Partidos políticos y cuestiones agrarias

Acercamiento a la organización de los partidos en México y a su rostro agrario. Enmarca a cada partido dentro de la dinámica Estado-sociedad civil: y analiza su fuerza por el número de sus integrantes: el grado de cohesión y organización real, la homogeneidad y el crecimiento en lo orgánico, político e ideológico, la capacidad de movilización, los intereses reales que representa.ITESO, A.C

A very early estimation of software development time and effort using neural networks

In spite of years of research and development, formal structured estimation of time and effort required to develop a Management Information System (MIS) is still an open problem. Usual estimation techniques applied by now are supported by the not so realistic premise of requirements stability, and often human experts are required to apply them. This paper considers models of estimation based on metrics available on early design phase. Our research work aims to develop formal estimation models for time and effort needed for MIS development. These models use development team efficiency, requirements volatility, development speed and system complexity as input parameters. We also identify which input metrics are adequate for measuring system’s cognitive complexity and found that useful metrics can be obtained automatically from the system users´ data views very early on the life cycle with independence of the technology used and without human intervention. We tested the metrics estimation capability using Artificial Neural Networks (ANN), and thus confirmed an existing functional relation among input and output metrics (time and effort). Once trained, the ANN predicts effort needed with a 15% average error and time needed with a 30% average error.Eje: I - Workshop de Ingeniería de Software y Base de DatosRed de Universidades con Carreras en Informática (RedUNCI

A very early estimation of software development time and effort using neural networks

In spite of years of research and development, formal structured estimation of time and effort required to develop a Management Information System (MIS) is still an open problem. Usual estimation techniques applied by now are supported by the not so realistic premise of requirements stability, and often human experts are required to apply them. This paper considers models of estimation based on metrics available on early design phase. Our research work aims to develop formal estimation models for time and effort needed for MIS development. These models use development team efficiency, requirements volatility, development speed and system complexity as input parameters. We also identify which input metrics are adequate for measuring system’s cognitive complexity and found that useful metrics can be obtained automatically from the system users´ data views very early on the life cycle with independence of the technology used and without human intervention. We tested the metrics estimation capability using Artificial Neural Networks (ANN), and thus confirmed an existing functional relation among input and output metrics (time and effort). Once trained, the ANN predicts effort needed with a 15% average error and time needed with a 30% average error.Eje: I - Workshop de Ingeniería de Software y Base de DatosRed de Universidades con Carreras en Informática (RedUNCI

A comparative study of electrochemical and optical properties of rhenium deposited on gold and platinum

Rhenium-containing films were grown on gold and platinum after different potentiostatic and potentiodynamic polarizations in the - 0.20 V to 0.70 V range (vs rhe) in aqueous acid perrhenate. Experimental data were obtained using cyclic voltammetry and ellipsometry, from which the thickness and optical indices of the electrodeposited rhenium layer were calculated. Metallic rhenium deposition on gold takes place at potentials within the hydrogen evolution reaction. Rhenium oxide on platinum is formed in the hydrogen adatom potential domain, whereas metallic rhenium is deposited concurrently with the hydrogen adsorption and evolution reactions on the same metal.Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicada

A comparative study of electrochemical and optical properties of rhenium deposited on gold and platinum

Rhenium-containing films were grown on gold and platinum after different potentiostatic and potentiodynamic polarizations in the - 0.20 V to 0.70 V range (vs rhe) in aqueous acid perrhenate. Experimental data were obtained using cyclic voltammetry and ellipsometry, from which the thickness and optical indices of the electrodeposited rhenium layer were calculated. Metallic rhenium deposition on gold takes place at potentials within the hydrogen evolution reaction. Rhenium oxide on platinum is formed in the hydrogen adatom potential domain, whereas metallic rhenium is deposited concurrently with the hydrogen adsorption and evolution reactions on the same metal.Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicada

Simulating the Influence of Conjugative-Plasmid Kinetic Values on the Multilevel Dynamics of Antimicrobial Resistance in a Membrane Computing Model

