Optimizing therapeutic targets for breast cancer using boolean network models.

Studying gene regulatory networks associated with cancer provides valuable insights for therapeutic purposes, given that cancer is fundamentally a genetic disease. However, as the number of genes in the system increases, the complexity arising from the interconnections between network components grows exponentially. In this study, using Boolean logic to adjust the existing relationships between network components has facilitated simplifying the modeling process, enabling the generation of attractors that represent cell phenotypes based on breast cancer RNA-seq data. A key therapeutic objective is to guide cells, through targeted interventions, to transition from the current cancer attractor to a physiologically distinct attractor unrelated to cancer. To achieve this, we developed a computational method that identifies network nodes whose inhibition can facilitate the desired transition from one tumor attractor to another associated with apoptosis, leveraging transcriptomic data from cell lines. To validate the model, we utilized previously published in vitro experiments where the downregulation of specific proteins resulted in cell growth arrest and death of a breast cancer cell line. The method proposed in this manuscript combines diverse data sources, conducts structural network analysis, and incorporates relevant biological knowledge on apoptosis in cancer cells. This comprehensive approach aims to identify potential targets of significance for personalized medicine.

Boolean model of the gene regulatory network of Pseudomonas aeruginosa CCBH4851.

Introduction: Pseudomonas aeruginosa infections are one of the leading causes of death in immunocompromised patients with cystic fibrosis, diabetes, and lung diseases such as pneumonia and bronchiectasis. Furthermore, P. aeruginosa is one of the main multidrug-resistant bacteria responsible for nosocomial infections worldwide, including the multidrug-resistant CCBH4851 strain isolated in Brazil. Methods: One way to analyze their dynamic cellular behavior is through computational modeling of the gene regulatory network, which represents interactions between regulatory genes and their targets. For this purpose, Boolean models are important predictive tools to analyze these interactions. They are one of the most commonly used methods for studying complex dynamic behavior in biological systems. Results and discussion: Therefore, this research consists of building a Boolean model of the gene regulatory network of P. aeruginosa CCBH4851 using data from RNA-seq experiments. Next, the basins of attraction are estimated, as these regions and the transitions between them can help identify the attractors, representing long-term behavior in the Boolean model. The essential genes of the basins were associated with the phenotypes of the bacteria for two conditions: biofilm formation and polymyxin B treatment. Overall, the Boolean model and the analysis method proposed in this work can identify promising control actions and indicate potential therapeutic targets, which can help pinpoint new drugs and intervention strategies.

A Data Science Approach for the Identification of Molecular Signatures of Aggressive Cancers.

The main hallmarks of cancer include sustaining proliferative signaling and resisting cell death. We analyzed the genes of the WNT pathway and seven cross-linked pathways that may explain the differences in aggressiveness among cancer types. We divided six cancer types (liver, lung, stomach, kidney, prostate, and thyroid) into classes of high (H) and low (L) aggressiveness considering the TCGA data, and their correlations between Shannon entropy and 5-year overall survival (OS). Then, we used principal component analysis (PCA), a random forest classifier (RFC), and protein-protein interactions (PPI) to find the genes that correlated with aggressiveness. Using PCA, we found GRB2, CTNNB1, SKP1, CSNK2A1, PRKDC, HDAC1, YWHAZ, YWHAB, and PSMD2. Except for PSMD2, the RFC analysis showed a different list, which was CAD, PSMD14, APH1A, PSMD2, SHC1, TMEFF2, PSMD11, H2AFZ, PSMB5, and NOTCH1. Both methods use different algorithmic approaches and have different purposes, which explains the discrepancy between the two gene lists. The key genes of aggressiveness found by PCA were those that maximized the separation of H and L classes according to its third component, which represented 19% of the total variance. By contrast, RFC classified whether the RNA-seq of a tumor sample was of the H or L type. Interestingly, PPIs showed that the genes of PCA and RFC lists were connected neighbors in the PPI signaling network of WNT and cross-linked pathways.

Galaxy and MEAN Stack to Create a User-Friendly Workflow for the Rational Optimization of Cancer Chemotherapy.

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.

Pulmonary Mesenchymal Stem Cells in Mild Cases of COVID-19 Are Dedicated to Proliferation; In Severe Cases, They Control Inflammation, Make Cell Dispersion, and Tissue Regeneration.

Mesenchymal stem cells (MSCs) are multipotent adult stem cells present in virtually all tissues; they have potent self-renewal capacity and differentiate into multiple cell types. For many reasons, these cells are a promising therapeutic alternative to treat patients with severe COVID-19 and pulmonary post-COVID sequelae. These cells are not only essential for tissue regeneration; they can also alter the pulmonary environment through the paracrine secretion of several mediators. They can control or promote inflammation, induce other stem cells differentiation, restrain the virus load, and much more. In this work, we performed single-cell RNA-seq data analysis of MSCs in bronchoalveolar lavage samples from control individuals and COVID-19 patients with mild and severe clinical conditions. When we compared samples from mild cases with control individuals, most genes transcriptionally upregulated in COVID-19 were involved in cell proliferation. However, a new set of genes with distinct biological functions was upregulated when we compared severely affected with mild COVID-19 patients. In this analysis, the cells upregulated genes related to cell dispersion/migration and induced the γ-activated sequence (GAS) genes, probably triggered by IFNGR1 and IFNGR2. Then, IRF-1 was upregulated, one of the GAS target genes, leading to the interferon-stimulated response (ISR) and the overexpression of many signature target genes. The MSCs also upregulated genes involved in the mesenchymal-epithelial transition, virus control, cell chemotaxis, and used the cytoplasmic RNA danger sensors RIG-1, MDA5, and PKR. In a non-comparative analysis, we observed that MSCs from severe cases do not express many NF-κB upstream receptors, such as Toll-like (TLRs) TLR-3, -7, and -8; tumor necrosis factor (TNFR1 or TNFR2), RANK, CD40, and IL-1R1. Indeed, many NF-κB inhibitors were upregulated, including PPP2CB, OPTN, NFKBIA, and FHL2, suggesting that MSCs do not play a role in the "cytokine storm" observed. Therefore, lung MSCs in COVID-19 sense immune danger and act protectively in concert with the pulmonary environment, confirming their therapeutic potential in cell-based therapy for COVID-19. The transcription of MSCs senescence markers is discussed.

