Differential Role of Human Choline Kinase α and β Enzymes in Lipid Metabolism: Implications in Cancer Onset and Treatment

11 pages, 6 figures, 1 table.Background The Kennedy pathway generates phosphocoline and phosphoethanolamine through its two branches. Choline Kinase (ChoK) is the first enzyme of the Kennedy branch of synthesis of 1phosphocholine, the major component of the plasma membrane. ChoK family of proteins is composed by ChoKα and ChoKβ isoforms, the first one with two different variants of splicing. Recently ChoKα has been implicated in the carcinogenic process, since it is over-expressed in a variety of human cancers. However, no evidence for a role of ChoKβ in carcinogenesis has been reported. Methodology/Principal Findings Here we compare the in vitro and in vivo properties of ChoKα1 and ChoKβ in lipid metabolism, and their potential role in carcinogenesis. Both ChoKα1 and ChoKβ showed choline and ethanolamine kinase activities when assayed in cell extracts, though with different affinity for their substrates. However, they behave differentially when overexpressed in whole cells. Whereas ChoKβ display an ethanolamine kinase role, ChoKα1 present a dual choline/ethanolamine kinase role, suggesting the involvement of each ChoK isoform in distinct biochemical pathways under in vivo conditions. In addition, while overexpression of ChoKα1 is oncogenic when overexpressed in HEK293T or MDCK cells, ChoKβ overexpression is not sufficient to induce in vitro cell transformation nor in vivo tumor growth. Furthermore, a significant upregulation of ChoKα1 mRNA levels in a panel of breast and lung cancer cell lines was found, but no changes in ChoKβ mRNA levels were observed. Finally, MN58b, a previously described potent inhibitor of ChoK with in vivo antitumoral activity, shows more than 20-fold higher efficiency towards ChoKα1 than ChoKβ. Conclusion/Significance This study represents the first evidence of the distinct metabolic role of ChoKα and ChoKβ isoforms, suggesting different physiological roles and implications in human carcinogenesis. These findings constitute a step forward in the design of an antitumoral strategy based on ChoK inhibition.This work has been supported by grants to JCL from Comunidad de Madrid (GR-SAL-0821-2004), Ministerio de Ciencia e Innovación (SAF2008-03750, RD06/0020/0016), Fundación Mutua Madrileña, and by a grant to ARM from Fundación Mutua Madrileña.Peer reviewe

