Signaling network of lipids as a comprehensive scaffold for omics data integration in sputum of COPD patients

STARGATE-X: a Python package for statistical analysis on the REACTOME network

Many important aspects of biological knowledge at the molecular level can be represented by pathways. Through their analysis, we gain mechanistic insights and interpret lists of interesting genes from experiments (usually omics and functional genomic experiments). As a result, pathways play a central role in the development of bioinformatics methods and tools for computing predictions from known molecular-level mechanisms. Qualitative as well as quantitative knowledge about pathways can be effectively represented through biochemical networks linking the biochemical reactions and the compounds (e.g., proteins) occurring in the considered pathways. So, repositories providing biochemical networks for known pathways play a central role in bioinformatics and in systems biology. Here we focus on Reactome, a free, comprehensive, and widely used repository for biochemical networks and pathways. In this paper, we: (1) introduce a tool StARGate-X (STatistical Analysis of the Reactome multi-GrAph Through nEtworkX) to carry out an automated analysis of the connectivity properties of Reactome biochemical reaction network and of its biological hierarchy (i.e., cell compartments, namely, the closed parts within the cytosol, usually surrounded by a membrane); the code is freely available at https://github.com/marinoandrea/stargate-x; (2) show the effectiveness of our tool by providing an analysis of the Reactome network, in terms of centrality measures, with respect to in- and out-degree. As an example of usage of StARGate-X, we provide a detailed automated analysis of the Reactome network, in terms of centrality measures. We focus both on the subgraphs induced by single compartments and on the graph whose nodes are the strongly connected components. To the best of our knowledge, this is the first freely available tool that enables automatic analysis of the large biochemical network within Reactome through easy-to-use APIs (Application Programming Interfaces).

Update on metabolomic findings in COPD patients

COPD is a heterogeneous disorder that shows diverse clinical presentations (phenotypes and "treatable traits") and biological mechanisms (endotypes). This heterogeneity implies that to carry out a more personalised clinical management, it is necessary to classify each patient accurately. With this objective, and in addition to clinical features, it would be very useful to have well-defined biological markers. The search for these markers may either be done through more conventional laboratory and hypothesis-driven techniques or relatively blind high-throughput methods, with the omics approaches being suitable for the latter. Metabolomics is the science that studies biological processes through their metabolites, using various techniques such as gas and liquid chromatography, mass spectrometry and nuclear magnetic resonance. The most relevant metabolomics studies carried out in COPD highlight the importance of metabolites involved in pathways directly related to proteins (peptides and amino acids), nucleic acids (nitrogenous bases and nucleosides), and lipids and their derivatives (especially fatty acids, phospholipids, ceramides and eicosanoids). These findings indicate the relevance of inflammatory-immune processes, oxidative stress, increased catabolism and alterations in the energy production. However, some specific findings have also been reported for different COPD phenotypes, demographic characteristics of the patients, disease progression profiles, exacerbations, systemic manifestations and even diverse treatments. Unfortunately, the studies carried out to date have some limitations and shortcomings and there is still a need to define clear metabolomic profiles with clinical utility for the management of COPD and its implicit heterogeneity.

Metabolomics in COPD

The clinical presentation of chronic obstructive pulmonary disease (COPD) is highly heterogeneous. Attempts have been made to define subpopulations of patients who share clinical characteristics (phenotypes and treatable traits) and/or biological characteristics (endotypes), in order to offer more personalized care. Assigning a patient to any of these groups requires the identification of both clinical and biological markers. Ideally, biological markers should be easily obtained from blood or urine, but these may lack specificity. Biomarkers can be identified initially using conventional or more sophisticated techniques. However, the more sophisticated techniques should be simplified in the future if they are to have clinical utility. The -omics approach offers a methodology that can assist in the investigation and identification of useful markers in both targeted and blind searches. Specifically, metabolomics is the science that studies biological processes involving metabolites, which can be intermediate or final products. The metabolites associated with COPD and their specific phenotypic and endotypic features have been studied using various techniques. Several compounds of particular interest have emerged, namely, several types of lipids and derivatives (mainly phospholipids, but also ceramides, fatty acids and eicosanoids), amino acids, coagulation factors, and nucleic acid components, likely to be involved in their function, protein catabolism, energy production, oxidative stress, immune-inflammatory response and coagulation disorders. However, clear metabolomic profiles of the disease and its various manifestations that may already be applicable in clinical practice still need to be defined.

