Unsupervised fuzzy pattern discovery in gene expression data

2010-2011 > Academic research: refereed > Publication in refereed journalpublished_fina

PETcofold: predicting conserved interactions and structures of two multiple alignments of RNA sequences

Motivation: Predicting RNA–RNA interactions is essential for determining the function of putative non-coding RNAs. Existing methods for the prediction of interactions are all based on single sequences. Since comparative methods have already been useful in RNA structure determination, we assume that conserved RNA–RNA interactions also imply conserved function. Of these, we further assume that a non-negligible amount of the existing RNA–RNA interactions have also acquired compensating base changes throughout evolution. We implement a method, PETcofold, that can take covariance information in intra-molecular and inter-molecular base pairs into account to predict interactions and secondary structures of two multiple alignments of RNA sequences

Full design automation of multi-state RNA devices to program gene expression using energy-based optimization

[EN] Small RNAs (sRNAs) can operate as regulatory agents to control protein expression by interaction with the 59 untranslated region of the mRNA. We have developed a physicochemical framework, relying on base pair interaction energies, to design multi-state sRNA devices by solving an optimization problem with an objective function accounting for the stability of the transition and final intermolecular states. Contrary to the analysis of the reaction kinetics of an ensemble of sRNAs, we solve the inverse problem of finding sequences satisfying targeted reactions. We show here that our objective function correlates well with measured riboregulatory activity of a set of mutants. This has enabled the application of the methodology for an extended design of RNA devices with specified behavior, assuming different molecular interaction models based on Watson-Crick interaction. We designed several YES, NOT, AND, and OR logic gates, including the design of combinatorial riboregulators. In sum, our de novo approach provides a new paradigm in synthetic biology to design molecular interaction mechanisms facilitating future high-throughput functional sRNA design.Work supported by the grants FP7-ICT-043338 (BACTOCOM) to AJ, and BIO2011-26741 (Ministerio de Economia y Competitividad, Spain) to JAD. GR is supported by an EMBO long-term fellowship co-funded by Marie Curie actions (ALTF-1177-2011), and TEL by a PhD fellowship from the AXA Research Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Rodrigo Tarrega, G.; Landrain, TE.; Majer, E.; Daros Arnau, JA.; Jaramillo, A. (2013). Full design automation of multi-state RNA devices to program gene expression using energy-based optimization. PLoS Computational Biology. 9(8):1003172-1003172. https://doi.org/10.1371/journal.pcbi.1003172S1003172100317298Isaacs, F. J., Dwyer, D. J., & Collins, J. J. (2006). RNA synthetic biology. Nature Biotechnology, 24(5), 545-554. doi:10.1038/nbt1208Isaacs, 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/nbt986Lucks, J. B., Qi, L., Mutalik, V. K., Wang, D., & Arkin, A. P. (2011). Versatile RNA-sensing transcriptional regulators for engineering genetic networks. Proceedings of the National Academy of Sciences, 108(21), 8617-8622. doi:10.1073/pnas.1015741108Mutalik, 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.919Bayer, T. S., & Smolke, C. D. (2005). Programmable ligand-controlled riboregulators of eukaryotic gene expression. Nature Biotechnology, 23(3), 337-343. doi:10.1038/nbt1069Nakashima, N., & Tamura, T. (2009). Conditional gene silencing of multiple genes with antisense RNAs and generation of a mutator strain of Escherichia coli. Nucleic Acids Research, 37(15), e103-e103. doi:10.1093/nar/gkp498Callura, 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.1203808109Beisel, C. L., Bayer, T. S., Hoff, K. G., & Smolke, C. D. (2008). Model‐guided design of ligand‐regulated RNAi for programmable control of gene expression. Molecular Systems Biology, 4(1), 224. doi:10.1038/msb.2008.62Qi, 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/gks168Carothers, J. M., Goler, J. A., Juminaga, D., & Keasling, J. D. (2011). Model-Driven Engineering of RNA Devices to Quantitatively Program Gene Expression. Science, 334(6063), 1716-1719. doi:10.1126/science.1212209Rodrigo, 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.1203831109Brantl, S. (2002). Antisense-RNA regulation and RNA interference. Biochimica et Biophysica Acta (BBA) - Gene Structure and Expression, 1575(1-3), 15-25. doi:10.1016/s0167-4781(02)00280-4Majdalani, N., Vanderpool, C. K., & Gottesman, S. (2005). Bacterial Small RNA Regulators. Critical Reviews in Biochemistry and Molecular Biology, 40(2), 93-113. doi:10.1080/10409230590918702Selinger, D. W., Cheung, K. J., Mei, R., Johansson, E. M., Richmond, C. S., Blattner, F. R., … Church, G. M. (2000). RNA expression analysis using a 30 base pair resolution Escherichia coli genome array. Nature Biotechnology, 18(12), 1262-1268. doi:10.1038/82367Yelin, R., Dahary, D., Sorek, R., Levanon, E. Y., Goldstein, O., Shoshan, A., … Rotman, G. (2003). Widespread occurrence of antisense transcription in the human genome. Nature Biotechnology, 21(4), 379-386. doi:10.1038/nbt808Wang, X.