[EN] Bacterial plasmids harboring antibiotic resistance genes are critical in the spread of antibiotic resistance. It is known that plasmids differ in their kinetic values, i.e., conjugation rate, segregation rate by copy number incompatibility with related plasmids, and rate of stochastic loss during replication. They also differ in cost to the cell in terms of reducing fitness and in the frequency of compensatory mutations compensating plasmid cost. However, we do not know how variation in these values influences the success of a plasmid and its resistance genes in complex ecosystems, such as the microbiota. Genes are in plasmids, plasmids are in cells, and cells are in bacterial populations and microbiotas, which are inside hosts, and hosts are in human communities at the hospital or the community under various levels of cross-colonization and antibiotic exposure. Differences in plasmid kinetics might have consequences on the global spread of antibiotic resistance. New membrane computing methods help to predict these consequences. In our simulation, conjugation frequency of at least 10(-3) influences the dominance of a strain with a resistance plasmid. Coexistence of different antibiotic resistances occurs if host strains can maintain two copies of similar plasmids. Plasmid loss rates of 10(-4) or 10(-5) or plasmid fitness costs of >= 0.06 favor plasmids located in the most abundant species. The beneficial effect of compensatory mutations for plasmid fitness cost is proportional to this cost at high mutation frequencies (10(-3) to 10(-5)). The results of this computational model clearly show how changes in plasmid kinetics can modify the entire population ecology of antibiotic resistance in the hospital setting.F. Baquero, M. Campos, and T. M. Coque were supported by EU Joint Programming Initiative JPIAMR2016-AC16/00043 (JPIonAMR-Third call on Transmission, ST131TS project), the Health Institute Carlos III of Spain (grants PI15-00818 and PI18-01942 and CIBER [CIBER in Epidemiology and Public Health, CIBERESP; CB06/02/0053]), and the Regional Government of Madrid (InGEMICS-C; S2017/BMD-3691), all of them cofinanced by the European Development Regional Fund (ERDF) "A Way to Achieve Europe." A. San Millan was supported by the European Research Council under the European Union's Horizon 2020 Research and Innovation Program (ERC grant agreement number 757440-PLASREVOLUTION)Campos Frances, M.; San Millan, A.; Sempere Luna, JM.; Lanza, VF.; Coque, TM.; Llorens, C.; Baquero, F. (2020). Simulating the Influence of Conjugative-Plasmid Kinetic Values on the Multilevel Dynamics of Antimicrobial Resistance in a Membrane Computing Model. Antimicrobial Agents and Chemotherapy. 64(8):1-19. https://doi.org/10.1128/AAC.00593-20S119648De Gelder, L., Ponciano, J. M., Joyce, P., & Top, E. M. (2007). Stability of a promiscuous plasmid in different hosts: no guarantee for a long-term relationship. Microbiology, 153(2), 452-463. doi:10.1099/mic.0.2006/001784-0Norman, A., Hansen, L. H., & Sørensen, S. J. (2009). Conjugative plasmids: vessels of the communal gene pool. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1527), 2275-2289. doi:10.1098/rstb.2009.0037Andam, C. P., Fournier, G. P., & Gogarten, J. P. (2011). Multilevel populations and the evolution of antibiotic resistance through horizontal gene transfer. FEMS Microbiology Reviews, 35(5), 756-767. doi:10.1111/j.1574-6976.2011.00274.xBaquero, F., Tedim, A. P., & Coque, T. M. (2013). Antibiotic resistance shaping multi-level population biology of bacteria. Frontiers in Microbiology, 4. doi:10.3389/fmicb.2013.00015Wein, T., Hülter, N. F., Mizrahi, I., & Dagan, T. (2019). Emergence of plasmid stability under non-selective conditions maintains antibiotic resistance. Nature Communications, 10(1). doi:10.1038/s41467-019-10600-7Yano, H., Shintani, M., Tomita, M., Suzuki, H., & Oshima, T. (2019). Reconsidering plasmid maintenance factors for computational plasmid design. Computational and Structural Biotechnology Journal, 17, 70-81. doi:10.1016/j.csbj.2018.12.001Gumpert, H., Kubicek-Sutherland, J. Z., Porse, A., Karami, N., Munck, C., Linkevicius, M., … Sommer, M. O. A. (2017). Transfer and Persistence of a Multi-Drug Resistance Plasmid in situ of the Infant Gut Microbiota in the Absence of Antibiotic Treatment. Frontiers in Microbiology, 8. doi:10.3389/fmicb.2017.01852Durão, P., Balbontín, R., & Gordo, I. (2018). Evolutionary Mechanisms Shaping the Maintenance of Antibiotic Resistance. Trends in Microbiology, 26(8), 677-691. doi:10.1016/j.tim.2018.01.005Campos, M., Llorens, C., Sempere, J. M., Futami, R., Rodriguez, I., Carrasco, P., … Baquero, F. (2015). A membrane computing simulator of trans-hierarchical antibiotic resistance evolution dynamics in nested ecological compartments (ARES). Biology Direct, 10(1). doi:10.1186/s13062-015-0070-9Campos, M., Capilla, R., Naya, F., Futami, R., Coque, T., Moya, A., … Baquero, F. (2019). Simulating Multilevel Dynamics of Antimicrobial Resistance in a Membrane Computing Model. mBio, 10(1), e02460-18. doi:10.1128/mbio.02460-1813. Baquero F, Campos M, Llorens C, Sempere JM. 2018. A model of antibiotic resistance evolution dynamics through P systems with active membranes and communication rules, p 33–44. In Graciani C, Agustín Riscos-Núñez A, Păun Gh, Rozenberg G, Salomaa A (ed), Enjoying natural computing. Springer, Cham, Switzerland.Leclerc, Q. J., Lindsay, J. A., & Knight, G. M. (2019). Mathematical modelling to study the horizontal transfer of antimicrobial resistance genes in bacteria: current state of the field and recommendations. Journal of The Royal Society Interface, 16(157), 20190260. doi:10.1098/rsif.2019.0260Blanquart, F. (2019). Evolutionary epidemiology models to predict the dynamics of antibiotic resistance. Evolutionary Applications, 12(3), 365-383. doi:10.1111/eva.1275316. Rozenberg G, Salomaa A, Păun G (ed). 