Development of a Blood-based Transcriptional Risk Score for Chronic Obstructive Pulmonary Disease

Moll, Matthew; Boueiz, Adel; Ghosh, Auyon; Saferali, Aabida; Lee, Sool; Yun, Jeong H.; Hobbs, Brian D.; Hersh, Craig P.; Sin, Don D.; Tal‐Singer, Ruth; Silverman, Edwin K.; Cho, Michael H.; Castaldi, Peter J.

doi:10.1164/rccm.202107-1584oc

Cited by 21 publications

(8 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To the best of our knowledge, the present study is the first to examine the effects of wb-TRS on nonobese T2D risk and dynamic change of related cardiometabolic traits by using peripheral blood transcriptional markers. Recently, several studies proposed the concept of TRS or polygenetic TRS, which used cumulative gene expression information to provide an additional layer of biological interpretability and performed better than both traditional risk factors and polygenic risk score for predicting Crohn’s disease, chronic obstructive pulmonary disease, and lung function [8, 26–28]. Our study is in line with previous studies showing transcriptional variance is a reliable intermediary mediating associations of genotypes with complex phenotypes [5, 6, 29] and provides evidence of implement of blood transcriptional profile could yield a promising better predictive effect on T2D and the dynamic changes in related cardiometabolic traits.…”

Section: Discussionmentioning

confidence: 99%

A whole blood-based transcriptional risk score for nonobese type 2 diabetes predicts dynamic changes in glucose metabolic traits

Hou

Dai

Chen

et al. 2023

Preprint

View full text Add to dashboard Cite

BackgroundThe performance of peripheral blood transcriptional markers in evaluating the risk of type 2 diabetes (T2D) with normal weight is unknown. We developed a whole blood-based transcriptional risk score (wb-TRS) for nonobese T2D and assessed its contributions to disease risk and dynamic changes in glucose metabolism.Methods and findingsWe developed the wb-TRS in 1105 participants aged ≥40 years and in normal weight for up to 10 years from a well-defined community-based cohort with blood transcriptome data and validated it in an external dataset (253 overweight/obese participants from a dietary intervention trial with 3 repeated transcriptome data). Potential biology significance and causal inference were also explored. The wb-TRS included 144 transcripts. Compared to the lowest tertile, wb-TRS in tertile 3 associated with 8.68-folds (95% confidence interval [CI], 3.51-21.5), and each 1-unit increment associated with 2.57-folds (95% CI, 1.86-3.56) higher risk of nonobese T2D, after adjustments for traditional risk factors. Furthermore, baseline wb-TRS was significantly associated with dynamic changes in average, daytime, nighttime and 24h glucose and HbA1c, and area under the curve of glucose measured in the continuous glucose monitoring during 6-month of intervention. The wb-TRS improved the predicting performance for nonobese T2D in a model with fasting glucose, triglycerides and demographic and anthropometric parameters. Mitch analysis implicated oxidative phosphorylation, cholesterol metabolism and mTORC1 signaling involved in nonobese T2D pathogenesis. Transcriptome-wide Mendelian randomization supported causal effects of gene transcripts such as RAB1A and GCC1-PAX4 on nonobese T2D risk.ConclusionsA whole blood based nonobese T2D associated TRS was validated to predict dynamic changes in glucose metabolism. These findings also suggested several genes and biological pathways that might involve in the pathogenesis of nonobese T2D.

show abstract

Section: Discussionmentioning

confidence: 99%

A whole blood-based transcriptional risk score for nonobese type 2 diabetes predicts dynamic changes in glucose metabolic traits

Hou

Dai

Chen

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Biomarkers allow the characterization of disease progression at the molecular level through noninvasive techniques, , holding promise for the diagnosis of COPD and exacerbations. ,, Recently, various gene (e.g., circulating miRNA) , and protein biomarkers (e.g., C-reactive protein) , have been reported for the diagnosis or evaluation of COPD, but their performance is suboptimal for clinical use due to their poor accuracy . Compared with genes and proteins, metabolites function as immediate indicators of biochemical activity and exhibit a close correlation with the COPD phenotype. , Mass spectrometry (MS), specifically laser desorption/ionization (LDI) MS, has emerged as a robust analytical instrument for the high-throughput and sensitive detection of various metabolites. − However, metabolic analysis is often impeded by the inherent challenges of concentration and purification, given the low concentration of metabolites and high complexity of samples in clinical specimens. ,− …”

Section: Introductionmentioning

confidence: 99%

Plasmonic Alloys Enhanced Metabolic Fingerprints for the Diagnosis of COPD and Exacerbations

Su,

Song,

Yang

et al. 2024

ACS Cent. Sci.

