Metabolic Biomarkers In Midtrimester Maternal Plasma Can Accurately Predict Adverse Pregnancy Outcome in Patients with SLE

Metabolomics profiling reveals low blood tyrosine levels as a metabolic feature of newborns from systemic lupus erythematosus pregnancies

Introduction

Pregnancy outcomes of patients with systemic lupus erythematosus (SLE) have improved over the past four decades, leading to an increased desire for pregnancy among this cohort. However, the offspring of patients with SLE still face the risks of preterm birth, low birth weight, learning disabilities, and neurological disorders, while the causes underlying these risks remain unclear.

Methods

In this study, we analyzed the blood metabolic features of neonates born to 30 SLE patients and 52 healthy control mothers by employing tandem mass spectrometry with the dual aims of identifying the etiology of metabolic features specific to infants born from mothers with SLE and providing new insights into the clinical management of such infants.

Results

We found significant differences in serum metabolite levels between infants born from mothers with SLE and those born from mothers without SLE, including 15 metabolites with reduced serum levels. Further analysis revealed a disrupted tyrosine metabolism pathway in the offspring of mothers with SLE.

Discussion

By constructing a composite model incorporating various factors, such as serum tyrosine levels, gestational age, and birth weight, we were able to accurately differentiate between newborns of SLE and non-SLE pregnancies. Our data reveal significant differences in serum concentrations of amino acids and acylcarnitines in newborns born to mothers with SLE. We conclude that the reduction of blood L-tyrosine levels is a feature that is characteristic of adverse neurological outcomes in infants born from mothers with SLE.

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.

Metabolomics meets systems immunology

Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.

A metabolomic-based biomarker discovery study for predicting phototherapy duration for neonatal hyperbilirubinemia

BACKGROUND

Phototherapy is a recommended method for the treatment of neonatal hyperbilirubinemia. However, biomarkers for predicting the more effective duration of phototherapy prior to treatment are lacking. Therefore, we aimed to determine novel predictors for the timing of phototherapy from the perspective of metabolomics.

METHODS

A total of 12 newborns with neonatal hyperbilirubinemia were recruited on the day of admission. The infants were divided into a short-duration (<30 hours) phototherapy group and a long-duration (≥30 hours) phototherapy group based on the length of phototherapy treatment. Metabolites in serum samples were then explored using an untargeted metabolomics strategy.

RESULTS

In total, 59 of 1,073 significantly different metabolites were identified between the short-duration and long-duration phototherapy groups, including 18 upregulated and 41 downregulated metabolites. The results of metabolomic analysis showed that the differentially expressed metabolites were enriched in glycerophospholipid metabolism, which is closely associated with the excretion of bilirubin. Moreover, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the metabolites were also enriched in alpha-Linolenic acid metabolism and fatty acid elongation. Spearman correlation hierarchical clustering analysis demonstrated that 9 metabolites were negatively correlated with the duration of phototherapy. Metabolites, especially phosphatidylethanolamine (PE) (22:1(13Z)/15:0), phosphatidylcholine (PC) (18:1(9Z)/18:1(9Z)), phosphatidylserine (PS) (22:0/15:0), 5,6-dihydrouridine, and PE (MonoMe(11,3)/MonoMe(13,5)), had better predictability for the duration of phototherapy [area under curve (AUC): 1; 95% confidence interval (CI): 1-1] than total serum total bilirubin and direct bilirubin (AUC: 0.806; 95% CI: 0.55-1), as revealed by receiver operating characteristic analysis.

CONCLUSIONS

Our research found that the differential metabolites were associated with the duration of neonatal jaundice and that glycerophospholipid metabolism might have played a role in this biological process. Moreover, metabolites such as PE (22:1(13Z)/15:0), PC (18:1(9Z)/18:1(9Z)), PS (22:0/15:0), 5,6-dihydrouridine, and PE (MonoMe(11,3)/MonoMe(13,5)) could be used as predictors for phototherapy duration in neonatal hyperbilirubinemia and assist with decision-making.

Toward a new taxonomy of obstetrical disease: improved performance of maternal blood biomarkers for the great obstetrical syndromes when classified according to placental pathology

BACKGROUND

The major challenge for obstetrics is the prediction and prevention of the great obstetrical syndromes. We propose that defining obstetrical diseases by the combination of clinical presentation and disease mechanisms as inferred by placental pathology will aid in the discovery of biomarkers and add specificity to those already known.

OBJECTIVE

To describe the longitudinal profile of placental growth factor (PlGF), soluble fms-like tyrosine kinase-1 (sFlt-1), and the PlGF/sFlt-1 ratio throughout gestation, and to determine whether the association between abnormal biomarker profiles and obstetrical syndromes is strengthened by information derived from placental examination, eg, the presence or absence of placental lesions of maternal vascular malperfusion.

STUDY DESIGN

This retrospective case cohort study was based on a parent cohort of 4006 pregnant women enrolled prospectively. The case cohort of 1499 pregnant women included 1000 randomly selected patients from the parent cohort and all additional patients with obstetrical syndromes from the parent cohort. Pregnant women were classified into six groups: 1) term delivery without pregnancy complications (n=540; control); 2) preterm labor and delivery (n=203); 3) preterm premature rupture of the membranes (n=112); 4) preeclampsia (n=230); 5) small-for-gestational-age neonate (n=334); and 6) other pregnancy complications (n=182). Maternal plasma concentrations of PlGF and sFlt-1 were determined by enzyme-linked immunosorbent assays in 7560 longitudinal samples. Placental pathologists, masked to clinical outcomes, diagnosed the presence or absence of placental lesions of maternal vascular malperfusion. Comparisons between mean biomarker concentrations in cases and controls were performed by utilizing longitudinal generalized additive models. Comparisons were made between controls and each obstetrical syndrome with and without subclassifying cases according to the presence or absence of placental lesions of maternal vascular malperfusion.

