36 research outputs found

    Recomendaciones de la Sociedad Argentina de Reumatología en el manejo de la arteritis de células gigantes

    Get PDF
    La arteritis de células gigantes (ACG) es una vasculitis sistémica que afecta a personas adultas; compromete vasos arteriales de mediano y gran calibre, con potenciales complicaciones de gravedad, como la ceguera, y es considerada una emergencia médica. El objetivo de estas guías fue desarrollar las primeras recomendaciones argentinas para su tratamiento, basadas en la revisión de la literatura mediante metodología GRADE. Un panel de expertos en vasculitis elaboró las preguntas en formato PICO (población, intervención, comparador y outcomes), y luego un panel de expertos en metodología efectuó la revisión de la bibliografía con la extracción de la evidencia para cada una de las preguntas. Se realizó un focus group de pacientes para conocer sus preferencias y experiencias. Finalmente, con la información recabada, el panel de expertos en vasculitis procedió a la votación de las recomendaciones que a continuación se presentan

    Recomendaciones de la Sociedad Argentina de ReumatologĂ­a para el tratamiento de las vasculitis asociadas a ANCA

    Get PDF
    Las vasculitis asociadas a ANCA representan un grupo de enfermedades autoinmunes, multisistémicas, que afectan principalmente a los vasos de pequeño calibre, pudiendo comprometer el tracto respiratorio superior e inferior, el aparato otorrinolaringológico, riñón y piel, aunque eventualmente cualquier órgano puede estar involucrado. Son enfermedades con potencial y severo compromiso de órganos y elevada morbimortalidad. El objetivo de estas guías fue desarrollar las primeras recomendaciones argentinas para su tratamiento, basadas en la revisión de la literatura mediante metodología GRADE. Un panel de expertos en vasculitis elaboró las preguntas en formato PICO (población, intervención, comparador y outcomes), y luego un panel de expertos en metodología efectuó la revisión de la bibliografía con la extracción de la evidencia para cada una de las preguntas. Se realizó un focus group de pacientes para conocer sus preferencias y experiencias. Finalmente, con la información recabada, el panel de expertos en vasculitis procedió a la votación de las recomendaciones que a continuación se presentan

    GuĂ­as Argentinas de Vasculitis

    Get PDF
    La arteritis de células gigantes (ACG) es una vasculitis sistémica que afecta a personas adultas; compromete vasos arteriales de mediano y gran calibre, con potenciales complicaciones de gravedad, como la ceguera, y es considerada una emergencia médica. El objetivo de estas guías fue desarrollar las primeras recomendaciones argentinas para su tratamiento, basadas en la revisión de la literatura mediante metodología GRADE. Un panel de expertos en vasculitis elaboró las preguntas en formato PICO (población, intervención, comparador y outcomes), y luego un panel de expertos en metodología efectuó la revisión de la bibliografía con la extracción de la evidencia para cada una de las preguntas. Se realizó un focus group de pacientes para conocer sus preferencias y experiencias. Finalmente, con la información recabada, el panel de expertos en vasculitis procedió a la votación de las recomendaciones que a continuación se presentan

    Recomendaciones de la Sociedad Argentina de Reumatología en el manejo de la arteritis de células gigantes

    Get PDF
    La arteritis de células gigantes (ACG) es una vasculitis sistémica que afecta a personas adultas; compromete vasos arteriales de mediano y gran calibre, con potenciales complicaciones de gravedad, como la ceguera, y es considerada una emergencia médica. El objetivo de estas guías fue desarrollar las primeras recomendaciones argentinas para su tratamiento, basadas en la revisión de la literatura mediante metodología GRADE. Un panel de expertos en vasculitis elaboró las preguntas en formato PICO (población, intervención, comparador y outcomes), y luego un panel de expertos en metodología efectuó la revisión de la bibliografía con la extracción de la evidencia para cada una de las preguntas. Se realizó un focus group de pacientes para conocer sus preferencias y experiencias. Finalmente, con la información recabada, el panel de expertos en vasculitis procedió a la votación de las recomendaciones que a continuación se presentan