Surfactant protein D inhibits lipid-laden foamy macrophages and lung inflammation in chronic obstructive pulmonary disease

Increased levels of surfactant protein D (SP-D) and lipid-laden foamy macrophages (FMs) are frequently found under oxidative stress conditions and/or in patients with chronic obstructive pulmonary disease (COPD) who are also chronically exposed to cigarette smoke (CS). However, the roles and molecular mechanisms of SP-D and FMs in COPD have not yet been determined. In this study, increased levels of SP-D were found in the bronchoalveolar lavage fluid (BALF) and sera of ozone- and CS-exposed mice. Furthermore, SP-D-knockout mice showed increased lipid-laden FMs and airway inflammation caused by ozone and CS exposure, similar to that exhibited by our study cohort of chronic smokers and COPD patients. We also showed that an exogenous recombinant fragment of human SP-D (rfhSP-D) prevented the formation of oxidized low-density lipoprotein (oxLDL)-induced FMs in vitro and reversed the airway inflammation and emphysematous changes caused by oxidative stress and CS exposure in vivo. SP-D upregulated bone marrow-derived macrophage (BMDM) expression of genes involved in countering the oxidative stress and lipid metabolism perturbations induced by CS and oxLDL. Our study demonstrates the crucial roles of SP-D in the lipid homeostasis of dysfunctional alveolar macrophages caused by ozone and CS exposure in experimental mouse emphysema, which may provide a novel opportunity for the clinical application of SP-D in patients with COPD.

Multi-omics subtyping pipeline for chronic obstructive pulmonary disease

Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of mortality in the United States; however, COPD has heterogeneous clinical phenotypes. This is the first large scale attempt which uses transcriptomics, proteomics, and metabolomics (multi-omics) to determine whether there are molecularly defined clusters with distinct clinical phenotypes that may underlie the clinical heterogeneity. Subjects included 3,278 subjects from the COPDGene cohort with at least one of the following profiles: whole blood transcriptomes (2,650 subjects); plasma proteomes (1,013 subjects); and plasma metabolomes (1,136 subjects). 489 subjects had all three contemporaneous -omics profiles. Autoencoder embeddings were performed individually for each -omics dataset. Embeddings underwent subspace clustering using MineClus, either individually by -omics or combined, followed by recursive feature selection based on Support Vector Machines. Clusters were tested for associations with clinical variables. Optimal single -omics clustering typically resulted in two clusters. Although there was overlap for individual -omics cluster membership, each -omics cluster tended to be defined by unique molecular pathways. For example, prominent molecular features of the metabolome-based clustering included sphingomyelin, while key molecular features of the transcriptome-based clusters were related to immune and bacterial responses. We also found that when we integrated the -omics data at a later stage, we identified subtypes that varied based on age, severity of disease, in addition to diffusing capacity of the lungs for carbon monoxide, and precent on atrial fibrillation. In contrast, when we integrated the -omics data at an earlier stage by treating all data sets equally, there were no clinical differences between subtypes. Similar to clinical clustering, which has revealed multiple heterogenous clinical phenotypes, we show that transcriptomics, proteomics, and metabolomics tend to define clusters of COPD patients with different clinical characteristics. Thus, integrating these different -omics data sets affords additional insight into the molecular nature of COPD and its heterogeneity.

