Network Analysis of Biomarkers Associated with Occupational Exposure to Benzene and Malathion.

Complex diseases are associated with the effects of multiple genes, proteins, and biological pathways. In this context, the tools of Network Medicine are compatible as a platform to systematically explore not only the molecular complexity of a specific disease but may also lead to the identification of disease modules and pathways. Such an approach enables us to gain a better understanding of how environmental chemical exposures affect the function of human cells, providing better perceptions about the mechanisms involved and helping to monitor/prevent exposure and disease to chemicals such as benzene and malathion. We selected differentially expressed genes for exposure to benzene and malathion. The construction of interaction networks was carried out using GeneMANIA and STRING. Topological properties were calculated using MCODE, BiNGO, and CentiScaPe, and a Benzene network composed of 114 genes and 2415 interactions was obtained. After topological analysis, five networks were identified. In these subnets, the most interconnected nodes were identified as: IL-8, KLF6, KLF4, JUN, SERTAD1, and MT1H. In the Malathion network, composed of 67 proteins and 134 interactions, HRAS and STAT3 were the most interconnected nodes. Path analysis, combined with various types of high-throughput data, reflects biological processes more clearly and comprehensively than analyses involving the evaluation of individual genes. We emphasize the central roles played by several important hub genes obtained by exposure to benzene and malathion.

ACE2 Interaction Networks in COVID-19: A Physiological Framework for Prediction of Outcome in Patients with Cardiovascular Risk Factors.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (coronavirus disease 2019; COVID-19) is associated with adverse outcomes in patients with cardiovascular disease (CVD). The aim of the study was to characterize the interaction between SARS-CoV-2 and Angiotensin-Converting Enzyme 2 (ACE2) functional networks with a focus on CVD. METHODS: Using the network medicine approach and publicly available datasets, we investigated ACE2 tissue expression and described ACE2 interaction networks that could be affected by SARS-CoV-2 infection in the heart, lungs and nervous system. We compared them with changes in ACE-2 networks following SARS-CoV-2 infection by analyzing public data of human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). This analysis was performed using the Network by Relative Importance (NERI) algorithm, which integrates protein-protein interaction with co-expression networks. We also performed miRNA-target predictions to identify which miRNAs regulate ACE2-related networks and could play a role in the COVID19 outcome. Finally, we performed enrichment analysis for identifying the main COVID-19 risk groups. RESULTS: We found similar ACE2 expression confidence levels in respiratory and cardiovascular systems, supporting that heart tissue is a potential target of SARS-CoV-2. Analysis of ACE2 interaction networks in infected hiPSC-CMs identified multiple hub genes with corrupted signaling which can be responsible for cardiovascular symptoms. The most affected genes were EGFR (Epidermal Growth Factor Receptor), FN1 (Fibronectin 1), TP53, HSP90AA1, and APP (Amyloid Beta Precursor Protein), while the most affected interactions were associated with MAST2 and CALM1 (Calmodulin 1). Enrichment analysis revealed multiple diseases associated with the interaction networks of ACE2, especially cancerous diseases, obesity, hypertensive disease, Alzheimer's disease, non-insulin-dependent diabetes mellitus, and congestive heart failure. Among affected ACE2-network components connected with the SARS-Cov-2 interactome, we identified AGT (Angiotensinogen), CAT (Catalase), DPP4 (Dipeptidyl Peptidase 4), CCL2 (C-C Motif Chemokine Ligand 2), TFRC (Transferrin Receptor) and CAV1 (Caveolin-1), associated with cardiovascular risk factors. We described for the first time miRNAs which were common regulators of ACE2 networks and virus-related proteins in all analyzed datasets. The top miRNAs regulating ACE2 networks were miR-27a-3p, miR-26b-5p, miR-10b-5p, miR-302c-5p, hsa-miR-587, hsa-miR-1305, hsa-miR-200b-3p, hsa-miR-124-3p, and hsa-miR-16-5p. CONCLUSION: Our study provides a complete mechanistic framework for investigating the ACE2 network which was validated by expression data. This framework predicted risk groups, including the established ones, thus providing reliable novel information regarding the complexity of signaling pathways affected by SARS-CoV-2. It also identified miRNAs that could be used in personalized diagnosis in COVID-19.