2010. The Oxford handbook of membrane computing. Oxford University Press, Oxford, England.17. Păun G. 2002. Membrane computing. An introduction. Springer-Verlag, Heidelberg, Germany.Novais, A., Cantón, R., Moreira, R., Peixe, L., Baquero, F., & Coque, T. M. (2006). Emergence and Dissemination of Enterobacteriaceae Isolates Producing CTX-M-1-Like Enzymes in Spain Are Associated with IncFII (CTX-M-15) and Broad-Host-Range (CTX-M-1, -3, and -32) Plasmids. Antimicrobial Agents and Chemotherapy, 51(2), 796-799. doi:10.1128/aac.01070-06Mathers, A. J., Peirano, G., & Pitout, J. D. D. (2015). The Role of Epidemic Resistance Plasmids and International High-Risk Clones in the Spread of Multidrug-Resistant Enterobacteriaceae. Clinical Microbiology Reviews, 28(3), 565-591. doi:10.1128/cmr.00116-1420. Poirel L, Madec JY, Lupo A, Schink AK, Kieffer N, Nordmann P, Schwarz S. 2018. Antimicrobial resistance in Escherichia coli, p 289–316. In Schwarz S, Cavaco LM, Shen J (ed), Antimicrobial resistance in bacteria from livestock and companion animals. ASM Press, Washington, DC.Livermore, D. M., & Hawkey, P. M. (2005). CTX-M: changing the face of ESBLs in the UK. Journal of Antimicrobial Chemotherapy, 56(3), 451-454. doi:10.1093/jac/dki23923. European Centre for Disease Prevention and Control. 2015. Antimicrobial resistance surveillance in Europe 2015. Annual report of the European Antimicrobial Resistance Surveillance Network (EARS-Net). European Centre for Disease Prevention and Control, Stockholm, Sweden.Bush, K., & Fisher, J. F. (2011). Epidemiological Expansion, Structural Studies, and Clinical Challenges of New β-Lactamases from Gram-Negative Bacteria. Annual Review of Microbiology, 65(1), 455-478. doi:10.1146/annurev-micro-090110-102911Bush, K. (2018). Past and Present Perspectives on β-Lactamases. Antimicrobial Agents and Chemotherapy, 62(10). doi:10.1128/aac.01076-18Hawser, S. P., Bouchillon, S. K., Hoban, D. J., Badal, R. E., Cantón, R., & Baquero, F. (2010). Incidence and Antimicrobial Susceptibility of Escherichia coli and Klebsiella pneumoniae with Extended-Spectrum β-Lactamases in Community- and Hospital-Associated Intra-Abdominal Infections in Europe: Results of the 2008 Study for Monitoring Antimicrobial Resistance Trends (SMART). Antimicrobial Agents and Chemotherapy, 54(7), 3043-3046. doi:10.1128/aac.00265-10Simonsen, L., Gordon, D. M., Stewart, F. M., & Levin, B. R. (1990). Estimating the rate of plasmid transfer: an end-point method. Journal of General Microbiology, 136(11), 2319-2325. doi:10.1099/00221287-136-11-2319Levin, B. R., Stewart, F. M., & Rice, V. A. (1979). The kinetics of conjugative plasmid transmission: Fit of a simple mass action model. Plasmid, 2(2), 247-260. doi:10.1016/0147-619x(79)90043-xTurner, P. E., Williams, E. S. C. P., Okeke, C., Cooper, V. S., Duffy, S., & Wertz, J. E. (2014). Antibiotic resistance correlates with transmission in plasmid evolution. Evolution, 68(12), 3368-3380. doi:10.1111/evo.12537Porse, A., Schønning, K., Munck, C., & Sommer, M. O. A. (2016). Survival and Evolution of a Large Multidrug Resistance Plasmid in New Clinical Bacterial Hosts. Molecular Biology and Evolution, 33(11), 2860-2873. doi:10.1093/molbev/msw163Smillie, C., Garcillán-Barcia, M. P., Francia, M. V., Rocha, E. P. C., & de la Cruz, F. (2010). Mobility of Plasmids. Microbiology and Molecular Biology Reviews, 74(3), 434-452. doi:10.1128/mmbr.00020-1038. Taylor DE, Gibreel A, Tracz DM, Lawley TD. 2004. Antibiotic resistance plasmids, p 473–492. In Funnell BE, Phillips GJ (ed), Plasmid biology. American Society of Microbiology, Washington, DC.Million-Weaver, S., & Camps, M. (2014). Mechanisms of plasmid segregation: Have multicopy plasmids been overlooked? Plasmid, 75, 27-36. doi:10.1016/j.plasmid.2014.07.002Lau, B. T. C., Malkus, P., & Paulsson, J. (2013). New quantitative methods for measuring plasmid loss rates reveal unexpected stability. Plasmid, 70(3), 353-361. doi:10.1016/j.plasmid.2013.07.007Vogwill, T., & MacLean, R. C. (2014). The genetic basis of the fitness costs of antimicrobial resistance: a meta-analysis approach. Evolutionary Applications, 8(3), 284-295. doi:10.1111/eva.12202Andersson, D. I., & Levin, B. R. (1999). The biological cost of antibiotic resistance. Current Opinion in Microbiology, 2(5), 489-493. doi:10.1016/s1369-5274(99)00005-3Andersson, D. I., & Hughes, D. (2010). Antibiotic resistance and its cost: is it possible to reverse resistance? Nature Reviews Microbiology, 8(4), 260-271. doi:10.1038/nrmicro2319Loftie-Eaton, W., Bashford, K., Quinn, H., Dong, K., Millstein, J., Hunter, S., … Top, E. M. (2017). Compensatory mutations improve general permissiveness to antibiotic resistance plasmids. Nature Ecology & Evolution, 1(9), 1354-1363. doi:10.1038/s41559-017-0243-2Zwanzig, M., Harrison, E., Brockhurst, M. A., Hall, J. P. J., Berendonk, T. U., & Berger, U. (2019). Mobile Compensatory Mutations Promote Plasmid Survival. mSystems, 4(1). doi:10.1128/msystems.00186-18Yang, Q. E., MacLean, C., Papkou, A., Pritchard, M., Powell, L., Thomas, D., … Walsh, T. R. (2020). Compensatory mutations modulate the competitiveness and dynamics of plasmid-mediated colistin resistance in Escherichia coli clones. The ISME