Identification of potential COPD genes based on multi-omics data at the functional level

Liu, Zhe; Li, Wan; Liu, Jun-Jie; Xie, Ruiqiang; Huang, Hao; Li, Yiran; He, Yuehan; Jiang, Jing; Chen, Binbin; Guo, Shanshan; Chen, Lina

doi:10.1039/c5mb00577a

Cited by 9 publications

(8 citation statements)

References 119 publications

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“…However, because dysregulation at different molecular levels and anatomical locations can dominate in different disease subgroups, multi-omics integration may be necessary to facilitate the diagnosis and understanding of disease mechanisms involved in the underlying COPD disease subgroups [4][5][6][7][8]. Several recent studies integrating two or three omics data blocks have indicated that integrating data from multiple molecular levels improves the identification of biomarkers [9,10], sub-phenotype prediction [11,12] and mechanistic understanding [13] of COPD. While these specific examples provide convincing evidence of the advantages of omics integration, no quantitative evaluation of the gain in statistical power beyond dual and triple omics integration has yet been published.…”

Section: Introductionmentioning

confidence: 99%

Integration of multi-omics datasets enables molecular classification of COPD

Wheelock

Sköld

et al. 2018

Eur Respir J

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

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Section: Introductionmentioning

confidence: 99%

Integration of multi-omics datasets enables molecular classification of COPD

Wheelock

Sköld

et al. 2018

Eur Respir J

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“…Our work reveals that key transcription factors involved in stem cell maintenance are deregulated in COPD, with in particular their expression repressed when in presence of TGF-β, bringing a molecular perspective to the cellular data. All the transcription factors that we identified are involved in lung epithelium stem/progenitor cells or in COPD, and SOX2 and ELF5 are particularly relevant as they have been shown to be required for maintenance and differentiation of lung epithelium stem/progenitor cells [14–18]. Slug knockdown shows that in COPD these transcription factors are repressed downstream of Slug.…”

Section: Discussionmentioning

confidence: 94%

Transcription factors involved in stem cell maintenance are downstream of Slug/Snail2 and repressed by TGF-β in bronchial basal stem/progenitor cells from COPD

P¹,

Jolly²,

Courageux³

et al. 2020

Preprint

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22Objectives: Patients with Chronic Obstructive Pulmonary Disease (COPD) have a bronchial 23 epithelium with many anomalies and basal/progenitor cells showing a decrease of self-renewal 24 and differentiation potential. The objective of this study was to identify deregulations in the 25 genetic program of COPD bronchial progenitors that could account for their exhaustion. The 26 transcription factor Slug/Snail2 is highly expressed in bronchial progenitors and we aimed at 27 identifying genes downstream of Slug whose expression is deregulated in COPD progenitors. 28 Results:We knocked down Slug in primary basal cells from COPD subjects and, since COPD 29 subjects have higher levels of Transforming Growth Factor (TGF)-β and Slug is regulated by 30 TGF-β, we selected genes downstream of Slug involved in differentiation that respond to We identified transcription factors involved in stem cell maintenance downstream of Slug and 32 repressed by TGF-β in COPD but not normal progenitors. We found that the effect of TGF-β on 33 the expression of these genes is correlated to Slug knockdown effect. We also found a correlation 34 between the mRNA levels of Slug and these genes only in presence of TGF-β. These results 35 reveal that stem cell maintenance genes are deregulated in COPD bronchial progenitors, Slug and 36 TGF-β being involved in that deregulation. 37

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“…Moreover, genes, proteins, and metabolite networks are particularly important, as the explanatory value of any single molecule is small compared with multiple biomarkers [8]. These biomarkers, either in multiple biomarker panels or integrated with other omics, may lead to novel diagnostic and prognostic tests for COPD phenotypes [9]. A system-biology platform that stems from advancements in medical diagnosis, omics, and bioinformatics could offer great potential to better understand the complexity of COPD [4].…”

Section: Discussionmentioning

confidence: 99%

“…Given the current progress in next-generation sequencing and mass spectrometry, considerable attention has been paid to both omics approaches and biomarker discovery for clarifying these heterogeneous diseases [2,3,9]. However, despite accumulating evidence from genomics and transcriptomics, the application of proteomics has been limited [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Proteomics of serum extracellular vesicles identifies a novel COPD biomarker, fibulin-3 from elastic fibres

et al. 2021

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There is an unmet need for novel biomarkers in the diagnosis of multifactorial COPD. We applied next-generation proteomics to serum extracellular vesicles (EVs) to discover novel COPD biomarkers.EVs from 10 patients with COPD and six healthy controls were analysed by tandem mass tag-based non-targeted proteomics, and those from elastase-treated mouse models of emphysema were also analysed by non-targeted proteomics. For validation, EVs from 23 patients with COPD and 20 healthy controls were validated by targeted proteomics.Using non-targeted proteomics, we identified 406 proteins, 34 of which were significantly upregulated in patients with COPD. Of note, the EV protein signature from patients with COPD reflected inflammation and remodelling. We also identified 63 upregulated candidates from 1956 proteins by analysing EVs isolated from mouse models. Combining human and mouse biomarker candidates, we validated 45 proteins by targeted proteomics, selected reaction monitoring. Notably, levels of fibulin-3, tripeptidyl-peptidase 2, fibulin-1, and soluble scavenger receptor cysteine-rich domain-containing protein were significantly higher in patients with COPD. Moreover, six proteins; fibulin-3, tripeptidyl-peptidase 2, UTP-glucose-1-phosphate uridylyl transferase, CD81, CD177, and oncoprotein-induced transcript 3, were correlated with emphysema. Upregulation of fibulin-3 was confirmed by immunoblotting of EVs and immunohistochemistry in lungs. Strikingly, fibulin-3 knockout mice spontaneously developed emphysema with age, as evidenced by alveolar enlargement and elastin destruction.We discovered potential pathogenic biomarkers for COPD using next-generation proteomics of EVs. This is a novel strategy for biomarker discovery and precision medicine.

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Identification of potential COPD genes based on multi-omics data at the functional level

Cited by 9 publications

References 119 publications

Integration of multi-omics datasets enables molecular classification of COPD

Integration of multi-omics datasets enables molecular classification of COPD

Transcription factors involved in stem cell maintenance are downstream of Slug/Snail2 and repressed by TGF-β in bronchial basal stem/progenitor cells from COPD

Proteomics of serum extracellular vesicles identifies a novel COPD biomarker, fibulin-3 from elastic fibres

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