Untargeted lipidomics reveals specific lipid abnormalities in Sjögren's syndrome

CYP51-mediated cholesterol biosynthesis is required for the proliferation of CD4+ T cells in Sjogren's syndrome

CYtochrome P450, family 51 (CYP51) is an important enzyme for de novo cholesterol synthesis in mammalian cells. In the present study, we found that the expression of CYP51 positively correlated with CD4+ T cell activation both in vivo and in vitro. The addition of ketoconazole, a pharmacological inhibitor of CYP51, prevented the proliferation and activation of anti-CD3/CD28-expanded mouse CD4+ T cells in a dose-dependent fashion. Liquid chromatography-tandem mass spectrometry indicated an increase in levels of lanosterol in T cells treated with ketoconazole during activation. Ketoconazole-induced blockade of the cholesterol synthesis pathway also caused Sterol regulatory element binding protein 2 (SREBP2) activation in CD4+ T cells. Additionally, ketoconazole treatment elicited an integrated stress response in T cells that up-regulated activating transcription factor 4 (ATF4) and DNA-damage inducible transcript 3 (DDIT3/CHOP) at the translational level. Furthermore, treatment with ketoconazole significantly decreased the amount of CD4+ T cells infiltrating lesions in the submandibular glands of NOD/Ltj mice. In summary, our results suggest that CYP51 plays an essential role in the proliferation and survival of CD4+ T cells, which makes ketoconazole an inhibitor of CD4+ T cell proliferation and of the SS-like autoimmune response through regulating the biosynthesis of cholesterol and inducing the integrated stress response.

Plasma lipidomics of primary biliary cholangitis and its comparison with Sjögren's syndrome

Background

Abnormal lipid metabolism is common in patients with primary biliary cholangitis (PBC). PBC and Sjögren's syndrome (SS) frequently coexist in clinical practice; however, the lipid characteristics of both diseases are unknown. Therefore, we aimed to analyze the plasma lipid profiles of both diseases.

Methods

Plasma samples from 60 PBC patients, 30 SS patients, and 30 healthy controls (HC) were collected, and untargeted lipidomics was performed using ultrahigh-performance liquid chromatography high-resolution mass spectrometry. Potential lipid biomarkers were screened through an orthogonal projection to latent structure discriminant analysis and further evaluated using receiver operating characteristic (ROC) analysis.

Results

A total of 115 lipids were differentially upregulated in PBC patients compared with HC. Seventeen lipids were positively associated with the disease activity of PBC, and ROC analysis showed that all of these lipids could differentiate between ursodeoxycholic acid (UDCA) responders and UDCA non-responders. The top six lipids based on the area under the curve (AUC) values were glycerophosphocholine (PC) (16:0/16:0), PC (18:1/18:1), PC (42:2), PC (16:0/18:1), PC (17:1/14:0), and PC (15:0/18:1). In comparison with SS, 44 lipids were found to be differentially upregulated in PBC. Additionally, eight lipids were found to have a good diagnostic performance of PBC because of the AUC values of more than 0.9 when identified from SS and HC groups, which were lysophosphatidylcholines (LysoPC) (16:1), PC (16:0/16:0), PC (16:0/16:1), PC (16:1/20:4), PC (18:0/20:3), PC (18:1/20:2), PC (20:0/22:5), and PC (20:1/22:5).

Conclusion

Our study revealed differentially expressed lipid signatures in PBC compared with HC and SS. PC is the main lipid species associated with disease activity and the UDCA response in patients with PBC.

Untargeted Lipidomics Reveals Characteristic Biomarkers in Patients with Ankylosing Spondylitis Disease

Objective. Ankylosing spondylitis (AS) is a chronic inflammatory disease of the axial skeleton. Early and accurate diagnosis is necessary for the timely and effective treatment of this disease and its common complications. Lipid metabolites form various kinds of bioactive molecules that regulate the initiation and progression of inflammation. However, there are currently few studies that investigate the alteration of serum lipid in AS patients. Methods. Blood samples were collected from 115 AS patients and 108 healthy controls (HCs). Serum-untargeted lipidomics were performed using ultrahigh-performance liquid chromatography coupled with Q-Exactive spectrometry, and the data were determined by multivariate statistical methods to explore potential lipid biomarkers. Results. Lipid phenotypes associated with disease activity were detected in the serum of patients with AS. Of all 586 identified lipids, there are 297 differential lipid metabolites between the AS and HC groups, of which 15 lipid metabolites are significant. In the AS groups, the levels of triacylglycerol (TAG) (18:0/18:1/20:0) were increased, and the levels of phosphatidylcholine (PC) (16:0e/26:4) and PC (18:1/22:6) were decreased. The areas under the receiver operating characteristic curve (AUC) of TAG (18:0/18:1/20:0), PC (16:0e/26:4), and PC (18:1/22:6) were 0.919, 0.843, and 0.907, respectively. Conclusion. Our findings uncovered that lipid deregulation is a crucial hallmark of AS, thereby providing new insights into the early diagnosis of AS.

Blood-based lipidomic signature of severe obstructive sleep apnoea in Alzheimer's disease

Background

Obstructive sleep apnoea (OSA) is the most frequent form of sleep-disordered breathing in patients with Alzheimer’s disease (AD). Available evidence demonstrates that both conditions are independently associated with alterations in lipid metabolism. However, it is unknown whether the expression of lipids is different between AD patients with and without severe OSA. In this context, we examined the plasma lipidome of patients with suspected OSA, aiming to identify potential diagnostic biomarkers and to provide insights into the pathophysiological mechanisms underlying the disease.

