Multi-omic signatures of atherogenic dyslipidaemia: pre-clinical target identification and validation in humans

Omics research in atherosclerosis

Atherosclerosis (AS) is a chronic inflammatory disease characterized by lipid deposition within the arterial intima, as well as fibrous tissue proliferation and calcification. AS has long been recognized as one of the primary pathological foundations of cardiovascular diseases in humans. Its pathogenesis is intricate and not yet fully elucidated. Studies have shown that AS is associated with oxidative stress, inflammatory response, lipid deposition, and changes in cell phenotype. Unfortunately, there is currently no effective prevention or targeted treatment for AS. The rapid advancement of omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, has opened up novel avenues to elucidate the fundamental pathophysiology and associated mechanisms of AS. Here, we review articles published over the past decade and focus on the current status, challenges, limitations, and prospects of omics in AS research and clinical practice. Emphasizing potential targets based on omics technologies will improve our understanding of this pathological condition and assist in the development of potential therapeutic approaches for AS-related diseases.

Multiomic integration analysis identifies atherogenic metabolites mediating between novel immune genes and cardiovascular risk

BACKGROUND

Understanding genetic-metabolite associations has translational implications for informing cardiovascular risk assessment. Interrogating functional genetic variants enhances our understanding of disease pathogenesis and the development and optimization of targeted interventions.

METHODS

In this study, a total of 187 plasma metabolite levels were profiled in 4974 individuals of European ancestry of the GCAT| Genomes for Life cohort. Results of genetic analyses were meta-analysed with additional datasets, resulting in up to approximately 40,000 European individuals. Results of meta-analyses were integrated with reference gene expression panels from 58 tissues and cell types to identify predicted gene expression associated with metabolite levels. This approach was also performed for cardiovascular outcomes in three independent large European studies (N = 700,000) to identify predicted gene expression additionally associated with cardiovascular risk. Finally, genetically informed mediation analysis was performed to infer causal mediation in the relationship between gene expression, metabolite levels and cardiovascular risk.

RESULTS

A total of 44 genetic loci were associated with 124 metabolites. Lead genetic variants included 11 non-synonymous variants. Predicted expression of 53 fine-mapped genes was associated with 108 metabolite levels; while predicted expression of 6 of these genes was also associated with cardiovascular outcomes, highlighting a new role for regulatory gene HCG27. Additionally, we found that atherogenic metabolite levels mediate the associations between gene expression and cardiovascular risk. Some of these genes showed stronger associations in immune tissues, providing further evidence of the role of immune cells in increasing cardiovascular risk.

CONCLUSIONS

These findings propose new gene targets that could be potential candidates for drug development aimed at lowering the risk of cardiovascular events through the modulation of blood atherogenic metabolite levels.

A fluorescence-based immunochromatographic assay using quantum dot-encapsulated nanoparticles for the rapid and sensitive detection of fetuin-B

Coronary artery disease (CAD) is the leading cause of death worldwide. Earlier detection of CAD improves treatment outcomes and secondary prevention. The circulating fetuin-B protein is considered to be a promising biomarker for the early detection of CAD. However, a facile and reliable clinical test for fetuin-B is still lacking. Herein, we describe a reliable fluorescent biosensor for detecting fetuin-B in plasma that combines quantum dots-doped polystyrene nanoparticles with an immunochromatographic assay strip (QNPs-ICAS). The QNPs served as detection signals in the QNPs-ICAS sensor system, which was based on a double-antibody sandwich structure. Under optimum experimental conditions, the biosensor exhibited a broad linear range of 1-200 ng mL-1 and a low detection limit of 0.299 ng mL-1. Furthermore, the proposed immunosensor demonstrated high sensitivity, satisfactory selectivity, good reproducibility, and excellent recovery. Finally, the performance and applicability of our QNPs-based ICAS system were validated in clinical samples using a commercial ELISA kit with excellent correlations (r = 0.98451, n = 116). To conclude, the proposed sensor served as a rapid, sensitive, and accurate method for detecting fetuin-B in actual clinical samples, thereby demonstrating its potential for preliminary CAD screening and diagnosis.

