Identifying a gene expression signature of frequent COPD exacerbations in peripheral blood using network methods

Blood Gene Expression and Immune Cell Subtypes Associated with Chronic Obstructive Pulmonary Disease Exacerbations

Rationale: Acute exacerbations of chronic obstructive pulmonary disease (AE-COPDs) are associated with a significant disease burden. Blood immune phenotyping may improve our understanding of a COPD endotype at increased risk of exacerbations. Objective: To determine the relationship between the transcriptome of circulating leukocytes and COPD exacerbations. Methods: Blood RNA sequencing data (n = 3,618) from the COPDGene (Genetic Epidemiology of COPD) study were analyzed. Blood microarray data (n = 646) from the ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) study were used for validation. We tested the association between blood gene expression and AE-COPDs. We imputed the abundance of leukocyte subtypes and tested their association with prospective AE-COPDs. Flow cytometry was performed on blood in SPIROMICS (Subpopulations and Intermediate Outcomes in COPD Study) (n = 127), and activation markers for T cells were tested for association with prospective AE-COPDs. Measurements and Main Results: Exacerbations were reported 4,030 and 2,368 times during follow-up in COPDGene (5.3 ± 1.7 yr) and ECLIPSE (3 yr), respectively. We identified 890, 675, and 3,217 genes associated with a history of AE-COPDs, persistent exacerbations (at least one exacerbation per year), and prospective exacerbation rate, respectively. In COPDGene, the number of prospective exacerbations in patients with COPD (Global Initiative for Chronic Obstructive Lung Disease stage ⩾2) was negatively associated with circulating CD8+ T cells, CD4+ T cells, and resting natural killer cells. The negative association with naive CD4+ T cells was replicated in ECLIPSE. In the flow-cytometry study, an increase in CTLA4 on CD4+ T cells was positively associated with AE-COPDs. Conclusions: Individuals with COPD with lower circulating lymphocyte counts, particularly decreased CD4+ T cells, are more susceptible to AE-COPDs, including persistent exacerbations.

Systemic alterations in neutrophils and their precursors in early-stage chronic obstructive pulmonary disease

Systemic inflammation is established as part of late-stage severe lung disease, but molecular, functional, and phenotypic changes in peripheral immune cells in early disease stages remain ill defined. Chronic obstructive pulmonary disease (COPD) is a major respiratory disease characterized by small-airway inflammation, emphysema, and severe breathing difficulties. Using single-cell analyses we demonstrate that blood neutrophils are already increased in early-stage COPD, and changes in molecular and functional neutrophil states correlate with lung function decline. Assessing neutrophils and their bone marrow precursors in a murine cigarette smoke exposure model identified similar molecular changes in blood neutrophils and precursor populations that also occur in the blood and lung. Our study shows that systemic molecular alterations in neutrophils and their precursors are part of early-stage COPD, a finding to be further explored for potential therapeutic targets and biomarkers for early diagnosis and patient stratification.

Severity of Lung Function Impairment Drives Transcriptional Phenotypes of COPD and Relates to Immune and Metabolic Processes

Purpose

This study sought to characterize transcriptional phenotypes of COPD through unsupervised clustering of sputum gene expression profiles, and further investigate mechanisms underlying the characteristics of these clusters.

Patients and methods

Induced sputum samples were collected from patients with stable COPD (n = 72) and healthy controls (n = 15). Induced sputum was collected for inflammatory cell counts, and RNA extracted. Transcriptional profiles were generated (Illumina Humanref-8 V2) and analyzed by GeneSpring GX14.9.1. Unsupervised hierarchical clustering and differential gene expression analysis were performed, and gene alterations validated in the ECLIPSE dataset (GSE22148).

Results

We identified 2 main clusters (Cluster 1 [n = 35] and Cluster 2 [n = 37]), which further divided into 4 sub-clusters (Sub-clusters 1.1 [n = 14], 1.2 [n = 21], 2.1 [n = 20] and 2.2 [n = 17]). Compared with Cluster 1, Cluster 2 was associated with significantly lower lung function (p = 0.014), more severe disease (p = 0.009) and breathlessness (p = 0.035), and increased sputum neutrophils (p = 0.031). Sub-cluster 1.1 had significantly higher proportion of people with comorbid cardiovascular disease compared to the other 3 sub-clusters (92.5% vs 57.1%, 50% and 52.9%, p < 0.013). Through supervised analysis we determined that degree of airflow limitation (GOLD stage) was the predominant factor driving gene expression differences in our transcriptional clusters. There were 452 genes (adjusted p < 0.05 and ≥2 fold) altered in GOLD stage 3 and 4 versus 1 and 2, of which 281 (62%) were also found to be significantly expressed between these GOLD stages in the ECLIPSE data set (GSE22148). Differentially expressed genes were largely downregulated in GOLD stages 3 and 4 and connected in 5 networks relating to lipoprotein and cholesterol metabolism; metabolic processes in oxidation/reduction and mitochondrial function; antigen processing and presentation; regulation of complement activation and innate immune responses; and immune and metabolic processes.

Conclusion

Severity of lung function drives 2 distinct transcriptional phenotypes of COPD and relates to immune and metabolic processes.

Recent evidence from omic analysis for redox signalling and mitochondrial oxidative stress in COPD

COPD is driven by exogenous and endogenous oxidative stress derived from inhaled cigarette smoke, air pollution and reactive oxygen species from dysregulated mitochondria in activated inflammatory cells within the airway and lung. This is compounded by the loss in antioxidant defences including FOXO and NRF2 and other antioxidant transcription factors together with various key enzymes that attenuate oxidant effects. Oxidative stress enhances inflammation; airway remodelling including fibrosis and emphysema; post-translational protein modifications leading to autoantibody generation; DNA damage and cellular senescence. Recent studies using various omics technologies in the airways, lungs and blood of COPD patients has emphasised the importance of oxidative stress, particularly that derived from dysfunctional mitochondria in COPD and its role in immunity, inflammation, mucosal barrier function and infection. Therapeutic interventions targeting oxidative stress should overcome the deleterious pathologic effects of COPD if targeted to the lung. We require novel, more efficacious antioxidant COPD treatments among which mitochondria-targeted antioxidants and Nrf2 activators are promising.

Candidate master microRNA regulator of arsenic-induced pancreatic beta cell impairment revealed by multi-omics analysis

Arsenic is a pervasive environmental toxin that is listed as the top priority for investigation by the Agency for Toxic Substance and Disease Registry. While chronic exposure to arsenic is associated with type 2 diabetes (T2D), the underlying mechanisms are largely unknown. We have recently demonstrated that arsenic treatment of INS-1 832/13 pancreatic beta cells impairs glucose-stimulated insulin secretion (GSIS), a T2D hallmark. We have also shown that arsenic alters the microRNA profile of beta cells. MicroRNAs have a well-established post-transcriptional regulatory role in both normal beta cell function and T2D pathogenesis. We hypothesized that there are microRNA master regulators that shape beta cell gene expression in pathways pertinent to GSIS after exposure to arsenicals. To test this hypothesis, we first treated INS-1 832/13 beta cells with either inorganic arsenic (iAsIII) or monomethylarsenite (MAsIII) and confirmed GSIS impairment. We then performed multi-omic analysis using chromatin run-on sequencing, RNA-sequencing, and small RNA-sequencing to define profiles of transcription, gene expression, and microRNAs, respectively. Integrating across these data sets, we first showed that genes downregulated by iAsIII treatment are enriched in insulin secretion and T2D pathways, whereas genes downregulated by MAsIII treatment are enriched in cell cycle and critical beta cell maintenance factors. We also defined the genes that are subject primarily to post-transcriptional control in response to arsenicals and demonstrated that miR-29a is the top candidate master regulator of these genes. Our results highlight the importance of microRNAs in arsenical-induced beta cell dysfunction and reveal both shared and unique mechanisms between iAsIII and MAsIII.

Identifying key genes and functionally enriched pathways in Th2-high asthma by weighted gene co-expression network analysis

Background

Asthma is a chronic lung disease characterized by reversible inflammation of the airways. The imbalance of Th1/Th2 cells plays a significant role in the mechanisms of asthma. The aim of this study was to identify asthma-related key genes and functionally enriched pathways in a Th2-high group by using weighted gene coexpression network analysis (WGCNA).