View full text Add to dashboard Cite

Accurate diagnosis of chronic obstructive pulmonary disease (COPD) and exacerbations by metabolic biomarkers enables individualized treatment. Advanced metabolic detection platforms rely on designed materials. Here, we design mesoporous PdPt alloys to characterize metabolic fingerprints for diagnosing COPD and exacerbations. As a result, the optimized PdPt alloys enable the acquisition of metabolic fingerprints within seconds, requiring only 0.5 μL of native plasma by laser desorption/ionization mass spectrometry owing to the enhanced electric field, photothermal conversion, and photocurrent response. Machine learning decodes metabolic profiles acquired from 431 individuals, achieving a precise diagnosis of COPD with an area under the curve (AUC) of 0.904 and an accurate distinction between stable COPD and acute exacerbations of COPD (AECOPD) with an AUC of 0.951. Notably, eight metabolic biomarkers identified accurately discriminate AECOPD from stable COPD while providing valuable information on disease progress. Our platform will offer an advanced nanoplatform for the management of COPD, complementing standard clinical techniques.

show abstract

“…Various advanced high-throughput sequencing technologies have generated several types of omics data. Although single-omics data, such as genomics ( 21 ), epigenomics ( 22 ), transcriptomics ( 23 , 24 ), proteomics ( 25 , 26 ), metabolomics ( 27 , 28 ), and scRNA-seq ( 29 , 30 ), have contributed to clarifying the mechanisms of COPD, the disease is still one of the three leading causes of deaths worldwide, and the burden of COPD is expected to increase in the next few decades ( 1 ). There is no simple correspondence between transcription and protein abundance; complex regulatory mechanisms affect transcription, translation, PTMs, and metabolic processes, and ultimately affect protein expression ( 31 , 32 ).…”

Section: Introductionmentioning

confidence: 99%

Associative analysis of multi-omics data indicates that acetylation modification is widely involved in cigarette smoke-induced chronic obstructive pulmonary disease

Gao

Liu²,

Wang³

et al. 2023

Front. Med.

View full text Add to dashboard Cite

We aimed to study the molecular mechanisms of chronic obstructive pulmonary disease (COPD) caused by cigarette smoke more comprehensively and systematically through different perspectives and aspects and to explore the role of protein acetylation modification in COPD. We established the COPD model by exposing C57BL/6J mice to cigarette smoke for 24 weeks, then analyzed the transcriptomics, proteomics, and acetylomics data of mouse lung tissue by RNA sequencing (RNA-seq) and liquid chromatography-tandem mass spectrometry (LC-MS/MS), and associated these omics data through unique algorithms. This study demonstrated that the differentially expressed proteins and acetylation modification in the lung tissue of COPD mice were co-enriched in pathways such as oxidative phosphorylation (OXPHOS) and fatty acid degradation. A total of 19 genes, namely, ENO3, PFKM, ALDOA, ACTN2, FGG, MYH1, MYH3, MYH8, MYL1, MYLPF, TTN, ACTA1, ATP2A1, CKM, CORO1A, EEF1A2, AKR1B8, MB, and STAT1, were significantly and differentially expressed at all the three levels of transcription, protein, and acetylation modification simultaneously. Then, we assessed the distribution and expression in different cell subpopulations of these 19 genes in the lung tissues of patients with COPD by analyzing data from single-cell RNA sequencing (scRNA-seq). Finally, we carried out the in vivo experimental verification using mouse lung tissue through quantitative real-time PCR (qRT-PCR), Western blotting (WB), immunofluorescence (IF), and immunoprecipitation (IP). The results showed that the differential acetylation modifications of mouse lung tissue are widely involved in cigarette smoke-induced COPD. ALDOA is significantly downregulated and hyperacetylated in the lung tissues of humans and mice with COPD, which might be a potential biomarker for the diagnosis and/or treatment of COPD.

show abstract

Development of a Blood-based Transcriptional Risk Score for Chronic Obstructive Pulmonary Disease

Cited by 21 publications

References 55 publications

A whole blood-based transcriptional risk score for nonobese type 2 diabetes predicts dynamic changes in glucose metabolic traits

A whole blood-based transcriptional risk score for nonobese type 2 diabetes predicts dynamic changes in glucose metabolic traits

Plasmonic Alloys Enhanced Metabolic Fingerprints for the Diagnosis of COPD and Exacerbations

Associative analysis of multi-omics data indicates that acetylation modification is widely involved in cigarette smoke-induced chronic obstructive pulmonary disease

Contact Info

Product

Resources

About