Development of a Comprehensive Gene Signature Linking Hypoxia, Glycolysis, Lactylation, and Metabolomic Insights in Gastric Cancer through the Integration of Bulk and Single-Cell RNA-Seq Data

BACKGROUND

Hypoxia and anaerobic glycolysis are cancer hallmarks and sources of the metabolite lactate. Intriguingly, lactate-induced protein lactylation is considered a novel epigenetic mechanism that predisposes cells toward a malignant state. However, the significance of comprehensive hypoxia-glycolysis-lactylation-related genes (HGLRGs) in cancer is unclear. We aimed to construct a model centered around HGLRGs for predicting survival, metabolic features, drug responsiveness, and immune response in gastric cancer.

METHODS

The integration of bulk and single-cell RNA-Seq data was achieved using data obtained from the TCGA and GEO databases to analyze HGLRG expression patterns. A HGLRG risk-score model was developed based on univariate Cox regression and a LASSO-Cox regression model and subsequently validated. Additionally, the relationships between the identified HGLRG signature and multiple metabolites, drug sensitivity and various cell clusters were explored.

RESULTS

Thirteen genes were identified as constituting the HGLRG signature. Using this signature, we established predictive models, including HGLRG risk scores and nomogram and Cox regression models. The stratification of patients into high- and low-risk groups based on HGLRG risk scores showed a better prognosis in the latter. The high-risk group displayed increased sensitivity to cytotoxic drugs and targeted inhibitors. The expression of the HGLRG BGN displayed a strong correlation with amino acids and lipid metabolites. Notably, a significant difference in immune infiltration, such as that of M1 macrophages and CD8 T cells, was correlated with the HGLRG signature. The abundant DUSP1 within the mesenchymal components was highlighted by single-cell transcriptomics.

CONCLUSION

The innovative HGLRG signature demonstrates efficacy in predicting survival and providing a practical clinical model for gastric cancer. The HGLRG signature reflects the internal metabolism, drug responsiveness, and immune microenvironment components of gastric cancer and is expected to boost patients' response to targeted therapy and immunotherapy.

Metabolic systems approaches update molecular insights of clinical phenotypes and cardiovascular risk in patients with homozygous familial hypercholesterolemia

BACKGROUND

Homozygous familial hypercholesterolemia (HoFH) is an orphan metabolic disease characterized by extremely elevated low-density lipoprotein cholesterol (LDL-C), xanthomas, aortic stenosis, and premature atherosclerotic cardiovascular disease (ASCVD). In addition to LDL-C, studies in experimental models and small clinical populations have suggested that other types of metabolic molecules might also be risk factors responsible for cardiovascular complications in HoFH, but definitive evidence from large-scale human studies is still lacking. Herein, we aimed to comprehensively characterize the metabolic features and risk factors of human HoFH by using metabolic systems strategies.

METHODS

Two independent multi-center cohorts with a total of 868 individuals were included in the cross-sectional study. First, comprehensive serum metabolome/lipidome-wide analyses were employed to identify the metabolomic patterns for differentiating HoFH patients (n = 184) from heterozygous FH (HeFH, n = 376) and non-FH (n = 100) subjects in the discovery cohort. Then, the metabolomic patterns were verified in the validation cohort with 48 HoFH patients, 110 HeFH patients, and 50 non-FH individuals. Subsequently, correlation/regression analyses were performed to investigate the associations of clinical/metabolic alterations with typical phenotypes of HoFH. In the prospective study, a total of 84 HoFH patients with available follow-up were enrolled from the discovery cohort. Targeted metabolomics, deep proteomics, and random forest approaches were performed to investigate the ASCVD-associated biomarkers in HoFH patients.