Methods

The gene expression profiles of GSE4302, which included 42 asthma patients and 28 controls, were selected from the Gene Expression Omnibus (GEO). A gene network was constructed, and genes were classified into different modules using WGCNA. Gene ontology (GO) was performed to further explore the potential function of the genes in the most related module. In addition, the expression profile and diagnostic capacity (ROC curve) of hub genes of interest were verified by dataset GSE67472.

Results

In dataset GSE4302, subjects with asthma were divided into Th2-high and Th2-low groups according to the expression of the SERPINB2, POSTN and CLCA1 genes. A weighted gene coexpression network was constructed, and genes were classified into 7 modules. Among them, the red module was most closely associated with Th2-high asthma, which contained 60 genes. These genes were significantly enriched in different biological processes and molecular functions. A total of 8 hub genes (TPSB2, CPA3, ITLN1, CST1, SERPINB10, CEACAM5, CHD26 and P2RY14) were identified, and the expression levels of these genes (except TPSB2) were confirmed in dataset GSE67472. ROC curve analysis validated that the expression of these 8 genes exhibited excellent diagnostic efficiency for Th2-high asthma and Th2-low asthma.

Conclusions

The study provides a novel perspective on Th2-high asthma by WGCNA, and the hub genes and potential pathways involved may be beneficial for the diagnosis and management of Th2-high asthma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01241-9.

Blood miRNAs Are Linked to Frequent Asthma Exacerbations in Childhood Asthma and Adult COPD

MicroRNAs have been independently associated with asthma and COPD; however, it is unclear if microRNA associations will overlap when evaluating retrospective acute exacerbations. Objective: We hypothesized that peripheral blood microRNAs would be associated with retrospective acute asthma exacerbations in a pediatric asthma cohort and that such associations may also be relevant to acute COPD exacerbations. Methods: We conducted small-RNA sequencing on 374 whole-blood samples from children with asthma ages 6-14 years who participated in the Genetics of Asthma in Costa Rica Study (GACRS) and 450 current and former adult smokers with and without COPD who participated in the COPDGene study. Measurements and Main Results: After QC, we had 351 samples and 649 microRNAs for Differential Expression (DE) analysis between the frequent (n = 183) and no or infrequent exacerbation (n = 168) groups in GACRS. Fifteen upregulated miRs had odds ratios (OR) between 1.22 and 1.59 for a doubling of miR counts, while five downregulated miRs had ORs between 0.57 and 0.8. These were assessed for generalization in COPDGene, where three of the upregulated miRs (miR-532-3p, miR-296-5p, and miR-766-3p) and two of the downregulated miRs (miR-7-5p and miR-451b) replicated. Pathway enrichment analysis showed MAPK and PI3K-Akt signaling pathways were strongly enriched for target genes of DE miRNAs and miRNAs generalizing to COPD exacerbations, as well as infection response pathways to various pathogens. Conclusion: miRs (451b; 7-5p; 532-3p; 296-5p and 766-3p) associated with both childhood asthma and adult COPD exacerbations may play a vital role in airflow obstruction and exacerbations and point to shared genomic regulatory machinery underlying exacerbations in both diseases.

Protein interaction networks provide insight into fetal origins of chronic obstructive pulmonary disease

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a leading cause of death in adults that may have origins in early lung development. It is a complex disease, influenced by multiple factors including genetic variants and environmental factors. Maternal smoking during pregnancy may influence the risk for diseases during adulthood, potentially through epigenetic modifications including methylation.

METHODS

In this work, we explore the fetal origins of COPD by utilizing lung DNA methylation marks associated with in utero smoke (IUS) exposure, and evaluate the network relationships between methylomic and transcriptomic signatures associated with adult lung tissue from former smokers with and without COPD. To identify potential pathobiological mechanisms that may link fetal lung, smoke exposure and adult lung disease, we study the interactions (physical and functional) of identified genes using protein-protein interaction networks.

RESULTS

We build IUS-exposure and COPD modules, which identify connected subnetworks linking fetal lung smoke exposure to adult COPD. Studying the relationships and connectivity among the different modules for fetal smoke exposure and adult COPD, we identify enriched pathways, including the AGE-RAGE and focal adhesion pathways.

CONCLUSIONS

The modules identified in our analysis add new and potentially important insights to understanding the early life molecular perturbations related to the pathogenesis of COPD. We identify AGE-RAGE and focal adhesion as two biologically plausible pathways that may reveal lung developmental contributions to COPD. We were not only able to identify meaningful modules but were also able to study interconnections between smoke exposure and lung disease, augmenting our knowledge about the fetal origins of COPD.

PhosVarDeep: deep-learning based prediction of phospho-variants using sequence information

Human DNA sequencing has revealed numerous single nucleotide variants associated with complex diseases. Researchers have shown that these variants have potential effects on protein function, one of which is to disrupt protein phosphorylation. Based on conventional machine learning algorithms, several computational methods for predicting phospho-variants have been developed, but their performance still leaves considerable room for improvement. In recent years, deep learning has been successfully applied in biological sequence analysis with its efficient sequence pattern learning ability, which provides a powerful tool for improving phospho-variant prediction based on protein sequence information. In the study, we present PhosVarDeep, a novel unified deep-learning framework for phospho-variant prediction. PhosVarDeep takes reference and variant sequences as inputs and adopts a Siamese-like CNN architecture containing two identical subnetworks and a prediction module. In each subnetwork, general phosphorylation sequence features are extracted by a pre-trained sequence feature encoding network and then fed into a CNN module for capturing variant-aware phosphorylation sequence features. After that, a prediction module is introduced to integrate the outputs of the two subnetworks and generate the prediction results of phospho-variants. Comprehensive experimental results on phospho-variant data demonstrates that our method significantly improves the prediction performance of phospho-variants and compares favorably with existing conventional machine learning methods.

Novel phenotype and genotype spectrum of WDR62 in two patients with associated primary autosomal recessive microcephaly

BACKGROUND

Microcephaly is a prominent feature of patients with primary autosomal recessive microcephaly 2 (MCPH2) caused by mutations in the WD Repeat Domain 62 (WDR62; OMIM: 613,583).

AIM

The study aimed to identify the underlying genetic factor(s) causing microcephaly in two patients in a consanguineous Iranian family.

METHODS

Two male patients (11 and 27 years old) were noticed due to microcephaly, neurodevelopmental delay, and occasional seizures. The younger patient (the proband) was subjected to paired-end whole-exome sequencing followed by Sanger sequencing to detect any underlying genetic factor.

RESULTS

Upon examination, both patients showed microcephaly as a prominent manifestation; they were under-weighted as well. The patients had a moderate gross motor impairment, severe cognitive disability and speech delay, increased deep tendon reflexes, flexible joint contractures, sensorineural hearing loss, and vertical nystagmus as a new ocular finding. The proband had more severe neurodevelopmental delay symptoms. The brain magnetic resonance imaging series revealed severe structural and cortical brain abnormalities in addition to hemiatrophy. Using Whole-exome Sequencing, a novel homozygous missense variant-NM_001083961.2; c.1598A > G: p.(His533Arg)-was identified in the WDR62. Subsequently, in silico analyses determined the possible impacts of the novel variant on the structure and function of WDR62 protein.

CONCLUSIONS

Herein, we identified a novel homozygous missense variant in the WDR62 in two patients with MCPH2. Vertical nystagmus and sensorineural hearing loss were detected as novel neurological findings. The present study expands the phenotype and genotype spectrum of MCPH2.

Effect of Smoking and Its Cessation on the Transcript Profile of Peripheral Monocytes in COPD Patients

Rationale

Smoking is the primary cause of chronic obstructive pulmonary disease (COPD); however, only 10–20% of smokers develop the disease suggesting possible genomic association in the causation of the disease. In the present study, we aimed to explore the whole genome transcriptomics of blood monocytes from COPD smokers (COPD-S), COPD Ex-smokers (COPD-ExS), Control smokers (CS), and Control Never-smokers (CNS) to understand the differential effects of smoking, COPD and that of smoking cessation.

