In atherosclerotic lesions, vascular smooth muscle cells (VSMCs) represent half of the foam cell population, characterized by an aberrant accumulation of undigested lipids within lysosomes. Loss of lysosome function impacts VSMCs homeostasis and disease progression. Understanding the molecular mechanisms underlying lysosome dysfunction in these cells is, therefore, crucial. We identify cholesteryl hemiazelate (ChA), a stable oxidation end-product of cholesteryl-polyunsaturated fatty acid esters, as an inducer of lysosome malfunction in VSMCs. ChA-treated VSMCs acquire a foam cell-like phenotype, characterized by enlarged lysosomes full of ChA and neutral lipids. The lysosomes are perinuclear and exhibit degradative capacity and cargo exit defects. Lysosome luminal pH is also altered. Even though, the transcriptional response machinery and autophagy are not activated by ChA, the addition of recombinant lysosomal acid lipase (LAL) is able to rescue lysosome dysfunction. ChA significantly affects VSMCs proliferation and migration impacting atherosclerosis. In sum, this work shows that: 1) ChA is sufficient to induce lysosomal dysfunction in VSMCs; 2) In ChA-treated VSMCs, neither lysosome biogenesis nor autophagy are triggered; and 3) Recombinant LAL can be a therapeutic approach for lysosomal dysfunction.
Background Localized stress and cell death in chronic inflammatory diseases may release tissue-specific lipids into the circulation causing the blood plasma lipidome to reflect the type of inflammation. However, deep lipid profiles of major chronic inflammatory diseases have not been compared. Methods Plasma lipidomes of patients suffering from two etiologically distinct chronic inflammatory diseases, atherosclerosis-related vascular disease, including cardiovascular (CVD) and ischemic stroke (IS), and systemic lupus erythematosus (SLE), were screened by a top-down shotgun mass spectrometry-based analysis without liquid chromatographic separation and compared to each other and to age-matched controls. Lipid profiling of 596 lipids was performed on a cohort of 427 individuals. Machine learning classifiers based on the plasma lipidomes were used to distinguish the two chronic inflammatory diseases from each other and from the controls. Findings Analysis of the lipidomes enabled separation of the studied chronic inflammatory diseases from controls based on independent validation test set classification performance (CVD vs control - Sensitivity: 0.94, Specificity: 0.88; IS vs control - Sensitivity: 1.0, Specificity: 1.0; SLE vs control – Sensitivity: 1, Specificity: 0.93) and from each other (SLE vs CVD ‒ Sensitivity: 0.91, Specificity: 1; IS vs SLE - Sensitivity: 1, Specificity: 0.82). Preliminary linear discriminant analysis plots using all data clearly separated the clinical groups from each other and from the controls, and partially separated CVD severities, as classified into five clinical groups. Dysregulated lipids are partially but not fully counterbalanced by statin treatment. Interpretation Dysregulation of the plasma lipidome is characteristic of chronic inflammatory diseases. Lipid profiling accurately identifies the diseases and in the case of CVD also identifies sub-classes. Funding Full list of funding sources at the end of the manuscript.
Background: Inflammation impacts several acute and chronic diseases causing localized stress and cell death, releasing tissue-specific lipids into the circulation from inflamed cells and tissues. The plasma lipidome may be expected to reflect the type of inflammation and the specific cells and tissues involved. However, deep lipid profiles of major chronic inflammatory diseases have not been compared. Methods: We compare the plasma lipidomes of patients suffering from two etiologically distinct chronic inflammatory diseases, atherosclerosis-related cardiovascular disease (CVD) including ischemic stroke (IS), and systemic lupus erythematosus (SLE), to each other and to age-matched controls. The controls had never suffered from any of these diseases. Blood plasma lipidomes were screened by a top-down shotgun MS-based analysis without liquid chromatographic separation. Lipid profiling based on MS was performed on a cohort of 427 individuals. The cohort constitutes 85 controls (control), 217 with cardiovascular disease (further classified into CVD 1-5), 21 ischemic stroke patients (IS), and 104 patients suffering from systemic lupus erythematosis (SLE). 596 lipids were profiled which were quality filtered for further evaluation and determination of potential biomarkers. Lipidomes were compared by linear regression and evaluated by machine learning classifiers. Results: Machine learning classifiers based on the plasma lipidomes of patients suffering from CVD and SLE allowed clear distinction of these two chronic inflammatory diseases from each other and from healthy age-matched controls and body mass index (BMI). We demonstrate convincing evidence for the capability of lipidomics to separate the studied chronic and inflammatory diseases from controls based on independent validation test set classification performance (CVD vs control, Sensitivity: 0.90, Specificity: 0.98; IS vs control, Sensitivity: 1.0, Specificity: 1.0; SLE vs control, Sensitivity: 1, Specificity: 0.88) and from each other (SLE vs CVD ‒ Sensitivity: 0.91, Specificity: 1). Preliminary linear discriminant analysis plots using all data clearly separated the clinical groups from each other and from the controls. In addition, CVD severities, as classified into five clinical groups, were partially separable by linear discriminant analysis. Notably, significantly dysregulated lipids between pathological groups versus control displayed a reverse lipid regulation pattern compared to statin treated controls versus non treated controls. Conclusion: Dysregulation of the plasma lipidome is characteristic of chronic inflammatory diseases. Lipid profiling accurately identifies the diseases and in the case of CVD also identifies sub-classes. Dysregulated lipids are partially but not fully counterbalanced by statin treatment.
Alkaptonuria (AKU) is an ultra‐rare disease caused by the deficient activity of homogentisate 1,2‐dioxygenase enzyme, leading the accumulation of homogentisic acid (HGA) in connective tissues implicating the formation of a black pigmentation called “ochronosis.” Although AKU is a multisystemic disease, the most affected tissue is the articular cartilage, which during the pathology appears to be highly damaged. In this study, a model of alkaptonuric chondrocytes and cartilage was realized to investigate the role of HGA in the alteration of the extracellular matrix (ECM). The AKU tissues lost its architecture composed of collagen, proteoglycans, and all the proteins that characterize the ECM. The cause of this alteration in AKU cartilage is attributed to a degeneration of the cytoskeletal network in chondrocytes caused by the accumulation of HGA. The three cytoskeletal proteins, actin, vimentin, and tubulin, were analyzed and a modification in their amount and disposition in AKU chondrocytes model was identified. Cytoskeleton is involved in many fundamental cellular processes; therefore, the aberration in this complex network is involved in the manifestation of AKU disease.
Background: Localized stress and cell death in chronic inflammatory diseases may release tissue-specific lipids into the circulation causing the blood plasma lipidome to reflect the type of inflammation. However, deep lipid profiles of major chronic inflammatory diseases have not been compared. Methods: Plasma lipidomes of patients suffering from two etiologically distinct chronic inflammatory diseases, atherosclerosis-related vascular disease, including cardiovascular (CVD) and ischemic stroke (IS), and systemic lupus erythematosus (SLE), were screened by a top-down shotgun mass spectrometry-based analysis without liquid chromatographic separation and compared to each other and to age-matched controls. Lipid profiling of 596 lipids was performed on a cohort of 427 individuals. Machine learning classifiers based on the plasma lipidomes were used to distinguish the two chronic inflammatory diseases from each other and from the controls. Findings: Analysis of the lipidomes enabled separation of the studied chronic inflammatory diseases from controls based on independent validation test set classification performance (CVD vs control -
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