Unique and shared systemic biomarkers for emphysema in Alpha-1 Antitrypsin deficiency and chronic obstructive pulmonary disease

Serban, Karina A.; Pratte, Katherine; Strange, Charlie; Sandhaus, Robert A.; Turner, Alice; Beiko, Tatsiana; Spittle, Daniella A; Maier, Lisa A.; Hamzeh, Nabeel; Silverman, Edwin K.; Hobbs, Brian D.; Hersh, Craig P.; DeMeo, Dawn L.; Cho, M.H.; Bowler, Russell P.

doi:10.1016/j.ebiom.2022.104262

Cited by 14 publications

(10 citation statements)

References 44 publications

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“…Our investigations examined the blood genes, isoforms, exons, and proteins, confirming many of the known emphysema-associated pathways and revealing additional ones that may also be at play. Systemic inflammation and immunological dysfunction due to noxious particle exposure potentiate alveolar damage in emphysema and increase susceptibility to viral infections (47)(48)(49)(50)(51)(52). Our pathway analysis identified neutrophil degranulation and the TNF pathway, both emphysema-related inflammatory signals previously implicated in COPD and murine models of emphysema (53)(54)(55)(56).…”

Section: Discussionmentioning

confidence: 70%

Blood-Based Transcriptomic and Proteomic Biomarkers of Emphysema

Suryadevara

Gregory

et al. 2022

Preprint

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Rationale: Emphysema is a key component of COPD with important prognostic implications. Identifying blood-based biomarkers of emphysema will facilitate early diagnosis and possible development of targeted therapies. Objectives: Discover blood transcriptomic and proteomic biomarkers for chest computed tomography-quantified emphysema in smokers and develop predictive biomarker panels. Methods: Emphysema blood biomarker discovery was performed using differential gene expression, alternative splicing, and protein association analyses in a training set of 2,370 COPDGene participants with available whole blood RNA sequencing, plasma SomaScan proteomics, and clinical data. Validation was conducted in a testing set of 1,016 COPDGene subjects. Since body mass index (BMI) and emphysema often co-occur, we performed a mediation analysis to quantify the effect of BMI on gene and protein associations with emphysema. Predictive models were also developed using elastic net to predict quantitative emphysema from cell blood count, RNA sequencing, and proteomic biomarkers. Model accuracy was assessed by area under the receiver-operator-characteristic-curves (AUROC) for subjects stratified into tertiles of emphysema severity. Measurements and Main Results: 4,913 genes, 1,478 isoforms, 386 exons, and 881 proteins were significantly associated with emphysema (FDR 10%). 75% and 77% of genes and proteins, respectively, were mediated by BMI. The significantly enriched biological pathways were involved in inflammation and cell differentiation, differing between the most and least BMI-mediated genes. The cell blood count plus protein model achieved the highest performance with an AUROC of 0.89. Conclusions: Blood transcriptome and proteome-wide analyses reveal key biological pathways of emphysema and enhance the prediction of emphysema.

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Section: Discussionmentioning

confidence: 70%

Blood-Based Transcriptomic and Proteomic Biomarkers of Emphysema

Suryadevara

Gregory

et al. 2022

Preprint

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“…Genetic factors are known to play a crucial role in the development and progression of COPD. Among the known genetic risk factors, a deficiency in α1-antitrypsin, an enzyme that protects the lungs from inflammation-induced damage, is well-established [31]. In addition to α1-antitrypsin deficiency, genome-wide association studies (GWAS) have identified numerous genetic variants associated with COPD susceptibility.…”

Section: Discussionmentioning

confidence: 99%

Impact of Lung-Related Polygenic Risk Scores on Chronic Obstructive Pulmonary Disease Risk and Their Interaction with w-3 Fatty Acid Intake in Middle-Aged and Elderly Individuals

