Absolute quantification and characterization of oxylipins in lupus nephritis and systemic lupus erythematosus

Intricating connections: the role of ferroptosis in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a chronic inflammatory and autoimmune disease with multiple tissue damage. However, the pathology remains elusive, and effective treatments are lacking. Multiple types of programmed cell death (PCD) implicated in SLE progression have recently been identified. Although ferroptosis, an iron-dependent form of cell death, has numerous pathophysiological features similar to those of SLE, such as intracellular iron accumulation, mitochondrial dysfunction, lipid metabolism disorders and concentration of damage associated-molecular patterns (DAMPs), only a few reports have demonstrated that ferroptosis is involved in SLE progression and that the role of ferroptosis in SLE pathogenesis continues to be neglected. Therefore, this review elucidates the potential intricate relationship between SLE and ferroptosis to provide a reliable theoretical basis for further research on ferroptosis in the pathogenesis of SLE.

The Octadecanoids: Synthesis and Bioactivity of 18-Carbon Oxygenated Fatty Acids in Mammals, Bacteria, and Fungi

The octadecanoids are a broad class of lipids consisting of the oxygenated products of 18-carbon fatty acids. Originally referring to production of the phytohormone jasmonic acid, the octadecanoid pathway has been expanded to include products of all 18-carbon fatty acids. Octadecanoids are formed biosynthetically in mammals via cyclooxygenase (COX), lipoxygenase (LOX), and cytochrome P450 (CYP) activity, as well as nonenzymatically by photo- and autoxidation mechanisms. While octadecanoids are well-known mediators in plants, their role in the regulation of mammalian biological processes has been generally neglected. However, there have been significant advancements in recognizing the importance of these compounds in mammals and their involvement in the mediation of inflammation, nociception, and cell proliferation, as well as in immuno- and tissue modulation, coagulation processes, hormone regulation, and skin barrier formation. More recently, the gut microbiome has been shown to be a significant source of octadecanoid biosynthesis, providing additional biosynthetic routes including hydratase activity (e.g., CLA-HY, FA-HY1, FA-HY2). In this review, we summarize the current field of octadecanoids, propose standardized nomenclature, provide details of octadecanoid preparation and measurement, summarize the phase-I metabolic pathway of octadecanoid formation in mammals, bacteria, and fungi, and describe their biological activity in relation to mammalian pathophysiology as well as their potential use as biomarkers of health and disease.

ALOX5 contributes to glioma progression by promoting 5-HETE-mediated immunosuppressive M2 polarization and PD-L1 expression of glioma-associated microglia/macrophages

BACKGROUND

Oxylipin metabolism plays an essential role in glioma progression and immune modulation in the tumor microenvironment. Lipid metabolic reprogramming has been linked to macrophage remodeling, while the understanding of oxylipins and their catalyzed enzymes lipoxygenases in the regulation of glioma-associated microglia/macrophages (GAMs) remains largely unexplored.

METHODS

To explore the pathophysiological relevance of oxylipin in human glioma, we performed Ultra-high performance liquid chromatography-MS/MS (UHPLC-MS/MS) analysis in human glioma and non-tumor brain tissues. To comprehensively investigate the role of arachidonate lipoxygenase 5 (ALOX5) in glioma, we performed in vivo bioluminescent imaging, immunofluorescence staining and flow cytometry analysis on tumors from orthotopic glioma-bearing mice. We developed an ALOX5-targeted nanobody, and tested its anti-glioma efficacy of combination therapy with α-programmed cell death protein-1 (PD-1).

RESULTS

In this study, we found that ALOX5 and its oxylipin 5-hydroxyeicosatetraenoic acid (5-HETE) are upregulated in glioma, accumulating programmed death-ligand 1 (PD-L1)+ M2-GAMs and orchestrating an immunosuppressive tumor microenvironment. Mechanistically, 5-HETE derived from ALOX5-overexpressing glioma cells, promotes GAMs migration, PD-L1 expression, and M2 polarization by facilitating nuclear translocation of nuclear factor erythroid 2-related factor 2. Additionally, a nanobody targeting ALOX5 is developed that markedly suppresses 5-HETE efflux from glioma cells, attenuates M2 polarization of GAMs, and consequently ameliorates glioma progression. Furthermore, the combination therapy of the ALOX5-targeted nanobody plus α-PD-1 exhibits superior anti-glioma efficacy.