Methods

The study included 103 consecutive patients from the memory unit of our institution with a diagnosis of AD. The individuals were subjected to overnight polysomnography (PSG) to diagnose severe OSA (apnoea-hypopnea index ≥30/h), and blood was collected the following morning. Untargeted plasma lipidomic profiling was performed using liquid chromatography coupled with mass spectrometry.

Results

We identified a subset of 44 lipids (mainly phospholipids and glycerolipids) that were expressed differently between patients with AD and severe and nonsevere OSA. Among the lipids in this profile, 30 were significantly correlated with specific PSG measures of OSA severity related to sleep fragmentation and hypoxemia. Machine learning analyses revealed a 4-lipid signature (phosphatidylcholine PC(35:4), cis-8,11,14,17-eicosatetraenoic acid and two oxidized triglycerides (OxTG(58:5) and OxTG(62:12)) that provided an accuracy (95% CI) of 0.78 (0.69–0.86) in the detection of OSA. These same lipids improved the predictive power of the STOP-Bang questionnaire in terms of the area under the curve (AUC) from 0.61 (0.50–0.74) to 0.80 (0.70–0.90).

Conclusion

Our results show a plasma lipidomic fingerprint that allows the identification of patients with AD and severe OSA, allowing the personalized management of these individuals. The findings suggest that oxidative stress and inflammation are potential prominent mechanisms underlying the association between OSA and AD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-022-01102-8.

Machine learning based on metabolomics reveals potential targets and biomarkers for primary Sjogren’s syndrome

Background: Using machine learning based on metabolomics, this study aimed to construct an effective primary Sjogren’s syndrome (pSS) diagnostics model and reveal the potential targets and biomarkers of pSS.

Methods: From a total of 39 patients with pSS and 38 healthy controls (HCs), serum specimens were collected. The samples were analyzed by ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry. Three machine learning algorithms, including the least absolute shrinkage and selection operator (LASSO), random forest (RF), and extreme gradient boosting (XGBoost), were used to build the pSS diagnosis models. Afterward, four machine learning methods were used to reduce the dimensionality of the metabolomics data. Finally, metabolites with significant differences were screened and pathway analysis was conducted.

Results: The area under the curve (AUC), sensitivity, and specificity of LASSO, RF and XGBoost test set all reached 1.00. Orthogonal partial least squares discriminant analysis was used to classify the metabolomics data. By combining the results of the univariate false discovery rate and the importance of the variable in projection, we identified 21 significantly different metabolites. Using these 21 metabolites for diagnostic modeling, the AUC, sensitivity, and specificity of LASSO, RF, and XGBoost all reached 1.00. Metabolic pathway analysis revealed that these 21 metabolites are highly correlated with amino acid and lipid metabolisms. On the basis of 21 metabolites, we screened the important variables in the models. Further, five common variables were obtained by intersecting the important variables of three models. Based on these five common variables, the AUC, sensitivity, and specificity of LASSO, RF, and XGBoost all reached 1.00.2-Hydroxypalmitic acid, L-carnitine and cyclic AMP were found to be potential targets and specific biomarkers for pSS.

Conclusion: The combination of machine learning and metabolomics can accurately distinguish between patients with pSS and HCs. 2-Hydroxypalmitic acid, L-carnitine and cyclic AMP were potential targets and biomarkers for pSS.

Enhancing the Signal-to-Noise of Diagnostic Fragment Ions of Unsaturated Glycerophospholipids via Precursor Exclusion Ultraviolet Photodissociation Mass Spectrometry (PEx-UVPD-MS)

Understanding and elucidating the diverse structures and functions of lipids has motivated the development of many innovative tandem mass spectrometry (MS/MS) strategies. Higher-energy activation methods, such as ultraviolet photodissociation (UVPD), generate unique fragment ions from glycerophospholipids that can be used to perform in-depth structural analysis and facilitate the deconvolution of isomeric lipid structures in complex samples. Although detailed characterization is central to the correlation of lipid structure to biological function, it is often impeded by the lack of sufficient instrument sensitivity for highly bioactive but low-abundance phospholipids. Here, we present precursor exclusion (PEx) UVPD, a simple yet powerful technique to enhance the signal-to-noise (S/N) of informative low-abundance fragment ions produced from UVPD of glycerophospholipids. Through the exclusion of the large population of undissociated precursor ions with an MS3 strategy, the S/N of diagnostic fragment ions from PC 18:0/18:2(9Z, 12Z) increased up to an average of 13x for PEx-UVPD compared to UVPD alone. These enhancements were extended to complex mixtures of lipids from bovine liver extract to confidently identify 35 unique structures using liquid chromatography PEx-UVPD. This methodology has the potential to advance lipidomics research by offering deeper structure elucidation and confident identification of biologically active lipids.

Integration of metabolomics and lipidomics reveals serum biomarkers for systemic lupus erythematosus with different organs involvement

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that affects various organs or systems. We performed metabolomic and lipidomic profiles analyses of 133 SLE patients and 30 HCs. Differential metabolites and lipids were integrated, and then the biomarker panel was identified using binary logistic regression. We found that a combination of four metabolites or lipids could distinguish SLE from HC with an AUC of 0.998. Three lipids were combined to differentiate inactive SLE and active SLE. The AUC was 0.767. In addition, we also identified the biomarkers for different organ phenotypes of SLE. The AUCs for diagnosing SLE patients with only kidney involvement, skin involvement, blood system involvement, and multisystem involvement were 0.766, 0.718, 0.951, and 0.909, respectively. Our study succeeded in identifying biomarkers associated with different clinical phenotypes in SLE patients, which could facilitate a more precise diagnosis and assessment of disease progression in SLE.