Journal, 14(3), 861-865. doi:10.1038/s41396-019-0578-6Gama, J. A., Zilhão, R., & Dionisio, F. (2018). Impact of plasmid interactions with the chromosome and other plasmids on the spread of antibiotic resistance. Plasmid, 99, 82-88. doi:10.1016/j.plasmid.2018.09.009Harrison, E., Dytham, C., Hall, J. P. J., Guymer, D., Spiers, A. J., Paterson, S., & Brockhurst, M. A. (2016). Rapid compensatory evolution promotes the survival of conjugative plasmids. Mobile Genetic Elements, 6(3), e1179074. doi:10.1080/2159256x.2016.1179074Hall, J. P. J., Brockhurst, M. A., Dytham, C., & Harrison, E. (2017). The evolution of plasmid stability: Are infectious transmission and compensatory evolution competing evolutionary trajectories? Plasmid, 91, 90-95. doi:10.1016/j.plasmid.2017.04.00354. Shintani M, Suzuki H. 2019. Plasmids and their hosts, p 109–133. In Nishida H, Oshima T (ed), DNA traffic in the environment. Springer, Singapore.Komp Lindgren, P., Karlsson, A., & Hughes, D. (2003). Mutation Rate and Evolution of Fluoroquinolone Resistance in Escherichia coli Isolates from Patients with Urinary Tract Infections. Antimicrobial Agents and Chemotherapy, 47(10), 3222-3232. doi:10.1128/aac.47.10.3222-3232.2003Krone, S. M., Lu, R., Fox, R., Suzuki, H., & Top, E. M. (2007). Modelling the spatial dynamics of plasmid transfer and persistence. Microbiology, 153(8), 2803-2816. doi:10.1099/mic.0.2006/004531-0Baquero, F., Coque, T. M., & de la Cruz, F. (2011). Ecology and Evolution as Targets: the Need for Novel Eco-Evo Drugs and Strategies To Fight Antibiotic Resistance. Antimicrobial Agents and Chemotherapy, 55(8), 3649-3660. doi:10.1128/aac.00013-11Buckner, M. M. C., Ciusa, M. L., & Piddock, L. J. V. (2018). Strategies to combat antimicrobial resistance: anti-plasmid and plasmid curing. FEMS Microbiology Reviews, 42(6), 781-804. doi:10.1093/femsre/fuy031Bush, K. (2008). Extended-spectrum β-lactamases in North America, 1987–2006. Clinical Microbiology and Infection, 14, 134-143. doi:10.1111/j.1469-0691.2007.01848.xJacoby, G. A., & Han, P. (1996). Detection of extended-spectrum beta-lactamases in clinical isolates of Klebsiella pneumoniae and Escherichia coli. Journal of clinical microbiology, 34(4), 908-911. doi:10.1128/jcm.34.4.908-911.1996Valverde, A., Coque, T. M., Sanchez-Moreno, M. P., Rollan, A., Baquero, F., & Canton, R. (2004). Dramatic Increase in Prevalence of Fecal Carriage of Extended-Spectrum  -Lactamase-Producing Enterobacteriaceae during Nonoutbreak Situations in Spain. Journal of Clinical Microbiology, 42(10), 4769-4775. doi:10.1128/jcm.42.10.4769-4775.2004Hernández, J. R., Martínez-Martínez, L., Cantón, R., Coque, T. M., & Pascual, A. (2005). Nationwide Study of Escherichia coli and Klebsiella pneumoniae Producing Extended-Spectrum β-Lactamases in Spain. Antimicrobial Agents and Chemotherapy, 49(5), 2122-2125. doi:10.1128/aac.49.5.2122-2125.2005PEREZ, F., ENDIMIANI, A., HUJER, K., & BONOMO, R. (2007). The continuing challenge of ESBLs. Current Opinion in Pharmacology, 7(5), 459-469. doi:10.1016/j.coph.2007.08.003Hernández-García, M., Pérez-Viso, B., Navarro-San Francisco, C., Baquero, F., Morosini, M. I., Ruiz-Garbajosa, P., & Cantón, R. (2019). Intestinal co-colonization with different carbapenemase-producing Enterobacterales isolates is not a rare event in an OXA-48 endemic area. EClinicalMedicine, 15, 72-79. doi:10.1016/j.eclinm.2019.09.005Jensen, R. B., & Gerdes, K. (1995). Programmed cell death in bacteria: proteic plasmid stabilization systems. Molecular Microbiology, 17(2), 205-210. doi:10.1111/j.1365-2958.1995.mmi_17020205.xStalder, T., Cornwell, B., Lacroix, J., Kohler, B., Dixon, S., Yano, H., … Top, E. M. (2020). Evolving Populations in Biofilms Contain More Persistent Plasmids. Molecular Biology and Evolution, 37(6), 1563-1576. doi:10.1093/molbev/msaa024McNally, A., Oren, Y., Kelly, D., Pascoe, B., Dunn, S., Sreecharan, T., … Corander, J. (2016). Combined Analysis of Variation in Core, Accessory and Regulatory Genome Regions Provides a Super-Resolution View into the Evolution of Bacterial Populations. PLOS Genetics, 12(9), e1006280. doi:10.1371/journal.pgen.1006280Baquero, M.-R., Galán, J. C., del Carmen Turrientes, M., Cantón, R., Coque, T. M., Martínez, J. L., & Baquero, F. (2005). Increased Mutation Frequencies in Escherichia coli Isolates Harboring Extended-Spectrum β-Lactamases. Antimicrobial Agents and Chemotherapy, 49(11), 4754-4756. doi:10.1128/aac.49.11.4754-4756.2005Baquero, F. (2004). From pieces to patterns: evolutionary engineering in bacterial pathogens. Nature Reviews Microbiology, 2(6), 510-518. doi:10.1038/nrmicro909Andersson, D. I., Balaban, N. Q., Baquero, F., Courvalin, P., Glaser, P., Gophna, U., … Tønjum, T. (2020). Antibiotic resistance: turning evolutionary principles into clinical reality. FEMS Microbiology Reviews, 44(2), 171-188. doi:10.1093/femsre/fuaa001Jernberg, C., Löfmark, S., Edlund, C., & Jansson, J. K. (2010). Long-term impacts of antibiotic exposure on the human intestinal microbiota. Microbiology, 156(11), 3216-3223. doi:10.1099/mic.0.040618-0Sommer, F., Anderson, J. M., Bharti, R., Raes, J., & Rosenstiel, P. (2017). The resilience of the intestinal microbiota influences health and disease. Nature Reviews Microbiology, 15(10), 630-638. doi:10.1038/nrmicro.2017.58Novais, C., Tedim, A. P., Lanza, V. F., Freitas, A. R., Silveira, E., Escada, R., … Coque, T. M. (2016). Co-diversification of Enterococcus faecium Core Genomes and PBP5: Evidences of pbp5 Horizontal Transfer. Frontiers in Microbiology, 7. doi:10.3389/fmicb.2016.0158