Methods

Exploratory analyses in form of principal component analysis (PCA) and hierarchical component analysis (uHCA) were performed to evaluate the similarity in gene expression patterns, while differential expression analyses of different supervised groups of smokers and never smokers were performed to study the differential effect of smoking, COPD and smoking cessation. Differentially expressed genes among groups were subjected to post-hoc enrichment analysis. Candidate genes were subjected to external validation by quantitative RT-PCR experiments.

Results

CNS made a cluster completely segregated from the other three subgroups (CS, COPDS and COPD-ExS). About 550, 8 and 5 genes showed differential expression, respectively, between CNS and CS, between CS and COPD-S, and between COPD-S and COPD-ExS. Apoptosis, immune response, cell adhesion, and inflammation were the top process networks identified in enrichment analysis. Two candidate genes (CASP9 and TNFRSF1A) found to be integral to several pathways in enrichment analysis were validated in an external validation experiment.

Conclusion

Control never smokers had formed a cluster distinctively separated from all smokers (COPDS, COPD-ExS, and CS), while amongst all smokers, control smokers had aggregated in a separate cluster. Smoking cessation appeared beneficial if started at an early stage as many genes altered due to smoking started reverting towards the baseline, whereas only a few COPD-related genes showed reversal after smoking cessation.

Identification of Up-Regulated ANXA3 Resulting in Fracture Non-Union in Patients With T2DM

Diabetes mellitus is a metabolic disorder that increases fracture risk and interferes with bone formation and impairs fracture healing. Genomic studies on diabetes and fracture healing are lacking. We used a weighted co-expression network analysis (WGCNA) method to identify susceptibility modules and hub genes associated with T2DM and fracture healing. First, we downloaded the GSE95849, GSE93213, GSE93215, and GSE142786 data from the Gene Expression Omnibus (GEO) website, analyzed differential expression genes and constructed a WGCNA network. Second, we screened out 30 hub genes, which were found to be enriched in neutrophil activation, translational initiation, RAGE receptor binding, propanoate metabolism, and other pathways through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) analyses. Third, we searched for genes related to bone metabolism and fracture healing in the published genome-wide single nucleotide polymorphism (SNP) data, built a protein-protein interaction (PPI) network with hub genes, and found that they were associated with metabolic process, blood vessel development, and extracellular matrix organization. ANXA3 was identified as the biomarker based on gene expression and correlation analysis. And the AUC value of it was 0.947. Fourth, we explored that ANXA3 was associated with neutrophils in fracture healing process by single-cell RNA sequencing analysis. Finally, we collected clinical patient samples and verified the expression of ANXA3 by qRT-PCR in patents with T2DM and fracture non-union. In conclusion, this is the first genomics study on the effect of T2DM on fracture healing. Our study identified some characteristic modules and hub genes in the etiology of T2DM-associated fracture non-union, which may help to further investigate the molecular mechanisms. Up-regulated ANXA3 potentially contributed to fracture non-union in T2DM by mediating neutrophils. It can be a prognostic biomarker and potential therapeutic target.

Host Factor Interaction Networks Identified by Integrative Bioinformatics Analysis Reveals Therapeutic Implications in COPD Patients With COVID-19

Background: The COVID-19 pandemic poses an imminent threat to humanity, especially for those who have comorbidities. Evidence of COVID-19 and COPD comorbidities is accumulating. However, data revealing the molecular mechanism of COVID-19 and COPD comorbid diseases is limited. Methods: We got COVID-19/COPD -related genes from different databases by restricted screening conditions (top500), respectively, and then supplemented with COVID-19/COPD-associated genes (FDR<0.05, |LogFC|≥1) from clinical sample data sets. By taking the intersection, 42 co-morbid host factors for COVID-19 and COPD were finally obtained. On the basis of shared host factors, we conducted a series of bioinformatics analysis, including protein-protein interaction analysis, gene ontology and pathway enrichment analysis, transcription factor-gene interaction network analysis, gene-microRNA co-regulatory network analysis, tissue-specific enrichment analysis and candidate drug prediction. Results: We revealed the comorbidity mechanism of COVID-19 and COPD from the perspective of host factor interaction, obtained the top ten gene and 3 modules with different biological functions. Furthermore, we have obtained the signaling pathways and concluded that dexamethasone, estradiol, progesterone, and nitric oxide shows effective interventions. Conclusion: This study revealed host factor interaction networks for COVID-19 and COPD, which could confirm the potential drugs for treating the comorbidity, ultimately, enhancing the management of the respiratory disease.

Application of Weighted Gene Coexpression Network Analysis to Identify Key Modules and Hub Genes in Systemic Juvenile Idiopathic Arthritis

Systemic juvenile idiopathic arthritis (sJIA) is a severe autoinflammatory disorder with a still not clearly defined molecular mechanism. To better understand the disease, we used scattered datasets from public domains and performed a weighted gene coexpression network analysis (WGCNA) to identify key modules and hub genes underlying sJIA pathogenesis. Two gene expression datasets, GSE7753 and GSE13501, were used to construct the WGCNA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were applied to the genes and hub genes in the sJIA modules. Cytoscape was used to screen and visualize the hub genes. We further compared the hub genes with the genome-wide association study (GWAS) genes and used a consensus WGCNA to verify that our conclusions were conservative and reproducible across multiple independent datasets. A total of 5,414 genes were obtained for WGCNA, from which highly correlated genes were divided into 17 modules. The red module demonstrated the highest correlation with the sJIA module (r = 0.8, p = 3e⁻²⁹), whereas the green-yellow module was found to be closely related to the non-sJIA module (r = 0.62, p = 1e⁻¹⁴). Functional enrichment analysis demonstrated that the red module was mostly enriched in the activation of immune responses, infection, nucleosomes, and erythrocytes, and the green-yellow module was mostly enriched in immune responses and inflammation. Additionally, the hub genes in the red module were highly enriched in erythrocyte differentiation, including ALAS2, AHSP, TRIM10, TRIM58, and KLF1. The hub genes from the green-yellow module were mainly associated with immune responses, as exemplified by the genes KLRB1, KLRF1, CD160, and KIRs. We identified sJIA-related modules and several hub genes that might be associated with the development of sJIA. Particularly, the modules may help understand the mechanisms of sJIA, and the hub genes may become biomarkers and therapeutic targets of sJIA in the future.

Identification of Metabolism-Associated Molecular Subtypes of Chronic Obstructive Pulmonary Disease

Purpose

This study aimed to identify the COPD molecular subtypes reflecting pulmonary function damage on the basis of metabolism-related gene expression, which provided the opportunity to study the metabolic heterogeneity and the association of metabolic pathways with pulmonary function damage.

Methods

Univariate linear regression and the Boruta algorithm were used to select metabolism-related genes associated with forced expiratory volume in the first second (FEV1) and FEV1/forced vital capacity (FVC) in the Evaluation of COPD to Longitudinally Identify Predictive Surrogate Endpoints (ECLIPSE) cohort. COPD subtypes were further identified by consensus clustering with best-fit. Then, we analyzed the differences in the clinical characteristics, metabolic pathways, immune cell characteristics, and transcription features among the subtypes.

Results

This study identified two subtypes (C1 and C2). C1 exhibited higher levels of lower pulmonary function and innate immunity than C2. Ten metabolic pathways were confirmed as key metabolic pathways. The pathways related to N-glycan, hexosamine, purine, alanine, aspartate and glutamate tended to be positively associated with the abundance of adaptive immune cells and negatively associated with the abundance of innate immune cells. In addition, other pathways had opposite trends. All results were verified in Genetic Epidemiology of COPD (COPDGene) datasets.

Conclusion

The two subtypes reflect the pulmonary function damage and help to further understand the metabolic mechanism of pulmonary function in COPD. Further studies are needed to prove the prognostic and therapeutic value of the subtypes.

A novel whole blood gene expression signature for asthma, dermatitis, and rhinitis multimorbidity in children and adolescents

BACKGROUND

Allergic diseases often occur in combination (multimorbidity). Human blood transcriptome studies have not addressed multimorbidity. Large-scale gene expression data were combined to retrieve biomarkers and signaling pathways to disentangle allergic multimorbidity phenotypes.

