Systems biology analyses of gene expression and genome wide association study data in obstructive sleep apnea

Lipid metabolism and neuromuscular junction as common pathways underlying the genetic basis of erectile dysfunction and obstructive sleep apnea

Erectile dysfunction (ED) incidence is higher in patients with obstructive sleep apnea (OSA). Studies have suggested that ED and OSA may activate similar pathways; however, few have investigated the links between their underlying genotypic profiles. Therefore, we conducted an in-silico analysis to test whether ED and OSA share genetic variants of risk and to identify any molecular, cellular and biological interactions between them. Two gene lists were manually curated through a literature review based on a PUBMED search, which resulted in one gene list associated with ED (total of 205 genes) and the other with OSA (total of 2622 genes). Between those gene sets, 35 were common for both lists (Fisher exact test, p-value = 0.027). The Protein-protein interaction (PPI) analysis using the intersect list as input showed that 3 of them had direct interactions (LPL, DGKB and PLCB1). In addition, the biological function of the genes contained in the intersect list suggested that pathways related to lipid metabolism and the neuromuscular junction were commonly found in the genetic basis of ED and OSA. From the shared genes between both conditions, the biological pathways highlighted in this study may serve as preliminary findings for future functional investigations on OSA and ED association.

Identification and Validation of Prognostic Factors of Lipid Metabolism in Obstructive Sleep Apnea

Background: Obstructive sleep apnea (OSA) is considered to be an independent factor affecting lipid metabolism. This study explored the relationship between immune genes and lipid metabolism in OSA.

Methods: Immune-related Differentially Expressed Genes (DEGs) were identified by analyzing microarray data sets from the Gene Expression Omnibus (GEO) database. Subsequently, we conducted protein-protein interaction (PPI) network analysis and calculated their Gene Ontology (GO) semantic similarity. The GO, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, Disease Ontology (DO), gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA) were employed for functional enrichment analyses and to determine the most significant functional terms. Combined with the results of boruta and random forest, we selected predictors to build a prognostic model, along with seeking out the potential TFs and target drugs for the predictive genes.

Results: Immune-related DEGs included 64 genes upregulated and 98 genes downregulated. The enrichment analysis might closely associate with cell adhesion and T cell-mediated immunity pathways and there were many DEGs involved in lipid and atherosclerosis signaling pathways. The highest-ranking hub gene in PPI network have been reported lowly expressed in OSA. In line with the enrichment analysis, DO analysis reveal that respiratory diseases may be associated with OSA besides immune system disorders. Consistent with the result of the KEGG pathway, the analysis of GSVA revealed that the pro-inflammation pathways are associated with OSA. Monocytes and CD8 T cells were the predominant immune cells in adipose tissue. We built a prognostic model with the top six genes, and the prognostic genes were involved in the polarization of macrophage and differentiation of T lymphocyte subsets. In vivo experimental verification revealed that EPGN, LGR5, NCK1 and VIP were significantly down-regulated while PGRMC2 was significantly up-regulated in mouse model of OSA.

Conclusions: Our study demonstrated strong associations between immune genes and the development of dyslipidemia in OSA. This work promoted the molecular mechanisms and potential targets for the regulation of lipid metabolism in OSA.

Study of Osteoarthritis-Related Hub Genes Based on Bioinformatics Analysis

Osteoarthritis (OA) is a common cause of morbidity and disability worldwide. However, the pathogenesis of OA is unclear. Therefore, this study was conducted to characterize the pathogenesis and implicated genes of OA. The gene expression profiles of GSE82107 and GSE55235 were downloaded from the Gene Expression Omnibus database. Altogether, 173 differentially expressed genes including 68 upregulated genes and 105 downregulated genes in patients with OA were selected based on the criteria of ∣log fold-change | >1 and an adjusted p value < 0.05. Protein-protein interaction network analysis showed that FN1, COL1A1, IGF1, SPP1, TIMP1, BGN, COL5A1, MMP13, CLU, and SDC1 are the top ten genes most closely related to OA. Quantitative reverse transcription-polymerase chain reaction showed that the expression levels of COL1A1, COL5A1, TIMP1, MMP13, and SDC1 were significantly increased in OA. This study provides clues for the molecular mechanism and specific biomarkers of OA.