NALP1 is a transcriptional target for cAMP-response-element-binding protein (CREB) in myeloid leukaemia cells

NALP1 (also called DEFCAP, NAC, CARD7) has been shown to play a central role in the activation of inflammatory caspases and processing of pro-IL1β (pro-interleukin-1β). Previous studies showed that NALP1 is highly expressed in peripheral blood mononuclear cells. In the present study, we report that expression of NALP1 is absent from CD34+ haematopoietic blast cells, and its levels are upregulated upon differentiation of CD34+ cells into granulocytes and to a lesser extent into monocytes. In peripheral blood cells, the highest levels of NALP1 were observed in CD3+ (T-lymphocytes), CD15+ (granulocytes) and CD14+ (monocytes) cell populations. Notably, the expression of NALP1 was significantly increased in the bone marrow blast cell population of some patients with acute leukaemia, but not among tissue samples from thyroid and renal cancer. A search for consensus sites within the NALP1 promoter revealed a sequence for CREB (cAMP-response-element-binding protein) that was required for transcriptional activity. Moreover, treatment of TF1 myeloid leukaemia cells with protein kinase C and protein kinase A activators induced CREB phosphorylation and upregulated the mRNA and protein levels of NALP1. Conversely, ectopic expression of a dominant negative form of CREB in TF1 cells blocked the transcriptional activity of the NALP1 promoter and significantly reduced the expression of NALP1. Thus NALP1 is transcriptionally regulated by CREB in myeloid cells, a mechanism that may contribute to modulate the response of these cells to pro-inflammatory stimuli

