ABSTRACT
Cancer is a genomic disease involving various intertwined pathways with complex cross-communication links. Conceptually, this complex interconnected system forms a network, which allows one to model the dynamic behavior of the elements that characterize it to describe the entire system's development in its various evolutionary stages of carcinogenesis. Knowing the activation or inhibition status of the genes that make up the network during its temporal evolution is necessary for the rational intervention on the critical factors for controlling the system's dynamic evolution. In this report, we proposed a methodology for building data-driven boolean networks that model breast cancer tumors. We defined the network components and topology based on gene expression data from RNA-seq of breast cancer cell lines. We used a Boolean logic formalism to describe the network dynamics. The combination of single-cell RNA-seq and interactome data enabled us to study the dynamics of malignant subnetworks of up-regulated genes. First, we used the same Boolean function construction scheme for each network node, based on canalyzing functions. Using single-cell breast cancer datasets from The Cancer Genome Atlas, we applied a binarization algorithm. The binarized version of scRNA-seq data allowed identifying attractors specific to patients and critical genes related to each breast cancer subtype. The model proposed in this report may serve as a basis for a methodology to detect critical genes involved in malignant attractor stability, whose inhibition could have potential applications in cancer theranostics.
ABSTRACT
One aspect of personalized medicine is aiming at identifying specific targets for therapy considering the gene expression profile of each patient individually. The real-world implementation of this approach is better achieved by user-friendly bioinformatics systems for healthcare professionals. In this report, we present an online platform that endows users with an interface designed using MEAN stack supported by a Galaxy pipeline. This pipeline targets connection hubs in the subnetworks formed by the interactions between the proteins of genes that are up-regulated in tumors. This strategy has been proved to be suitable for the inhibition of tumor growth and metastasis in vitro. Therefore, Perl and Python scripts were enclosed in Galaxy for translating RNA-seq data into protein targets suitable for the chemotherapy of solid tumors. Consequently, we validated the process of target diagnosis by (i) reference to subnetwork entropy, (ii) the critical value of density probability of differential gene expression, and (iii) the inhibition of the most relevant targets according to TCGA and GDC data. Finally, the most relevant targets identified by the pipeline are stored in MongoDB and can be accessed through the aforementioned internet portal designed to be compatible with mobile or small devices through Angular libraries.
ABSTRACT
Cancer is a genetic disease for which traditional treatments cause harmful side effects. After two decades of genomics technological breakthroughs, personalized medicine is being used to improve treatment outcomes and mitigate side effects. In mathematical modeling, it has been proposed that cancer matches an attractor in Waddington's epigenetic landscape. The use of Hopfield networks is an attractive modeling approach because it requires neither previous biological knowledge about protein-protein interactions nor kinetic parameters. In this report, Hopfield network modeling was used to analyze bulk RNA-Seq data of paired breast tumor and control samples from 70 patients. We characterized the control and tumor attractors with respect to their size and potential energy and correlated the Euclidean distances between the tumor samples and the control attractor with their corresponding clinical data. In addition, we developed a protocol that outlines the key genes involved in tumor state stability. We found that the tumor basin of attraction is larger than that of the control and that tumor samples are associated with a more substantial negative energy than control samples, which is in agreement with previous reports. Moreover, we found a negative correlation between the Euclidean distances from tumor samples to the control attractor and patient overall survival. The ascending order of each node's density in the weight matrix and the descending order of the number of patients that have the target active only in the tumor sample were the parameters that withdrew more tumor samples from the tumor basin of attraction with fewer gene inhibitions. The combinations of therapeutic targets were specific to each patient. We performed an initial validation through simulation of trastuzumab treatment effects in HER2+ breast cancer samples. For that, we built an energy landscape composed of single-cell and bulk RNA-Seq data from trastuzumab-treated and non-treated HER2+ samples. The trajectory from the non-treated bulk sample toward the treated bulk sample was inferred through the perturbation of differentially expressed genes between these samples. Among them, we characterized key genes involved in the trastuzumab response according to the literature.
ABSTRACT
Drought stress is the main limiting factor of soybean yield. Currently, genetic engineering has been one important tool in the development of drought-tolerant cultivars. A widely used strategy is the fusion of genes that confer tolerance under the control of the CaMV35S constitutive promoter; however, stress-responsive promoters would constitute the best alternative to the generation of drought-tolerant crops. We characterized the promoter of α-galactosidase soybean (GlymaGAL) gene that was previously identified as highly up-regulated by drought stress. The ß-glucuronidase (GUS) activity of Arabidopsis transgenic plants bearing 1000- and 2000-bp fragments of the GlymaGAL promoter fused to the uidA gene was evaluated under air-dried, polyethylene glycol (PEG) and salt stress treatments. After 24 h of air-dried and PEG treatments, the pGAL-2kb led to an increase in GUS expression in leaf and root samples when compared to the control samples. These results were corroborated by qPCR expression analysis of the uidA gene. The pGAL-1kb showed no difference in GUS activity between control and treated samples. The pGAL-2kb promoter was evaluated in transgenic soybean roots, leading to an increase in EGFP expression under air-dried treatment. Our data indicates that pGAL-2kb could be a useful tool in developing drought-tolerant cultivars by driving gene expression.
ABSTRACT
Abstract Drought stress is the main limiting factor of soybean yield. Currently, genetic engineering has been one important tool in the development of drought-tolerant cultivars. A widely used strategy is the fusion of genes that confer tolerance under the control of the CaMV35S constitutive promoter; however, stress-responsive promoters would constitute the best alternative to the generation of drought-tolerant crops. We characterized the promoter of α-galactosidase soybean (GlymaGAL) gene that was previously identified as highly up-regulated by drought stress. The β-glucuronidase (GUS) activity of Arabidopsis transgenic plants bearing 1000- and 2000-bp fragments of the GlymaGAL promoter fused to the uidA gene was evaluated under air-dried, polyethylene glycol (PEG) and salt stress treatments. After 24 h of air-dried and PEG treatments, the pGAL-2kb led to an increase in GUS expression in leaf and root samples when compared to the control samples. These results were corroborated by qPCR expression analysis of the uidA gene. The pGAL-1kb showed no difference in GUS activity between control and treated samples. The pGAL-2kb promoter was evaluated in transgenic soybean roots, leading to an increase in EGFP expression under air-dried treatment. Our data indicates that pGAL-2kb could be a useful tool in developing drought-tolerant cultivars by driving gene expression.