Integrated microRNA and mRNA network analysis of the human myometrial transcriptome in the transition from quiescence to labor

We conducted integrated transcriptomics network analyses of miRNA and mRNA interactions in human myometrium to identify novel molecular candidates potentially involved in human parturition. Myometrial biopsies were collected from women undergoing primary Cesarean deliveries in well-characterized clinical scenarios: (1) spontaneous term labor (TL, n = 5); (2) term nonlabor (TNL, n = 5); (3) spontaneous preterm birth (PTB) with histologic chorioamnionitis (PTB-HCA, n = 5); and (4) indicated PTB nonlabor (PTB-NL, n = 5). RNAs were profiled using RNA sequencing, and miRNA-target interaction networks were mined for key discriminatory subnetworks. Forty miRNAs differed between TL and TNL myometrium, while seven miRNAs differed between PTB-HCA vs. PTB-NL specimens; six of these were cross-validated using quantitative PCR. Based on the combined sequencing data, unsupervised clustering revealed two nonoverlapping cohorts that differed primarily by absence or presence of uterine quiescence, rather than gestational age or original clinical cohort. The intersection of differentially expressed miRNAs and their targets predicted 22 subnetworks with enriched representation of miR-146b-5p, miR-223-3p, and miR-150-5p among miRNAs, and of myocyte enhancer factor-2C (MEF2C) among mRNAs. Of four known MEF2 transcription factors, decreased MEF2A and MEF2C expression in women with uterine nonquiescence was observed in the sequencing data, and validated in a second cohort by quantitative PCR. Immunohistochemistry localized MEF2A and MEF2C to myometrial smooth muscle cells and confirmed decreased abundance with labor. Collectively, these results suggest altered MEF2 expression may represent a previously unrecognized process through which miRNAs contribute to the phenotypic switch from quiescence to labor in human myometrium.

Personalised medicine in sleep respiratory disorders: focus on obstructive sleep apnoea diagnosis and treatment

In all fields of medicine, major efforts are currently dedicated to improve the clinical, physiological and therapeutic understanding of disease, and obstructive sleep apnoea (OSA) is no exception. The personalised medicine approach is relevant for OSA, given its complex pathophysiology and variable clinical presentation, the interactions with comorbid conditions and its possible contribution to poor outcomes. Treatment with continuous positive airway pressure (CPAP) is effective, but CPAP is poorly tolerated or not accepted in a considerable proportion of OSA patients. This review summarises the available studies on the physiological phenotypes of upper airway response to obstruction during sleep, and the clinical presentations of OSA (phenotypes and clusters) with a special focus on our changing attitudes towards approaches to treatment. Such major efforts are likely to change and expand treatment options for OSA beyond the most common current choices (i.e CPAP, mandibular advancement devices, positional treatment, lifestyle changes or upper airway surgery). More importantly, treatment for OSA may become more effective, being tailored to each patient's need.

Genomics of human pulmonary tuberculosis: from genes to pathways

Purpose of review

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), remains a major public health threat globally. Several lines of evidence support a role for host genetic factors in resistance/susceptibility to TB disease and MTB infection. However, results across candidate gene and genome-wide association studies (GWAS) are largely inconsistent, so a cohesive genetic model underlying TB risk has not emerged.

Recent Findings

Despite the difficulties in identifying consistent genetic associations, genetic studies of TB and MTB infection have revealed a few well-documented loci. These well validated genes are presented in this review, but there remains a large gap in how these genes translate into better understanding of TB. To address this, we present a pathway based extension of standard association analyses, seeding the results with the best validated genes from candidate gene and GWAS studies.

Summary

Several pathways were significantly enriched using pathway analyses that may help to explain population patterns of TB risk. In conclusion, we advocate for novel approaches to the study of host genetic analysis of TB that extend traditional association approaches.