CONCLUSIONS

Our findings reveal a pivotal role of the ALOX5/5-HETE axis in regulating GAMs and highlight the ALOX5-targeted nanobody as a potential therapeutic agent, which could potentiate immune checkpoint therapy for glioma.

Targeted metabolomics combined with machine learning to identify and validate new biomarkers for early SLE diagnosis and disease activity

BACKGROUND

The early diagnosis of systemic lupus erythematosus (SLE) and the assessment of disease activity progression remain a great challenge. Targeted metabolomics has great potential to identify new biomarkers of SLE.

METHODS

Serum from 44 healthy participants and 89 SLE patients were analyzed using HM400 high-throughput targeted metabolomics. Machine learning (ML) with seven learning models and trained the model several times iteratively selected the two best prediction model in a competitive way, which were independent validated by enzyme-linked immunosorbent (ELISA) with 90 SLE patients.

RESULTS

In this study, 146 differential metabolites, most of them organic acids, amino acids, and bile acids, were detected between patients with initial SLE and healthy participants, and 8 potential biomarkers were found by intersection of ML and statistics (area under the curve [AUC] > 0.95) showing a significant positive correlation with clinical indicators. In addition, we identified and validated 2 potential biomarkers for SLE classification (P < 0.05, AUC > 0.775; N-Methyl-L-glutamic acid, L-2-aminobutyric acid) showing a significant correlation with the SLE Disease Activity Index. These differential metabolites were mainly involved in metabolic pathways, amino acid biosynthesis, 2-oxocarboxylic acid metabolism and other pathways.

CONCLUSION

This study indicated that the tricarboxylic acid cycle might be associated with SLE drug therapy. We identified 8 diagnostic models biomarkers and 2 biomarkers that could be used to identify initial SLE and distinguish different activity degree, which will promote the development of new tools for the diagnosis and evaluation of SLE.

Systemic lupus in the era of machine learning medicine

Artificial intelligence and machine learning applications are emerging as transformative technologies in medicine. With greater access to a diverse range of big datasets, researchers are turning to these powerful techniques for data analysis. Machine learning can reveal patterns and interactions between variables in large and complex datasets more accurately and efficiently than traditional statistical methods. Machine learning approaches open new possibilities for studying SLE, a multifactorial, highly heterogeneous and complex disease. Here, we discuss how machine learning methods are rapidly being integrated into the field of SLE research. Recent reports have focused on building prediction models and/or identifying novel biomarkers using both supervised and unsupervised techniques for understanding disease pathogenesis, early diagnosis and prognosis of disease. In this review, we will provide an overview of machine learning techniques to discuss current gaps, challenges and opportunities for SLE studies. External validation of most prediction models is still needed before clinical adoption. Utilisation of deep learning models, access to alternative sources of health data and increased awareness of the ethics, governance and regulations surrounding the use of artificial intelligence in medicine will help propel this exciting field forward.

Advances in Mass Spectrometry-Based Blood Metabolomics Profiling for Non-Cancer Diseases: A Comprehensive Review

Blood metabolomics profiling using mass spectrometry has emerged as a powerful approach for investigating non-cancer diseases and understanding their underlying metabolic alterations. Blood, as a readily accessible physiological fluid, contains a diverse repertoire of metabolites derived from various physiological systems. Mass spectrometry offers a universal and precise analytical platform for the comprehensive analysis of blood metabolites, encompassing proteins, lipids, peptides, glycans, and immunoglobulins. In this comprehensive review, we present an overview of the research landscape in mass spectrometry-based blood metabolomics profiling. While the field of metabolomics research is primarily focused on cancer, this review specifically highlights studies related to non-cancer diseases, aiming to bring attention to valuable research that often remains overshadowed. Employing natural language processing methods, we processed 507 articles to provide insights into the application of metabolomic studies for specific diseases and physiological systems. The review encompasses a wide range of non-cancer diseases, with emphasis on cardiovascular disease, reproductive disease, diabetes, inflammation, and immunodeficiency states. By analyzing blood samples, researchers gain valuable insights into the metabolic perturbations associated with these diseases, potentially leading to the identification of novel biomarkers and the development of personalized therapeutic approaches. Furthermore, we provide a comprehensive overview of various mass spectrometry approaches utilized in blood metabolomics research, including GC-MS, LC-MS, and others discussing their advantages and limitations. To enhance the scope, we propose including recent review articles supporting the applicability of GC×GC-MS for metabolomics-based studies. This addition will contribute to a more exhaustive understanding of the available analytical techniques. The Integration of mass spectrometry-based blood profiling into clinical practice holds promise for improving disease diagnosis, treatment monitoring, and patient outcomes. By unraveling the complex metabolic alterations associated with non-cancer diseases, researchers and healthcare professionals can pave the way for precision medicine and personalized therapeutic interventions. Continuous advancements in mass spectrometry technology and data analysis methods will further enhance the potential of blood metabolomics profiling in non-cancer diseases, facilitating its translation from the laboratory to routine clinical application.