METHODS

Integrated transcriptomic analysis was conducted in 1233 participants with a discovery phase using gene expression data (Human Transcriptome Array 2.0) from whole blood of 786 children from three European birth cohorts (MeDALL), and a replication phase using RNA Sequencing data from an independent cohort (EVA-PR, n = 447). Allergic diseases (asthma, atopic dermatitis, rhinitis) were considered as single disease or multimorbidity (at least two diseases), and compared with no disease.

RESULTS

Fifty genes were differentially expressed in allergic diseases. Thirty-two were not previously described in allergy. Eight genes were consistently overexpressed in all types of multimorbidity for asthma, dermatitis, and rhinitis (CLC, EMR4P, IL5RA, FRRS1, HRH4, SLC29A1, SIGLEC8, IL1RL1). All genes were replicated the in EVA-PR cohort. RT-qPCR validated the overexpression of selected genes. In MeDALL, 27 genes were differentially expressed in rhinitis alone, but none was significant for asthma or dermatitis alone. The multimorbidity signature was enriched in eosinophil-associated immune response and signal transduction. Protein-protein interaction network analysis identified IL5/JAK/STAT and IL33/ST2/IRAK/TRAF as key signaling pathways in multimorbid diseases. Synergistic effect of multimorbidity on gene expression levels was found.

CONCLUSION

A signature of eight genes identifies multimorbidity for asthma, rhinitis, and dermatitis. Our results have clinical and mechanistic implications, and suggest that multimorbidity should be considered differently than allergic diseases occurring alone.

Biomarker discovery in attention deficit hyperactivity disorder: RNA sequencing of whole blood in discordant twin and case-controlled cohorts

Background

A variety of DNA-based methods have been applied to identify genetic markers of attention deficit hyperactivity disorder (ADHD), but the connection to RNA-based gene expression has not been fully exploited.

Methods

Using well defined cohorts of discordant, monozygotic twins from the Michigan State University Twin Registry, and case-controlled ADHD cases in adolescents, the present studies utilized advanced single molecule RNA sequencing to identify expressed changes in whole blood RNA in ADHD. Multiple analytical strategies were employed to narrow differentially expressed RNA targets to a small set of potential biomarkers of ADHD.

Results

RNA markers common to both the discordant twin study and case-controlled subjects further narrowed the putative targets, some of which had been previously associated with ADHD at the DNA level. The potential role of several differentially expressed genes, including ABCB5, RGS2, GAK, GIT1 and 3 members of the galactose metabolism pathway (GALE, GALT, GALK1) are substantiated by prior associations to ADHD and by established mechanistic connections to molecular pathways relevant to ADHD and behavioral control.

Conclusions

The convergence of DNA, RNA, and metabolic data suggests these may be promising targets for diagnostics and therapeutics in ADHD.

Crucial transcripts predict response to initial immunoglobulin treatment in acute Kawasaki disease

Although intravenous immunoglobulin (IVIG) can effectively treat Kawasaki disease (KD), 10–20% of KD patients show no beneficial clinical response. Developing reliable criteria to discriminate non-responders is important for early planning of appropriate regimens. To predict the non-responders before IVIG treatment, gene expression dataset of 110 responders and 61 non-responders was obtained from Gene Expression Omnibus. After weighted gene co-expression network analysis, we found that modules positively correlated with the non-responders were mainly associated with myeloid cell activation. Transcripts up-regulated in the non-responders, IL1R2, GK, HK3, C5orf32, CXCL16, NAMPT and EMILIN2, were proven to play key roles via interaction with other transcripts in co-expression network. The crucial transcripts may affect the clinical response to IVIG treatment in acute KD. And these transcripts may serve as biomarkers and therapeutic targets for precise diagnosis and treatment of the non-responders.

Exploration of the sputum methylome and omics deconvolution by quadratic programming in molecular profiling of asthma and COPD: the road to sputum omics 2.0

BACKGROUND

To date, most studies involving high-throughput analyses of sputum in asthma and COPD have focused on identifying transcriptomic signatures of disease. No whole-genome methylation analysis of sputum cells has been performed yet. In this context, the highly variable cellular composition of sputum has potential to confound the molecular analyses.

METHODS

Whole-genome transcription (Agilent Human 4 × 44 k array) and methylation (Illumina 450 k BeadChip) analyses were performed on sputum samples of 9 asthmatics, 10 healthy and 10 COPD subjects. RNA integrity was checked by capillary electrophoresis and used to correct in silico for bias conferred by RNA degradation during biobank sample storage. Estimates of cell type-specific molecular profiles were derived via regression by quadratic programming based on sputum differential cell counts. All analyses were conducted using the open-source R/Bioconductor software framework.

RESULTS

A linear regression step was found to perform well in removing RNA degradation-related bias among the main principal components of the gene expression data, increasing the number of genes detectable as differentially expressed in asthma and COPD sputa (compared to controls). We observed a strong influence of the cellular composition on the results of mixed-cell sputum analyses. Exemplarily, upregulated genes derived from mixed-cell data in asthma were dominated by genes predominantly expressed in eosinophils after deconvolution. The deconvolution, however, allowed to perform differential expression and methylation analyses on the level of individual cell types and, though we only analyzed a limited number of biological replicates, was found to provide good estimates compared to previously published data about gene expression in lung eosinophils in asthma. Analysis of the sputum methylome indicated presence of differential methylation in genomic regions of interest, e.g. mapping to a number of human leukocyte antigen (HLA) genes related to both major histocompatibility complex (MHC) class I and II molecules in asthma and COPD macrophages. Furthermore, we found the SMAD3 (SMAD family member 3) gene, among others, to lie within differentially methylated regions which has been previously reported in the context of asthma.

CONCLUSIONS

In this methodology-oriented study, we show that methylation profiling can be easily integrated into sputum analysis workflows and exhibits a strong potential to contribute to the profiling and understanding of pulmonary inflammation. Wherever RNA degradation is of concern, in silico correction can be effective in improving both sensitivity and specificity of downstream analyses. We suggest that deconvolution methods should be integrated in sputum omics analysis workflows whenever possible in order to facilitate the unbiased discovery and interpretation of molecular patterns of inflammation.

Identification of recent exacerbations in COPD patients by electronic nose

Molecular profiling of exhaled breath by electronic nose (eNose) might be suitable as a noninvasive tool that can help in monitoring of clinically unstable COPD patients. However, supporting data are still lacking. Therefore, as a first step, this study aimed to determine the accuracy of exhaled breath analysis by eNose to identify COPD patients who recently exacerbated, defined as an exacerbation in the previous 3 months. Data for this exploratory, cross-sectional study were extracted from the multicentre BreathCloud cohort. Patients with a physician-reported diagnosis of COPD (n=364) on maintenance treatment were included in the analysis. Exacerbations were defined as a worsening of respiratory symptoms requiring treatment with oral corticosteroids, antibiotics or both. Data analysis involved eNose signal processing, ambient air correction and statistics based on principal component (PC) analysis followed by linear discriminant analysis (LDA). Before analysis, patients were randomly divided into a training (n=254) and validation (n=110) set. In the training set, LDA based on PCs 1-4 discriminated between patients with a recent exacerbation or no exacerbation with high accuracy (receiver operating characteristic (ROC)-area under the curve (AUC)=0.98, 95% CI 0.97-1.00). This high accuracy was confirmed in the validation set (AUC=0.98, 95% CI 0.94-1.00). Smoking, health status score, use of inhaled corticosteroids or vital capacity did not influence these results. Exhaled breath analysis by eNose can discriminate with high accuracy between COPD patients who experienced an exacerbation within 3 months prior to measurement and those who did not. This suggests that COPD patients who recently exacerbated have their own exhaled molecular fingerprint that could be valuable for monitoring purposes.

