Partial correlation network analysis identifies coordinated gene expression within a regional cluster of COPD genome-wide association signals.

Chronic obstructive pulmonary disease (COPD) is a complex disease influenced by well-established environmental exposures (most notably, cigarette smoking) and incompletely defined genetic factors. The chromosome 4q region harbors multiple genetic risk loci for COPD, including signals near HHIP, FAM13A, GSTCD, TET2, and BTC. Leveraging RNA-Seq data from lung tissue in COPD cases and controls, we estimated the co-expression network for genes in the 4q region bounded by HHIP and BTC (~70MB), through partial correlations informed by protein-protein interactions.

Temporal Exploration of COPD Phenotypes: Insights from the COPDGene and SPIROMICS Cohorts.

Background: Chronic obstructive pulmonary disease (COPD) exhibits considerable progression heterogeneity. We hypothesized that elastic principal graph analysis (EPGA) would identify distinct clinical phenotypes and their longitudinal relationships.

Methods: Cross-sectional data from 8,972 tobacco-exposed COPDGene participants, with and without COPD, were used to train a model with EPGA, using thirty clinical, physiologic and CT features.

Vitamin D constrains inflammation by modulating the expression of key genes on Chr17q12-21.1.

Vitamin D possesses immunomodulatory functions and vitamin D deficiency has been associated with the rise in chronic inflammatory diseases, including asthma (Litonjua and Weiss, 2007). Vitamin D supplementation studies do not provide insight into the molecular genetic mechanisms of vitamin D-mediated immunoregulation. Here, we provide evidence for vitamin D regulation of two human chromosomal loci, Chr17q12-21.

Cellular Heterogeneity of Activated Primary Human Macrophages and Associated Drug-Gene Networks: From Biology to Precision Therapeutics.

Background: Interferon-γ (IFNγ) signaling plays a complex role in atherogenesis. IFNγ stimulation of macrophages permits in vitro exploration of proinflammatory mechanisms and the development of novel immune therapies. We hypothesized that the study of macrophage subpopulations could lead to anti-inflammatory interventions.

Phenomics and Robust Multiomics Data for Cardiovascular Disease Subtyping.

The complex landscape of cardiovascular diseases encompasses a wide range of related pathologies arising from diverse molecular mechanisms and exhibiting heterogeneous phenotypes. This variety of manifestations poses significant challenges in the development of treatment strategies. The increasing availability of precise phenotypic and multiomics data of cardiovascular disease patient populations has spurred the development of a variety of computational disease subtyping techniques to identify distinct subgroups with unique underlying pathogeneses.

Single Cell Transcriptomic Analysis Reveals Organ Specific Pericyte Markers and Identities.

Pericytes are mesenchymal-derived mural cells that wrap around capillaries and directly contact endothelial cells. Present throughout the body, including the cardiovascular system, pericytes are proposed to have multipotent cell-like properties and are involved in numerous biological processes, including regulation of vascular development, maturation, permeability, and homeostasis. Despite their physiological importance, the functional heterogeneity, differentiation process, and pathological roles of pericytes are not yet clearly understood, in part due to the inability to reliably distinguish them from other mural cell populations.

Genomic signatures of the unjamming transition in compressed human bronchial epithelial cells.

Epithelial tissue can transition from a jammed, solid-like, quiescent phase to an unjammed, fluid-like, migratory phase, but the underlying molecular events of the unjamming transition (UJT) remain largely unexplored. Using primary human bronchial epithelial cells (HBECs) and one well-defined trigger of the UJT, compression mimicking the mechanical effects of bronchoconstriction, here, we combine RNA sequencing data with protein-protein interaction networks to provide the first genome-wide analysis of the UJT. Our results show that compression induces an early transcriptional activation of the membrane and actomyosin network and a delayed activation of the extracellular matrix (ECM) and cell-matrix networks.

Systems Approaches to Treatment Response to Imatinib in Severe Asthma: A Pilot Study.

There is an acute need for advances in pharmacologic therapies and a better understanding of novel drug targets for severe asthma. Imatinib, a tyrosine kinase inhibitor, has been shown to improve forced expiratory volume in 1 s (FEV) in a clinical trial of patients with severe asthma. In a pilot study, we applied systems biology approaches to epithelium gene expression from these clinical trial patients treated with imatinib to better understand lung function response with imatinib treatment.

Robustness and lethality in multilayer biological molecular networks.

Robustness is a prominent feature of most biological systems. Most previous related studies have been focused on homogeneous molecular networks. Here we propose a comprehensive framework for understanding how the interactions between genes, proteins and metabolites contribute to the determinants of robustness in a heterogeneous biological network.

Early-pregnancy transcriptome signatures of preeclampsia: from peripheral blood to placenta.

Several studies have linked maternal asthma, excess BMI, and low vitamin D status with increased risk of Preeclampsia (PE) development. Given prior evidence in the literature and our observations from the subjects in the Vitamin D Antenatal Asthma Reduction Trial (VDAART), we hypothesized that PE, maternal asthma, vitamin D insufficiency, and excess body mass index (BMI) might share both peripheral blood and placental gene signatures that link these conditions together. We used samples collected in the VDAART to investigate relationships between these four conditions and gene expression patterns in peripheral blood obtained at early pregnancy.

Discovering the genes mediating the interactions between chronic respiratory diseases in the human interactome.

The molecular and clinical features of a complex disease can be influenced by other diseases affecting the same individual. Understanding disease-disease interactions is therefore crucial for revealing shared molecular mechanisms among diseases and designing effective treatments. Here we introduce Flow Centrality (FC), a network-based approach to identify the genes mediating the interaction between two diseases in a protein-protein interaction network.

Network Diffusion Approach to Predict LncRNA Disease Associations Using Multi-Type Biological Networks: LION.

Recently, long-non-coding RNAs (lncRNAs) have attracted attention because of their emerging role in many important biological mechanisms. The accumulating evidence indicates that the dysregulation of lncRNAs is associated with complex diseases. However, only a few lncRNA-disease associations have been experimentally validated and therefore, predicting potential lncRNAs that are associated with diseases become an important task.

A Novel Neural Networks Ensemble Approach for Modeling Electrochemical Cells.

Accurate modeling of electrochemical cells is nowadays mandatory for achieving effective upgrades in the fields of energetic efficiency and sustainable mobility. Indeed, these models are often used for performing accurate State-of-Charge (SoC) estimations in energy storage systems used in microgrids or powering pure electric and hybrid cars. To this aim, a novel neural networks ensemble approach for modeling electrochemical cells is proposed in this paper.

Learning an unknown transformation via a genetic approach.

Recent developments in integrated photonics technology are opening the way to the fabrication of complex linear optical interferometers. The application of this platform is ubiquitous in quantum information science, from quantum simulation to quantum metrology, including the quest for quantum supremacy via the boson sampling problem. Within these contexts, the capability to learn efficiently the unitary operation of the implemented interferometers becomes a crucial requirement.

A generative model for protein contact networks.

In this paper, we present a generative model for protein contact networks (PCNs). The soundness of the proposed model is investigated by focusing primarily on mesoscopic properties elaborated from the spectra of the graph Laplacian. To complement the analysis, we also study the classical topological descriptors, such as statistics of the shortest paths and the important feature of modularity.

General rules for bosonic bunching in multimode interferometers.

We perform a comprehensive set of experiments that characterize bosonic bunching of up to three photons in interferometers of up to 16 modes. Our experiments verify two rules that govern bosonic bunching. The first rule, obtained recently, predicts the average behavior of the bunching probability and is known as the bosonic birthday paradox.