Exploring the causal correlations between 486 serum metabolites and systemic lupus erythematosus: a bidirectional Mendelian randomization study

Objective: The observational association between circulating metabolites and systemic lupus erythematosus (SLE) has been well documented. However, whether the association is causal remains unclear. In this study, bidirectional Mendelian randomization (MR) was introduced to analyse the causal relationships and possible mechanisms. Methods: We conducted a two-sample bidirectional MR study. A genome-wide association study (GWAS) with 7,824 participants provided data on 486 human blood metabolites. Outcome information was obtained from a large-scale GWAS summary, which contained 5,201 single nucleotide polymorphisms (SNPs) cases and 9,066 control cases of Europeans and yielded a total of 7,071,163 SNPs. The inverse variance weighted (IVW) model was recruited as the primary two-sample MR analysis approach, followed by sensitivity analyses such as the heterogeneity test, horizontal pleiotropy test, leave-one-out analysis, and linkage disequilibrium score (LDSC) regression. Results: In this study, we discovered that 24 metabolites belonging to the lipid, carbohydrate, xenobiotic and amino acid superpathways may increase the risk of SLE occurrence (p < 0.05). In addition, the metabolic disorders of 51 metabolites belonging to the amino acid, energy, xenobiotics, peptide and lipid superpathways were affected by SLE (p < 0.05). Palmitoleate belonging to the lipid superpathway and isobutyrylcarnitine and phenol sulfate belonging to the amino acid superpathway were factors with two-way causation. The metabolic enrichment pathway of bile acid biosynthesis was significant in the forward MR analysis (p = 0.0435). Linolenic acid and linoleic acid metabolism (p = 0.0260), betaine metabolism (p = 0.0314), and glycerolipid metabolism (p = 0.0435) were the significant metabolically enriched pathways in the reverse MR analysis. Conclusion: The levels of some specific metabolites may either contribute to the immune response inducing SLE, or they may be intermediates in the development and progression of SLE. These metabolites can be used as auxiliary diagnostic tools for SLE and for the evaluation of disease progression and therapeutic effects.

Serum Metabolomics Analysis of Skin-Involved Systemic Lupus Erythematosus: Association of Anti-SSA Antibodies with Photosensitivity

Purpose

Systemic lupus erythematosus is a heterogeneous autoimmune disease in which skin involvement is a common manifestation. It is currently thought that the photosensitivity of SLE skin involvement is associated with anti-SSA antibodies. This study aimed to expand the current state of knowledge surrounding the molecular pathophysiology of SLE skin photosensitivity through Serum metabolomics analysis.

Patients and Methods

The serum metabolites of 23 cases of skin-involved SLE (SI) group, 14 cases of no SI (NSI) group, and 30 cases of healthy controls (HC) were analyzed by using UPLC-MS/MS technology, and subgroup analysis was performed according to the expression of anti-SSA antibodies in SI. MetaboAnalyst 5.0 was used for enrichment analysis and ROC curve construction, identifying serum metabolic markers of skin-involved SLE associated with anti-SSA antibodies.

Results

We identified several metabolites and metabolic pathways associated with SLE photosensitivity. Two metabolites, SM (d18:1/24:0) and gamma-CEHC can distinguish between anti-SSA antibody-positive and negative SI, with AUC of 0.829 and 0.806. These two photosensitization-related substances may be potential markers of skin involvement in SLE associated with anti-SSA antibody.

Conclusion

This study provides new insights into the pathogenesis of SI patients, and provides a new molecular biological basis for the association between anti-SSA antibodies and skin photoallergic manifestations of SLE.