Omics and the Search for Blood Biomarkers in Chronic Obstructive Pulmonary Disease. Insights from COPDGene

There is an unmet need for blood biomarkers in diagnosis and prognosis of chronic obstructive pulmonary disease (COPD). The search for these biomarkers has been revolutionized by high-throughput sequencing techniques and multiplex platforms that can measure thousands of gene transcripts, proteins, or metabolites. We review COPDGene (Genetic Epidemiology of COPD) project publications that include DNA methylation, transcriptomic, proteomic, and metabolomic blood biomarkers and discuss their impact on COPD. Key contributions from COPDGene include identification of DNA methylation effects from smoking and genetic variation, new transcriptomic signatures in the blood, identification of protein biomarkers associated with severity and progression (e.g., sRAGE [soluble receptor for advanced glycosylation end products], inflammatory cytokines IL-6 and IL-8), and identification of small molecules (ceramides and sphingomyelin) that may be pathogenic. COPDGene studies have revealed that some of the COPD genome-wide association study polymorphisms are strongly associated with blood biomarkers (e.g., rs2070600 in AGER is a pQTL [protein quantitative trait locus] for sRAGE), underscoring the importance of combining omics results. Investigators have developed molecular networks identifying lower CD4⁺ resting memory cells associated with COPD. Genes, proteins, and metabolite networks are particularly important because the explanatory value of any single molecule is small (1-10%) compared with panels of multiple markers. COPDGene has been a useful resource in the identification and validation of multiple biomarkers for COPD. These biomarkers, either combined in multiple biomarker panels or integrated with other omics data types, may lead to novel diagnostic and prognostic tests for COPD phenotypes and may be relevant for assessing novel therapies.

Adaptive NKG2C+ natural killer cells are related to exacerbations and nutritional abnormalities in COPD patients

Abstract

Chronic obstructive pulmonary disease (COPD) is a chronic and often progressive disorder with a heterogeneous presentation and frequent systemic manifestations. Several aspects like persistence in smoking habit, continuous exacerbations, alpha-1-antitrypsin deficiency and inflammatory-immune response, are involved in the pathophysiology and progression of the disease. However, the role of natural killer (NK) cells remains controversial. Otherwise, human cytomegalovirus (HCMV) infection has been reported to induce an adaptive differentiation and expansion of an NK cell subset which carries the CD94/NKG2C receptor, which may contribute to an upset immune defense. For these reasons, our objective is to assess the distribution of NK cells and their subset in COPD patients and some of its phenotypes.

Methods

Peripheral blood samples were obtained from 66 COPD patients. HCMV serology and the proportions of total NK cells and the NKG2C+ and NKG2A+ subsets were evaluated by flow cytometry. The NKG2C genotype was also assessed.

Results

Eighty-eight per cent of COPD patients were HCMV(+), and the proportions of total NK cells were higher in patients with severe-very severe airway obstruction than in those with only mild-moderate involvement. There were no differences in the proportions of NKG2C+ cells between controls and COPD, either among COPD patients classified by severity of the disease. However, the percentage of NKG2C+ cells were higher in COPD patients with frequent exacerbations than in occasional exacerbators, and higher in cases with reduced lean mass (Fat free mass index) than in those with normal nutritional status.

Conclusion

These results suggest a relationship between levels of NKG2C+ cells in COPD patients and clinical variables closely linked to a poor/worse prognosis.

Using Physiologic, Genetic, and Epigenetic Information to Provide Care to Clients Who Are Obese

The pathology of obesity is a complex process involving interactions among behavioral, environmental, immunologic, genetic, and epigenetic factors. This article gives a broad overview of obesity. The physiology of fat storage, influence of eating behaviors on obesity, and the genetic relationship between eating and food sources are discussed. Specific genes that have been associated with obesity are introduced, with information on leptin and genes such as FTO, GLUT4, and others. This synopsis of obesity expands into environmental influences and epigenetic factors. These include food selection, gut microbiota, pregnancy, and exercise. The nurse will gain specific knowledge to assist in tailoring therapies specific to clients who are working to overcome the long-term effects of this disorder.

CD36 identified by weighted gene co-expression network analysis as a hub candidate gene in lupus nephritis

Lupus nephritis (LN) is a severe manifestation of systemic lupus erythematosus (SLE), which often progresses to end-stage renal disease (ESRD) and ultimately leads to death. At present, there are no definitive therapies towards LN, so that illuminating the molecular mechanism behind the disease has become an urgent task for researchers. Bioinformatics has become a widely utilized method for exploring genes related to disease. This study set out to conduct weighted gene co-expression network analysis (WGCNA) and screen the hub gene of LN. We performed WGCNA on the microarray expression profile dataset of GSE104948 from the Gene Expression Omnibus (GEO) database with 18 normal and 21 LN samples of glomerulus. A total of 5,942 genes were divided into 5 co-expression modules, one of which was significantly correlated to LN. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted on the LN-related module, and the module was proved to be associated mainly with the activation of inflammation, immune response, cytokines, and immune cells. Genes in the most significant GO terms were extracted for sub-networks of WGNCA. We evaluated the centrality of genes in the sub-networks by Maximal Clique Centrality (MCC) method and CD36 was ultimately screened out as a hub candidate gene of the pathogenesis of LN. The result was verified by its differentially expressed level between normal and LN in GSE104948 and the other three multi-microarray datasets of GEO. Moreover, we further demonstrated that the expression level of CD36 is related to the WHO Lupus Nephritis Class of LN patients with the help of Nephroseq database. The current study proposed CD36 as a vital candidate gene in LN for the first time and CD36 may perform as a brand-new biomarker or therapeutic target of LN in the future.

Differential expression and functional analysis of lung cancer gene expression datasets: A systems biology perspective

There is an inherent need to identify differentially expressed genes (DEGs), characterize these genes and provide functional enrichment analysis to the publicly available lung cancer datasets, primarily coming from next-generation sequencing data or microarray gene expression studies. The risk of lung cancer in patients with smokers is manifold, and with chronic obstructive pulmonary disease (COPD) it is 2- to 5-fold greater, compared with smokers without COPD. In the present study, differential expression analysis and gene functional enrichment analysis of lung cancer gene expression datasets obtained from NCBI-GEO were performed. The result identifies a significant number of DEGs which have at least a 2-fold change in their expression. Among them, six genes were found to have a 4-fold change in the expression level, and 47 genes exhibited a 3-fold change in the expression. It was also observed that most of the genes were upregulated and few genes were downregulated.

RNA-sequencing across three matched tissues reveals shared and tissue-specific gene expression and pathway signatures of COPD

BACKGROUND

Multiple gene expression studies have been performed separately in peripheral blood, lung, and airway tissues to study COPD. We performed RNA-sequencing gene expression profiling of large-airway epithelium, alveolar macrophage and peripheral blood samples from the same subset of COPD cases and controls from the COPDGene study who underwent bronchoscopy at a single center. Using statistical and gene set enrichment approaches, we sought to improve the understanding of COPD by studying gene sets and pathways across these tissues, beyond the individual genomic determinants.

METHODS

We performed differential expression analysis using RNA-seq data obtained from 63 samples from 21 COPD cases and controls (includes four non-smokers) via the R package DESeq2. We tested associations between gene expression and variables related to lung function, smoking history, and CT scan measures of emphysema and airway disease. We examined the correlation of differential gene expression across the tissues and phenotypes, hypothesizing that this would reveal preserved and private gene expression signatures. We performed gene set enrichment analyses using curated databases and findings from prior COPD studies to provide biological and disease relevance.

RESULTS

The known smoking-related genes CYP1B1 and AHRR were among the top differential expression results for smoking status in the large-airway epithelium data. We observed a significant overlap of genes primarily across large-airway and macrophage results for smoking and airway disease phenotypes. We did not observe specific genes differentially expressed in all three tissues for any of the phenotypes. However, we did observe hemostasis and immune signaling pathways in the overlaps across all three tissues for emphysema, and amyloid and telomere-related pathways for smoking. In peripheral blood, the emphysema results were enriched for B cell related genes previously identified in lung tissue studies.

CONCLUSIONS

Our integrative analyses across COPD-relevant tissues and prior studies revealed shared and tissue-specific disease biology. These replicated and novel findings in the airway and peripheral blood have highlighted candidate genes and pathways for COPD pathogenesis.

Do sputum or circulating blood samples reflect the pulmonary transcriptomic differences of COPD patients? A multi-tissue transcriptomic network META-analysis

BACKGROUND

Previous studies have identified lung, sputum or blood transcriptomic biomarkers associated with the severity of airflow limitation in COPD. Yet, it is not clear whether the lung pathobiology is mirrored by these surrogate tissues. The aim of this study was to explore this question.