    Inferring the Regulatory Network of the miRNA-mediated Response to Biotic and Abiotic Stress in Melon

    Full text link
    [EN] Background: MiRNAs have emerged as key regulators of stress response in plants, suggesting their potential as candidates for knock-in/out to improve stress tolerance in agricultural crops. Although diverse assays have been performed, systematic and detailed studies of miRNA expression and function during exposure to multiple environments in crops are limited. Results: Here, we present such pioneering analysis in melon plants in response to seven biotic and abiotic stress conditions. Deep-sequencing and computational approaches have identified twenty-four known miRNAs whose expression was significantly altered under at least one stress condition, observing that down-regulation was preponderant. Additionally, miRNA function was characterized by high scale degradome assays and quantitative RNA measurements over the intended target mRNAs, providing mechanistic insight. Clustering analysis provided evidence that eight miRNAs showed a broad response range under the stress conditions analyzed, whereas another eight miRNAs displayed a narrow response range. Transcription factors were predominantly targeted by stressresponsive miRNAs in melon. Furthermore, our results show that the miRNAs that are down-regulated upon stress predominantly have as targets genes that are known to participate in the stress response by the plant, whereas the miRNAs that are up-regulated control genes linked to development. Conclusion: Altogether, this high-resolution analysis of miRNA-target interactions, combining experimental and computational work, Illustrates the close interplay between miRNAs and the response to diverse environmental conditions, in melon.The authors thank Dr. A. Monforte for providing melon seeds and Dra. B. Pico (Cucurbits Group - COMAV) for providing melon seeds and Monosporascus isolate respectively. This work was supported by grants AGL2016-79825-R, BIO2014-61826-EXP (GG), and BFU2015-66894-P (GR) from the Spanish Ministry of Economy and Competitiveness (co-supported by FEDER). The funders had no role in the experiment design, data analysis, decision to publish, or preparation of the manuscript.Sanz-Carbonell, A.; Marques Romero, MC.; Bustamante-GonzĂĄlez, AJ.; Fares Riaño, MA.; Rodrigo Tarrega, G.; Gomez, GG. (2019). Inferring the Regulatory Network of the miRNA-mediated Response to Biotic and Abiotic Stress in Melon. BMC Plant Biology. 1-17. https://doi.org/10.1186/s12870-019-1679-0S117Zhang B. MicroRNAs: a new target for improving plant tolerance to abiotic stress. J Exp Bot. 2015;66:1749–61.Zhu JK. Abiotic stress signaling and responses in plants. Cell. 2016;167:313–24.Bielach A, Hrtyan M, Tognetti VB. Plants under stress: involvement of auxin and Cytokinin. Int J Mol Sci. 2017;4(18):7.Zarattini M, Forlani G. Toward unveiling the mechanisms for transcriptional regulation of proline biosynthesis in the plant cell response to biotic and abiotic stress conditions. Front Plant Sci. 2017;2(8):927.Nolan T, Chen J, Yin Y. Cross-talk of Brassinosteroid signaling in controlling growth and stress responses. Biochem J. 2017;474:2641–61.Mittler R. Abiotic stress, the field environment and stress combinations. Trends Plant Sci. 2006;11:15–9.Djami-Tchatchou AT, Sanan-Mishra N, Ntushelo K, Dubery IA. Functional roles of microRNAs in Agronomically important plants—potential as targets for crop improvement and protection. Front Plant Sci. 2017;8:378.Baxter A, Mittler R, Suzuki N. ROS as key players in plant stress signaling. J Exp Bot. 2014;65:1229–40.Golldack D, Li C, Mohan H, Probst