Is Lactate an Oncometabolite? Evidence Supporting a Role for Lactate in the Regulation of Transcriptional Activity of Cancer-Related Genes in MCF7 Breast Cancer Cells.

Lactate is a ubiquitous molecule in cancer. In this exploratory study, our aim was to test the hypothesis that lactate could function as an oncometabolite by evaluating whether lactate exposure modifies the expression of oncogenes, or genes encoding transcription factors, cell division, and cell proliferation in MCF7 cells, a human breast cancer cell line. Gene transcription was compared between MCF7 cells incubated in (a) glucose/glutamine-free media (control), (b) glucose-containing media to stimulate endogenous lactate production (replicating some of the original Warburg studies), and (c) glucose-containing media supplemented with L-lactate (10 and 20 mM). We found that both endogenous, glucose-derived lactate and exogenous, lactate supplementation significantly affected the transcription of key oncogenes (MYC, RAS, and PI3KCA), transcription factors (HIF1A and E2F1), tumor suppressors (BRCA1, BRCA2) as well as cell cycle and proliferation genes involved in breast cancer (AKT1, ATM, CCND1, CDK4, CDKN1A, CDK2B) (0.001 < p < 0.05 for all genes). Our findings support the hypothesis that lactate acts as an oncometabolite in MCF7 cells. Further research is necessary on other cell lines and biopsy cultures to show generality of the findings and reveal the mechanisms by which dysregulated lactate metabolism could act as an oncometabolite in carcinogenesis

Explosive higher-order Kuramoto dynamics on simplicial complexes

The higher-order interactions of complex systems, such as the brain, are captured by their simplicial complex structure and have a significant effect on dynamics. However the existing dynamical models defined on simplicial complexes make the strong assumption that the dynamics resides exclusively on the nodes. Here we formulate the higher-order Kuramoto model which describes the interactions between oscillators placed not only on nodes but also on links, triangles, and so on. We show that higher-order Kuramoto dynamics can lead to explosive synchronization transition by using an adaptive coupling dependent on the solenoidal and the irrotational component of the dynamics

Unsupervised machine learning algorithms as support tools in molecular dynamics simulations

Unsupervised Machine Learning algorithms such as clustering offer convenient features for data analysis tasks. When combined with other tools like visualization software, the possibilities of automated analysis may be greatly enhanced. In the context of Molecular Dynamics simulations, in particular asymmetric granular collisions which typically consist of thousands of particles, it is key to distinguish the fragments in which the system is divided after a collision for classification purposes. In this work we explore the unsupervised Machine Learning algorithms k-means and AGNES to distinguish groups of particles in molecular dynamics simulations, with encouraging results according to performance metrics such as accuracy and precision. We also report computational times for each algorithm, where k-means results faster than AGNES. Finally, we delineate the integration of these type of algorithms with a well-known analysis and visualization tool widely used in the physics community.Sociedad Argentina de Informática e Investigación Operativ

