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Iglesias-Martinez, Luis F.
Preferred name
Iglesias-Martinez, Luis F.
Official Name
Iglesias-Martinez, Luis F.
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Now showing 1 - 3 of 3
- PublicationAn Integrated Global Analysis of Compartmentalized HRAS Signaling(Elsevier, 2019-03-12)
; ; ; ; ; ; ; ; ; Modern omics technologies allow us to obtain global information on different types of biological networks. However, integrating these different types of analyses into a coherent framework for a comprehensive biological interpretation remains challenging. Here, we present a conceptual framework that integrates protein interaction, phosphoproteomics, and transcriptomics data. Applying this method to analyze HRAS signaling from different subcellular compartments shows that spatially defined networks contribute specific functions to HRAS signaling. Changes in HRAS protein interactions at different sites lead to different kinase activation patterns that differentially regulate gene transcription. HRAS-mediated signaling is the strongest from the cell membrane, but it regulates the largest number of genes from the endoplasmic reticulum. The integrated networks provide a topologically and functionally resolved view of HRAS signaling. They reveal distinct HRAS functions including the control of cell migration from the endoplasmic reticulum and TP53-dependent cell survival when signaling from the Golgi apparatus.Scopus© Citations 32 444 - PublicationA role for adrenergic receptors in the uterotonic effects of ergometrine in isolated human term non-laboring myometrium(Lippincott, Williams and Wilkins, 2017-02)
; ; ; ; ; ; ; Background: Ergometrine is a uterotonic agent that is recommended in the prevention and management of post partum hemorrhage. Despite its long-standing use the mechanism by which it acts in humans has never been fully elucidated. The objective of this study was to investigate the role of adrenoreceptors in ergometrine's mechanism of action in human myometrium. The study examined the hypothesis that alpha adrenoreceptor antagonism would result in the reversal of the uterotonic effects of ergometrine. Methods: Myometrial samples were obtained from women undergoing elective cesarean delivery. The samples were then dissected into strips and mounted in organ bath chambers. Following generation of an ergometrine concentration-response curve (10-15 to 10-5 M), strips were treated with increasing concentrations of ergometrine (10-15 to 10-7 M) alone and ergometrine (10-7 to 10-5 M) in the presence of phentolamine (10-7 M), prazosin (10-7 M), propranolol (10-6 M) or yohimbine (10-6 M). The effects of adding ergometrine and the effect of drug combinations were analysed using linear mixed effects models with measures of amplitude (g), frequency (contractions/10min) and motility index (g*contractions/10min). Results: A total of 157 experiments were completed on samples obtained from 33 women. There was a significant increase in the motility index (adding 0.342 g*counts/10min/µM; 95% CI from 0.253 to 0.431, P<0.001), amplitude (0.078 g/µM; 95% CI, from 0.0344 to 0.121, P=5e-04) and frequency (0.051 counts/10min/µM; 95% CI, 0.038 to 0.063, P<0.001) in the presence of ergometrine. The α adrenergic antagonist phentolamine and the more selective α1 adrenergic antagonist prazosin, inhibited the ergometrine mediated increase in motility index, amplitude and frequency (-1.63 g*counts/10mins/µM and -16.70 g*counts/10mins/µM for motility index, respectively). Conclusions: These results provide novel evidence for a role for α adrenergic signaling mechanisms in the action of ergometrine on human myometrial smooth muscle in the in vitro setting. Information that sheds light on the mechanism of action of ergometrine may have implications for the development of further uterotonic agents.775Scopus© Citations 9 - PublicationBGRMI: A method for inferring gene regulatory networks from time-course gene expression data and its application in breast cancer researchReconstructing gene regulatory networks (GRNs) from gene expression data is a challenging problem. Existing GRN reconstruction algorithms can be broadly divided into model-free and model–based methods. Typically, model-free methods have high accuracy but are computation intensive whereas model-based methods are fast but less accurate. We propose Bayesian Gene Regulation Model Inference (BGRMI), a model-based method for inferring GRNs from time-course gene expression data. BGRMI uses a Bayesian framework to calculate the probability of different models of GRNs and a heuristic search strategy to scan the model space efficiently. Using benchmark datasets, we show that BGRMI has higher/comparable accuracy at a fraction of the computational cost of competing algorithms. Additionally, it can incorporate prior knowledge of potential gene regulation mechanisms and TF hetero-dimerization processes in the GRN reconstruction process. We incorporated existing ChIP-seq data and known protein interactions between TFs in BGRMI as sources of prior knowledge to reconstruct transcription regulatory networks of proliferating and differentiating breast cancer (BC) cells from time-course gene expression data. The reconstructed networks revealed key driver genes of proliferation and differentiation in BC cells. Some of these genes were not previously studied in the context of BC, but may have clinical relevance in BC treatment.
380Scopus© Citations 31