Gene expression profile of whole blood cells differs in pregnant women with positive screening and negative diagnosis for gestational diabetes.

OBJECTIVE: To evaluate the gene expression profile of whole blood cells in pregnant women without diabetes (with positive screening and negative diagnosis for gestational diabetes mellitus (GDM)) compared with pregnant women with negative screening for GDM. RESEARCH DESIGN AND METHODS: Pregnant women were recruited in the Diabetes Perinatal Research Centre-Botucatu Medical School-UNESP and Botucatuense Mercy Hospital (UNIMED). Distributed into 2 groups: control (n=8), women with negative screening and non-diabetic (ND, n=13), with positive screening and negative diagnosis of GDM. A peripheral blood sample was collected for glucose, glycated hemoglobin, and microarray gene expression analyses. RESULTS: The evaluation of gene expression profiles showed significant differences between the control group and the ND group, with 22 differentially expressed gene sequences. Gene networks and interaction tables were generated to evaluate the biological processes associated with differentially expressed genes of interest. CONCLUSIONS: In the group with positive screening, there is an apparent regulatory balance between the functions of the differentially expressed genes related to the pathogenesis of diabetes and a compensatory attempt to mitigate the possible etiology. These results support the 'two-step Carpenter-Coustan' strategy because pregnant women with negative screening do not need to continue on diagnostic investigation of gestational diabetes, thus reducing the cost of healthcare and the medicalization of pregnancy. Although not diabetic, they do have risk factors, and thus attention to these genes is important when considering disease evolution because this pregnant women are a step toward developing diabetes compared with women without these risk factors.

Signaling transcript profile of the asexual intraerythrocytic development cycle of Plasmodium falciparum induced by melatonin and cAMP.

According to the World Health Organization (WHO), Plasmodium falciparum is the deadliest parasite among all species. This parasite possesses the ability to sense molecules, including melatonin (MEL) and cAMP, and modulate its cell cycle accordingly. MEL synchronizes the development of this malaria parasite by activating several cascades, including the generation of the second messenger cAMP. Therefore, we performed RNA sequencing (RNA-Seq) analysis in P. falciparum erythrocytic stages (ring, trophozoite and schizont) treated with MEL and cAMP. To investigate the expression profile of P. falciparum genes regulated by MEL and cAMP, we performed RNA-Seq analysis in three P. falciparum strains (control, 3D7; protein kinase 7 knockout, PfPK7-; and PfPK7 complement, PfPK7C). In the 3D7 strain, 38 genes were differentially expressed upon MEL treatment; however, none of the genes in the trophozoite (T) stage PfPK7- knockout parasites were differentially expressed upon MEL treatment for 5 hours compared to untreated controls, suggesting that PfPK7 may be involved in the signaling leading to differential gene expression. Moreover, we found that MEL modified the mRNA expression of genes encoding membrane proteins, zinc ion-binding proteins and nucleic acid-binding proteins, which might influence numerous functions in the parasite. The RNA-Seq data following treatment with cAMP show that this molecule modulates different genes throughout the intraerythrocytic cycle, namely, 75, 101 and 141 genes, respectively, in the ring (R), T and schizont (S) stages. Our results highlight P. falciparum's perception of the external milieu through the signaling molecules MEL and cAMP, which are able to drive to changes in gene expression in the parasite.

NERI: network-medicine based integrative approach for disease gene prioritization by relative importance.