Optimizing CIGB-300 intralesional delivery in locally advanced cervical cancer

Background:We conducted a phase 1 trial in patients with locally advanced cervical cancer by injecting 0.5 ml of the CK2-antagonist CIGB-300 in two different sites on tumours to assess tumour uptake, safety, pharmacodynamic activity and identify the recommended dose.Methods:Fourteen patients were treated with intralesional injections containing 35 or 70 mg of CIGB-300 in three alternate cycles of three consecutive days each before standard chemoradiotherapy. Tumour uptake was determined using 99 Tc-radiolabelled peptide. In situ B23/nucleophosmin was determined by immunohistochemistry.Results:Maximum tumour uptake for CIGB-300 70-mg dose was significantly higher than the one observed for 35 mg: 16.1±8.9 vs 31.3±12.9 mg (P=0.01). Both, AUC 24h and biological half-life were also significantly higher using 70 mg of CIGB-300 (P<0.001). Unincorporated CIGB-300 diffused rapidly to blood and was mainly distributed towards kidneys, and marginally in liver, lungs, heart and spleen. There was no DLT and moderate allergic-like reactions were the most common systemic side effect with strong correlation between unincorporated CIGB-300 and histamine levels in blood. CIGB-300, 70 mg, downregulated B23/nucleophosmin (P=0.03) in tumour specimens.Conclusion:Intralesional injections of 70 mg CIGB-300 in two sites (0.5 ml per injection) and this treatment plan are recommended to be evaluated in phase 2 studies.Fil: Sarduy, M. R.. Medical-surgical Research Center; CubaFil: García, I.. Centro de Ingeniería Genética y Biotecnología; CubaFil: Coca, M. A.. Clinical Investigation Center; CubaFil: Perera, A.. Clinical Investigation Center; CubaFil: Torres, L. A.. Clinical Investigation Center; CubaFil: Valenzuela, C. M.. Centro de Ingeniería Genética y Biotecnología; CubaFil: Baladrón, I.. Centro de Ingeniería Genética y Biotecnología; CubaFil: Solares, M.. Hospital Materno Ramón González Coro; CubaFil: Reyes, V.. Center For Genetic Engineering And Biotechnology Havana; CubaFil: Hernández, I.. Isotope Center; CubaFil: Perera, Y.. Centro de Ingeniería Genética y Biotecnología; CubaFil: Martínez, Y. M.. Medical-surgical Research Center; CubaFil: Molina, L.. Medical-surgical Research Center; CubaFil: González, Y. M.. Medical-surgical Research Center; CubaFil: Ancízar, J. A.. Centro de Ingeniería Genética y Biotecnología; CubaFil: Prats, A.. Clinical Investigation Center; CubaFil: González, L.. Centro de Ingeniería Genética y Biotecnología; CubaFil: Casacó, C. A.. Clinical Investigation Center; CubaFil: Acevedo, B. E.. Centro de Ingeniería Genética y Biotecnología; CubaFil: López Saura, P. A.. Centro de Ingeniería Genética y Biotecnología; CubaFil: Alonso, Daniel Fernando. Universidad Nacional de Quilmes; ArgentinaFil: Gómez, R.. Elea Laboratories; ArgentinaFil: Perea Rodríguez, S. E.. Center For Genetic Engineering And Biotechnology Havana; Cuba. Centro de Ingeniería Genética y Biotecnología; Cub

Clinical Audits in Outpatient Clinics for Chronic Obstructive Pulmonary Disease: Methodological Considerations and Workflow

Objectives: Previous clinical audits for chronic obstructive pulmonary disease (COPD) have provided valuable information on the clinical care delivered to patients admitted to medical wards because of COPD exacerbations. However, clinical audits of COPD in an outpatient setting are scarce and no methodological guidelines are currently available. Based on our previous experience, herein we describe a clinical audit for COPD patients in specialized outpatient clinics with the overall goal of establishing a potential methodological workflow.Methods: A pilot clinical audit of COPD patients referred to respiratory outpatient clinics in the region of Andalusia, Spain (over 8 million inhabitants), was performed. The audit took place between October 2013 and September 2014, and 10 centers (20% of all public hospitals) were invited to participate. Cases with an established diagnosis of COPD based on risk factors, clinical symptoms, and a post-bronchodilator FEV1/FVC ratio of less than 0.70 were deemed eligible. The usefulness of formally scheduled regular follow-up visits was assessed. Two different databases (resources and clinical database) were constructed. Assessments were planned over a year divided by 4 three-month periods, with the goal of determining seasonal-related changes. Exacerbations and survival served as the main endpoints.Conclusions: This paper describes a methodological framework for conducting a clinical audit of COPD patients in an outpatient setting. Results from such audits can guide health information systems development and implementation in real-world settings.This study was financially supported by an unrestricted grant from Laboratorios Menarini, SA (Barcelona, Spain)