Kim

Park

2023

Nutrients

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Chronic obstructive pulmonary disease (COPD) is a complex, progressive respiratory disorder with persistent airflow limitation and tissue destruction. We aimed to explore the genetic impact of COPD and its interaction with nutrient intake in 8840 middle-aged and elderly individuals from the Ansan/Ansung cohorts. Participants were diagnosed with COPD if the ratio of forced expiratory volume in 1 s (FEV1) to forced vital capacity (FVC) was less than 0.7 using spirometry, and if they were previously diagnosed with COPD by a physician. Genome-wide association studies (GWAS) were performed to screen for genetic variants associated with COPD risk. Among them, we selected the genetic variants that exhibited interactions using the generalized multifactor dimensionality reduction (GMDR) method. The polygenic risk score (PRS) was computed by summing the number of risk alleles in the SNP-SNP interaction models that adhered to specific rules. Subsequently, participants were categorized into low-PRS, medium-PRS, and high-PRS groups. The participants with COPD exhibited significantly lower FEV1/FVC ratios (0.64) than those without COPD (0.82). It was positively associated with inflammation markers (serum C-reactive protein and white blood cell levels). A higher proportion of COPD participants were smokers and engaged in regular exercise. The 5-SNP model consisted of FAM13A_rs1585258, CAV1_rs1997571, CPD_rs719601, PEPD_rs10405598, and ITGA1_rs889294, and showed a significant association with COPD risk (p < 0.001). Participants in the high-PRS group of this model had a 2.2-fold higher risk of COPD than those in the low-PRS group after adjusting for covariates. The PRS interacted with w-3 fatty acid intake and exercise, thus influencing the risk of COPD. There was an increase in COPD incidence among individuals with a higher PRS, particularly those with low consumption of w-3 fatty acid and engaged in high levels of exercise. In conclusion, adults with a high-PRS are susceptible to COPD risk, and w-3 fatty acid intake and exercise may impact the risk of developing COPD, potentially applying to formulate precision medicines to prevent COPD.

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“…We show that proteomic data can identify behavioral features (e.g., smoking) even after removing the features that allow matching to genomes. The ability to accurately identify someone by linking their proteome to a genome, identify risk for protein related disease such as alpha-1 antitrypsin deficiency 18 , infer sex, genetic ancestry, or relatedness and also characterize other characteristics such as body fat, renal function, fitness, smoking, alcohol consumption, diabetes, cardiovascular risk 19 , and age 20 implies that proteomic data should have at least the same (if not more rigorous) privacy protections as genetic and genomic datasets.…”

Section: Discussionmentioning

confidence: 99%

Large scale proteomic studies create novel privacy considerations

Hill¹,

Guo²,

Litkowski³

et al. 2023

Sci Rep

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Privacy protection is a core principle of genomic but not proteomic research. We identified independent single nucleotide polymorphism (SNP) quantitative trait loci (pQTL) from COPDGene and Jackson Heart Study (JHS), calculated continuous protein level genotype probabilities, and then applied a naïve Bayesian approach to link SomaScan 1.3K proteomes to genomes for 2812 independent subjects from COPDGene, JHS, SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) and Multi-Ethnic Study of Atherosclerosis (MESA). We correctly linked 90–95% of proteomes to their correct genome and for 95–99% we identify the 1% most likely links. The linking accuracy in subjects with African ancestry was lower (~ 60%) unless training included diverse subjects. With larger profiling (SomaScan 5K) in the Atherosclerosis Risk Communities (ARIC) correct identification was > 99% even in mixed ancestry populations. We also linked proteomes-to-proteomes and used the proteome only to determine features such as sex, ancestry, and first-degree relatives. When serial proteomes are available, the linking algorithm can be used to identify and correct mislabeled samples. This work also demonstrates the importance of including diverse populations in omics research and that large proteomic datasets (> 1000 proteins) can be accurately linked to a specific genome through pQTL knowledge and should not be considered unidentifiable.

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Unique and shared systemic biomarkers for emphysema in Alpha-1 Antitrypsin deficiency and chronic obstructive pulmonary disease

Cited by 14 publications

References 44 publications

Blood-Based Transcriptomic and Proteomic Biomarkers of Emphysema

Blood-Based Transcriptomic and Proteomic Biomarkers of Emphysema

Impact of Lung-Related Polygenic Risk Scores on Chronic Obstructive Pulmonary Disease Risk and Their Interaction with w-3 Fatty Acid Intake in Middle-Aged and Elderly Individuals

Large scale proteomic studies create novel privacy considerations

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