Real-Time and Low-Cost Sensing Technique Based on Photonic Bandgap Structures

This paper was published in OPTICS LETTERS and is made available as an electronic reprint with the permission of OSA. The paper can be found at the following URL on the OSA website: http://dx.doi.org/10.1364/OL.36.002707. Systematic or multiple reproduction or distribution to multiple locations via electronic or other means is prohibited and is subject to penalties under law[EN] A technique for the development of low-cost and high-sensitivity photonic biosensing devices is proposed and experimentally demonstrated. In this technique, a photonic bandgap structure is used as transducer, but its readout is performed by simply using a broadband source, an optical filter, and a power meter, without the need of obtaining the transmission spectrum of the structure; thus, a really low-cost system and real-time results are achieved. Experimental results show that it is possible to detect very low refractive index variations, achieving a detection limit below 2 x 10(-6) refractive index units using this low-cost measuring technique. (C) 2011 Optical Society of America[This work was funded by the Spanish Ministerio de Ciencia e Innovacion (MICINN) under contracts TEC2008-06333, JCI-009-5805, and TEC2008-05490. Support by the Universidad Politecnica de Valencia through program PAID-06-09 and the Conselleria d'Educacio through program GV-2010-031 is acknowledged.García Castelló, J.; Toccafondo, V.; Pérez Millán, P.; Sánchez Losilla, N.; Cruz, JL.; Andres, MV.; García-Rupérez, J. (2011). Real-Time and Low-Cost Sensing Technique Based on Photonic Bandgap Structures. Optics Letters. 36(14):2707-2709. https://doi.org/10.1364/OL.36.002707S270727093614Fan, X., White, I. M., Shopova, S. I., Zhu, H., Suter, J. D., & Sun, Y. (2008). Sensitive optical biosensors for unlabeled targets: A review. 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IEEE Journal of Selected Topics in Quantum Electronics, 16(3), 654-661. doi:10.1109/jstqe.2009.2032510Xu, D.-X., Vachon, M., Densmore, A., Ma, R., Delâge, A., Janz, S., … Schmid, J. H. (2010). Label-free biosensor array based on silicon-on-insulator ring resonators addressed using a WDM approach. Optics Letters, 35(16), 2771. doi:10.1364/ol.35.002771Skivesen, N., Têtu, A., Kristensen, M., Kjems, J., Frandsen, L. H., & Borel, P. I. (2007). Photonic-crystal waveguide biosensor. Optics Express, 15(6), 3169. doi:10.1364/oe.15.003169Lee, M. R., & Fauchet, P. M. (2007). Nanoscale microcavity sensor for single particle detection. Optics Letters, 32(22), 3284. doi:10.1364/ol.32.003284García-Rupérez, J., Toccafondo, V., Bañuls, M. J., Castelló, J. G., Griol, A., Peransi-Llopis, S., & Maquieira, Á. (2010). Label-free antibody detection using band edge fringes in SOI planar photonic crystal waveguides in the slow-light regime. Optics Express, 18(23), 24276. doi:10.1364/oe.18.024276Toccafondo, V., García-Rupérez, J., Bañuls, M. J., Griol, A., Castelló, J. G., Peransi-Llopis, S., & Maquieira, A. (2010). Single-strand DNA detection using a planar photonic-crystal-waveguide-based sensor. Optics Letters, 35(21), 3673. doi:10.1364/ol.35.003673Luff, B. J., Wilson, R., Schiffrin, D. J., Harris, R. D., & Wilkinson, J. S. (1996). Integrated-optical directional coupler biosensor. Optics Letters, 21(8), 618. doi:10.1364/ol.21.000618Sepúlveda, B., Río, J. S. del, Moreno, M., Blanco, F. J., Mayora, K., Domínguez, C., & Lechuga, L. M. (2006). Optical biosensor microsystems based on the integration of highly sensitive Mach–Zehnder interferometer devices. Journal of Optics A: Pure and Applied Optics, 8(7), S561-S566. doi:10.1088/1464-4258/8/7/s41Densmore, A., Vachon, M., Xu, D.-X., Janz, S., Ma, R., Li, Y.-H., … Schmid, J. H. (2009). Silicon photonic wire biosensor array for multiplexed real-time and label-free molecular detection. Optics Letters, 34(23), 3598. doi:10.1364/ol.34.003598Povinelli, M. L., Johnson, S. G., & Joannopoulos, J. D. (2005). Slow-light, band-edge waveguides for tunable time delays. Optics Express, 13(18), 7145. doi:10.1364/opex.13.007145Garcia, J., Sanchis, P., Martinez, A., & Marti, J. (2008). 1D periodic structures for slow-wave induced non-linearity enhancement. Optics Express, 16(5), 3146. doi:10.1364/oe.16.003146Pérez-Millán, P., Torres-Peiró, S., Cruz, J. L., & Andrés, M. V. (2008). Fabrication of chirped fiber Bragg gratings by simple combination of stretching movements. Optical Fiber Technology, 14(1), 49-53. doi:10.1016/j.yofte.2007.07.00

Triadic percolation induces dynamical topological patterns in higher-order networks

Triadic interactions are higher-order interactions which occur when a set of nodes affects the interaction between two other nodes. Examples of triadic interactions are present in the brain when glia modulate the synaptic signals among neuron pairs or when interneuron axo-axonic synapses enable presynaptic inhibition and facilitation, and in ecosystems when one or more species can affect the interaction among two other species. On random graphs, triadic percolation has been recently shown to turn percolation into a fully fledged dynamical process in which the size of the giant component undergoes a route to chaos. However, in many real cases, triadic interactions are local and occur on spatially embedded networks. Here, we show that triadic interactions in spatial networks induce a very complex spatio-temporal modulation of the giant component which gives rise to triadic percolation patterns with significantly different topology. We classify the observed patterns (stripes, octopus, and small clusters) with topological data analysis and we assess their information content (entropy and complexity). Moreover, we illustrate the multistability of the dynamics of the triadic percolation patterns, and we provide a comprehensive phase diagram of the model. These results open new perspectives in percolation as they demonstrate that in presence of spatial triadic interactions, the giant component can acquire a time-varying topology. Hence, this work provides a theoretical framework that can be applied to model realistic scenarios in which the giant component is time dependent as in neuroscience