Putative regulatory functions of SNPs associated with bronchial asthma, arterial hypertension and their comorbid phenotype

Identification of Circulating Proteins Associated With Blood Pressure

BACKGROUND

Circulating proteins in blood are involved in various physiological processes, but their contributions to blood pressure regulation remain partially understood. In traditional observational studies, identifying circulating proteins causally associated with blood pressure is challenging because of potentially unmeasured confounding and possible reverse causality.

METHODS

Two-sample Mendelian randomization analyses were conducted to estimate the causal effects of 2270 circulating proteins (data sourced from 8 genome-wide association studies) on diastolic blood pressure, systolic blood pressure, and pulse pressure. Colocalization analyses were then used to investigate whether the circulating proteins and blood pressure traits shared causal genetic variants. To further verify the findings, we subsequently performed Steiger filtering analyses, annotation of protein-altering variants, assessment of overlap between protein quantitative trait loci and expression quantitative trait loci, protein-protein interaction and functional enrichment analyses, and drug target evaluation. To provide more potential biomarkers, we further evaluated the epidemiological associations of 2923 circulating proteins with blood pressure and hypertension by cross-sectional and longitudinal analyses using individual data in the UK Biobank.

RESULTS

Mendelian randomization and colocalization analyses identified 121 circulating proteins with putative causal effects on at least 1 blood pressure trait. Many of the identified proteins are enriched in the pathways relevant to blood pressure regulation, and a majority of these proteins are either known drug targets or druggable candidates.

CONCLUSIONS

This study has uncovered numerous circulating proteins potentially causally associated with blood pressure, providing insights into the regulatory mechanisms of blood pressure and potential therapeutic targets to facilitate blood pressure management.

Inflammation and Neurodegeneration in Glaucoma: Isolated Eye Disease or a Part of a Systemic Disorder? - Serum Proteomic Analysis

Introduction

Glaucoma is the most common optic neuropathy and the leading cause of irreversible blindness worldwide, which affects 3.54% of the population aged 40-80 years. Despite numerous published studies, some aspects of glaucoma pathogenesis, serum biomarkers, and their potential link with other diseases remain unclear. Recent articles have proposed that autoimmune, oxidative stress and inflammation may be involved in the pathogenesis of glaucoma.

Methods

We investigated the serum expression of 92 inflammatory and neurotrophic factors in glaucoma patients. The study group consisted of 26 glaucoma patients and 192 healthy subjects based on digital fundography.

Results

Patients with glaucoma had significantly lower serum expression of IL-2Rβ, TWEAK, CX3CL1, CD6, CD5, LAP TGF-beta1, LIF-R, TRAIL, NT-3, and CCL23 and significantly higher expression of IL-22Rα1.

Conclusion

Our results indicate that patients with glaucoma tend to have lower levels of neuroprotective proteins and higher levels of neuroinflammatory proteins, similar to those observed in psychiatric, neurodegenerative and autoimmune diseases, indicating a potential link between these conditions and glaucoma pathogenesis.

Development of a peripheral blood transcriptomic gene signature to predict bronchopulmonary dysplasia

Bronchopulmonary dysplasia (BPD) is the most common lung disease of extreme prematurity, yet mechanisms that associate with or identify neonates with increased susceptibility for BPD are largely unknown. Combining artificial intelligence with gene expression data is a novel approach that may assist in better understanding mechanisms underpinning chronic lung disease and in stratifying patients at greater risk for BPD. The objective of this study is to develop an early peripheral blood transcriptomic signature that can predict preterm neonates at risk for developing BPD. Secondary analysis of whole blood microarray data from 97 very low birth weight neonates on day of life 5 was performed. BPD was defined as positive pressure ventilation or oxygen requirement at 28 days of age. Participants were randomly assigned to a training (70%) and testing cohort (30%). Four gene-centric machine learning models were built, and their discriminatory abilities were compared with gestational age or birth weight. This study adheres to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) statement. Neonates with BPD (n = 62 subjects) exhibited a lower median gestational age (26.0 wk vs. 30.0 wk, P < 0.01) and birth weight (800 g vs. 1,280 g, P < 0.01) compared with non-BPD neonates. From an initial pool (33,252 genes/patient), 4,523 genes exhibited a false discovery rate (FDR) <1%. The area under the receiver operating characteristic curve (AUC) for predicting BPD utilizing gestational age or birth weight was 87.8% and 87.2%, respectively. The machine learning models, using a combination of five genes, revealed AUCs ranging between 85.8% and 96.1%. Pathways integral to T cell development and differentiation were associated with BPD. A derived five-gene whole blood signature can accurately predict BPD in the first week of life.