Plexin C1 deficiency permits synaptotagmin 7–mediated macrophage migration and enhances mammalian lung fibrosis

Peng, Xueyan; Moore, Meagan W.; Mathur, Aditi; Zhou, Yang; Sun, Huanxing; Gan, Ye; Herazo-Maya, José D.; Kaminski, Naftali; Hu, Xinyuan; Pan, Heng; Ryu, Changwan; Osafo-Addo, Awo; Homer, Robert J.; Feghali‐Bostwick, Carol; Fares, Wassim H.; Gulati, Mridu; Hu, Buqu; Lee, Chang-Min; Elias, Jack A.; Herzog, Erica L.

doi:10.1096/fj.201600373r

Cited by 67 publications

(48 citation statements)

References 48 publications

(87 reference statements)

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“…Based on their gene expression profiles (Zhang and Horvath 2005), similar modules were combined by the eigengene through the moduleEigengenes function (Langfelder and Horvath 2008). Case Control GSE47460 COPD a 71 91 Lung biopsy GPL14550 Agilent-028004 SurePrint G3 Human GE 8x60K Microarray (Peng et al 2016) GSE47460 IPF 122 91 Lung biopsy GPL14550 Agilent-028004 SurePrint G3 Human GE 8x60K Microarray (Peng et al 2016) GSE23611 asthma 27 13 Lung biopsy GPL6480 Agilent-014850 Whole Human Genome Microarray 4x44K G4112F (Choy et al 2011) a GOLD stage II…”

Section: Co-expression Network Construction and Disease-specific Modumentioning

confidence: 99%

Identification of biomarkers in common chronic lung diseases by co-expression networks and drug-target interactions analysis

Maghsoudloo

Jamalkandi²,

Najafi³

et al. 2020

Mol Med

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Background: asthma, chronic obstructive pulmonary disease (COPD), and idiopathic pulmonary fibrosis (IPF) are three serious pulmonary diseases that contain common and unique characteristics. Therefore, the identification of biomarkers that differentiate these diseases is of importance for preventing misdiagnosis. In this regard, the present study aimed to identify the disorders at the early stages, based on lung transcriptomics data and drug-target interactions. Methods: To this end, the differentially expressed genes were found in each disease. Then, WGCNA was utilized to find specific and consensus gene modules among the three diseases. Finally, the disease-disease similarity was analyzed, followed by determining candidate drug-target interactions. Results: The results confirmed that the asthma lung transcriptome was more similar to COPD than IPF. In addition, the biomarkers were found in each disease and thus were proposed for further clinical validations. These genes included RBM42, STX5, and TRIM41 in asthma, CYP27A1, GM2A, LGALS9, SPI1, and NLRC4 in COPD, ATF3, PPP1R15A, ZFP36, SOCS3, NAMPT, and GADD45B in IPF, LRRC48 and CETN2 in asthma-COPD, COL15A1, GIMAP6, and JAM2 in asthma-IPF and LMO7, TSPAN13, LAMA3, and ANXA3 in COPD-IPF. Finally, analyzing drug-target networks suggested anti-inflammatory candidate drugs for treating the above mentioned diseases. Conclusion: In general, the results revealed the unique and common biomarkers among three chronic lung diseases. Eventually, some drugs were suggested for treatment purposes.

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Section: Co-expression Network Construction and Disease-specific Modumentioning

confidence: 99%

Identification of biomarkers in common chronic lung diseases by co-expression networks and drug-target interactions analysis

Maghsoudloo

Jamalkandi²,

Najafi³

et al. 2020

Mol Med

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show abstract

“…Datasets GSE47460 dataset. The first dataset analyzed for the present study is available through the GEO public repository at accession number GSE47460 published on May 30, 2013 [Peng 2016, Anathy 2018]. This dataset includes microarray gene expression profiling obtained from total RNA extracted from whole lung homogenates from subjects undergoing thoracic surgery for clinical indications.…”

Section: Methodsmentioning

confidence: 99%

“…The network-based analysis implemented by the SWIM software (see Materials and Methods section) was exploited to identify disease genes and network modules associated with COPD status by using the GSE47460 dataset (training set) containing microarray gene expression profiling of lung tissue samples from 219 COPD cases and 108 controls [Peng 2016, Anathy 2018.…”

Section: Copd Correlation Networkmentioning

confidence: 99%

Integrated transcriptomic correlation network analysis identifies COPD molecular determinants