N. Tolerance to drought and salt stress in plants: unraveling the signaling networks. Front Plant Sci. 2014;5:151.Lee SH, Li HW, Koh KW, Chuang HY, Chen YR, Lin CS, Chan MT. MSRB7 reverses oxidation of GSTF2/3 to confer tolerance of Arabidopsis thaliana to oxidative stress. J Exp Bot. 2014;65:5049–62.Carrera J, Rodrigo G, Jaramillo A, Elena SF. Reverse-engineering the Arabidopsis thaliana transcriptional network under changing environmental conditions. Genome Biol. 2009;10(9):R96.Shriram V, Kumar V, Devarumath RM, Khare TS, Wani SH. MicroRNAs as potential targets for abiotic stress tolerance in plants. Front Plant Sci. 2016;7:817.Sunkar R, Chinnusamy V, Zhu J, Zhu JH. Small RNAs as big players in plant abiotic stress responses and nutrient deprivation. Trends Plant Sci. 2007;12:301–9.Kumar R. Role of microRNAs in biotic and abiotic stress responses in crop plants. Appl Biochem Biotechnology. 2014;174:93–115.Reis RS, Eamens AL, Waterhouse PM. Missing pieces in the puzzle of plant MicroRNAs. Trends Plant Sci. 2015;20:721–8.Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116:281–97.Borges F, Martienssen RA. The expanding world of small RNAs in plants. Nat Rev Mol Cell Biol. 2015;16:727–41.Axtell MJ, Bartel DP. Antiquity of microRNAs and their targets in land-plants. Plant Cell. 2005;17:1658–73.Cuperus JT, Fahlgren N, Carrington JC. Evolution and functional diversification of MIRNA genes. Plant Cell. 2011;23:431–42.Cui J, You C, Chen X. The evolution of microRNAs in plants. Current Opinions in Plant Biology. 2016;35:61–7.Sunkar R, Li YF, Jagadeeswaran G. Functions of microRNAs in plant stress responses. Trends Plant Sci. 2012;17:196–203.Zhang T, Zhao YL, Zhao JH, Wang S, Jin Y, Chen ZQ, Fang YY, Hua CL, Ding SW, Guo HS. Cotton plants export microRNAs to inhibit virulence gene expression in a fungal pathogen. Nature Plants. 2016;2(10):16153.Chaloner T, vanKan JA, Grant-Downton R. RNA ‘Information Warfare’ in pathogenic and mutualistic interactions. Trends Plant Sci. 2016;9:738–48.Niu D, Wang Z, Wang S, Qiao L Zhao H. Profiling of small RNAs involved in plant-pathogen interactions. Methods Molecular Biology. 2015;1287:61–79.Wei S, Wang L, Zhang Y, Huang D. Identification of early response genes to salt stress in roots of melon (Cucumis melo L.) seedlings. Molecular Biology Report. 2013;40:2915–26.Clepet C, Joobeur T, Zheng Y, Jublot D, Huang M, Truniger V, et al. Analysis of expressed sequence tags generated from full-length enriched cDNA libraries of melon. BMC Genomics. 2011;12:252.GonzĂĄlez M, Xu M, Esteras C, Roig C, Monforte AJ, Troadec C, et al. Towards a TILLING platform for functional genomics in Piel de Sapo melons. BMC Research Notes. 2011;4:289.GarcĂ­a MJ. The genome of melon (Cucumis melo L.). Proc Natl Acad Sci U S A. 2012;109:11872–7.Pollack FG, Uecker FA. Monosporascus cannonballus: an unusual ascomycete in cantaloupe roots. Mycologia. 1974;66:346–9.Kofalvi S, Marcos J, Cañizares MC, Pallas V, Candresse T. Hop stunt viroid (HSVd) sequence variants from Prunus species: evidence for recombination between HSVd isolates. J Gen Virol. 1997;78:3177–86.Sattar S, Song Y, Anstead J, Sunkar R, Thompson G. Cucumis melo expression profile during aphid herbivory in a resistant and susceptible interaction. Mol Plant-Microbe Interact. 2012;25:839–48.Herranz MC, Navarro JA, Sommen E, Pallas V. Comparative analysis among the small RNA populations of source, sink and conductive tissues in two different plant-virus pathosystems. BMC Genomics. 2015;16:117.Jagadeeswaran G, Nimmakayala P, Zheng Y, Gowdu K, Reddy UK, Sunkar R. Characterization of the small RNA component of leaves and fruits from four different cucurbit species. BMC Genomics. 