Metabolomic and Proteomic Analyses of Persistent Valvular Atrial Fibrillation and Non-Valvular Atrial Fibrillation

Alterations of gut microbiota and its metabolomics in children with 6PPDQ, PBDE, PCB, and metal(loid) exposure

The composition and metabolites of the gut microbiota can be altered by environmental pollutants. However, the effect of co-exposure to multiple pollutants on the human gut microbiota has not been sufficiently studied. In this study, gut microorganisms and their metabolites were compared between 33 children from Guiyu, an e-waste dismantling and recycling area, and 34 children from Haojiang, a healthy environment. The exposure level was assessed by estimating the daily intake (EDI) of polybrominated diphenyl ethers (PBDEs), polychlorinated biphenyls (PCBs), 6PPD-quinone (6PPDQ), and metal(loid)s in kindergarten dust. Significant correlations were found between the EDIs of 6PPDQ, BDE28, PCB52, Ni, Cu, and the composition of gut microbiota and specific metabolites. The Bayesian kernel machine regression model showed negative correlations between the EDIs of five pollutants (6PPDQ, BDE28, PCB52, Ni, and Cu) and the composition of gut microbiota. The EDIs of these five pollutants were positively correlated with the levels of the metabolite 2,4-diaminobutyric acid, while negatively correlated with the levels of d-erythro-sphingosine and d-threitol. Our study suggests that exposure to 6PPDQ, BDE28, PCB52, Ni, and Cu in kindergarten dust is associated with alterations in the composition and metabolites of the gut microbiota. These alterations may be associated with children's health.

Integrated proteomic and metabolomic profiling reveals novel insights on the inflammation and immune response in HFpEF

BACKGROUND

The precise mechanisms leading to the development of heart failure with preserved ejection fraction (HFpEF) remain incompletely defined. In this study, an integrative approach utilizing untargeted proteomics and metabolomics was employed to delineate the altered proteomic and metabolomic profiles in patients with HFpEF compared to healthy controls.

MATERIALS AND METHODS

Data were collected from a prospective cohort consisting of 30 HFpEF participants and 30 healthy controls, matched by gender and age. plasma samples were analyzed by multi-omics platforms. The quantification of plasma proteins and metabolites was performed using data-independent acquisition-based liquid chromatography-tandem mass spectrometry (LC-MS/MS) and ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS), respectively. Additionally, Proteomic and metabolomic results were analyzed separately and integrated using correlation and pathway analysis. This was followed by the execution of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment studies to elucidate the biological relevance of the observed results.

RESULTS

A total of 46 significantly differentially expressed proteins (DEPs) and 102 differentially expressed metabolites (DEMs) were identified. Then, GO and KEGG pathway enrichment analyses were performed by DEPs and DEMs. Integrated analysis of proteomics and metabolomics has revealed Tuberculosis and African trypanosomiasis pathways that are significantly enriched and the DEPs and DEMs enriched within them, are associated with inflammation and immune response.

CONCLUSIONS

Integrated proteomic and metabolomic analyses revealed distinct inflammatory and immune response pathways in HFpEF, highlighting novel therapeutic avenues.

Using machine learning to identify proteomic and metabolomic signatures of stroke in atrial fibrillation

Atrial fibrillation (AF) is a common cardiac arrhythmia, with stroke being its most detrimental comorbidity. The exact mechanism of AF related stroke (AFS) still needs to be explored. In this study, we integrated proteomics and metabolomics platform to explore disordered plasma proteins and metabolites between AF patients and AFS patients. There were 22 up-regulated and 31 down-regulated differentially expressed proteins (DEPs) in AFS plasma samples. Moreover, 63 up-regulated and 51 down-regulated differentially expressed metabolites (DEMs) were discovered in AFS plasma samples. We integrated proteomics and metabolomics based on the topological interactions of DEPs and DEMs, which yielded revealed several related pathways such as arachidonic acid metabolism, serotonergic synapse, purine metabolism, tyrosine metabolism and steroid hormone biosynthesis. We then performed a machine learning model to identify potential biomarkers of stroke in AF. Finally, we selected 6 proteins and 6 metabolites as candidate biomarkers for predicting stroke in AF by random forest, the area under the curve being 0.976. In conclusion, this study provides new perspectives for understanding the progressive mechanisms of AF related stroke and discovering innovative biomarkers for determining the prognosis of stroke in AF.

The role of lactate in cardiovascular diseases

Cardiovascular diseases pose a major threat worldwide. Common cardiovascular diseases include acute myocardial infarction (AMI), heart failure, atrial fibrillation (AF) and atherosclerosis. Glycolysis process often has changed during these cardiovascular diseases. Lactate, the end-product of glycolysis, has been overlooked in the past but has gradually been identified to play major biological functions in recent years. Similarly, the role of lactate in cardiovascular disease is gradually being recognized. Targeting lactate production, regulating lactate transport, and modulating circulating lactate levels may serve as potential strategies for the treatment of cardiovascular diseases in the future. The purpose of this review is to integrate relevant clinical and basic research on the role of lactate in the pathophysiological process of cardiovascular disease in recent years to clarify the important role of lactate in cardiovascular disease and to guide further studies exploring the role of lactate in cardiovascular and other diseases. Video Abstract.