RESULTS

Beyond LDL-C, various bioactive metabolites in multiple pathways were discovered and validated for differentiating HoFH from HoFH and non-FH. Our results demonstrated that the inflammation and oxidative stress-related metabolites in the pathways of arachidonic acid and lipoprotein(a) metabolism were independently associated with the prevalence of corneal arcus, xanthomas, and supravalvular/valvular aortic stenosis in HoFH patients. Our results also identified a small marker panel consisting of high-density lipoprotein cholesterol, lipoprotein(a), apolipoprotein A1, and eight proinflammatory and proatherogenic metabolites in the pathways of arachidonic acid, phospholipid, carnitine, and sphingolipid metabolism that exhibited significant performances on predicting first ASCVD events in HoFH patients.

CONCLUSIONS

Our findings demonstrate that human HoFH is associated with a variety of metabolic abnormalities and is more complex than previously known. Furthermore, this study provides additional metabolic alterations that hold promise as residual risk factors in HoFH population.

Ethinylestradiol in combined hormonal contraceptive has a broader effect on serum proteome compared with estradiol valerate: a randomized controlled trial

STUDY QUESTION

Does an estradiol-based combined oral contraceptive (COC) have a milder effect on the serum proteome than an ethinylestradiol (EE)-based COC or dienogest (DNG) only?

SUMMARY ANSWER

The changes in serum proteome were multifold after the use of a synthetic EE-based COC compared to natural estrogen COC or progestin-only preparation.

WHAT IS KNOWN ALREADY

EE-based COCs widely affect metabolism, inflammation, hepatic protein synthesis and blood coagulation. Studies comparing serum proteomes after the use of COCs containing EE and natural estrogens are lacking.

STUDY DESIGN, SIZE, DURATION

This was a spin-off from a randomized, controlled, two-center clinical trial. Women (n = 59) were randomized to use either EE + DNG, estradiol valerate (EV) + DNG or DNG only continuously for 9 weeks.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Participants were healthy, young, white volunteer women. Serum samples were collected before and after 9 weeks of hormonal exposure. Samples from 44 women were available for analysis (EE + DNG n = 14, EV + DNG n = 16 and DNG only n = 14). Serum proteins were analyzed by quantitative, discovery-type label-free proteomics.

MAIN RESULTS AND THE ROLE OF CHANCE

Altogether, 446 proteins/protein families with two or more unique peptides were detected and quantified. The number of proteins/families that altered over the 9-week period within the study groups was 121 for EE + DNG and 5 for EV + DNG, while no changes were detected for DNG only. When alterations were compared between the groups, significant differences were detected for 63 proteins/protein families, of which 58 were between the EE + DNG and EV + DNG groups. The most affected functions during the use of EE + DNG were the complement system, acute phase response signaling, metabolism and the coagulation system. The results were validated by fetuin-B and cortisol-binding globulin ELISA and sex hormone-binding globulin immunoassay.

LARGE SCALE DATA

Data are available via ProteomeXchange with identifiers PXD033617 (low abundance fraction) and PXD033618 (high abundance fraction).

LIMITATIONS, REASONS FOR CAUTION

The power analysis of the trial was not based on the proteomic analysis of this spin-off study. In the future, targeted proteomic analysis with samples from another trial should be carried out in order to confirm the results.

WIDER IMPLICATIONS OF THE FINDINGS

The EE-based COC exerted a broader effect on the serum proteome than the EV-based COC or the DNG-only preparation. These results demonstrate that the effects of EE in COCs go far beyond the established endpoint markers of estrogen action, while the EV combination is closer to the progestin-only preparation. The study indicates that EV could provide a preferable option to EE in COCs in the future and signals a need for further studies comparing the clinical health outcomes of COCs containing EE and natural estrogens.