2012;13:329.Kozomara A, Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2014;42:D68–73.Barciszewska-Pacak M, Milanowska K, Knop K, Bielewicz D, Nuc P, Plewka P, et al. Arabidopsis microRNA expression regulation in a wide range of abiotic stress responses. Front Plant Sci. 2015;6:410.Zhou L, Liu Y, Liu Z, Kong D, Duan M, Luo L. Genome-wide identification and analysis of drought-responsive microRNAs in Oryza sativa. J Exp Bot. 2010;61:4157–68.Samad A, Sajad M, Nazaruddin N, Fauzi I, Murad A, Zainal Z, Ismanizan Ismail I. MicroRNA and transcription factor: key players in plant regulatory network. Front Plant Sci. 2017;8:565.Danisman S. TCP transcription factors at the Interface between environmental challenges and the Plant’s growth responses. Front Plant Sci. 2016;7:1930.Llave C, Xie Z, Kasschau KD, Carrington JC. Cleavage of scarecrow-like mRNA targets directed by a class of Arabidopsis miRNA. Science. 2002;297:2053–6.Gupta OP, Meena NL, Sharma I, et al. Differential regulation of microRNAs in response to osmotic, salt and cold stresses in wheat. Mol Biol Rep. 2014;41:4623.Wang M, Wang Q, Zhang B. 2013. Response of miRNAs and their targets to salt and drought stresses in cotton (Gossypium hirsutum ). Gene 30: 26–32.Savageau MA. Demand theory of gene regulation. I. Quantitative development of the theory. Genetics. 1998;149:1665–76.NegrĂŁo S, Schmöckel SM, Tester M. Evaluating physiological responses of plants to salinity stress. Ann Bot. 2017;119:1–11.Barabasi AL, Oltvai ZN. Network biology: understanding the cell's functional organization. Nat Rev Genet. 2004;5(2):101–13.Megraw M, Cumbie J, Ivanchenko M, Filichkin S. Small genetic circuits and MicroRNAs: big players in polymerase II transcriptional control in plants. Plant Cell. 2016;28:286–303.Wang St, Sun Xl, Hoshino Y, Yu Y, Jia B, et al. 2014. MicroRNA319 Positively Regulates Cold Tolerance by Targeting OsPCF6 and OsTCP21 in Rice (Oryza sativa). PLoS ONE 9(3): e91357.Fang Y, Xie K, Xiong L. Conserved miR164-targeted NAC genes regulate drought resistence in rice. J Exp Bot. 2014;65:2119–35.Goossens A, de la Fuente N, Forment J, Serrano R, Portillo F. Regulation of yeast H+-ATPase by protein kinases belonging to a family dedicated to activation of plasma membrane transporters. Mol Cell Biol. 2000;20:7654–61.Roig C, Fita A, RĂ­os G, Hammond JP, Nuez F, PicĂł B. Root transcriptional responses of two melon genotypes with contrasting resistance to Monosporascus cannonballus (Pollack et Uecker) infection. BMC Genomics. 2012;13:601.Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet Journal. 2011;17:10–2.R Core Team 2013. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3–900051–07-0, URL http://www.R-project.org /.Tarazona S, FuriĂł-TarĂ­ P, TurrĂ  D, Di Pietro A, Nueda MJ, Ferrer A, Conesa A. Data quality aware analysis of differential expression in RNA-seq with NOISeq R/bioc package. Nucleic Acids Res. 2015;43:e140.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.Czimmerer Z, Hulvely J, Simandi Z, Varallyay E, Havelda Z, Szabo E, Balint BL. A versatile method to design stem-loop primer-based quantitative PCR assays for detecting small regulatory RNA molecules. PLoS One. 2013;8(1):e55168.Zhai J, Arikit S, Simon S, Kingham B, Meyers B. Rapid construction of parallel analysis of RNA end (PARE) libraries for Illumina sequencing. Methods. 2014;67:84–90.Pink S, Vogel S. 2014. D3NETWORK: Stata module to create network visualizations using D3.js http://EconPapers.repec.org/RePEc:boc:bocode:s457844 .Csardi G, Nepusz T. The igraph software package for complex network research. Int J Complex Systems. 2006;1695:1–9