-J., Gaasterland, T., & Chua, N.-H. (2005). Genome Biology, 6(4), R30. doi:10.1186/gb-2005-6-4-r30Stojanovic, M. N., & Stefanovic, D. (2003). A deoxyribozyme-based molecular automaton. Nature Biotechnology, 21(9), 1069-1074. doi:10.1038/nbt862Seelig, G., Soloveichik, D., Zhang, D. Y., & Winfree, E. (2006). Enzyme-Free Nucleic Acid Logic Circuits. Science, 314(5805), 1585-1588. doi:10.1126/science.1132493Yin, P., Choi, H. M. T., Calvert, C. R., & Pierce, N. A. (2008). Programming biomolecular self-assembly pathways. Nature, 451(7176), 318-322. doi:10.1038/nature06451Ran, T., Kaplan, S., & Shapiro, E. (2009). Molecular implementation of simple logic programs. Nature Nanotechnology, 4(10), 642-648. doi:10.1038/nnano.2009.203Penchovsky, R., & Breaker, R. R. (2005). Computational design and experimental validation of oligonucleotide-sensing allosteric ribozymes. Nature Biotechnology, 23(11), 1424-1433. doi:10.1038/nbt1155Salis, H. M., Mirsky, E. A., & Voigt, C. A. (2009). Automated design of synthetic ribosome binding sites to control protein expression. Nature Biotechnology, 27(10), 946-950. doi:10.1038/nbt.1568Laidler, K. J., & King, M. C. (1983). Development of transition-state theory. The Journal of Physical Chemistry, 87(15), 2657-2664. doi:10.1021/j100238a002Sosnick, T. R., & Pan, T. (2003). RNA folding: models and perspectives. Current Opinion in Structural Biology, 13(3), 309-316. doi:10.1016/s0959-440x(03)00066-6Yurke, B. (2003). Genetic Programming and Evolvable Machines, 4(2), 111-122. doi:10.1023/a:1023928811651Bandyra, K. J., Said, N., Pfeiffer, V., Górna, M. W., Vogel, J., & Luisi, B. F. (2012). The Seed Region of a Small RNA Drives the Controlled Destruction of the Target mRNA by the Endoribonuclease RNase E. Molecular Cell, 47(6), 943-953. doi:10.1016/j.molcel.2012.07.015Dawid, A., Cayrol, B., & Isambert, H. (2009). RNA synthetic biology inspired from bacteria: construction of transcription attenuators under antisense regulation. Physical Biology, 6(2), 025007. doi:10.1088/1478-3975/6/2/025007Lioliou, E., Romilly, C., Romby, P., & Fechter, P. (2010). RNA-mediated regulation in bacteria: from natural to artificial systems. New Biotechnology, 27(3), 222-235. doi:10.1016/j.nbt.2010.03.002Dirks, R. M., Bois, J. S., Schaeffer, J. M., Winfree, E., & Pierce, N. A. (2007). Thermodynamic Analysis of Interacting Nucleic Acid Strands. SIAM Review, 49(1), 65-88. doi:10.1137/060651100Das, R., Karanicolas, J., & Baker, D. (2010). Atomic accuracy in predicting and designing noncanonical RNA structure. Nature Methods, 7(4), 291-294. doi:10.1038/nmeth.1433Vogel, J., & Luisi, B. F. (2011). Hfq and its constellation of RNA. Nature Reviews Microbiology, 9(8), 578-589. doi:10.1038/nrmicro2615Friedland, A. E., Lu, T. K., Wang, X., Shi, D., Church, G., & Collins, J. J. (2009). Synthetic Gene Networks That Count. Science, 324(5931), 1199-1202. doi:10.1126/science.1172005Rodrigo, G., Carrera, J., Landrain, T. E., & Jaramillo, A. (2012). Perspectives on the automatic design of regulatory systems for synthetic biology. FEBS Letters, 586(15), 2037-2042. doi:10.1016/j.febslet.2012.02.031Chin, J. W. (2006). Modular approaches to expanding the functions of living matter. Nature Chemical Biology, 2(6), 304-311. doi:10.1038/nchembio789McCaskill, J. S. (1990). The equilibrium partition function and base pair binding probabilities for RNA secondary structure. Biopolymers, 29(6-7), 1105-1119. doi:10.1002/bip.360290621Chitsaz, H., Salari, R., Sahinalp, S. C., & Backofen, R. (2009). A partition function algorithm for interacting nucleic acid strands. Bioinformatics, 25(12), i365-i373. doi:10.1093/bioinformatics/btp212Hofacker, I. L., Fontana, W., Stadler, P. F., Bonhoeffer, L. S., Tacker, M., & Schuster, P. (1994). Fast folding and comparison of RNA secondary structures. Monatshefte f�r Chemie Chemical Monthly, 125(2), 167-188. doi:10.1007/bf00818163Andronescu, M., Zhang, Z. C., & Condon, A. (2005). Secondary Structure Prediction of Interacting RNA Molecules. Journal of Molecular Biology, 345(5), 987-1001. doi:10.1016/j.jmb.2004.10.082Mathews, 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.1999.2700Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. doi:10.1126/science.220.4598.67