Modeling Basins of Attraction for Breast Cancer Using Hopfield Networks.

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.

Signaling Complexity Measured by Shannon Entropy and Its Application in Personalized Medicine.

Traditional approaches to cancer therapy seek common molecular targets in tumors from different patients. However, molecular profiles differ between patients, and most tumors exhibit inherent heterogeneity. Hence, imprecise targeting commonly results in side effects, reduced efficacy, and drug resistance. By contrast, personalized medicine aims to establish a molecular diagnosis specific to each patient, which is currently feasible due to the progress achieved with high-throughput technologies. In this report, we explored data from human RNA-seq and protein-protein interaction (PPI) networks using bioinformatics to investigate the relationship between tumor entropy and aggressiveness. To compare PPI subnetworks of different sizes, we calculated the Shannon entropy associated with vertex connections of differentially expressed genes comparing tumor samples with their paired control tissues. We found that the inhibition of up-regulated connectivity hubs led to a higher reduction of subnetwork entropy compared to that obtained with the inhibition of targets selected at random. Furthermore, these hubs were described to be participating in tumor processes. We also found a significant negative correlation between subnetwork entropies of tumors and the respective 5-year survival rates of the corresponding cancer types. This correlation was also observed considering patients with lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) based on the clinical data from The Cancer Genome Atlas database (TCGA). Thus, network entropy increases in parallel with tumor aggressiveness but does not correlate with PPI subnetwork size. This correlation is consistent with previous reports and allowed us to assess the number of hubs to be inhibited for therapy to be effective, in the context of precision medicine, by reference to the 100% patient survival rate 5 years after diagnosis. Large standard deviations of subnetwork entropies and variations in target numbers per patient among tumor types characterize tumor heterogeneity.

Suspected Adverse Drug Reactions Related to Breast Cancer Chemotherapy: Disproportionality Analysis of the Brazilian Spontaneous Reporting System.

Spontaneous reporting systems may generate a large volume of information in real world conditions with a relatively low cost. Disproportionality measures are useful to indicate and quantify unexpected safety issues associated with a given drug-event pair (signals of disproportionality), based upon differences compared to the background reporting frequency. This cross-sectional study (2008 to 2013) aimed to analyse the feasibility of detecting such signals in the Brazilian Pharmacovigilance Database comprising suspected adverse drug reactions related to the use of doxorubicin, cyclophosphamide, carboplatin, trastuzumab, docetaxel, and paclitaxel for breast cancer chemotherapy. We first accessed overall database features (patient information and suspected adverse drug reactions) and further conducted a disproportionality analysis based on Reporting Odds Ratios with a confidence interval of 95% in order to identify possible signals of disproportionate reporting, only among serious suspected adverse drug reactions. Of all data reports of adverse reactions (n = 2603), 83% were classified as serious, with the highest prevalence with docetaxel (78.1%). The final analysis was performed using 1,309 reports with 3,139 drug-reaction pairs. The following signals of disproportionate reporting, some rare or not mentioned on labels, were observed: tachypnea with docetaxel; bronchospasm, syncope, cyanosis, and anaphylactic reaction with paclitaxel; and anaphylactic shock with trastuzumab. Structured management of spontaneous adverse drug reaction reporting is essential for monitoring the safe use of drugs and detecting early safety signals. Disproportionality signal analysis represents a viable and applicable strategy for oncology signal screening in the Brazilian Pharmacovigilance Database.

Gene regulatory network inference and analysis of multidrug-resistant Pseudomonas aeruginosa

BACKGROUND Healthcare-associated infections caused by bacteria such as Pseudomonas aeruginosa are a major public health problem worldwide. Gene regulatory networks (GRN) computationally represent interactions among regulatory genes and their targets. They are an important approach to help understand bacterial behaviour and to provide novel ways of overcoming scientific challenges, including the identification of potential therapeutic targets and the development of new drugs. OBJECTIVES The goal of this study was to reconstruct the multidrug-resistant (MDR) P. aeruginosa GRN and to analyse its topological properties. METHODS The methodology used in this study was based on gene orthology inference using the reciprocal best hit method. We used the genome of P. aeruginosa CCBH4851 as the basis of the reconstruction process. This MDR strain is representative of the sequence type 277, which was involved in an endemic outbreak in Brazil. FINDINGS We obtained a network with a larger number of regulatory genes, target genes and interactions as compared to the previously reported network. Topological analysis results are in accordance with the complex network representation of biological processes. MAIN CONCLUSIONS The properties of the network were consistent with the biological features of P. aeruginosa. To the best of our knowledge, the P. aeruginosa GRN presented here is the most complete version available to date.