Biomarkers and key pathways in atrial fibrillation associated with mitral valve disease identified by multi-omics study

Abstract

Background: Mitral valve disease (MVD)-associated atrial fibrillation (AF) is one of the most common arrhythmias with an increased risk of thromboembolic events. This study aimed to identify the molecular mechanisms and possible biomarkers for chronic AF in MVD by using multi-omics methods.Methods: This prospective study enrolled patients with MVD (n=100) undergoing mitral valve replacement surgery. The patients were allocated into chronic AF and sinus rhythm (SR) groups. Plasma samples were collected preoperatively. Proteomics was performed with isobaric tags for relative and absolute quantitation (iTRAQ) to identify differential proteins (DPs) between the two groups. The selected DPs were then validated in a new cohort of patients by enzyme-linked immunosorbent assay (ELISA). A gas chromatography-mass spectrometer was used in the metabolomics study to identify differential metabolites (DMs). Bioinformatics analyses were performed to analyze the results.Results: Among the 447 plasma proteins and 322 metabolites detected, 57 proteins and 55 metabolites, including apolipoprotein A-I (ApoA-I), apolipoprotein A-II (ApoA-II), LIM domain only protein 7 (LMO7), and vitronectin (VN) were differentially expressed between AF and SR patients. Bioinformatics analyses identified enriched pathways related to AF, including peroxisome proliferator-activated receptor alpha (PPARα), the renin angiotensin aldosterone system (RAAS), galactose, biosynthesis of unsaturated fatty acids, and linoleic acid metabolism.Conclusions: Using integrated multi-omics technologies in MVD-associated AF patients, the present study, for the first time, revealed important signaling pathways, such as PPARα, as well as possible roles of other signaling pathways, including the RAAS and galactose metabolism to understand the molecular mechanism of MVD-associated AF. It also identified a large number of DPs and DMs. Some identified proteins and metabolites, such as ApoA-I, ApoA-II, LMO7, and VN, may be further developed as biomarkers for MVD-associated AF.

Further Advances in Atrial Fibrillation Research: A Metabolomic Perspective

“…In many cases, the patients were volunteers, so there may be a possible selection and observation bias. Further validation of markers in larger independent data sets are needed [13][14][15][16][17], especially in human studies, where highlighted plasma biomarkers may not reflect changes in heart tissue with enough confidence to be used in patient stratification yet [13,15,16,18] and further research in this field is needed to define sensitive predictive or diagnostic biomarkers for AF. Besides that, proposed metabolic biomarkers were analyzed only in certain metabolic pathways, although the participation in more complex processes is well known [13,14,16].…”

“…After the bioinformatic analysis, it was observed that some of the molecules found as possible biomarkers have not yet been described, thus their clinical impact is unknown [18]. Likewise, it is possible that there are still undiscovered markers that can be used for patient stratification [6,[13][14][15][16][17][18][19].…”

“…Strategies such as proteomics and metabolomics have been shown to be powerful tools for biomarker identification and pathological studies [6,[13][14][15][16][17][18][19]. This technology is becoming a powerful ally in overcoming the current challenges of achieving a better understanding of proteins and molecules as well as the metabolic pathways and processes in which they participate [6].…”

“…Jing Li et al [17] studied the shifts in gut microbiome and metabolome associated with risk of recurrent AF after percutaneous radiofrequency catheter ablation, which represents an important treatment strategy for AF patients. Compared with non-AF controls, gut microbiota composition and metabolomic profile from serum and feces samples were significantly altered between patients with recurrent AF and non-recurrent AF group.…”

“…LysoPC(15:0), lysoPE(0:0/16:0), chenodeoxycholic acid, sebacic acid, lysoPE(0:0/20:0), corticosterone, α-linolenic acid, and uracil Ming-Yang Li et al[17] taurine, α-ketoacid, and α-ketoisovale Jie et al[18] …”