Shotgun mass spectrometry-based lipid profiling identifies and distinguishes between chronic inflammatory diseases

Matthiesen, Rune; Lauber, Chris; Sampaio, Júlio L.; Domingues, Neuza; Alves, Liliana; Gerl, Mathias J.; Almeida, Jorge; Rodrigues, Gustavo; Gonçalves, Pedro Araújo; Ferreira, Jorge; Borbinha, Cláudia; Marto, João Pedro; Neves, Marisa; Batista, Frederico; Viana-Baptista, Miguel; Alves, José Delgado; Simons, Kai; Vaz, Winchil L.C.; Vieira, Otília V.

doi:10.1016/j.ebiom.2021.103504

Cited by 18 publications

(24 citation statements)

References 66 publications

(87 reference statements)

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“…The risk factors are ordered based on the model itself, which provided more directions and basis for us to improve the SLE classification criteria. On the other hand, there are 3 studies [ 20 , 22 , 23 ] that performed analyses for blood polypeptides and lipids, and 1 study [ 21 ] distinguished SLE patients from normal people through skin imaging examination. All of these studies yielded decent results and provided more directions for early identification of SLE in clinical practice.…”

Section: Discussionmentioning

confidence: 99%

“…Eleven hundred and sixty-six (1166) ineligible articles were excluded after browsing the abstracts and titles, and full texts of the remaining sixty (60) articles were read. Finally, a total of eighteen (18) studies were included, in which ten (10) [15][16][17][18][19][20][21][22][23][24] focused on SLE and the remaining eight (8) [25][26][27][28][29][30][31][32] on NPSLE. Te study selection process is shown in Figure 1, and the characteristics of included studies are shown in Tables 1 (for SLE) and 2 (for NPSLE).…”

Section: Study Selection and Risk Of Bias Assessmentmentioning

confidence: 99%

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Machine Learning for Diagnosis of Systemic Lupus Erythematosus: A Systematic Review and Meta-Analysis

Zhou

Wang

Zhao

et al. 2022

Computational Intelligence and Neuroscience

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Background. Application of machine learning (ML) for identification of systemic lupus erythematosus (SLE) has been recently drawing increasing attention, while there is still lack of evidence-based support. Methods. Systematic review and meta-analysis are conducted to evaluate its diagnostic accuracy and application prospect. PubMed, Embase, Cochrane Library, and Web of Science libraries are searched, in combination with manual searching and literature retrospection, for studies regarding machine learning for identifying SLE and neuropsychiatric systemic lupus erythematosus (NPSLE). Quality Assessment of Diagnostic Accuracy Studies (QUADA-2) is applied to assess the quality of included studies. Diagnostic accuracy of the SLE model and NPSLE model is assessed using the bivariate fixed-effect model, and the data are pooled. Summary receiver operator characteristic curve (SROC) is plotted, and area under the curve (AUC) is calculated. Results. Eighteen (18) studies are included, in which ten (10) focused on SLE and eight (8) on NPSLE. The AUC of SLE identification is 0.95, the sensitivity is 0.90, the specificity is 0.89, the PLR is 8.4, the NLR is 0.12, and the DOR is 73. AUC of NPSLE identification is 0.89, the sensitivity is 0.83, the specificity is 0.83, the PLR is 5.0, the NLR is 0.20, and the DOR is 25. Conclusion. Machine learning presented remarkable performance in identification of SLE and NPSLE. Based on the convenience for inclusion factor collection and non-invasiveness of detection, machine learning is expected to be widely applied in clinical practice to assist medical decision making.

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Section: Discussionmentioning

confidence: 99%

Section: Study Selection and Risk Of Bias Assessmentmentioning

confidence: 99%

Machine Learning for Diagnosis of Systemic Lupus Erythematosus: A Systematic Review and Meta-Analysis

Zhou

Wang

Zhao

et al. 2022

Computational Intelligence and Neuroscience

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“…Regarding the ChA concentrations employed, one should bear in mind that liposomes are not very efficient vehicles for ChA delivery (less than 3% of ChA is internalized) , and so we expect the "real" ChA concentrations to be on the micromolar range. This corresponds to the range of concentrations found in the plasma of CVD patients (Domingues et al, 2021;Matthiesen et al, 2021).…”

Section: Discussionmentioning

confidence: 56%

“…In contrast with the vast amount of literature on lysosome dysfunction in macrophages, very little is known about lysosome dysfunction in VSMCs. Here we found that ChA, a stable end oxidation product of cholesteryl linoleate and arachidonate found increased in plasma of CVD patients (Domingues et al, 2021;Matthiesen et al, 2021) accumulates in VSMCs and causes lysosome malfunction, recapitulating many of the effects seen with oxLDL, including induction of foam cell formation. We also demonstrate that, as consequence of these alterations, these cells become stiffer, migrating and proliferating less.…”

Section: Discussionmentioning

confidence: 85%

Cholesteryl hemiazelate causes lysosome dysfunction impacting vascular smooth muscle cell homeostasis

Alves

Marques

Padrão

et al. 2021

Journal of Cell Science

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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.

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“…They would also partition via passive diffusion and trans-membrane translocation into all membranes and the cytosol of neighboring cells. Considering that cholesteryl linoleate, the predicted precursor of ChA, is the most abundant cholesteryl ester in plasma (Matthiesen, Lauber et al 2021),…”

Section: Discussionmentioning

confidence: 99%

Cholesteryl hemiazelate Identified in Cardiovascular Disease Patients Causesin vitroandin vivoInflammation

Domingues¹,

Gaifem²,

Matthiesen³

et al. 2023

Preprint

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Oxidation of polyunsaturated fatty acids (PUFA) in low-density lipoproteins (LDL) trapped in the arterial intima plays a critical role in atherosclerosis. Though there have been many studies on the atherogenicity of oxidized derivatives of unsaturated fatty acid esters of cholesterol, the effects of the oxidation end-products of these esters has been ignored in the literature. Through lipidomics analyses of the plasma of cardiovascular disease patients and human endarterectomy specimens we identified and quantified cholesteryl hemiesters (ChE), end-products of oxidation of polyunsaturated-fatty acid esters of cholesterol. Cholesteryl hemiazelate (ChA) was the most prevalent ChE identified. Importantly human monocytes, monocyte-derived macrophages (MDM) and neutrophils exhibit inflammatory features when exposed to sub-toxic concentrations of ChA in vitro. ChA increases the secretion of proinflammatory cytokines such as IL-1β and IL-6 and modulates the surface markers profile of monocytes and MDM. In vivo, when zebrafish larvae were fed with a ChA-enriched diet they exhibited neutrophil and macrophage accumulation in the vasculature in a caspase 1- and cathepsin B-dependent manner. ChA also triggered lipid accumulation at the bifurcation sites of the vasculature of the zebrafish larvae and negatively impacted their life expectancy. We conclude that ChA has pro-atherogenic properties and can be considered part of a damage-associated molecular pattern (DAMP) in the development of atherosclerosis.

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Shotgun mass spectrometry-based lipid profiling identifies and distinguishes between chronic inflammatory diseases

Cited by 18 publications

References 66 publications

Machine Learning for Diagnosis of Systemic Lupus Erythematosus: A Systematic Review and Meta-Analysis

Machine Learning for Diagnosis of Systemic Lupus Erythematosus: A Systematic Review and Meta-Analysis

Cholesteryl hemiazelate causes lysosome dysfunction impacting vascular smooth muscle cell homeostasis

Cholesteryl hemiazelate Identified in Cardiovascular Disease Patients Causesin vitroandin vivoInflammation

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