Molecular Mechanisms of Lipid Metabolism Disorders in Infectious Exacerbations of Chronic Obstructive Pulmonary Disease

Exacerbations largely determine the character of the progression and prognosis of chronic obstructive pulmonary disease (COPD). Exacerbations are connected with changes in the microbiological landscape in the bronchi due to a violation of their immune homeostasis. Many metabolic and immune processes involved in COPD progression are associated with bacterial colonization of the bronchi. The objective of this review is the analysis of the molecular mechanisms of lipid metabolism and immune response disorders in the lungs in COPD exacerbations. The complex role of lipid metabolism disorders in the pathogenesis of some infections is only beginning to be understood, however, there are already fewer and fewer doubts even now about its significance both in the pathogenesis of infectious exacerbations of COPD and in general in the progression of the disease. It is shown that the lipid rafts of the plasma membranes of cells are involved in many processes related to the detection of pathogens, signal transduction, the penetration of pathogens into the cell. Smoking disrupts the normally proceeded processes of lipid metabolism in the lungs, which is a part of the COPD pathogenesis.

Serum Glycerophospholipid Profile in Acute Exacerbation of Chronic Obstructive Pulmonary Disease

Studies have shown that glycerophospholipids are involved in the pathogenesis of chronic obstructive pulmonary disease (COPD). This study adopted targeted metabolomic analysis to investigate the changes in serum glycerophospholipids in acute exacerbation of chronic obstructive pulmonary disease (AECOPD) and their differential expression in patients with different inflammatory subtypes. Patients with AECOPD admitted between January 2015 and December 2017 were enrolled, and their clinical data were collected. The patients' gender, age, body mass index, and lung function were recorded. Routine blood and induced sputum tests were performed. Liquid chromatography-mass spectrometry was used to detect the serum glycerophospholipid metabolic profiles and to analyze the metabolic profile changes between the acute exacerbation and recovery stages as well as the differences between different inflammatory subtypes. A total of 58 patients were hospitalized for AECOPD, including 49 male patients with a mean age of 74.8 ± 10.0 years. In the metabolic profiles, the expression of lysophosphatidylcholine (LPC) 18:3, lysophosphatidylethanolamine (LPE) 16:1, and phosphatidylinositol (PI) 32:1 was significantly reduced in the acute exacerbation stage compared to the recovery stage (P < 0.05). The three glycerophospholipids were used to plot the receiver operating characteristic curves to predict the acute exacerbation/recovery stage, and the areas under the curves were all above 70%. There were no differential metabolites between the two groups of patients with blood eosinophil percentage (EOS%) ≥2% and <2% at exacerbation. The expression of LPC 18:3, LPE 16:1, and PI 32:1 was significantly reduced in the acute exacerbation stage compared to the recovery stage in the inflammatory subtype with blood EOS <2% (P < 0.05). Abnormalities in the metabolism of glycerophospholipids may be involved in the onset of AECOPD, especially in the non-eosinophilic subtype.

Predictive diagnosis of chronic obstructive pulmonary disease using serum metabolic biomarkers and least-squares support vector machine

Objective

Development of biofluid‐based biomarkers is attractive for the diagnosis of chronic obstructive pulmonary disease (COPD) but still lacking. Thus, here we aimed to identify serum metabolic biomarkers for the diagnosis of COPD.

Methods

In this study, we investigated serum metabolic features between COPD patients (n = 54) and normal individuals (n = 74) using a ¹H NMR‐based metabolomics approach and developed an integrated method of least‐squares support vector machine (LS‐SVM) and serum metabolic biomarkers to assist COPD diagnosis.

Results

We observed a hypometabolic state in serum of COPD patients, as indicated by decreases in N‐acetyl‐glycoprotein (NAG), lipoprotein (LOP, mainly LDL/VLDL), polyunsaturated fatty acid (pUFA), glucose, alanine, leucine, histidine, valine, and lactate. Using an integrated method of multivariable and univariate analyses, NAG and LOP were identified as two important metabolites for distinguishing between COPD patients and controls. Subsequently, we developed a LS‐SVM classifier using these two markers and found that LS‐SVM classifiers with linear and polynomial kernels performed better than the classifier with RBF kernel. Linear and polynomial LS‐SVM classifiers can achieve the total accuracy rates of 80.77% and 84.62% and the AUC values of 0.87 and 0.90 for COPD diagnosis, respectively.

Conclusions

This study suggests that artificial intelligence integrated with serum metabolic biomarkers has a great potential for auxiliary diagnosis of COPD.