IODNE: An integrated optimization method for identifying the deregulated subnetwork for precision medicine in cancer

Subnetwork analysis can explore complex patterns of entire molecular pathways for the purpose of drug target identification. In this article, the gene expression profiles of a cohort of patients with breast cancer are integrated with protein‐protein interaction (PPI) networks using, simultaneously, both edge scoring and node scoring. A novel optimization algorithm, integrated optimization method to identify deregulated subnetwork (IODNE), is developed to search for the optimal dysregulated subnetwork of the merged gene and protein network. IODNE is applied to select subnetworks for Luminal‐A breast cancer from The Cancer Genome Atlas (TCGA) data. A large fraction of cancer‐related genes and the well‐known clinical targets, ER1/PR and HER2, are found by IODNE. This validates the utility of IODNE. When applying IODNE to the triple‐negative breast cancer (TNBC) subtype data, we identified subnetworks that contain genes such as ERBB2, HRAS, PGR, CAD, POLE, and SLC2A1.

Finding lost genes in GWAS via integrative-omics analysis reveals novel sub-networks associated with preterm birth

Maternal genome influences associate with up to 40% of spontaneous preterm births (PTB). Multiple genome wide association studies (GWAS) have been completed to identify genetic variants associated with PTB. Disappointingly, no highly significant SNPs have replicated in independent cohorts so far. We developed an approach combining protein-protein interaction (PPI) network data with tissue specific gene expression data to "find" SNPs of modest significance to identify candidate genes of functional importance that would otherwise be overlooked. This approach is based on the assumption that "high-ranking" SNPs falling short of genome wide significance may nevertheless indicate genes that have substantial biological value in understanding PTB. We mapped highly-ranked candidate SNPs from a meta-analysis of PTB-GWAS to coding genes and developed a PPI network enriched with PTB-SNP carrying genes. This network was scored with gene expression data from term and preterm myometrium to identify subnetworks of PTB-SNP associated genes coordinately expressed with labour onset in myometrial tissue. Our analysis consistently identified significant sub-networks associated with the interacting transcription factors MEF2C and TWIST1, genes not previously associated with PTB, both of which regulate processes clearly relevant to birth timing. Other genes in the significant sub-networks were also associated with inflammatory pathways, as well as muscle function and ion channels. Gene expression level dysregulation was confirmed for eight of these networks by qRT-PCR in an independent set of term and pre-term subjects. Our method identifies novel genes dysregulated in PTB and provides a generalized framework to identify GWAS SNPs that would otherwise be overlooked.

Pediatric OSA Syndrome Morbidity Biomarkers: The Hunt Is Finally On!

Since initial reports 40 years ago on pediatric OSA syndrome (OSAS) as a distinct and prevalent clinical entity, substantial advances have occurred in the delineation of diagnostic and treatment approaches. However, despite emerging and compelling evidence that OSAS increases the risk for cognitive, cardiovascular, and metabolic end-organ morbidities, routine assessment of such morbidities is seldom conducted in clinical practice. One of the major reasons for such discrepancies resides in the relatively labor-intensive and onerous steps that would be required to detect the presence of any of such morbidities, further adding to the already elevated cost of diagnosing the disorder. To circumvent these obstacles, the search for biomarker signatures of pediatric OSA and its cognitive and cardiometabolic consequences was launched, and considerable progress has occurred since then. Here, we review the current evidence for the presence of morbidity-related biomarkers among children with OSAS, and explore future opportunities in this promising arena.

The Role of Big Data in the Management of Sleep-Disordered Breathing

Analysis of large-volume data holds promise for improving the application of precision medicine to sleep, including improving identification of patient subgroups who may benefit from alternative therapies. Big data used within the health care system also promises to facilitate end-to-end screening, diagnosis, and management of sleep disorders; improve the recognition of differences in presentation and susceptibility to sleep apnea; and lead to improved management and outcomes. To meet the vision of personalized, precision therapeutics and diagnostics and improving the efficiency and quality of sleep medicine will require ongoing efforts, investments, and change in our current medical and research cultures.