METHODS

We used Weighted Gene Co-expression Network Analysis (WGCNA) to identify shared pathological mechanisms across four COPD gene-expression datasets: two sets of lung tissues (L1 n = 70; L2 n = 124), and one each of induced sputum (S; n = 121) and peripheral blood (B; n = 121).

RESULTS

WGCNA analysis identified twenty-one gene co-expression modules in L1. A robust module preservation between the two L datasets was observed (86%), with less preservation in S (33%) and even less in B (23%). Three modules preserved across lung tissues and sputum (not blood) were associated with the severity of airflow limitation. Ontology enrichment analysis showed that these modules included genes related to mitochondrial function, ion-homeostasis, T cells and RNA processing. These findings were largely reproduced using the consensus WGCNA network approach.

CONCLUSIONS

These observations indicate that major differences in lung tissue transcriptomics in patients with COPD are poorly mirrored in sputum and are unrelated to those determined in blood, suggesting that the systemic component in COPD is independently regulated. Finally, the fact that one of the preserved modules associated with FEV1 was enriched in mitochondria-related genes supports a role for mitochondrial dysfunction in the pathobiology of COPD.

Identification of surrogate prognostic biomarkers for allergic asthma in nasal epithelial brushing samples by WGCNA

BACKGROUND

Allergic asthma is a lower respiratory tract disease of Th2 inflammation with multiple molecular mechanisms. The upper and lower airways can be unified by the concept of a united airway and, as such, gene expression studies of upper epithelial cells may provide effective surrogate biomarkers for the prognostic study of allergic asthma.

OBJECTIVE

To identify surrogate biomarkers in upper airway epithelial cells for the prognostic study of allergic asthma.

METHODS

Nasal epithelial cell gene expression in 40 asthmatic and 17 healthy control subjects were analyzed by weighted gene coexpression network analysis (WGCNA) to identify gene network modules and profiles in allergic asthma. Functional enrichment analysis was performed on the coexpression genes in certain highlighted modules.

RESULTS

A total of 13 coexpression modules were constructed by WGCNA from 2804 genes in nasal epithelial brushing samples of the 40 asthmatic and 17 healthy subjects. The number of genes in these modules ranged from 1086 (Turquoise module) to 45 (Salmon). Eight coexpression modules were found to be significantly correlated (P < 0.05) with two clinic traits, namely disease status, and severity. Four modules were positively correlated ( P < 0.05) with the traits and these, therefore, contained genes that are mostly overexpressed in asthma. Contrastingly, the four other modules were found to be negatively correlated with the clinic traits. Functional enrichment analysis of the positively correlated modules showed that one (Magenta) was mainly enriched in mast cell activation and degranulation; another (Pink) was largely involved in immune cell response; the third (Yellow) was predominantly enriched in transmembrane signal pathways; and the last (Blue) was mainly enriched in substructure components of the cells. The hub genes in the modules were KIT, KITLG, GATA2, CD44, PTPRC, and CFTR, and these were confirmed as having significantly higher expression in the nasal epithelial cells. Combining the six hub genes enabled a relatively high capacity for discrimination between asthmatics and healthy subjects with an area under the receiver operating characteristic (ROC) curve of 0.924.

CONCLUSIONS

Our findings provide a framework of coexpression gene modules from nasal epithelial brushing samples that could be used for the prognostic study of allergic asthma.

Epigenetic regulation of inflammation in stroke

Despite extensive research, treatments for clinical stroke are still limited only to the administration of tissue plasminogen activator and the recent introduction of mechanical thrombectomy, which can be used in only a limited proportion of patients due to time constraints. A plethora of inflammatory events occur during stroke, arising in part due to the body's immune response to brain injury. Neuroinflammation contributes significantly to neuronal cell death and the development of functional impairment and death in stroke patients. Therefore, elucidating the molecular and cellular mechanisms underlying inflammatory damage following stroke injury will be essential for the development of useful therapies. Research findings increasingly point to the likelihood that epigenetic mechanisms play a role in the pathophysiology of stroke. Epigenetics involves the differential regulation of gene expression, including those involved in brain inflammation and remodelling after stroke. Hence, it is conceivable that epigenetic mechanisms may contribute to differential interindividual vulnerability and injury responses to cerebral ischaemia. In this review, we summarize recent findings on the emerging role of epigenetics in the regulation of neuroinflammation in stroke. We also discuss potential epigenetic targets that may be assessed for the development of stroke therapies.

Systems biology and in vitro validation identifies family with sequence similarity 129 member A (FAM129A) as an asthma steroid response modulator

BACKGROUND

Variation in response to the most commonly used class of asthma controller medication, inhaled corticosteroids, presents a serious challenge in asthma management, particularly for steroid-resistant patients with little or no response to treatment.

OBJECTIVE

We applied a systems biology approach to primary clinical and genomic data to identify and validate genes that modulate steroid response in asthmatic children.

METHODS

We selected 104 inhaled corticosteroid-treated asthmatic non-Hispanic white children and determined a steroid responsiveness endophenotype (SRE) using observations of 6 clinical measures over 4 years. We modeled each subject's cellular steroid response using data from a previously published study of immortalized lymphoblastoid cell lines under dexamethasone (DEX) and sham treatment. We integrated SRE with immortalized lymphoblastoid cell line DEX responses and genotypes to build a genome-scale network using the Reverse Engineering, Forward Simulation modeling framework, identifying 7 genes modulating SRE.

RESULTS

Three of these genes were functionally validated by using a stable nuclear factor κ-light-chain-enhancer of activated B cells luciferase reporter in A549 human lung epithelial cells, IL-1β cytokine stimulation, and DEX treatment. By using small interfering RNA transfection, knockdown of family with sequence similarity 129 member A (FAM129A) produced a reduction in steroid treatment response (P < .001).

CONCLUSION

With this systems-based approach, we have shown that FAM129A is associated with variation in clinical asthma steroid responsiveness and that FAM129A modulates steroid responsiveness in lung epithelial cells.

Somatic polyploidy is associated with the upregulation of c-MYC interacting genes and EMT-like signature

The dependence of cancer on overexpressed c-MYC and its predisposition for polyploidy represents a double puzzle. We address this conundrum by cross-species transcription analysis of c-MYC interacting genes in polyploid vs. diploid tissues and cells, including human vs. mouse heart, mouse vs. human liver and purified 4n vs. 2n mouse decidua cells. Gene-by-gene transcriptome comparison and principal component analysis indicated that c-MYC interactants are significantly overrepresented among ploidy-associated genes. Protein interaction networks and gene module analysis revealed that the most upregulated genes relate to growth, stress response, proliferation, stemness and unicellularity, as well as to the pathways of cancer supported by MAPK and RAS coordinated pathways. A surprising feature was the up-regulation of epithelial-mesenchymal transition (EMT) modules embodied by the N-cadherin pathway and EMT regulators from SNAIL and TWIST families. Metabolic pathway analysis also revealed the EMT-linked features, such as global proteome remodeling, oxidative stress, DNA repair and Warburg-like energy metabolism. Genes associated with apoptosis, immunity, energy demand and tumour suppression were mostly down-regulated. Noteworthy, despite the association between polyploidy and ample features of cancer, polyploidy does not trigger it. Possibly it occurs because normal polyploidy does not go that far in embryonalisation and linked genome destabilisation. In general, the analysis of polyploid transcriptome explained the evolutionary relation of c-MYC and polyploidy to cancer.

From systems biology to P4 medicine: applications in respiratory medicine

Human health and disease are emergent properties of a complex, nonlinear, dynamic multilevel biological system: the human body. Systems biology is a comprehensive research strategy that has the potential to understand these emergent properties holistically. It stems from advancements in medical diagnostics, “omics” data and bioinformatic computing power. It paves the way forward towards “P4 medicine” (predictive, preventive, personalised and participatory), which seeks to better intervene preventively to preserve health or therapeutically to cure diseases. In this review, we: 1) discuss the principles of systems biology; 2) elaborate on how P4 medicine has the potential to shift healthcare from reactive medicine (treatment of illness) to predict and prevent illness, in a revolution that will be personalised in nature, probabilistic in essence and participatory driven; 3) review the current state of the art of network (systems) medicine in three prevalent respiratory diseases (chronic obstructive pulmonary disease, asthma and lung cancer); and 4) outline current challenges and future goals in the field.