    Dynamic signal processing by ribozyme-mediated RNA circuits to control gene expression

    Get PDF
    [EN] Organisms have different circuitries that allow converting signal molecule levels to changes in gene expression. An important challenge in synthetic biology involves the de novo design of RNA modules enabling dynamic signal processing in live cells. This requires a scalable methodology for sensing, transmission, and actuation, which could be assembled into larger signaling networks. Here, we present a biochemical strategy to design RNA-mediated signal transduction cascades able to sense small molecules and small RNAs. We design switchable functional RNA domains by using strand-displacement techniques. We experimentally characterize the molecular mechanism underlying our synthetic RNA signaling cascades, show the ability to regulate gene expression with transduced RNA signals, and describe the signal processing response of our systems to periodic forcing in single live cells. The engineered systems integrate RNA-RNA interaction with available ribozyme and aptamer elements, providing new ways to engineer arbitrary complex gene circuits.EVOPROG [FP7-ICT-610730]; PROMYS [FP7-KBBE-613745 to A.J.]; Ministerio de Economia y Competitividad, Spain [BIO2011-26741 to J.-A.D.]; PRES Paris Sud grant (S.S.); EMBO long-term fellowship co-funded by Marie Curie actions [ALTF-1177-2011 A.J., G.R.]; AXA research fund; Ministerio de Educacion, Cultura y Deporte, Spain [AP2012-3751 to E.M.]. Funding for open access charge: EVOPROG [FP7-ICT-610730]; PROMYS [FP7-KBBE-613745].Shen, S.; Rodrigo Tarrega, G.; Prakash, S.; Majer, E.; Landrain, T.; Kirov, B.; Daros Arnau, JA.... (2015). Dynamic signal processing by ribozyme-mediated RNA circuits to control gene expression. Nucleic Acids Research. 43(10):5158-5170. https://doi.org/10.1093/nar/gkv287S515851704310Ulrich, L. E., Koonin, E. V., & Zhulin, I. B. (2005). One-component systems dominate signal transduction in prokaryotes. Trends in Microbiology, 13(2), 52-56. doi:10.1016/j.tim.2004.12.006Kiel, C., Yus, E., & Serrano, L. (2010). Engineering Signal Transduction Pathways. Cell, 140(1), 33-47. doi:10.1016/j.cell.2009.12.028Isaacs, F. J., Dwyer, D. J., & Collins, J. J. (2006). RNA synthetic biology. Nature Biotechnology, 24(5), 545-554. doi:10.1038/nbt1208Liang, J. C., Bloom, R. J., & Smolke, C. D. (2011). Engineering Biological Systems with Synthetic RNA Molecules. Molecular Cell, 43(6), 915-926. doi:10.1016/j.molcel.2011.08.023Dueber, J. E. (2003). Reprogramming Control of an Allosteric Signaling Switch Through Modular Recombination. Science, 301(5641), 1904-1908. doi:10.1126/science.1085945Sallee, N. A., Yeh, B. J., & Lim, W. A. (2007). Engineering Modular Protein Interaction Switches by Sequence Overlap. Journal of the American Chemical Society, 129(15), 4606-4611. doi:10.1021/ja0672728Rodrigo, G., Landrain, T. E., Shen, S., & Jaramillo, A. (2013). A new frontier in synthetic biology: automated design of small RNA devices in bacteria. Trends in Genetics, 29(9), 529-536. doi:10.1016/j.tig.2013.06.005Callura, J. M., Dwyer, D. J., Isaacs, F. J., Cantor, C. R., & Collins, J. J. (2010). Tracking, tuning, and terminating microbial physiology using synthetic riboregulators. Proceedings of the National Academy of Sciences, 107(36), 15898-15903. doi:10.1073/pnas.1009747107Callura, J. M., Cantor, C. R., & Collins, J. J. (2012). Genetic switchboard for synthetic biology applications. Proceedings of the National Academy of Sciences, 109(15), 5850-5855. doi:10.1073/pnas.1203808109Werstuck, G. (1998). Controlling Gene Expression in Living Cells Through Small Molecule-RNA Interactions. Science, 282(5387), 