MOBAS: identification of disease-associated protein subnetworks using modularity-based scoring

Network-based analyses are commonly used as powerful tools to interpret the findings of genome-wide association studies (GWAS) in a functional context. In particular, identification of disease-associated functional modules, i.e., highly connected protein-protein interaction (PPI) subnetworks with high aggregate disease association, are shown to be promising in uncovering the functional relationships among genes and proteins associated with diseases. An important issue in this regard is the scoring of subnetworks by integrating two quantities: disease association of individual gene products and network connectivity among proteins. Current scoring schemes either disregard the level of connectivity and focus on the aggregate disease association of connected proteins or use a linear combination of these two quantities. However, such scoring schemes may produce arbitrarily large subnetworks which are often not statistically significant or require tuning of parameters that are used to weigh the contributions of network connectivity and disease association.

Here, we propose a parameter-free scoring scheme that aims to score subnetworks by assessing the disease association of interactions between pairs of gene products. We also incorporate the statistical significance of network connectivity and disease association into the scoring function. We test the proposed scoring scheme on a GWAS dataset for two complex diseases type II diabetes (T2D) and psoriasis (PS). Our results suggest that subnetworks identified by commonly used methods may fail tests of statistical significance after correction for multiple hypothesis testing. In contrast, the proposed scoring scheme yields highly significant subnetworks, which contain biologically relevant proteins that cannot be identified by analysis of genome-wide association data alone. We also show that the proposed scoring scheme identifies subnetworks that are reproducible across different cohorts, and it can robustly recover relevant subnetworks at lower sampling rates.

The association of 5-HT2A, 5-HTT, and LEPR polymorphisms with obstructive sleep apnea syndrome: a systematic review and meta-analysis

Objective

A consensus has not been reached regarding the association of several different gene polymorphisms and susceptibility to obstructive sleep apnea syndrome (OSAS). We performed a meta-analysis to better evaluate the associations between 5-HT2A, 5-HTT, and LEPR polymorphisms, and OSAS.

Method

5-HT2A, 5-HTT, and LEPR polymorphisms and OSAS were identified in PubMed and EMBASE. The pooled odd rates (ORs) with 95%CIs were estimated using a fixed-effect or random-effect models. The associations between these polymorphisms and OSAS risk were assessed using dominant, recessive and additive models.

Results

Twelve publications were included in this study. The -1438 “A” allele of 5-HT2A was identified as a candidate genetic risk factor for OSAS (OR: 2.33, 95%CI 1.49–3.66). Individuals carrying the -1438 “G” allele had a nearly 70% reduced risk of OSAS when compared with AA homozygotes (OR: 0.30, 95%CI 0.23–0.40). There was no significant association between 5-HT2A 102C/T and OSAS risk, using any model. The “S” allele of 5-HTTLPR conferred protection against OSAS (OR: 0.80, 95%CI 0.67–0.95), while the “10” allele of 5-HTTVNTR contributed to the risk of OSAS (OR: 2.08, 95%CI: 1.58–2.73). The “GG” genotype of LEPR was associated with a reduced risk of OSAS (OR: 0.39, 95%CI 0.17–0.88).

Conclusion

The meta-analysis demonstrated that 5-HTR-1438 “A” and 5-HTTVNTR “10” alleles were significantly associated with OSAS. The “S” allele of 5-HTTLPR and the “GG” genotype of LEPR conferred protection against OSAS. Further studies, such as Genome-Wide Association study (GWAS), should be conducted in a large cohort of OSAS patients to confirm our findings.

Genomic convergence and network analysis approach to identify candidate genes in Alzheimer's disease

Background

Alzheimer’s disease (AD) is one of the leading genetically complex and heterogeneous disorder that is influenced by both genetic and environmental factors. The underlying risk factors remain largely unclear for this heterogeneous disorder. In recent years, high throughput methodologies, such as genome-wide linkage analysis (GWL), genome-wide association (GWA) studies, and genome-wide expression profiling (GWE), have led to the identification of several candidate genes associated with AD. However, due to lack of consistency within their findings, an integrative approach is warranted. Here, we have designed a rank based gene prioritization approach involving convergent analysis of multi-dimensional data and protein-protein interaction (PPI) network modelling.