Systems biology and network medicine have the potential to transform medical research and practicehttp://ow.ly/r3jR30hf35x

Molecular Genetic Analysis of Human Endometrial Mesenchymal Stem Cells That Survived Sublethal Heat Shock

High temperature is a critical environmental and personal factor. Although heat shock is a well-studied biological phenomenon, hyperthermia response of stem cells is poorly understood. Previously, we demonstrated that sublethal heat shock induced premature senescence in human endometrial mesenchymal stem cells (eMSC). This study aimed to investigate the fate of eMSC-survived sublethal heat shock (SHS) with special emphasis on their genetic stability and possible malignant transformation using methods of classic and molecular karyotyping, next-generation sequencing, and transcriptome functional analysis. G-banding revealed random chromosome breakages and aneuploidy in the SHS-treated eMSC. Molecular karyotyping found no genomic imbalance in these cells. Gene module and protein interaction network analysis of mRNA sequencing data showed that compared to untreated cells, SHS-survived progeny revealed some difference in gene expression. However, no hallmarks of cancer were found. Our data identified downregulation of oncogenic signaling, upregulation of tumor-suppressing and prosenescence signaling, induction of mismatch, and excision DNA repair. The common feature of heated eMSC is the silence of MYC, AKT1/PKB oncogenes, and hTERT telomerase. Overall, our data indicate that despite genetic instability, SHS-survived eMSC do not undergo transformation. After long-term cultivation, these cells like their unheated counterparts enter replicative senescence and die.

Meta-analysis of peripheral blood gene expression modules for COPD phenotypes

Chronic obstructive pulmonary disease (COPD) occurs typically in current or former smokers, but only a minority of people with smoking history develops the disease. Besides environmental factors, genetics is an important risk factor for COPD. However, the relationship between genetics, environment and phenotypes is not well understood. Sample sizes for genome-wide expression studies based on lung tissue have been small due to the invasive nature of sample collection. Increasing evidence for the systemic nature of the disease makes blood a good alternative source to study the disease, but there have also been few large-scale blood genomic studies in COPD. Due to the complexity and heterogeneity of COPD, examining groups of interacting genes may have more relevance than identifying individual genes. Therefore, we used Weighted Gene Co-expression Network Analysis to find groups of genes (modules) that are highly connected. However, module definitions may vary between individual data sets. To alleviate this problem, we used a consensus module definition based on two cohorts, COPDGene and ECLIPSE. We studied the relationship between the consensus modules and COPD phenotypes airflow obstruction and emphysema. We also used these consensus module definitions on an independent cohort (TESRA) and performed a meta analysis involving all data sets. We found several modules that are associated with COPD phenotypes, are enriched in functional categories and are overrepresented for cell-type specific genes. Of the 14 consensus modules, three were strongly associated with airflow obstruction (meta p ≤ 0.0002), and two had some association with emphysema (meta p ≤ 0.06); some associations were stronger in the case-control cohorts, and others in the cases-only subcohorts. Gene Ontology terms that were overrepresented included “immune response” and “defense response.” The cell types whose type-specific genes were overrepresented in modules (p < 0.05) included natural killer cells, dendritic cells, and neutrophils. Together, this is the largest investigation of gene blood expression in COPD with 469 cases in COPDGene, ECLIPSE and TESRA combined, with 6267 genes common to all data sets. Additional, we have 42 and 83 controls in COPDGene and ECLIPSE, respectively.

What does endotyping mean for treatment in chronic obstructive pulmonary disease?

Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous disease, both at the clinical and biological level. However, COPD is still diagnosed and treated according to simple clinical measures (level of airflow limitation, symptoms, and frequency of previous exacerbations). To address this clinical and biological complexity and to move towards precision medicine in COPD, we need to integrate (bioinformatics) and interpret (clinical science) the vast amount of high-throughput information that existing technology provides (systems biology and network medicine) so diagnosis, stratification, and treatment of patients with COPD can occur on the basis of their pathobiological mechanism (ie, endotypes). Therefore, this Series paper discusses a possible new taxonomy of COPD, the role of endotypes and associated biomarkers and phenotypes, the gaps (and opportunities) in existing knowledge of COPD pathobiology, how systems biology and network medicine can improve understanding of the disease and help to identify relevant endotypes and their specific biomarkers, and how endotypes and their biomarkers can improve the precision, effectiveness, and safety of the treatment of patients with COPD.

Measurement and Clinical Significance of Biomarkers of Oxidative Stress in Humans

Oxidative stress is the result of the imbalance between reactive oxygen species (ROS) formation and enzymatic and nonenzymatic antioxidants. Biomarkers of oxidative stress are relevant in the evaluation of the disease status and of the health-enhancing effects of antioxidants. We aim to discuss the major methodological bias of methods used for the evaluation of oxidative stress in humans. There is a lack of consensus concerning the validation, standardization, and reproducibility of methods for the measurement of the following: (1) ROS in leukocytes and platelets by flow cytometry, (2) markers based on ROS-induced modifications of lipids, DNA, and proteins, (3) enzymatic players of redox status, and (4) total antioxidant capacity of human body fluids. It has been suggested that the bias of each method could be overcome by using indexes of oxidative stress that include more than one marker. However, the choice of the markers considered in the global index should be dictated by the aim of the study and its design, as well as by the clinical relevance in the selected subjects. In conclusion, the clinical significance of biomarkers of oxidative stress in humans must come from a critical analysis of the markers that should give an overall index of redox status in particular conditions.

Network-based analysis reveals novel gene signatures in peripheral blood of patients with chronic obstructive pulmonary disease

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is currently the third leading cause of death and there is a huge unmet clinical need to identify disease biomarkers in peripheral blood. Compared to gene level differential expression approaches to identify gene signatures, network analyses provide a biologically intuitive approach which leverages the co-expression patterns in the transcriptome to identify modules of co-expressed genes.

METHODS

A weighted gene co-expression network analysis (WGCNA) was applied to peripheral blood transcriptome from 238 COPD subjects to discover co-expressed gene modules. We then determined the relationship between these modules and forced expiratory volume in 1 s (FEV₁). In a second, independent cohort of 381 subjects, we determined the preservation of these modules and their relationship with FEV₁. For those modules that were significantly related to FEV₁, we determined the biological processes as well as the blood cell-specific gene expression that were over-represented using additional external datasets.

RESULTS

Using WGCNA, we identified 17 modules of co-expressed genes in the discovery cohort. Three of these modules were significantly correlated with FEV₁ (FDR < 0.1). In the replication cohort, these modules were highly preserved and their FEV₁ associations were reproducible (P < 0.05). Two of the three modules were negatively related to FEV₁ and were enriched in IL8 and IL10 pathways and correlated with neutrophil-specific gene expression. The positively related module, on the other hand, was enriched in DNA transcription and translation and was strongly correlated to CD4+, CD8+ T cell-specific gene expression.

CONCLUSIONS

Network based approaches are promising tools to identify potential biomarkers for COPD.

TRIAL REGISTRATION

The ECLIPSE study was funded by GlaxoSmithKline, under ClinicalTrials.gov identifier NCT00292552 and GSK No. SCO104960.

The recent advances of phenotypes in acute exacerbations of COPD

Exacerbations of COPD are clinically relevant events with therapeutic and prognostic implications. Yet, significant heterogeneity of clinical presentation and disease progression exists within acute exacerbations of COPD (AECOPD). Currently, different phenotypes have been widely used to describe the characteristics among patients with AECOPD. This has proved to be significant in the treatment and prediction of the outcomes of the disease. In this review of published literature, the phenotypes of AECOPD were classified according to etiology, inflammatory biomarkers, clinical manifestation, comorbidity, the frequency of exacerbations, and so on. This review concentrates on advancements in the use of phenotypes of AECOPD.