296-298. doi:10.1126/science.282.5387.296Wieland, M., & Hartig, J. S. (2008). Improved Aptazyme Design and In Vivo Screening Enable Riboswitching in Bacteria. Angewandte Chemie International Edition, 47(14), 2604-2607. doi:10.1002/anie.200703700Win, M. N., & Smolke, C. D. (2007). A modular and extensible RNA-based gene-regulatory platform for engineering cellular function. Proceedings of the National Academy of Sciences, 104(36), 14283-14288. doi:10.1073/pnas.0703961104Klauser, B., & Hartig, J. S. (2013). An engineered small RNA-mediated genetic switch based on a ribozyme expression platform. Nucleic Acids Research, 41(10), 5542-5552. doi:10.1093/nar/gkt253Bayer, T. S., & Smolke, C. D. (2005). Programmable ligand-controlled riboregulators of eukaryotic gene expression. Nature Biotechnology, 23(3), 337-343. doi:10.1038/nbt1069Qi, L., Lucks, J. B., Liu, C. C., Mutalik, V. K., & Arkin, A. P. (2012). Engineering naturally occurring trans -acting non-coding RNAs to sense molecular signals. Nucleic Acids Research, 40(12), 5775-5786. doi:10.1093/nar/gks168Looger, L. L., Dwyer, M. A., Smith, J. J., & Hellinga, H. W. (2003). Computational design of receptor and sensor proteins with novel functions. Nature, 423(6936), 185-190. doi:10.1038/nature01556Kortemme, T., & Baker, D. (2004). Computational design of protein–protein interactions. Current Opinion in Chemical Biology, 8(1), 91-97. doi:10.1016/j.cbpa.2003.12.008Rodrigo, G., Landrain, T. E., & Jaramillo, A. (2012). De novo automated design of small RNA circuits for engineering synthetic riboregulation in living cells. Proceedings of the National Academy of Sciences, 109(38), 15271-15276. doi:10.1073/pnas.1203831109Isaacs, F. J., Dwyer, D. J., Ding, C., Pervouchine, D. D., Cantor, C. R., & Collins, J. J. (2004). Engineered riboregulators enable post-transcriptional control of gene expression. Nature Biotechnology, 22(7), 841-847. doi:10.1038/nbt986Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. doi:10.1126/science.220.4598.671Hofacker, I. L., Fontana, W., Stadler, P. F., Bonhoeffer, L. S., Tacker, M., & Schuster, P. (1994). Fast folding and comparison of RNA secondary structures. Monatshefte fïżœr Chemie Chemical Monthly, 125(2), 167-188. doi:10.1007/bf00818163PĂ©delacq, J.-D., Cabantous, S., Tran, T., Terwilliger, T. C., & Waldo, G. S. (2005). Engineering and characterization of a superfolder green fluorescent protein. Nature Biotechnology, 24(1), 79-88. doi:10.1038/nbt1172Hersch, G. L., Baker, T. A., & Sauer, R. T. (2004). SspB delivery of substrates for ClpXP proteolysis probed by the design of improved degradation tags. Proceedings of the National Academy of Sciences, 101(33), 12136-12141. doi:10.1073/pnas.0404733101Rodrigo, G., Kirov, B., Shen, S., & Jaramillo, A. (2013). Theoretical and experimental analysis of the forced LacI-AraC oscillator with a minimal gene regulatory model. Chaos: An Interdisciplinary Journal of Nonlinear Science, 23(2), 025109. doi:10.1063/1.4809786Danino, T., MondragĂłn-Palomino, O., Tsimring, L., & Hasty, J. (2010). A synchronized quorum of genetic clocks. Nature, 463(7279), 326-330. doi:10.1038/nature08753Mathews, D. H., Sabina, J., Zuker, M., & Turner, D. H. (1999). Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. Journal of Molecular Biology, 288(5), 911-940. doi:10.1006/jmbi.1999.2700Paige, J. S., Nguyen-Duc, T., Song, W., & Jaffrey, S. R. (2012). Fluorescence Imaging of Cellular Metabolites with RNA. Science, 335(6073), 1194-1194. doi:10.1126/science.1218298Chen, X., & Ellington, A. D. (2009). Design Principles for Ligand-Sensing, Conformation-Switching Ribozymes. PLoS Computational Biology, 5(12), e1000620. doi:10.1371/journal.pcbi.1000620Quarta, G., Sin, K., & Schlick, T. (2012). Dynamic Energy Landscapes of Riboswitches Help Interpret Conformational Rearrangements and Function. PLoS Computational Biology, 8(2), e1002368. doi:10.1371/journal.pcbi.1002368Freeman, J. B., & Dale, R. (2012). Assessing bimodality to detect the presence of a dual cognitive process. Behavior Research Methods, 45(1), 83-97. doi:10.3758/s13428-012-0225-xWieland, M., Benz, A., Klauser, B., & Hartig, J. S. (2009). Artificial Ribozyme Switches Containing Natural Riboswitch Aptamer Domains. Angewandte Chemie International Edition, 48(15), 2715-2718. doi:10.1002/anie.200805311Penchovsky, R., & Breaker, R. R. (2005). Computational design and experimental validation of oligonucleotide-sensing allosteric ribozymes. Nature Biotechnology, 23(11), 1424-1433. doi:10.1038/nbt1155Chushak, Y., & Stone, M. O. (2009). In silico selection of RNA aptamers. Nucleic Acids Research, 37(12), e87-e87. doi:10.1093/nar/gkp408Bartel, D., & Szostak, J. (1993). Isolation of new ribozymes from a large pool of random sequences [see comment]. Science, 261(5127), 1411-1418. doi:10.1126/science.7690155Lutz, R. (1997). Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 25(6), 1203-1210. doi:10.1093/nar/25.6.1203Mutalik, V. K., Qi, L., Guimaraes, J. C., Lucks, J. B., & Arkin, A. P. (2012). Rationally designed families of orthogonal RNA regulators of translation. Nature Chemical Biology, 8(5), 447-454. doi:10.1038/nchembio.919Bennett, M. R., & Hasty, J. (2009). Microfluidic devices for measuring gene network dynamics in single cells. Nature Reviews Genetics, 10(9), 628-638. doi:10.1038/nrg2625Cookson, N. A., Mather, W. H., Danino, T., MondragĂłn‐Palomino, O., Williams, R. J., Tsimring, L. S., & Hasty, J. (2011). Queueing up for enzymatic processing: correlated signaling through coupled degradation. Molecular Systems Biology, 7(1), 561. doi:10.1038/msb.2011.94Hermann, T. (2000). Adaptive Recognition by Nucleic Acid Aptamers. Science, 287(5454), 820-825. doi:10.1126/science.287.5454.820Lou, C., Stanton, B., Chen, Y.-J., Munsky, B., & Voigt, C. A. (2012). Ribozyme-based insulator parts buffer synthetic circuits from genetic context. Nature Biotechnology, 30(11), 1137-1142. doi:10.1038/nbt.2401Qi, L., Haurwitz, R. E., Shao, W., Doudna, J. A., & Arkin, A. P. (2012). RNA processing enables predictable programming of gene expression. Nature Biotechnology, 30(10), 1002-1006. doi:10.1038/nbt.2355Liu, C. C., Qi, L., Lucks, J. B., Segall-Shapiro, T. H., Wang, D., Mutalik, V. K., & Arkin, A. P. (2012). An adaptor from translational to transcriptional control enables predictable assembly of complex regulation. Nature Methods, 9(11), 1088-1094. doi:10.1038/nmeth.2184Qi, L. S., Larson, M. H., Gilbert, L. A., Doudna, J. A., Weissman, J. S., Arkin, A. P., & Lim, W. A. (2013). Repurposing CRISPR as an RNA-Guided Platform for Sequence-Specific Control of Gene Expression. Cell, 152(5), 1173-1183. doi:10.1016/j.cell.2013.02.022Gilbert, L. A., Larson, M. H., Morsut, L., Liu, Z., Brar, G. A., Torres, S. E., 
 Qi, L. S. (2013). CRISPR-Mediated Modular RNA-Guided Regulation of Transcription in Eukaryotes. Cell, 154(2), 442-451. doi:10.1016/j.cell.2013.06.044Bashor, C. J., Horwitz, A. A., Peisajovich, S. G., & Lim, W. A. (2010). Rewiring Cells: Synthetic Biology as a Tool to Interrogate the Organizational Principles of Living Systems. Annual Review of Biophysics, 39(1), 515-537. doi:10.1146/annurev.biophys.050708.133652Yen, L., Svendsen, J., Lee, J.-S., Gray, J. T., Magnier, M., Baba, T., 
 Mulligan, R. C. (2004). Exogenous control of mammalian gene expression through modulation of RNA self-cleavage. Nature, 431(7007), 471-476. doi:10.1038/nature0284