Results

Our approach employs integration of three different AD datasets- GWL,GWA and GWE to identify overlapping candidate genes ranked using a novel cumulative rank score (S_R) based method followed by prioritization using clusters derived from PPI network. S_R for each gene is calculated by addition of rank assigned to individual gene based on either p value or score in three datasets. This analysis yielded 108 plausible AD genes. Network modelling by creating PPI using proteins encoded by these genes and their direct interactors resulted in a layered network of 640 proteins. Clustering of these proteins further helped us in identifying 6 significant clusters with 7 proteins (EGFR, ACTB, CDC2, IRAK1, APOE, ABCA1 and AMPH) forming the central hub nodes. Functional annotation of 108 genes revealed their role in several biological activities such as neurogenesis, regulation of MAP kinase activity, response to calcium ion, endocytosis paralleling the AD specific attributes. Finally, 3 potential biochemical biomarkers were found from the overlap of 108 AD proteins with proteins from CSF and plasma proteome. EGFR and ACTB were found to be the two most significant AD risk genes.

Conclusions

With the assumption that common genetic signals obtained from different methodological platforms might serve as robust AD risk markers than candidates identified using single dimension approach, here we demonstrated an integrated genomic convergence approach for disease candidate gene prioritization from heterogeneous data sources linked to AD.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-199) contains supplementary material, which is available to authorized users.

Network.assisted analysis to prioritize GWAS results: principles, methods and perspectives

Genome-wide association studies (GWAS) have rapidly become a powerful tool in genetic studies of complex diseases and traits. Traditionally, single marker-based tests have been used prevalently in GWAS and have uncovered tens of thousands of disease-associated SNPs. Network-assisted analysis (NAA) of GWAS data is an emerging area in which network-related approaches are developed and utilized to perform advanced analyses of GWAS data in order to study various human diseases or traits. Progress has been made in both methodology development and applications of NAA in GWAS data, and it has already been demonstrated that NAA results may enhance our interpretation and prioritization of candidate genes and markers. Inspired by the strong interest in and high demand for advanced GWAS data analysis, in this review article, we discuss the methodologies and strategies that have been reported for the NAA of GWAS data. Many NAA approaches search for subnetworks and assess the combined effects of multiple genes participating in the resultant subnetworks through a gene set analysis. With no restriction to pre-defined canonical pathways, NAA has the advantage of defining subnetworks with the guidance of the GWAS data under investigation. In addition, some NAA methods prioritize genes from GWAS data based on their interconnections in the reference network. Here, we summarize NAA applications to various diseases and discuss the available options and potential caveats related to their practical usage. Additionally, we provide perspectives regarding this rapidly growing research area.

A pathway-based analysis on the effects of obstructive sleep apnea in modulating visceral fat transcriptome

RATIONALE

Obstructive sleep apnea (OSA) has been associated with metabolic dysregulation and systemic inflammation. This may be due to pathophysiologic effects of OSA on visceral adipose tissue. We sought to assess the transcriptional consequences of OSA on adipocytes by utilizing pathway-focused analyses.

METHODS

Patients scheduled to undergo ventral hernia repair surgery were recruited to wear a portable home sleep monitor for 2 nights prior to surgery. Visceral fat biopsies were obtained intraoperatively. RNA was extracted and whole-genome expression profiling was performed. Gene Set Enrichment Analysis (GSEA) was used to identify curated gene sets that were differentially enriched in OSA subjects. Network analysis was applied to a select set of highly enriched pathways.

RESULTS

Ten patients with OSA and 8 control subjects were recruited. There were no differences in age, gender, or body mass index between the 2 groups, but the OSA subjects had a significantly higher respiratory disturbance index (19.2 vs. 0.6, P = 0.05) and worse hypoxemia (minimum oxygen saturation 79.7% vs. 87.8%, P < 0.001). GSEA identified a number of gene sets up-regulated in adipose tissue of OSA patients, including the pro-inflammatory NF-κB pathway and the proteolytic ubiquitin/proteasome module. A critical metabolic pathway, the peroxisome proliferator-activated receptor (PPAR), was down-regulated in subjects with OSA. Network analysis linked members of these modules together and identified regulatory hubs.