Functional interactors of three genome-wide association study genes are differentially expressed in severe chronic obstructive pulmonary disease lung tissue

In comparison to genome-wide association studies (GWAS), there has been poor replication of gene expression studies in chronic obstructive pulmonary disease (COPD). We performed microarray gene expression profiling on a large sample of resected lung tissues from subjects with severe COPD. Comparing 111 COPD cases and 40 control smokers, 204 genes were differentially expressed; none were at significant GWAS loci. The top differentially expressed gene was HMGB1, which interacts with AGER, a known COPD GWAS gene. Differentially expressed genes showed enrichment for putative interactors of the first three identified COPD GWAS genes IREB2, HHIP, and FAM13A, based on gene sets derived from protein and RNA binding studies, RNA-interference, a murine smoking model, and expression quantitative trait locus analyses. The gene module most highly associated for COPD in Weighted Gene Co-Expression Network Analysis (WGCNA) was enriched for B cell pathways, and shared seventeen genes with a mouse smoking model and twenty genes with previous emphysema studies. As in other common diseases, genes at COPD GWAS loci were not differentially expressed; however, using a combination of network methods, experimental studies and careful phenotype definition, we found differential expression of putative interactors of these genes, and we replicated previous human and mouse microarray results.

Linking childhood allergic asthma phenotypes with endotype through integrated systems biology: current evidence and research needs

Asthma and other complex diseases results from a complex web of interactions involving inflammation, immunity, cell cycle, apoptosis, and metabolic perturbations across multiple organ systems. The extent to which various degrees of the age at onset, symptom severity, and the natural progression of the disease reflect multiple disease subtypes, influenced by unique process of development remains unknown. One of the most critical challenges to our understanding stems from incomplete understanding of the mechanisms. Within this review, we focus on the phenotypes of childhood allergic asthma as the basis to better understand the endotype for quantitative define subtypes of asthma. We highlight some of the known mechanistic pathways associated with the key hallmark events before the asthma onset. In particular, we examine how the recent advent of multiaxial -omics technologies and systems biology could help to clarify our current understanding of the pathway. We review how a large volume of molecular, genomic data generated by multiaxial technologies could be digested to identify cogent pathophysiologic molecular networks. We highlight some recent successes in application of these technologies within the context of other disease conditions for therapeutic interventions. We conclude by summarizing the research needs for the predictive value of preclinical biomarkers.

Molecular mechanisms underlying COPD-muscle dysfunction unveiled through a systems medicine approach

MOTIVATION

Skeletal muscle dysfunction is a systemic effect in one-third of patients with chronic obstructive pulmonary disease (COPD), characterized by high reactive-oxygen-species (ROS) production and abnormal endurance training-induced adaptive changes. However, the role of ROS in COPD remains unclear, not least because of the lack of appropriate tools to study multifactorial diseases.

RESULTS

We describe a discrete model-driven method combining mechanistic and probabilistic approaches to decipher the role of ROS on the activity state of skeletal muscle regulatory network, assessed before and after an 8-week endurance training program in COPD patients and healthy subjects. In COPD, our computational analysis indicates abnormal training-induced regulatory responses leading to defective tissue remodeling and abnormal energy metabolism. Moreover, we identified tnf, insr, inha and myc as key regulators of abnormal training-induced adaptations in COPD. The tnf-insr pair was identified as a promising target for therapeutic interventions. Our work sheds new light on skeletal muscle dysfunction in COPD, opening new avenues for cost-effective therapies. It overcomes limitations of previous computational approaches showing high potential for the study of other multi-factorial diseases such as diabetes or cancer.

CONTACT

jroca@clinic.ub.es or martacascante@ub.eduSupplementary information: Supplementary data are available at Bioinformatics online.

The global threat of Zika virus to pregnancy: epidemiology, clinical perspectives, mechanisms, and impact

Zika virus (ZIKV) is a mosquito-borne flavivirus that has newly emerged as a significant global threat, especially to pregnancy. Recent major outbreaks in the Pacific and in Central and South America have been associated with an increased incidence of microcephaly and other abnormalities of the central nervous system in neonates. The causal link between ZIKV infection during pregnancy and microcephaly is now strongly supported. Over 2 billion people live in regions conducive to ZIKV transmission, with ~4 million infections in the Americas predicted for 2016. Given the scale of the current pandemic and the serious and long-term consequences of infection during pregnancy, the impact of ZIKV on health services and affected communities could be enormous. This further highlights the need for a rapid global public health and research response to ZIKV to limit and prevent its impact through the development of therapeutics, vaccines, and improved diagnostics. Here we review the epidemiology of ZIKV; the threat to pregnancy; the clinical consequences and broader impact of ZIKV infections; and the virus biology underpinning new interventions, diagnostics, and insights into the mechanisms of disease.

Identifying a biomarker network for corticosteroid resistance in asthma from bronchoalveolar lavage samples

Corticosteroid resistance (CR) is a major barrier to the effective treatment of severe asthma. Hence, a better understanding of the molecular mechanisms involved in this condition is a priority. Network analysis is an emerging strategy to explore this complex heterogeneous disorder at system level to identify a small own network for CR in asthma. Gene expression profile of GSE7368 from bronchoalveolar lavage (BAL) of CR in subjects with asthma was downloaded from the gene expression omnibus (GEO) database and compared to BAL of corticosteroid-sensitive (CS) patients. DEGs were identified by the Limma package in R language. In addition, DEGs were mapped to STRING to acquire protein-protein interaction (PPI) pairs. Topological properties of PPI network were calculated by Centiscape, ClusterOne and BINGO. Subsequently, text-mining tools were applied to design one own cell signalling for CR in asthma. Thirty-five PPI networks were obtained; including a major network consisted of 370 nodes, connected by 777 edges. After topological analysis, a minor PPI network composed by 48 nodes was indentified, which is composed by most relevant nodes of major PPI network. In this subnetwork, several receptors (EGFR, EGR1, ESR2, PGR), transcription factors (MYC, JAK), cytokines (IL8, IL6, IL1B), one chemokine (CXCL1), one kinase (SRC) and one cyclooxygenase (PTGS2) were described to be associated with inflammatory environment and steroid resistance in asthma. We suggest a biomarker network composed by 48 nodes that could be potentially explored with diagnostic or therapeutic use.

Challenges and Current Efforts in the Development of Biomarkers for Chronic Inflammatory and Remodeling Conditions of the Lungs

This review discusses biomarkers that are being researched for their usefulness to phenotype chronic inflammatory lung diseases that cause remodeling of the lung's architecture. The review focuses on asthma, chronic obstructive pulmonary disease (COPD), and pulmonary hypertension. Bio-markers of environmental exposure and specific classes of biomarkers (noncoding RNA, metabolism, vitamin, coagulation, and microbiome related) are also discussed. Examples of biomarkers that are in clinical use, biomarkers that are under development, and biomarkers that are still in the research phase are discussed. We chose to present examples of the research in biomarker development by diseases, because asthma, COPD, and pulmonary hypertension are distinct entities, although they clearly share processes of inflammation and remodeling.

LC-MS based metabolomics identification of novel biomarkers of tobacco smoke-induced chronic bronchitis

Tobacco smoke (TS) is a major causative agent to lead to chronic bronchitis (CB). However the mechanisms of CB induced by TS are unclear. In this report, rats were exposed to different concentrations of TS and the metabolic features of CB were characterized by using a nontargeted metabolic profiling method based on liquid chromatography-mass spectrometry (LC-MS) to detect the altered metabolic patterns in serum from CB rats and investigate the mechanisms of CB. 11 potential biomarkers were identified in serum of rats. Among them, the levels of lysophosphatidylethanolamine (18:1), lysophosphatidic acid (18:1), lysophosphatidylethanolamine (18:0), lysophosphatidylethanolamine (16:0), lysophosphatidylethanolamine (20:4), docosahexaenoic acid, 5-hydroxyindoleacetic acid and 5'-carboxy-γ-tocopherol were higher in TS group compared to control group. Conversely, the levels of 4-imidazolone-5-propionic acid, 12-hydroxyeicosatetraenoic acid and uridine were lower in TS group. The results indicated that the mechanism of CB was related to amino acid metabolism and lipid metabolism, particularly lipid metabolism. In addition, lysophosphatidylethanolamines were proved to be important mediators, which could be used as biomarkers to diagnose CB. These results also suggested that metabolomics was suitable for diagnosing CB and elucidating the possible metabolic pathways of TS-induced CB.