Selecting cash management models from a multiobjective perspective

[EN] This paper addresses the problem of selecting cash management models under different operating conditions from a multiobjective perspective considering not only cost but also risk. A number of models have been proposed to optimize corporate cash management policies. The impact on model performance of different operating conditions becomes an important issue. Here, we provide a range of visual and quantitative tools imported from Receiver Operating Characteristic (ROC) analysis. More precisely, we show the utility of ROC analysis from a triple perspective as a tool for: (1) showing model performance; (2) choosingmodels; and (3) assessing the impact of operating conditions on model performance. We illustrate the selection of cash management models by means of a numerical example.Work partially funded by projects Collectiveware TIN2015-66863-C2-1-R (MINECO/FEDER) and 2014 SGR 118.Salas-Molina, F.; Rodríguez-Aguilar, JA.; Díaz-García, P. (2018). Selecting cash management models from a multiobjective perspective. Annals of Operations Research. 261(1-2):275-288. https://doi.org/10.1007/s10479-017-2634-9S2752882611-2Ballestero, E. (2007). Compromise programming: A utility-based linear-quadratic composite metric from the trade-off between achievement and balanced (non-corner) solutions. European Journal of Operational Research, 182(3), 1369–1382.Ballestero, E., & Romero, C. (1998). Multiple criteria decision making and its applications to economic problems. Berlin: Springer.Bi, J., & Bennett, K. P. (2003). Regression error characteristic curves. In Proceedings of the 20th international conference on machine learning (ICML-03), pp. 43–50.Bradley, A. P. (1997). The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145–1159.da Costa Moraes, M. B., Nagano, M. S., & Sobreiro, V. A. (2015). Stochastic cash flow management models: A literature review since the 1980s. In Decision models in engineering and management (pp. 11–28). New York: Springer.Doumpos, M., & Zopounidis, C. (2007). Model combination for credit risk assessment: A stacked generalization approach. Annals of Operations Research, 151(1), 289–306.Drummond, C., & Holte, R. C. (2000). Explicitly representing expected cost: An alternative to roc representation. In Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 98–207). New York: ACM.Drummond, C., & Holte, R. C. (2006). Cost curves: An improved method for visualizing classifier performance. Machine Learning, 65(1), 95–130.Elkan, C. (2001). The foundations of cost-sensitive learning. In International joint conference on artificial intelligence (Vol. 17, pp. 973–978). Lawrence Erlbaum associates Ltd.Fawcett, T. (2006). An introduction to roc analysis. Pattern Recognition Letters, 27(8), 861–874.Flach, P. A. (2003). The geometry of roc space: understanding machine learning metrics through roc isometrics. In Proceedings of the 20th international conference on machine learning (ICML-03), pp. 194–201.Garcia-Bernabeu, A., Benito, A., Bravo, M., & Pla-Santamaria, D. (2016). Photovoltaic power plants: a multicriteria approach to investment decisions and a case study in western spain. Annals of Operations Research, 245(1–2), 163–175.Glasserman, P. (2003). Monte Carlo methods in financial engineering (Vol. 53). New York: Springer.Gregory, G. (1976). Cash flow models: a review. Omega, 4(6), 643–656.Hernández-Orallo, J. (2013). Roc curves for regression. Pattern Recognition, 46(12), 3395–3411.Hernández-Orallo, J., Flach, P., & Ferri, C. (2013). Roc curves in cost space. Machine Learning, 93(1), 71–91.Hernández-Orallo, J., Lachiche, N., & Martınez-Usó, A. (2014). Predictive models for multidimensional data when the resolution context changes. In Workshop on learning over multiple contexts at ECML, volume 2014.Metz, C. E. (1978). Basic principles of roc analysis. In Seminars in nuclear medicine (Vol. 8, pp. 283–298). Amsterdam: Elsevier.Miettinen, K. (2012). Nonlinear multiobjective optimization (Vol. 12). Berlin: Springer.Ringuest, J. L. (2012). Multiobjective optimization: Behavioral and computational considerations. Berlin: Springer.Ross, S. A., Westerfield, R., & Jordan, B. D. (2002). Fundamentals of corporate finance (sixth ed.). New York: McGraw-Hill.Salas-Molina, F., Pla-Santamaria, D., & Rodriguez-Aguilar, J. A. (2016). A multi-objective approach to the cash management problem. Annals of Operations Research, pp. 1–15.Srinivasan, V., & Kim, Y. H. (1986). Deterministic cash flow management: State of the art and research directions. Omega, 14(2), 145–166.Steuer, R. E., Qi, Y., & Hirschberger, M. (2007). Suitable-portfolio investors, nondominated frontier sensitivity, and the effect of multiple objectives on standard portfolio selection. Annals of Operations Research, 152(1), 297–317.Stone, B. K. (1972). The use of forecasts and smoothing in control limit models for cash management. Financial Management, 1(1), 72.Torgo, L. (2005). Regression error characteristic surfaces. In Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining (pp. 697–702). ACM.Yu, P.-L. (1985). Multiple criteria decision making: concepts, techniques and extensions. New York: Plenum Press.Zeleny, M. (1982). Multiple criteria decision making. New York: McGraw-Hill