    AsociaciĂłn entre Artritis Reumatoidea y otras enfermedades autoinmunes

    Get PDF
    Objetivos: determinar la frecuencia de enfermedades autoinmunes (EAI) en pacientes con Artritis Reumatoidea (AR) y comparar la frecuencia de EAI entre pacientes con AR y sin AR ni otra EAI reumatológica. Material y Métodos: estudio multicéntrico, observacional, analítico, retrospectivo. Se incluyeron pacientes consecutivos con AR (ACR/EULAR 2010) y como grupo control pacientes con diagnóstico inicial de Osteoartritis primaria (OA).

    Measurement of charged particle spectra in deep-inelastic ep scattering at HERA

    Get PDF
    Charged particle production in deep-inelastic ep scattering is measured with the H1 detector at HERA. The kinematic range of the analysis covers low photon virtualities, 5 LT Q(2) LT 100 GeV2, and small values of Bjorken-x, 10(-4) LT x LT 10(-2). The analysis is performed in the hadronic centre-of-mass system. The charged particle densities are measured as a function of pseudorapidity (n(*)) and transverse momentum (p(T)(*)) in the range 0 LT n(*) LT 5 and 0 LT p(T)(*) LT 10 GeV in bins of x and Q(2). The data are compared to predictions from different Monte Carlo generators implementing various options for hadronisation and parton evolutions

    Correction to: Cluster identification, selection, and description in Cluster randomized crossover trials: the PREP-IT trials

    Get PDF
    An amendment to this paper has been published and can be accessed via the original article
    corecore