Molecular Network Analysis in Rabies Pathogenesis Using Cooperative Game Theory

Background and Purpose

Recently, many researchers from different fields of science have been used networks to analyze complex relational big data. The identification of which nodes are more important than the others, known as centrality analysis, is a key issue in biological network analysis. Although, several centralities have been introduced degree, closeness, and betweenness centralities are the most popular. These centralities are based on the individual position of each node and the cooperation and synergies between nodes have been ignored.

Objectives

Since in many cases, the network function is a consequence of cooperation and interaction between nodes, classical centralities were extended to a group of nodes instead of only individual nodes using cooperative game theory concepts. In this study, we analyze the protein interaction network inferred in rabies disease and rank gene products based on group centrality measurements to identify the novel gene candidates.

Materials and Methods

For this purpose, we used a game-theoretic approach at three scenarios, where the power of a coalition of genes assessed using different criteria including the neighbors of genes in the network, and predefined importance of the genes in its neighborhood. The Shapley value of such a game was considered as a new centrality. In this study, we analyze the network of gene products implicates rabies. The network has 1059 nodes and 8844 edges and centrality analysis was performed using CINNA package in R software.

Results

Based on three scenarios, we selected genes among the highest Shapley value that had low ranking from classical centralities. The enrichment analysis among the selected genes in scenario 1 indicates important pathways in rabies pathogenesis. Pair-wise correlation analysis reveals that changing the weights of nodes at different scenarios can significantly affect the results of ranking genes in the network.

Conclusion

A prior knowledge about the disease and the topology of the network, enable us to design an appropriate game and consequently infer some biological important nodes (genes) in the network. Obviously, a single centrality cannot capture all significant features embedded in the network.

Can We Assume the Gene Expression Profile as a Proxy for Signaling Network Activity?

Studying relationships among gene products by expression profile analysis is a common approach in systems biology. Many studies have generalized the outcomes to the different levels of central dogma information flow and assumed a correlation of transcript and protein expression levels. However, the relation between the various types of interaction (i.e., activation and inhibition) of gene products to their expression profiles has not been widely studied. In fact, looking for any perturbation according to differentially expressed genes is the common approach, while analyzing the effects of altered expression on the activity of signaling pathways is often ignored. In this study, we examine whether significant changes in gene expression necessarily lead to dysregulated signaling pathways. Using four commonly used and comprehensive databases, we extracted all relevant gene expression data and all relationships among directly linked gene pairs. We aimed to evaluate the ratio of coherency or sign consistency between the expression level as well as the causal relationships among the gene pairs. Through a comparison with random unconnected gene pairs, we illustrate that the signaling network is incoherent, and inconsistent with the recorded expression profile. Finally, we demonstrate that, to infer perturbed signaling pathways, we need to consider the type of relationships in addition to gene-product expression data, especially at the transcript level. We assert that identifying enriched biological processes via differentially expressed genes is limited when attempting to infer dysregulated pathways.

Integrated use of bioinformatic resources reveals that co-targeting of histone deacetylases, IKBK and SRC inhibits epithelial-mesenchymal transition in cancer

With the advent of high-throughput technologies leading to big data generation, increasing number of gene signatures are being published to predict various features of diseases such as prognosis and patient survival. However, to use these signatures for identifying therapeutic targets, use of additional bioinformatic tools is indispensible part of research. Here, we have generated a pipeline comprised of nearly 15 bioinformatic tools and enrichment statistical methods to propose and validate a drug combination strategy from already approved drugs and present our approach using published pan-cancer epithelial-mesenchymal transition (EMT) signatures as a case study. We observed that histone deacetylases were critical targets to tune expression of multiple epithelial versus mesenchymal genes. Moreover, SRC and IKBK were the principal intracellular kinases regulating multiple signaling pathways. To confirm the anti-EMT efficacy of the proposed target combination in silico, we validated expression of targets in mesenchymal versus epithelial subtypes of ovarian cancer. Additionally, we inhibited the pinpointed proteins in vitro using an invasive lung cancer cell line. We found that whereas low-dose mono-therapy failed to limit cell dispersion from collagen spheroids in a microfluidic device as a metric of EMT, the combination fully inhibited dissociation and invasion of cancer cells toward cocultured endothelial cells. Given the approval status and safety profiles of the suggested drugs, the proposed combination set can be considered in clinical trials.