Genetics of CM-proteins (A-hordeins) in barley

The CM-proteins, which are the main components of the A-hordeins, include four previously described proteins (CMa-1, CMb-1, CMc-1, CMd-1), plus a new one, CMe-1, which has been tentatively included in this group on the basis of its solubility properties and electrophoretic mobility. The variability of the five proteins has been investigated among 38 Hordeum vulgare cultivars and 17 H. spontaneum accessions. Proteins CMa-1, CMc-1 and CMd-1 were invariant within the cultivated species; CMd was also invariant in the wild one. The inheritance of variants CMb-1/CMb-2 and CMe-1/CMe-2,2 was studied in a cross H. spontaneum x H. vulgare. The first two proteins were inherited as codominantly expressed allelic variations of a single mendelian gene. Components CMe-2,2 were jointly inherited and codominantly expressed with respect to CMe-1. Gene CMb and gene(s) CMe were found to be unlinked. The chromosomal locations of genes encoding CM-proteins were investigated using wheat-barley addition lines. Genes CMa and CMc were associated with chromosome 1, and genes CMb and CMd with chromosome 4. These gene locations further support the proposed homoeology of chromosomes 1 and 4 of barley with chromosomes groups 7 and 4 of wheat, respectively. Gene(s) CMe has been assigned to chromosome 3 of barley. The accumulation of protein CMe-1 is totally blocked in the high lysine mutant Riso 1508 and partially so in the high lysine barley Hiproly

The Genomic Signature of Crop-Wild Introgression in Maize

The evolutionary significance of hybridization and subsequent introgression has long been appreciated, but evaluation of the genome-wide effects of these phenomena has only recently become possible. Crop-wild study systems represent ideal opportunities to examine evolution through hybridization. For example, maize and the conspecific wild teosinte Zea mays ssp. mexicana, (hereafter, mexicana) are known to hybridize in the fields of highland Mexico. Despite widespread evidence of gene flow, maize and mexicana maintain distinct morphologies and have done so in sympatry for thousands of years. Neither the genomic extent nor the evolutionary importance of introgression between these taxa is understood. In this study we assessed patterns of genome-wide introgression based on 39,029 single nucleotide polymorphisms genotyped in 189 individuals from nine sympatric maize-mexicana populations and reference allopatric populations. While portions of the maize and mexicana genomes were particularly resistant to introgression (notably near known cross-incompatibility and domestication loci), we detected widespread evidence for introgression in both directions of gene flow. Through further characterization of these regions and preliminary growth chamber experiments, we found evidence suggestive of the incorporation of adaptive mexicana alleles into maize during its expansion to the highlands of central Mexico. In contrast, very little evidence was found for adaptive introgression from maize to mexicana. The methods we have applied here can be replicated widely, and such analyses have the potential to greatly informing our understanding of evolution through introgressive hybridization. Crop species, due to their exceptional genomic resources and frequent histories of spread into sympatry with relatives, should be particularly influential in these studies

Clinical course of sepsis, severe sepsis, and septic shock in a cohort of infected patients from ten Colombian hospitals

ABSTARCT: Sepsis has several clinical stages, and mortality rates are different for each stage. Our goal was to establish the evolution and the determinants of the progression of clinical stages, from infection to septic shock, over the first week, as well as their relationship to 7-day and 28-day mortality