STUDY FUNDING/COMPETING INTEREST(S)

Funding for this researcher-initiated study was obtained from the Helsinki University Hospital research funds, the Hospital District of Helsinki and Uusimaa, the Sigrid Juselius Foundation, the Academy of Finland, the Finnish Medical Association, the University of Oulu Graduate School, the Emil Aaltonen Foundation, the Swedish Cultural Foundation in Finland, the Novo Nordisk Foundation, Orion Research Foundation and the Northern Ostrobothnia Regional Fund. The funders had no role in study design, data collection and analysis, publishing decisions or manuscript preparation. T.P. has received honoraria for lectures, consultations and research grants from Exeltis, Gedeon Richter, MSD, Merck, Pfizer, Roche, Stragen and Mithra Pharmaceuticals. O.H. occasionally serves on advisory boards for Bayer AG and Gedeon Richter and has designed and lectured at educational events for these companies. The other authors have nothing to disclose. O.H. occasionally serves on advisory boards for Bayer AG and Gedeon Richter and has designed and lectured at educational events for these companies. The other authors have nothing to disclose.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov NCT02352090.

TRIAL REGISTRATION DATE

27 January 2015.

DATE OF FIRST PATIENT’S ENROLMENT

1 April 2015.

Comprehensive Metabolomics and Machine Learning Identify Profound Oxidative Stress and Inflammation Signatures in Hypertensive Patients with Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) can aggravate blood pressure and increase the risk of cardiovascular diseases in hypertensive individuals, yet the underlying pathophysiological process is still incompletely understood. More importantly, OSA remains a significantly undiagnosed condition. In this study, a total of 559 hypertensive patients with and without OSA were included. Metabolome and lipidome-wide analyses were performed to explore the pathophysiological processes of hypertension comorbid OSA and derive potential biomarkers for diagnosing OSA in hypertensive subjects. Compared to non-OSA hypertensive patients (discovery set = 120; validation set = 116), patients with OSA (discovery set = 165; validation set = 158) demonstrated a unique sera metabolic phenotype dominated by abnormalities in biological processes of oxidative stress and inflammation. By integrating three machine learning algorithms, six discriminatory metabolites (including 5-hydroxyeicosatetraenoic acid, taurine, histidine, lysophosphatidic acid 16:0, lysophosphatidylcholine 18:0, and dihydrosphingosine) were selected for constructing diagnostic and classified model. Notably, the established multivariate-model could accurately identify OSA subjects. The corresponding area under the curve values and the correct classification rates were 0.995 and 96.8% for discovery sets, 0.997 and 99.1% for validation sets. This work updates the molecular insights of hypertension comorbid OSA and paves the way for the use of metabolomics for the diagnosis of OSA in hypertensive individuals.

Data-Independent Acquisition Proteomics Reveals Long-Term Biomarkers in the Serum of C57BL/6J Mice Following Local High-Dose Heart Irradiation

Background and Purpose: Cardiotoxicity is a well-known adverse effect of radiation therapy. Measurable abnormalities in the heart function indicate advanced and often irreversible heart damage. Therefore, early detection of cardiac toxicity is necessary to delay and alleviate the development of the disease. The present study investigated long-term serum proteome alterations following local heart irradiation using a mouse model with the aim to detect biomarkers of radiation-induced cardiac toxicity.

Materials and Methods: Serum samples from C57BL/6J mice were collected 20 weeks after local heart irradiation with 8 or 16 Gy X-ray; the controls were sham-irradiated. The samples were analyzed by quantitative proteomics based on data-independent acquisition mass spectrometry. The proteomics data were further investigated using bioinformatics and ELISA.

Results: The analysis showed radiation-induced changes in the level of several serum proteins involved in the acute phase response, inflammation, and cholesterol metabolism. We found significantly enhanced expression of proinflammatory cytokines (TNF-α, TGF-β, IL-1, and IL-6) in the serum of the irradiated mice. The level of free fatty acids, total cholesterol, low-density lipoprotein (LDL), and oxidized LDL was increased, whereas that of high-density lipoprotein was decreased by irradiation.

Conclusions: This study provides information on systemic effects of heart irradiation. It elucidates a radiation fingerprint in the serum that may be used to elucidate adverse cardiac effects after radiation therapy.