Case Study of Effectiveness Evaluation of Staff Training Courses in Refah Bank

One of the newest and most well-known train patterns for evaluating the effectiveness of in-service staffs training is Kircpatrick model. In this paper, the effectiveness of staff training courses of Refah-bank is evaluated. A questionnaire consisted of five components which include: reaction, learning, of behavior, the results and the innovation in role of confounding factors is handed out. The survey results show that three factors (reactions, behavior and innovation) have a significant effect on the teachings effectiveness according to Kircpatrick model. And that two factors (learning and results of the courses) have not a significant effect

Learning Formal Definitions for Snomed CT from Text

Abstract. Snomed CT is a widely used medical ontology which is formally expressed in a fragment of the Description Logic EL++. The underlying logics allow for expressive querying, yet make it costly to maintain and extend the ontology. In this paper we present an approach for the extraction of Snomed CT definitions from natural language text. We test and evaluate the approach using two types of texts.

Vogt-Koyanagi-Harada syndrome presenting with encephalopathy

VogtKoyanagi-Harada (VKH) is a rare syndrome affecting tissues with melanocytes. The possibility that VKH syndrome has an autoimmune pathogenesis is supported by the high frequency of human leukocyte antigen-DR4 commonly associated with other autoimmune diseases. Eyes are the main affected organ, resulting in blindness. Brain disease as a late onset event is extremely rare. Here, we are reporting a 57-year-old woman with previously diagnosed VKH syndrome, presenting with a late-onset brain encephalopathy. She was treated with corticosteroids and discharged from hospital with good general condition

High-Throughput Strategy for Glycine Oxidase Biosensor Development Reveals Glycine Release from Cultured Cells

Glycine is an important biomarker in clinical analysis due to its involvement in multiple physiological processes. As such, the need for low-cost analytical tools for glycine detection is growing. As a neurotransmitter, glycine is involved in inhibitory and excitatory neurochemical transmission in the central nervous system. In this work, we present a 10 \u3bcM Pt-based electrochemical enzymatic biosensor based on the flavoenzyme glycine oxidase (GO) for localized real-time measurements of glycine. Among GO variants at position 244, the H244K variant with increased glycine turnover was selected to develop a functional biosensor. This biosensor relies on amperometric readouts and does not require additional redox mediators. The biosensor was characterized and applied for glycine detection from cells, mainly HEK 293 cells and primary rat astrocytes. We have identified an enzyme, GO H244K, with increased glycine turnover using mutagenesis but which can be developed into a functional biosensor. Noteworthy, a glycine release of 395.7 \ub1 123 \u3bcM from primary astrocytes was measured, which is 3cfivefold higher than glycine release from HEK 293 cells (75.4 \ub1 3.91 \u3bcM) using the GO H244K biosensor