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.

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.

Initial findings of striatum tripartite model in OCD brain samples based on transcriptome analysis.

Obsessive-compulsive disorder (OCD) is a psychiatric disorder characterized by obsessions and/or compulsions. Different striatal subregions belonging to the cortico-striato-thalamic circuitry (CSTC) play an important role in the pathophysiology of OCD. The transcriptomes of 3 separate striatal areas (putamen (PT), caudate nucleus (CN) and accumbens nucleus (NAC)) from postmortem brain tissue were compared between 6 OCD and 8 control cases.

Transcriptome Analysis of Mesenchymal Stem Cells from Multiple Myeloma Patients Reveals Downregulation of Genes Involved in Cell Cycle Progression, Immune Response, and Bone Metabolism.

A growing body of evidence suggests a key role of tumor microenvironment, especially for bone marrow mesenchymal stem cells (MSC), in the maintenance and progression of multiple myeloma (MM), through direct and indirect interactions with tumor plasma cells. Thus, this study aimed to investigate the gene expression and functional alterations of MSC from MM patients (MM-MSC) in comparison with their normal counterparts from normal donors (ND-MSC). Gene expression analysis (Affymetrix) was performed in MM-MSC and ND-MSC after in vitro expansion.

Assessment of complementarity of WGCNA and NERI results for identification of modules associated to schizophrenia spectrum disorders.

Psychiatric disorders involve both changes in multiple genes as well different types of variations. As such, gene co-expression networks allowed the comparison of different stages and parts of the brain contributing to an integrated view of genetic variation. Two methods based on co-expression networks presents appealing results: Weighted Gene Correlation Network Analysis (WGCNA) and Network-Medicine Relative Importance (NERI).

Genome-wide analysis of the human malaria parasite transcription factor PfNF-YB shows interaction with a CCAAT motif.

Little is known about transcription factor regulation during the intraerythrocytic cycle. In order to elucidate the role of the (Pf)NF-YB transcription factor we searched for target genes in the entire genome. PfNF-YB mRNA is highly expressed in late trophozoite and schizont stages relative to the ring stage.

Multidimensional integrative analysis uncovers driver candidates and biomarkers in penile carcinoma.

Molecular data generation and their combination in penile carcinomas (PeCa), a significant public health problem in poor and underdeveloped countries, remain virtually unexplored. An integrativemethodology combin ing genome-wide copy number alteration, DNA methylation, miRNA and mRNA expression analysis was performed in a set of 20 usual PeCa. The well-ranked 16 driver candidates harboring genomic alterations and regulated by a set of miRNAs, including hsa-miR-31, hsa-miR-34a and hsa-miR-130b, were significantly associated with over-represented pathways in cancer, such as immune-inflammatory system, apoptosis and cell cycle.

GeNICE: A Novel Framework for Gene Network Inference by Clustering, Exhaustive Search, and Multivariate Analysis.

Gene network (GN) inference from temporal gene expression data is a crucial and challenging problem in systems biology. Expression data sets usually consist of dozens of temporal samples, while networks consist of thousands of genes, thus rendering many inference methods unfeasible in practice. To improve the scalability of GN inference methods, we propose a novel framework called GeNICE, based on probabilistic GNs; the main novelty is the introduction of a clustering procedure to group genes with related expression profiles and to provide an approximate solution with reduced computational complexity.

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

According to the World Health Organization (WHO), 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.

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.

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.

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.

Feature selection environment for genomic applications.

Background: Feature selection is a pattern recognition approach to choose important variables according to some criteria in order to distinguish or explain certain phenomena (i.e., for dimensionality reduction).

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.