Paci

Fiscon

Conte

et al. 2019

Preprint

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Chronic obstructive pulmonary disease (COPD) is a heterogeneous and complex syndrome. Network-based analysis implemented by SWIM software can be exploited to identify key molecular switches -called "switch genes" -for disease. Genes contributing to common biological processes or define given cell types are frequently co-regulated and co-expressed, giving rise to expression network modules. Consistently, we found that the COPD correlation network built by SWIM consists of three well-characterized modules: one populated by switch genes, all up-regulated in COPD cases and related to the regulation of immune response, inflammatory response, and hypoxia (like TIMP1, HIF1A, SYK, LY96, BLNK and PRDX4); one populated by well-recognized immune signature genes, all up-regulated in COPD cases; one where the GWAS genes AGER and CAVIN1 are the most representative module genes, both down-regulated in COPD cases. Interestingly, 70% of AGER negative interactors are switch genes including PRDX4, whose activation strongly correlates with the activation of known COPD GWAS interactors SERPINE2, CD79A, and POUF2AF1. These results suggest that SWIM analysis can identify key network modules related to complex diseases like COPD. growth and development, and ineffective lung repair. However, the pathobiological mechanisms for COPD remain incompletely understood [Silverman 2018].COPD susceptibility, like other complex diseases, is rarely caused by a single gene mutation, but is likely influenced by multiple genetic determinants with interconnections between different molecular components. Studying the effects of these interconnections on disease susceptibility could lead to improved understanding of COPD pathogenesis and the identification of new therapeutic targets. Previous efforts to identify the network of interacting genes and proteins in COPD have included protein-protein interaction (PPI) network studies. McDonald and colleagues [McDonald 2014] used dmGWAS software to identify a consensus network module within the PPI network based on COPD GWAS evidence. Sharma and colleagues [Sharma 2018] started with "seed" genes based on well-established COPD GWAS genes or Mendelian syndromes that include COPD as part of the syndrome constellation with a random walk approach to build a COPD network module of 163 proteins.

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Section: Copd Correlation Networkmentioning

confidence: 99%

Integrated transcriptomic correlation network analysis identifies COPD molecular determinants

Paci

Fiscon

Conte

et al. 2020

Sci Rep

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Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous syndrome. Networkbased analysis implemented by SWIM software can be exploited to identify key molecular switches-called "switch genes"-for the disease. Genes contributing to common biological processes or defining given cell types are usually co-regulated and co-expressed, forming expression network modules. Consistently, we found that the COPD correlation network built by SWIM consists of three wellcharacterized modules: one populated by switch genes, all up-regulated in COPD cases and related to the regulation of immune response, inflammatory response, and hypoxia (like TIMP1, HIF1A, SYK, LY96, BLNK and PRDX4); one populated by well-recognized immune signature genes, all up-regulated in COPD cases; one where the GWAS genes AGER and CAVIN1 are the most representative module genes, both down-regulated in COPD cases. Interestingly, 70% of AGER negative interactors are switch genes including PRDX4, whose activation strongly correlates with the activation of known COPD GWAS interactors SERPINE2, CD79A, and POUF2AF1. These results suggest that SWIM analysis can identify key network modules related to complex diseases like COPD. Chronic obstructive pulmonary disease (COPD) is a devastating lung disease characterized by progressive and incompletely reversible airflow obstruction. Like many other common diseases, COPD is a heterogeneous and complex syndrome influenced by both genetic and environmental determinants and is one of the main causes of morbidity and mortality worldwide. Cigarette smoking is a major environmental risk factor for COPD, but the substantial heritability of COPD indicates an important role for genetic determinants as well 1. Although multiple genetic loci for COPD have been identified by genome-wide association studies (GWAS), the key genes in those regions are largely undefined. Various contributors to COPD pathogenesis have been also suggested, including protease-antiprotease imbalance, oxidant-antioxidant imbalance, cellular senescence, autoimmunity, chronic inflammation, deficient lung growth and development, and ineffective lung repair. However, the pathobiological mechanisms for COPD remain incompletely understood 2. COPD susceptibility, like other complex diseases, is rarely caused by a single gene mutation, but is likely influenced by multiple genetic determinants with interconnections between different molecular components. Studying the effects of these interconnections on disease susceptibility could lead to improved understanding of COPD pathogenesis and the identification of new therapeutic targets. Previous efforts to identify the network of interacting genes and proteins in COPD have included protein-protein interaction (PPI) network studies. McDonald and colleagues 3 used dmGWAS software to identify a consensus network module within the PPI network based on COPD GWAS evidence. Sharma and colleagues 4 started with "seed" genes based on well-established COPD GWAS genes or Mendelian syndromes that include COPD ...

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Plexin C1 deficiency permits synaptotagmin 7–mediated macrophage migration and enhances mammalian lung fibrosis

Cited by 67 publications

References 48 publications

Identification of biomarkers in common chronic lung diseases by co-expression networks and drug-target interactions analysis

Identification of biomarkers in common chronic lung diseases by co-expression networks and drug-target interactions analysis

Integrated transcriptomic correlation network analysis identifies COPD molecular determinants

Integrated transcriptomic correlation network analysis identifies COPD molecular determinants

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