Iron and Sphingolipids as Common Players of (Mal)Adaptation to Hypoxia in Pulmonary Diseases

Hypoxia, or lack of oxygen, can occur in both physiological (high altitude) and pathological conditions (respiratory diseases). In this narrative review, we introduce high altitude pulmonary edema (HAPE), acute respiratory distress syndrome (ARDS), Chronic Obstructive Pulmonary Disease (COPD), and Cystic Fibrosis (CF) as examples of maladaptation to hypoxia, and highlight some of the potential mechanisms influencing the prognosis of the affected patients. Among the specific pathways modulated in response to hypoxia, iron metabolism has been widely explored in recent years. Recent evidence emphasizes hepcidin as highly involved in the compensatory response to hypoxia in healthy subjects. A less investigated field in the adaptation to hypoxia is the sphingolipid (SPL) metabolism, especially through Ceramide and sphingosine 1 phosphate. Both individually and in concert, iron and SPL are active players of the (mal)adaptation to physiological hypoxia, which can result in the pathological HAPE. Our aim is to identify some pathways and/or markers involved in the physiological adaptation to low atmospheric pressures (high altitudes) that could be involved in pathological adaptation to hypoxia as it occurs in pulmonary inflammatory diseases. Hepcidin, Cer, S1P, and their interplay in hypoxia are raising growing interest both as prognostic factors and therapeutical targets.

Bioactive Sphingolipids in the Pathogenesis of Chronic Obstructive Pulmonary Disease

A better understanding of the pathogenesis of distinct chronic obstructive pulmonary disease (COPD) phenotypes will improve diagnostic and therapeutic options for this common disease. We present evidence that sphingolipids such as ceramides are involved in the emphysema pathogenesis. Whereas distinct ceramide species cause cell death by apoptosis and necroptosis, cell adaptation leads to accumulation of other sphingolipid metabolites that extend cell survival by triggering autophagy. Cigarette smoke-released sphingolipids have been involved in both the initiation and persistence of lung injury via intracellular signaling and paracrine effects mediated via exosomes and plasma membrane-bound microparticles. Strategies to control sphingolipid metabolite production may promote cellular repair and maintenance to treat COPD.

Three-way interaction model with switching mechanism as an effective strategy for tracing functionally-related genes

Introduction: Identification of functionally-related genes is an important step in understanding biological systems. The most popular strategy to infer functional dependence is to study pairwise correlations between gene expression levels. However, certain functionally-related genes may have a low expression correlation due to their nonlinear interactions. The use of a three-way interaction (3WI) model with switching mechanism (SM) is a relatively new strategy to trace functionally-related genes. The 3WI model traces the dynamic and nonlinear nature of the co-expression relationship of two genes by introducing their link to the expression level of a third gene. Areas covered: In this paper, we reviewed a variety of existing methods for tracing the 3WIs. Furthermore, we provide a comprehensive review of the previous biological studies based on 3WI models. Expert commentary: Comparison of features of these methods indicates that the modified liquid association algorithm has the best efficiency for tracing 3WI between others. The limited number of biological studies based on the 3WI suggests that high computational demand of the available algorithms is a major challenge to apply this approach for analyzing high-throughput omics data.

Smoking is associated with quantifiable differences in the human lung DNA virome and metabolome

BACKGROUND

The role of commensal viruses in humans is poorly understood, and the impact of the virome on lung health and smoking-related disease is particularly understudied.

METHODS

Genetic material from acellular bronchoalveolar lavage fluid was sequenced to identify and quantify viral members of the lower respiratory tract which were compared against concurrent bronchoalveolar lavage bacterial, metabolite, cytokine and cellular profiles, and clinical data. Twenty smoker and 10 nonsmoker participants with no significant comorbidities were studied.

