6, and AUC = 0.653. The repositioning was performed on 2218 molecules described in DrugBank. Within the top-ranked drugs, we found: temsirolimus, paclitaxel, sirolimus, everolimus, and cabazitaxel, which are antineoplastic drugs described in clinical trials for cancer in general. Interestingly, we found several broad-spectrum antibiotics and antiretroviral agents. This powerful model predicts several drugs that should be studied in depth to find new chemotherapy regimens and to propose new strategies for osteosarcoma treatment.Universidad de Las Américas (Quito, Ecuador); ENF.RCA.18.01Gobierno Vasco; IT1045-16)-2016–202

Gene Prioritization through Consensus Strategy, Enrichment Methodologies Analysis, and Networking for Osteosarcoma Pathogenesis

[Abstract] Osteosarcoma is the most common subtype of primary bone cancer, affecting mostly adolescents. In recent years, several studies have focused on elucidating the molecular mechanisms of this sarcoma; however, its molecular etiology has still not been determined with precision. Therefore, we applied a consensus strategy with the use of several bioinformatics tools to prioritize genes involved in its pathogenesis. Subsequently, we assessed the physical interactions of the previously selected genes and applied a communality analysis to this protein–protein interaction network. The consensus strategy prioritized a total list of 553 genes. Our enrichment analysis validates several studies that describe the signaling pathways PI3K/AKT and MAPK/ERK as pathogenic. The gene ontology described TP53 as a principal signal transducer that chiefly mediates processes associated with cell cycle and DNA damage response It is interesting to note that the communality analysis clusters several members involved in metastasis events, such as MMP2 and MMP9, and genes associated with DNA repair complexes, like ATM, ATR, CHEK1, and RAD51. In this study, we have identified well-known pathogenic genes for osteosarcoma and prioritized genes that need to be further explored.Instituto Carlos III; PI17/01826Xunta de Galicia; ED431C 2018/49Xunta de Galicia; ED431G/0

Prediction of Breast Cancer Proteins Involved in Immunotherapy, Metastasis, and RNA-Binding Using Molecular Descriptors and Artifcial Neural Networks

[Abstract] Breast cancer (BC) is a heterogeneous disease where genomic alterations, protein expression deregulation, signaling pathway alterations, hormone disruption, ethnicity and environmental determinants are involved. Due to the complexity of BC, the prediction of proteins involved in this disease is a trending topic in drug design. This work is proposing accurate prediction classifer for BC proteins using six sets of protein sequence descriptors and 13 machine-learning methods. After using a univariate feature selection for the mix of fve descriptor families, the best classifer was obtained using multilayer perceptron method (artifcial neural network) and 300 features. The performance of the model is demonstrated by the area under the receiver operating characteristics (AUROC) of 0.980±0.0037, and accuracy of 0.936±0.0056 (3-fold cross-validation). Regarding the prediction of 4,504 cancer-associated proteins using this model, the best ranked cancer immunotherapy proteins related to BC were RPS27, SUPT4H1, CLPSL2, POLR2K, RPL38, AKT3, CDK3, RPS20, RASL11A and UBTD1; the best ranked metastasis driver proteins related to BC were S100A9, DDA1, TXN, PRNP, RPS27, S100A14, S100A7, MAPK1, AGR3 and NDUFA13; and the best ranked RNA-binding proteins related to BC were S100A9, TXN, RPS27L, RPS27, RPS27A, RPL38, MRPL54, PPAN, RPS20 and CSRP1. This powerful model predicts several BC-related proteins that should be deeply studied to fnd new biomarkers and better therapeutic targets. Scripts can be downloaded at https://github.com/muntisa/ neural-networks-for-breast-cancer-proteins.This work was supported by a) Universidad UTE (Ecuador), b) the Collaborative Project in Genomic Data Integration (CICLOGEN) PI17/01826 funded by the Carlos III Health Institute from the Spanish National plan for Scientific and Technical Research and Innovation 2013-2016 and the European Regional Development Funds (FEDER) - “A way to build Europe”; c) the General Directorate of Culture, Education and University Management of Xunta de Galicia ED431D 2017/16 and “Drug Discovery Galician Network” Ref. ED431G/01 and the “Galician Network for Colorectal Cancer Research” (Ref. ED431D 2017/23); d) the Spanish Ministry of Economy and Competitiveness for its support through the funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER) by the European Union; e) the Consolidation and Structuring of Competitive Research Units - Competitive Reference Groups (ED431C 2018/49), funded by the Ministry of Education, University and Vocational Training of the Xunta de Galicia endowed with EU FEDER funds; f) research grants from Ministry of Economy and Competitiveness, MINECO, Spain (FEDER CTQ2016-74881-P), Basque government (IT1045-16), and kind support of Ikerbasque, Basque Foundation for Science; and, g) Sociedad Latinoamericana de Farmacogenómica y Medicina Personalizada (SOLFAGEM)Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431C 2018/49Gobierno Vasco; IT1045-1

OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine

[Abstract] Breast cancer (BC) is the leading cause of cancer-related death among women and the most commonly diagnosed cancer worldwide. Although in recent years large-scale efforts have focused on identifying new therapeutic targets, a better understanding of BC molecular processes is required. Here we focused on elucidating the molecular hallmarks of BC heterogeneity and the oncogenic mutations involved in precision medicine that remains poorly defined. To fill this gap, we established an OncoOmics strategy that consists of analyzing genomic alterations, signaling pathways, protein-protein interactome network, protein expression, dependency maps in cell lines and patient-derived xenografts in 230 previously prioritized genes to reveal essential genes in breast cancer. As results, the OncoOmics BC essential genes were rationally filtered to 140. mRNA up-regulation was the most prevalent genomic alteration. The most altered signaling pathways were associated with basal-like and Her2-enriched molecular subtypes. RAC1, AKT1, CCND1, PIK3CA, ERBB2, CDH1, MAPK14, TP53, MAPK1, SRC, RAC3, BCL2, CTNNB1, EGFR, CDK2, GRB2, MED1 and GATA3 were essential genes in at least three OncoOmics approaches. Drugs with the highest amount of clinical trials in phases 3 and 4 were paclitaxel, docetaxel, trastuzumab, tamoxifen and doxorubicin. Lastly, we collected ~3,500 somatic and germline oncogenic variants associated with 50 essential genes, which in turn had therapeutic connectivity with 73 drugs. In conclusion, the OncoOmics strategy reveals essential genes capable of accelerating the development of targeted therapies for precision oncology.Instituto de Salud Carlos III; PI17/0182

HDL cholesterol efflux capacity in rheumatoid arthritis patients: contributing factors and relationship with subclinical atherosclerosis

Background: Lipid profiles appear to be altered in rheumatoid arthritis (RA) patients because of disease activity and inflammation. Cholesterol efflux capacity (CEC), which is the ability of high-density lipoprotein cholesterol to accept cholesterol from macrophages, has been linked not only to cardiovascular events in the general population but also to being impaired in patients with RA. The aim of this study was to establish whether CEC is related to subclinical carotid atherosclerosis in patients with RA. Methods: We conducted a cross-sectional study that encompassed 401 individuals, including 178 patients with RA and 223 sex-matched control subjects. CEC, using an in vitro assay, lipoprotein serum concentrations, and standard lipid profile, was assessed in patients and control subjects. Carotid intima-media thickness (CIMT) and carotid plaques were assessed in patients with RA. A multivariable analysis was performed to evaluate the relationship of CEC with RA-related data, lipid profile, and subclinical carotid atherosclerosis. Results: Mean (SD) CEC was not significantly different between patients with RA (18.9 ± 9.0%) and control subjects (16.9 ± 10.4%) (p = 0.11). Patients with RA with low (? coefficient ?5.2 [?10.0 to 0.3]%, p = 0.039) and moderate disease activity (? coefficient ?4.6 [?8.5 to 0.7]%, p = 0.020) were associated with lower levels of CEC than patients in remission. Although no association with CIMT was found, higher CEC was independently associated with a lower risk for the presence of carotid plaque in patients with RA (odds ratio 0.94 [95% CI 0.89?0.98], p = 0.015). Conclusions: CEC is independently associated with carotid plaque in patients with RA

Influence of elevated-CRP level-related polymorphisms in non-rheumatic Caucasians on the risk of subclinical atherosclerosis and cardiovascular disease in rheumatoid arthritis

Association between elevated C-reactive protein (CRP) serum levels and subclinical atherosclerosis and cardiovascular (CV) events was described in rheumatoid arthritis (RA). CRP, HNF1A, LEPR, GCKR, NLRP3, IL1F10, PPP1R3B, ASCL1, HNF4A and SALL1 exert an influence on elevated CRP serum levels in non-rheumatic Caucasians. Consequently, we evaluated the potential role of these genes in the development of CV events and subclinical atherosclerosis in RA patients. Three tag CRP polymorphisms and HNF1A, LEPR, GCKR, NLRP3, IL1F10, PPP1R3B, ASCL1, HNF4A and SALL1 were genotyped in 2,313 Spanish patients by TaqMan. Subclinical atherosclerosis was determined in 1,298 of them by carotid ultrasonography (by assessment of carotid intima-media thickness-cIMT-and presence/absence of carotid plaques). CRP serum levels at diagnosis and at the time of carotid ultrasonography were measured in 1,662 and 1,193 patients, respectively, by immunoturbidimetry. Interestingly, a relationship between CRP and CRP serum levels at diagnosis and at the time of the carotid ultrasonography was disclosed. However, no statistically significant differences were found when CRP, HNF1A, LEPR, GCKR, NLRP3, IL1F10, PPP1R3B, ASCL1, HNF4A and SALL1 were evaluated according to the presence/absence of CV events, carotid plaques and cIMT after adjustment. Our results do not confirm an association between these genes and CV disease in RA

Las redes como dispositivos para la gestión educativa en Iberoamérica

La presente aportación se focaliza en cómo el trabajo en red puede ayudar a mejorar la organización y gestión de los centros educativos. Presenta referentes normativos pero también identifica redes centradas en la gestión y pone ejemplos de alguna de ellas. Recoge las aportaciones de 36 especialistas, que han elaborado los informes de 11 países iberoamericanos