CONCLUSIONS

OSA is associated with alterations in visceral fat gene expression. Pathway-based network analysis highlighted perturbations in several key pathways whose coordinated interactions may contribute to the metabolic dysregulation observed in this complex disorder.

Network biology methods integrating biological data for translational science

The explosion of biomedical data, both on the genomic and proteomic side as well as clinical data, will require complex integration and analysis to provide new molecular variables to better understand the molecular basis of phenotype. Currently, much data exist in silos and is not analyzed in frameworks where all data are brought to bear in the development of biomarkers and novel functional targets. This is beginning to change. Network biology approaches, which emphasize the interactions between genes, proteins and metabolites provide a framework for data integration such that genome, proteome, metabolome and other -omics data can be jointly analyzed to understand and predict disease phenotypes. In this review, recent advances in network biology approaches and results are identified. A common theme is the potential for network analysis to provide multiplexed and functionally connected biomarkers for analyzing the molecular basis of disease, thus changing our approaches to analyzing and modeling genome- and proteome-wide data.

Integrative analysis of common neurodegenerative diseases using gene association, interaction networks and mRNA expression data

Alzheimer's and Parkinson's diseases (AD and PD) are two common neurodegenerative diseases primarily affecting memory and motor functions, respectively. In this study, we integrated data from various sources, and took a systems-biology approach to compare and contrast the molecular and network based dysregulation associated with AD and PD and we integrated these data with known pathways of drug treatment. First, we identified genes that exhibit consistent prior evidence of association with each disease. Then, we extracted disease-specific sub-networks from a human interactome database using associated genes as seeds. To rank the sub-networks we used existing gene expression data from cases and controls. Comparison of resulting disease-associated genes and networks revealed significant overlap between AD and PD. In addition, the identified sub-networks correlated with known drug interdiction pathways, and suggested new potential targets for intervention.

Identification of candidate genes related to bovine marbling using protein-protein interaction networks

Complex traits are determined by the combined effects of many loci and are affected by gene networks or biological pathways. Systems biology approaches have an important role in the identification of candidate genes related to complex diseases or traits at the system level. The present study systemically analyzed genes associated with bovine marbling score and identified their relationships. The candidate nodes were obtained using MedScan text-mining tools and linked by protein-protein interaction (PPI) from the Human Protein Reference Database (HPRD). To determine key node of marbling, the degree and betweenness centrality (BC) were used. The hub nodes and biological pathways of our network are consistent with the previous reports about marbling traits, and also suggest unknown candidate genes associated with intramuscular fat. Five nodes were identified as hub genes, which was consistent with the network analysis using quantitative reverse-transcription PCR (qRT-PCR). Key nodes of the PPI network have positive roles (PPARγ, C/EBPα, and RUNX1T1) and negative roles (RXRA, CAMK2A) in the development of intramuscular fat by several adipogenesis-related pathways. This study provides genetic information for identifying candidate genes for the marbling trait in bovine.

A novel integrative network approach to understand the interplay between cardiovascular disease and other complex disorders

There is accumulating evidence that the proteins encoded by the genes associated with a common disorder interact with each other, participate in similar pathways and share GO terms. It has been anticipated that the functional modules in a disease related functional linkage network can be integrated with bibliomics to reveal association with other complex disorders. In this study, the cardiovascular disease functional linkage network (CFN) containing 1536 nodes and 3345 interactions was constructed using proteins encoded by 234 genes associated with the disease. Integration of CFN with bibliomics showed that 227 out of 566 functional modules are significantly associated with one or more diseases. Analysis of functional modules revealed the possible regulatory roles of SP1 and CXCL12 in the pathogenesis of cardiovascular disease (CVD) and modulation of their activities may be considered as potential therapeutic tools. The integration of CFN with bibliomics also indicated significant relations of CVD with other complex disorders. In a stratified map the members of 227 functional modules and 58 diseases in 15 disease classes were combined. In this map, leprosy, listeria monocytogenes, myasthenia, hemorrhagic diathesis and Protein S deficiency, which were not previously reported to be associated with CVD, showed significant associations. Several cancers arising from epithelial cells were also found to be linked to other diseases through hub proteins, VEGFA and PTGS2.