RESULTS

Viruses that infect bacteria (phages) represented the vast majority of viruses in the lung. Though bacterial communities were statistically indistinguishable across smokers and nonsmokers as observed in previous studies, lung viromes and metabolic profiles were significantly different between groups. Statistical analyses revealed that changes in viral communities correlate most with changes in levels of arachidonic acid and IL-8, both potentially relevant for chronic obstructive pulmonary disease (COPD) pathogenesis based on prior studies.

CONCLUSIONS

Our assessment of human lung DNA viral communities reveals that commensal viruses are present in the lower respiratory tract and differ between smokers and nonsmokers. The associations between viral populations and local immune and metabolic tone suggest a significant role for virome-host interaction in smoking related lung disease.

A systematic survey of centrality measures for protein-protein interaction networks

Background

Numerous centrality measures have been introduced to identify “central” nodes in large networks. The availability of a wide range of measures for ranking influential nodes leaves the user to decide which measure may best suit the analysis of a given network. The choice of a suitable measure is furthermore complicated by the impact of the network topology on ranking influential nodes by centrality measures. To approach this problem systematically, we examined the centrality profile of nodes of yeast protein-protein interaction networks (PPINs) in order to detect which centrality measure is succeeding in predicting influential proteins. We studied how different topological network features are reflected in a large set of commonly used centrality measures.

Results

We used yeast PPINs to compare 27 common of centrality measures. The measures characterize and assort influential nodes of the networks. We applied principal component analysis (PCA) and hierarchical clustering and found that the most informative measures depend on the network’s topology. Interestingly, some measures had a high level of contribution in comparison to others in all PPINs, namely Latora closeness, Decay, Lin, Freeman closeness, Diffusion, Residual closeness and Average distance centralities.

Conclusions

The choice of a suitable set of centrality measures is crucial for inferring important functional properties of a network. We concluded that undertaking data reduction using unsupervised machine learning methods helps to choose appropriate variables (centrality measures). Hence, we proposed identifying the contribution proportions of the centrality measures with PCA as a prerequisite step of network analysis before inferring functional consequences, e.g., essentiality of a node.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0598-2) contains supplementary material, which is available to authorized users.

Valproic acid inhibits the protective effects of stromal cells against chemotherapy in breast cancer: Insights from proteomics and systems biology

Interaction between tumor and stromal cells is beginning to be decoded as a contributor to chemotherapy resistance. Here, we aim to take a system-level approach to explore a mechanism by which stromal cells induce chemoresistance in cancer cells and subsequently identify a drug that can inhibit such interaction. Using a proteomic dataset containing quantitative data on secretome of stromal cells, we performed multivariate analyses and found that bone-marrow mesenchymal stem cells (BM-MSCs) play the most protective role against chemotherapeutics. Pathway enrichment tests showed that secreted cytokines from BM-MSCs activated 4 signaling pathways including Janus kinase-signal transducer and activator of transcription, phosphatidylinositol 3-kinase-protein kinase B, and mitogen-activated protein kinase, transforming growth factor-β in cancer cells collectively leading to nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB) transcription factor activation. Based on the data from integrated Library of Integrated Network-Based Cellular Signatures (iLINCs) program, we found that among different drugs, valproic acid (VA) affected the expression of 34 genes within the identified pathways that are activated by stromal cells. Our in vitro experiments confirmed that VA inhibits NF-kB activation in cancer cells. In addition, analyzing gene expression data in patients taking oral VA showed that this drug decreased expression of antioxidant enzymes culminating in increased oxidative stress in tumor cells. These results suggest that VA confines the protective role of stromal cells by inhibiting the adaptation mechanisms toward oxidative stress which is potentiated by stromal cells. Since VA is an already prescribed drug manifesting anticancer effects, this study provides a mechanistic insight for combination of VA with chemotherapy in the clinical setting.

