Bielza, C. (Concha)

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    BN-BacArena: Bayesian network extension of BacArena for the dynamic simulation of microbial communities
    (Oxford University Press, 2024) Balzerani, F. (Francesco); Valcárcel-García, L.V. (Luis Vitores); Blasco, T. (Telmo); Larrañaga, P. (Pedro); Rufián-Henares, J.Á. (Ángel José); Francino, M.P. (M. Pilar); Planes-Pedreño, F.J. (Francisco Javier); Bielza, C. (Concha); Pérez-Burillo, S. (Sergio)
    Motivation: Simulating gut microbial dynamics is extremely challenging. Several computational tools, notably the widely used BacArena, enable modeling of dynamic changes in the microbial environment. These methods, however, do not comprehensively account for microbe–microbe stimulant or inhibitory effects or for nutrient–microbe inhibitory effects, typically observed in different compounds present in the daily diet. Results: Here, we present BN-BacArena, an extension of BacArena consisting on the incorporation within the native computational framework of a Bayesian network model that accounts for microbe–microbe and nutrient–microbe interactions. Using in vitro experiments, 16S rRNA gene sequencing data and nutritional composition of 55 foods, the output Bayesian network showed 23 significant nutrient–bacteria interactions, suggesting the importance of compounds such as polyols, ascorbic acid, polyphenols and other phytochemicals, and 40 bacteria–bacteria significant relationships. With test data, BN-BacArena demonstrates a statistically significant improvement over BacArena to predict the time-dependent relative abundance of bacterial species involved in the gut microbiota upon different nutritional interventions. As a result, BN-BacArena opens new avenues for the dynamic modeling and simulation of the human gut microbiota metabolism.
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    A community-based transcriptomics classifcation and nomenclature of neocortical cell types
    (Nature Publishing Group, 2020) Yuste, R. (Rafael); Hawrylycz, M. (Michael); Aalling, N. (Nadia); Aguilar-Valles, A. (Argel); Arendt, D. (Detlev); Armañanzas-Arnedillo, R. (Ruben); Ascoli, G.A. (Giorgio A.); Bielza, C. (Concha); Bokharaie, V. (Vahid); Borgtoft-Bergmann, T. (Tobias); Bystron, I. (Irina); Capogna, M. (Marco); Chang, Y.J. (Yoon Jeung); Clemens, A. (Ann); Kock, C.P.J. (Christiaan P. J.) de; De-Felipe, J. (Javier); Dos-Santos, S.E. (Sandra Esmeralda); Dunville, K. (Keagan); Feldmeyer, D. (Dirk); Fiáth, R. (Richárd); Fishell, G.J. (Gordon James); Foggetti, A. (Angelica); Gao, X. (Xuefan); Ghaderi, P. (Parviz); Goriounova, N.A. (Natalia A.); Güntürkün, O. (Onur); Hagihara, K. (Kenta); Hall, V.J. (Vanessa Jane); Helmstaedter, M. (Moritz); Herculano-Houzel, S. (Suzana)
    To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body.