Browsing by Author "Jiang, Xian-Li"
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Item Engineering Repressors with Coevolutionary Cues Facilitates Toggle Switches with a Master Reset(Oxford University Press, 2019-04-24) Dimas, R. P.; Jiang, Xian-Li; Alberto de la Paz, Jose; Morcos, Faruck; Chan, C. T. Y.; Jiang, Xian-Li; Alberto de la Paz, Jose; Morcos, FaruckEngineering allosteric transcriptional repressors containing an environmental sensing module (ESM) and a DNA recognition module (DRM) has the potential to unlock a combinatorial set of rationally designed biological responses. We demonstrated that constructing hybrid repressors by fusing distinct ESMs and DRMs provides a means to flexibly rewire genetic networks for complex signal processing. We have used coevolutionary traits among LacI homologs to develop a model for predicting compatibility between ESMs and DRMs. Our predictions accurately agree with the performance of 40 engineered repressors. We have harnessed this framework to develop a system of multiple toggle switches with a master OFF signal that produces a unique behavior: each engineered biological activity is switched to a stable ON state by different chemicals and returned to OFF in response to a common signal. One promising application of this design is to develop living diagnostics for monitoring multiple parameters in complex physiological environments and it represents one of many circuit topologies that can be explored with modular repressors designed with coevolutionary information. © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.Item Genotypic and Phenotypic Factors Influencing Drug Response in Mexican Patients with Type 2 Diabetes Mellitus(Frontiers Media SA) Sanchez-Ibarra, Hector E.; Reyes-Cortes, Luisa M.; Jiang, Xian-Li; Luna-Aguirre, Claudia M.; Aguirre-Trevino, Dionicio; Morales-Alvarado, Ivan A.; Leon-Cachon, Rafael B.; Lavalle-Gonzalez, Fernando; Morcos, Faruck; Barrera-Saldana, Hugo A.; Jiang, Xian-Li; Morcos, FaruckThe treatment of Type 2 Diabetes Mellitus (T2DM) consists primarily of oral antidiabetic drugs (OADs) that stimulate insulin secretion, such as sulfonylureas (SUs) and reduce hepatic glucose production (e.g., biguanides), among others. The marked inter-individual differences among T2DM patients' response to these drugs have become an issue on prescribing and dosing efficiently. In this study, fourteen polymorphisms selected from Genome-wide association studies (GWAS) were screened in 495 T2DM Mexican patients previously treated with OADs to find the relationship between the presence of these polymorphisms and response to the OADs. Then, a novel association screening method, based on global probabilities, was used to globally characterize important relationships between the drug response to OADs and genetic and clinical parameters, including polymorphisms, patient information, and type of treatment. Two polymorphisms, ABCC8-Ala1369Ser and KCNJ11-Glu23Lys, showed a significant impact on response to SUs. Heterozygous ABCC8-Ala1369Ser variant (A/C) carriers exhibited a higher response to SUs compared to homozygous ABCC8-Ala1369Ser variant (A/A) carriers (p-value = 0.029) and to homozygous wild-type genotypes (C/C) (p- value = 0.012). The homozygous KCNJ11-Glu23Lys variant (C/C) and wild-type (T/T) genotypes had a lower response to SUs compared to heterozygous (C/T) carriers (p-value = 0.039). The screening of OADs response related genetic and clinical factors could help improve the prescribing and dosing of OADs for T2DM patients and thus contribute to the design of personalized treatments.Item Revealing Protein Networks and Gene-Drug Connectivity in Cancer from Direct Information(Nature Publishing Group, 2018-08-20) Jiang, Xian-Li; Martinez-Ledesma, Emmanuel; Morcos, Faruck; 0000-0003-1697-8575 (Jiang, X-L); Jiang, Xian-Li; Morcos, FaruckThe connection between genetic variation and drug response has long been explored to facilitate the optimization and personalization of cancer therapy. Crucial to the identification of drug response related genetic features is the ability to separate indirect correlations from direct correlations across abundant datasets with large number of variables. Here we analyzed proteomic and pharmacogenomic data in cancer tissues and cell lines using a global statistical model connecting protein pairs, genes and anti-cancer drugs. We estimated this model using direct coupling analysis (DCA), a powerful statistical inference method that has been successfully applied to protein sequence data to extract evolutionary signals that provide insights on protein structure, folding and interactions. We used Direct Information (DI) as a metric of connectivity between proteins as well as gene-drug pairs. We were able to infer important interactions observed in cancer-related pathways from proteomic data and predict potential connectivities in cancer networks. We also identified known and potential connections for anti-cancer drugs and gene mutations using DI in pharmacogenomic data. Our findings suggest that gene-drug connections predicted with direct couplings can be used as a reliable guide to cancer therapy and expand our understanding of the effects of gene alterations on drug efficacies.