Integration of multi-omics datasets enables molecular classification of COPD

Chronic obstructive pulmonary disease (COPD) is an umbrella diagnosis caused by a multitude of underlying mechanisms, and molecular sub-phenotyping is needed to develop molecular diagnostic/prognostic tools and efficacious treatments.The objective of these studies was to investigate whether multi-omics integration improves the accuracy of molecular classification of COPD in small cohorts.Nine omics data blocks (comprising mRNA, micro RNA, proteomes and metabolomes) collected from several anatomical locations from 52 female subjects were integrated by similarity network fusion (SNF). Multi-omics integration significantly improved the accuracy of group classification of COPD patients from healthy never-smokers and from smokers with normal spirometry, reducing required group sizes from n=30 to n=6 at 95% power. Seven different combinations of four to seven omics platforms achieved >95% accuracy.For the first time, a quantitative relationship between multi-omics data integration and accuracy of data-driven classification power has been demonstrated across nine omics data blocks. Integrating five to seven omics data blocks enabled 100% correct classification of COPD diagnosis with groups as small as n=6 individuals, despite strong confounding effects of current smoking. These results can serve as guidelines for the design of future systems-based multi-omics investigations, with indications that integrating five to six data blocks from several molecular levels and anatomical locations suffices to facilitate unsupervised molecular classification in small cohorts.

Modulation of Haemophilus influenzae interaction with hydrophobic molecules by the VacJ/MlaA lipoprotein impacts strongly on its interplay with the airways

Airway infection by nontypeable Haemophilus influenzae (NTHi) associates to chronic obstructive pulmonary disease (COPD) exacerbation and asthma neutrophilic airway inflammation. Lipids are key inflammatory mediators in these disease conditions and consequently, NTHi may encounter free fatty acids during airway persistence. However, molecular information on the interplay NTHi-free fatty acids is limited, and we lack evidence on the importance of such interaction to infection. Maintenance of the outer membrane lipid asymmetry may play an essential role in NTHi barrier function and interaction with hydrophobic molecules. VacJ/MlaA-MlaBCDEF prevents phospholipid accumulation at the bacterial surface, being the only system involved in maintaining membrane asymmetry identified in NTHi. We assessed the relationship among the NTHi VacJ/MlaA outer membrane lipoprotein, bacterial and exogenous fatty acids, and respiratory infection. The vacJ/mlaA gene inactivation increased NTHi fatty acid and phospholipid global content and fatty acyl specific species, which in turn increased bacterial susceptibility to hydrophobic antimicrobials, decreased NTHi epithelial infection, and increased clearance during pulmonary infection in mice with both normal lung function and emphysema, maybe related to their shared lung fatty acid profiles. Altogether, we provide evidence for VacJ/MlaA as a key bacterial factor modulating NTHi survival at the human airway upon exposure to hydrophobic molecules.

Human T-lymphotropic virus 1 (HTLV-1) pathogenesis: A systems virology study

The main mechanisms of interaction between Human T-lymphotropic virus type 1 (HTLV-1) and its hosts in the manifestation of the related disease including HTLV-1 associated myelopathy/tropical spastic paraparesis (HAM/TSP) and Adult T-cell leukemia/lymphoma (ATLL) are yet to be determined. It is pivotal to find out the changes in the genes expression toward an asymptomatic or symptomatic states. To this end, the systems virology analysis was performed. Firstly, the differentially expressed genes (DEGs) were taken pairwise among the four sample sets of Normal, Asymptomatic Carriers (ACs), ATLL, and HAM/TSP. Afterwards, the protein-protein interaction networks were reconstructed utilizing the hub genes. In conclusion, the pathways of cells proliferation and transformation were identified in the ACs state. In addition to immune pathways in ATLL, the inflammation and cancer pathways were discened in both diseases of ATLL and HAM/TSP. The outcomes can specify the genes involved in the pathogenesis and help to design the drugs in the future.

What do polymorphisms tell us about the mechanisms of COPD?

COPD (chronic obstructive pulmonary disease) is characterized by irreversible lung airflow obstruction. Cigarette smoke is the major risk factor for COPD development. However, only a minority number of smokers develop COPD, and there are substantial variations in lung function among smokers, suggesting that genetic determinants in COPD susceptibility. During the past decade, genome-wide association studies and exome sequencing have been instrumental to identify the genetic determinants of complex traits, including COPD. Focused studies have revealed mechanisms by which genetic variants contribute to COPD and have led to novel insights in COPD pathogenesis. Through functional investigations of causal variants in COPD, from the proteinase-antiproteinase theory to emerging roles of developmental pathways (such as Hedgehog and Wnt pathways) in COPD, we have greatly expanded our understanding on this complex pulmonary disease. In this review, we critically review functional investigations on roles of genetic polymorphisms in COPD, and discuss future challenges and opportunities in discovering novel mechanisms of functional variants.