Phosphoproteomic analysis of neoadjuvant breast cancer suggests that increased sensitivity to paclitaxel is driven by CDK4 and filamin A

Precision oncology research is challenging outside the contexts of oncogenic addiction and/or targeted therapies. We previously showed that phosphoproteomics is a powerful approach to reveal patient subsets of interest characterized by the activity of a few kinases where the underlying genomics is complex. Here, we conduct a phosphoproteomic screening of samples from HER2-negative female breast cancer receiving neoadjuvant paclitaxel (N = 130), aiming to find candidate biomarkers of paclitaxel sensitivity. Filtering 11 candidate biomarkers through 2 independent patient sets (N = 218) allowed the identification of a subgroup of patients characterized by high levels of CDK4 and filamin-A who had a 90% chance of achieving a pCR in response to paclitaxel. Mechanistically, CDK4 regulates filamin-A transcription, which in turn forms a complex with tubulin and CLIP-170, which elicits increased binding of paclitaxel to microtubules, microtubule acetylation and stabilization, and mitotic catastrophe. Thus, phosphoproteomics allows the identification of explainable factors for predicting response to paclitaxel.

Integration of proteomic and metabolomic characterization in atrial fibrillation-induced heart failure

BACKGROUND

The exact mechanism of atrial fibrillation (AF)-induced heart failure (HF) remains unclear. Proteomics and metabolomics were integrated to in this study, as to describe AF patients' dysregulated proteins and metabolites, comparing patients without HF to patients with HF.

METHODS

Plasma samples of 20 AF patients without HF and another 20 with HF were analyzed by multi-omics platforms. Proteomics was performed with data independent acquisition-based liquid chromatography-tandem mass spectrometry (LC-MS/MS), as metabolomics was performed with LC-MS/MS platform. Proteomic and metabolomic results were analyzed separately and integrated using univariate statistical methods, multivariate statistical methods or machine learning model.

RESULTS

We found 35 up-regulated and 15 down-regulated differentially expressed proteins (DEPs) in AF patients with HF compared to AF patients without HF. Moreover, 121 up-regulated and 14 down-regulated differentially expressed metabolites (DEMs) were discovered in HF patients compared to AF patients without HF. An integrated analysis of proteomics and metabolomics revealed several significantly enriched pathways, including Glycolysis or Gluconeogenesis, Tyrosine metabolism and Pentose phosphate pathway. A total of 10 DEPs and DEMs selected as potential biomarkers provided excellent predictive performance, with an AUC of 0.94. In addition, subgroup analysis of HF classification was performed based on metabolomics, which yielded 9 DEMs that can distinguish between AF and HF for HF classification.

CONCLUSIONS

This study provides novel insights to understanding the mechanisms of AF-induced HF progression and identifying novel biomarkers for prognosis of AF with HF by using metabolomics and proteomics analyses.

Comprehensive metabolomic characterization of atrial fibrillation

Background

Using human humoral metabolomic profiling, we can discover the diagnostic biomarkers and pathogenesis of disease. The specific characterization of atrial fibrillation (AF) subtypes with metabolomics may facilitate effective and targeted treatment, especially in early stages.

Objectives

By investigating disturbed metabolic pathways, we could evaluate the diagnostic value of biomarkers based on metabolomics for different types of AF.

Methods

A cohort of 363 patients was enrolled and divided into a discovery and validation set. Patients underwent an electrocardiogram (ECG) for suspected AF. Groups were divided as follows: healthy individuals (Control), suspected AF (Sus-AF), first diagnosed AF (Fir-AF), paroxysmal AF (Par-AF), persistent AF (Per-AF), and AF causing a cardiogenic ischemic stroke (Car-AF). Serum metabolomic profiles were determined by gas chromatography–mass spectrometry (GC-MS) and liquid chromatography–quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). Metabolomic variables were analyzed with clinical information to identify relevant diagnostic biomarkers.

Results

The metabolic disorders were characterized by 16 cross-comparisons. We focused on comparing all of the types of AF (All-AFs) plus Car-AF vs. Control, All-AFs vs. Car-AF, Par-AF vs. Control, and Par-AF vs. Per-AF. Then, 117 and 94 metabolites were identified by GC/MS and LC-QTOF-MS, respectively. The essential altered metabolic pathways during AF progression included D-glutamine and D-glutamate metabolism, glycerophospholipid metabolism, etc. For differential diagnosis, the area under the curve (AUC) of specific metabolomic biomarkers ranged from 0.8237 to 0.9890 during the discovery phase, and the predictive values in the validation cohort were 78.8–90.2%.

Conclusions

Serum metabolomics is a powerful way to identify metabolic disturbances. Differences in small–molecule metabolites may serve as biomarkers for AF onset, progression, and differential diagnosis.