Induction of Nod2 in Myelomonocytic and Intestinal Epithelial Cells via Nuclear Factor-kB Activation

Nod2, a member of the Apaf1/Nod protein family, confers responsiveness to bacterial products and activates NF-kB, a ranscription factor that plays a central role in innate immunity. Recently, genetic variation in Nod2 has been associated with susceptibility to Crohn’s disease. Here, we report that expression of Nod2 is induced upon differentiation of CD34+ hematopoietic progenitor cells into granulocyte or monocyte/macrophages. In peripheral blood cells, the highest levels of Nod2 were observed in CD14+ (monocytes), CD15+ (granulocytes), and CD40+/CD86+ (dendritic cells) cell populations. Notably, stimulation of myeloblastic and epithelial cells with bacterial lipopolysaccharide or TNF resulted in up-regulation of Nod2. A search for consensus sites within the Nod2 promoter revealed a NF-kB binding element that was required for transcriptional activity in response to TNF . Moreover, ectopic expression of p65 induced transactivation, whereas that of dominant-negative I B blocked the transcriptional activity of the Nod2 promoter. Upon stimulation with TNF or lipopolysaccharide, both p50 and p65 subunits of NF-kB were bound to the Nod2 promoter. Thus, Nod2 expression is enhanced by proinflammatory cytokines and bacterial components via NF-kB, a mechanism that may contribute to the amplification of the innate immune response and susceptibility to inflammatory disease