Chronic obstructive pulmonary disease candidate gene prioritization based on metabolic networks and functional information

Chronic obstructive pulmonary disease (COPD) is a multi-factor disease, in which metabolic disturbances played important roles. In this paper, functional information was integrated into a COPD-related metabolic network to assess similarity between genes. Then a gene prioritization method was applied to the COPD-related metabolic network to prioritize COPD candidate genes. The gene prioritization method was superior to ToppGene and ToppNet in both literature validation and functional enrichment analysis. Top-ranked genes prioritized from the metabolic perspective with functional information could promote the better understanding about the molecular mechanism of this disease. Top 100 genes might be potential markers for diagnostic and effective therapies.

Systems Biomedicine of Rabies Delineates the Affected Signaling Pathways

The prototypical neurotropic virus, rabies, is a member of the Rhabdoviridae family that causes lethal encephalomyelitis. Although there have been a plethora of studies investigating the etiological mechanism of the rabies virus and many precautionary methods have been implemented to avert the disease outbreak over the last century, the disease has surprisingly no definite remedy at its late stages. The psychological symptoms and the underlying etiology, as well as the rare survival rate from rabies encephalitis, has still remained a mystery. We, therefore, undertook a systems biomedicine approach to identify the network of gene products implicated in rabies. This was done by meta-analyzing whole-transcriptome microarray datasets of the CNS infected by strain CVS-11, and integrating them with interactome data using computational and statistical methods. We first determined the differentially expressed genes (DEGs) in each study and horizontally integrated the results at the mRNA and microRNA levels separately. A total of 61 seed genes involved in signal propagation system were obtained by means of unifying mRNA and microRNA detected integrated DEGs. We then reconstructed a refined protein–protein interaction network (PPIN) of infected cells to elucidate the rabies-implicated signal transduction network (RISN). To validate our findings, we confirmed differential expression of randomly selected genes in the network using Real-time PCR. In conclusion, the identification of seed genes and their network neighborhood within the refined PPIN can be useful for demonstrating signaling pathways including interferon circumvent, toward proliferation and survival, and neuropathological clue, explaining the intricate underlying molecular neuropathology of rabies infection and thus rendered a molecular framework for predicting potential drug targets.

The relationship between COPD and lung cancer

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COPD is a risk factor for lung cancer beyond their shared aetiology.

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Both are driven by oxidative stress.

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Both are linked to cellular aging, senescence and telomere shortening.

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Both have been linked to genetic predisposition.

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Both show altered epigenetic regulation of gene expression.

Both COPD and lung cancer are major worldwide health concerns owing to cigarette smoking, and represent a huge, worldwide, preventable disease burden. Whilst the majority of smokers will not develop either COPD or lung cancer, they are closely related diseases, occurring as co-morbidities at a higher rate than if they were independently triggered by smoking.

Lung cancer and COPD may be different aspects of the same disease, with the same underlying predispositions, whether this is an underlying genetic predisposition, telomere shortening, mitochondrial dysfunction or premature aging. In the majority of smokers, the burden of smoking may be dealt with by the body’s defense mechanisms: anti-oxidants such as superoxide dismutases, anti-proteases and DNA repair mechanisms. However, in the case of both diseases these fail, leading to cancer if mutations occur or COPD if damage to the cell and proteins becomes too great.

Alternatively COPD could be a driving factor in lung cancer, by increasing oxidative stress and the resulting DNA damage, chronic exposure to pro-inflammatory cytokines, repression of the DNA repair mechanisms and increased cellular proliferation. Understanding the mechanisms that drive these processes in primary cells from patients with these diseases along with better disease models is essential for the development of new treatments.