BACKGROUND: Complex diseases are characterized as being polygenic and multifactorial, so this poses a challenge regarding the search for genes related to them. With the advent of high-throughput technologies for genome sequencing, gene expression measurements (transcriptome), and protein-protein interactions, complex diseases have been sistematically investigated. Particularly, Protein-Protein Interaction (PPI) networks have been used to prioritize genes related to complex diseases according to its topological features. However, PPI networks are affected by ascertainment bias, in which more studied proteins tend to have more connections, degrading the results quality. Additionally, methods using only PPI networks can provide only static and non-specific results, since the topologies of these networks are not specific of a given disease. RESULTS: The goal of this work is to develop a methodology that integrates PPI networks with disease specific data sources, such as GWAS and gene expression, to find genes more specific of a given complex disease. After the integration of PPI networks and gene expression data, the resulting network is used to connect genes related to the disease through the shortest paths that have the greatest concordance between their gene expressions. Both case and control expression data are used separately and, at the end, the most altered genes between the two conditions are selected. To evaluate the method, schizophrenia was adopted as case study. CONCLUSION: Results show that the proposed method successfully retrieves differentially coexpressed genes in two conditions, while avoiding the bias from literature. Moreover we were able to achieve a greater concordance in the selection of important genes from different microarray studies of the same disease and to produce a more specific gene set related to the studied disease.

Gene regulatory networks inference using a multi-GPU exhaustive search algorithm.

BACKGROUND: Gene regulatory networks (GRN) inference is an important bioinformatics problem in which the gene interactions need to be deduced from gene expression data, such as microarray data. Feature selection methods can be applied to this problem. A feature selection technique is composed by two parts: a search algorithm and a criterion function. Among the search algorithms already proposed, there is the exhaustive search where the best feature subset is returned, although its computational complexity is unfeasible in almost all situations. The objective of this work is the development of a low cost parallel solution based on GPU architectures for exhaustive search with a viable cost-benefit. We use CUDA™, a general purpose parallel programming platform that allows the usage of NVIDIA® GPUs to solve complex problems in an efficient way. RESULTS: We developed a parallel algorithm for GRN inference based on multiple GPU cards and obtained encouraging speedups (order of hundreds), when assuming that each target gene has two multivariate predictors. Also, experiments using single and multiple GPUs were performed, indicating that the speedup grows almost linearly with the number of GPUs. CONCLUSION: In this work, we present a proof of principle, showing that it is possible to parallelize the exhaustive search algorithm in GPUs with encouraging results. Although our focus in this paper is on the GRN inference problem, the exhaustive search technique based on GPU developed here can be applied (with minor adaptations) to other combinatorial problems.

A feature selection approach for identification of signature genes from SAGE data.

BACKGROUND: One goal of gene expression profiling is to identify signature genes that robustly distinguish different types or grades of tumors. Several tumor classifiers based on expression profiling have been proposed using microarray technique. Due to important differences in the probabilistic models of microarray and SAGE technologies, it is important to develop suitable techniques to select specific genes from SAGE measurements. RESULTS: A new framework to select specific genes that distinguish different biological states based on the analysis of SAGE data is proposed. The new framework applies the bolstered error for the identification of strong genes that separate the biological states in a feature space defined by the gene expression of a training set. Credibility intervals defined from a probabilistic model of SAGE measurements are used to identify the genes that distinguish the different states with more reliability among all gene groups selected by the strong genes method. A score taking into account the credibility and the bolstered error values in order to rank the groups of considered genes is proposed. Results obtained using SAGE data from gliomas are presented, thus corroborating the introduced methodology. CONCLUSION: The model representing counting data, such as SAGE, provides additional statistical information that allows a more robust analysis. The additional statistical information provided by the probabilistic model is incorporated in the methodology described in the paper. The introduced method is suitable to identify signature genes that lead to a good separation of the biological states using SAGE and may be adapted for other counting methods such as Massive Parallel Signature Sequencing (MPSS) or the recent Sequencing-By-Synthesis (SBS) technique. Some of such genes identified by the proposed method may be useful to generate classifiers.