RESULTS

1) When obstetrical syndromes are classified based on the presence or absence of placental lesions of maternal vascular malperfusion, significant differences in the mean plasma concentrations of PlGF, sFlt-1, and the PlGF/sFlt-1 ratio between cases and controls emerge earlier in gestation; 2) the strength of association between an abnormal PlGF/sFlt-1 ratio and the occurrence of obstetrical syndromes increases when placental lesions of maternal vascular malperfusion are present (adjusted odds ratio [aOR], 13.6 vs 6.7 for preeclampsia; aOR, 8.1 vs 4.4 for small-for-gestational-age neonates; aOR, 5.5 vs 2.1 for preterm premature rupture of the membranes; and aOR, 3.3 vs 2.1 for preterm labor (all P<0.05); and 3) the PlGF/sFlt-1 ratio at 28 to 32 weeks of gestation is abnormal in patients who subsequently delivered due to preterm labor with intact membranes and in those with preterm premature rupture of the membranes if both groups have placental lesions of maternal vascular malperfusion. Such association is not significant in patients with these obstetrical syndromes who do not have placental lesions.

CONCLUSION

Classification of obstetrical syndromes according to the presence or absence of placental lesions of maternal vascular malperfusion allows biomarkers to be informative earlier in gestation and enhances the strength of association between biomarkers and clinical outcomes. We propose that a new taxonomy of obstetrical disorders informed by placental pathology will facilitate the discovery and implementation of biomarkers as well as the prediction and prevention of such disorders.

Predictors Associated with Adverse Pregnancy Outcomes in a Cohort of Women with Systematic Lupus Erythematosus from Romania-An Observational Study (Stage 2)

Background: Pregnancy in women with systemic lupus erythematosus (SLE) is accompanied by adverse pregnancy outcomes (APOs). We aimed to investigate the association between clinical, sonographic, and laboratory parameters and APOs (preeclampsia, intrauterine growth restriction, premature birth, and maternal mortality). Methods: This observational retrospective study included all pregnancies in women with SLE who attended two tertiary maternity hospitals from Romania between January 2013 and December 2020. Clinical, sonographic, and laboratory variables were examined. Bivariate associations of APO status and each predictor variable were evaluated, and significant predictors were further included in a classification model based on discriminant analysis. Results: Predictors of APOs included BMI > 25 kg/m2, personal history of lupus nephritis or chronic hypertension, proteinuria, low C3, SLE Disease Activity Index 2000 (SLEDAI-2k score ≥ 4 and physician’s global-assessment (PGA) score ≥ 1 throughout pregnancy, increased mean uterine arteries pulsatility index in the first and second trimesters, cerebroplacental ratio < 1 in the second and third trimesters, and small fetal abdominal circumference in the third trimester. Glucocorticoids, methyldopa, and aspirin use appeared to be protective against APOs. Conclusions: This study provides a comprehensive analysis of the most important predictors for APOs in pregnant patients with SLE, which could constitute a basis for further research.

Potential genetic biomarkers predict adverse pregnancy outcome during early and mid-pregnancy in women with systemic lupus erythematosus

BACKGROUND

Effectively predicting the risk of adverse pregnancy outcome (APO) in women with systemic lupus erythematosus (SLE) during early and mid-pregnancy is a challenge. This study was aimed to identify potential markers for early prediction of APO risk in women with SLE.

METHODS

The GSE108497 gene expression dataset containing 120 samples (36 patients, 84 controls) was downloaded from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was performed, and differentially expressed genes (DEGs) were screened to define candidate APO marker genes. Next, three individual machine learning methods, random forest, support vector machine-recursive feature elimination, and least absolute shrinkage and selection operator, were combined to identify feature genes from the APO candidate set. The predictive performance of feature genes for APO risk was assessed using area under the receiver operating characteristic curve (AUC) and calibration curves. The potential functions of these feature genes were finally analyzed by conventional gene set enrichment analysis and CIBERSORT algorithm analysis.

RESULTS

We identified 321 significantly up-regulated genes and 307 down-regulated genes between patients and controls, along with 181 potential functionally associated genes in the WGCNA analysis. By integrating these results, we revealed 70 APO candidate genes. Three feature genes, SEZ6, NRAD1, and LPAR4, were identified by machine learning methods. Of these, SEZ6 (AUC = 0.753) showed the highest in-sample predictive performance for APO risk in pregnant women with SLE, followed by NRAD1 (AUC = 0.694) and LPAR4 (AUC = 0.654). After performing leave-one-out cross validation, corresponding AUCs for SEZ6, NRAD1, and LPAR4 were 0.731, 0.668, and 0.626, respectively. Moreover, CIBERSORT analysis showed a positive correlation between regulatory T cell levels and SEZ6 expression (P < 0.01), along with a negative correlation between M2 macrophages levels and LPAR4 expression (P < 0.01).

CONCLUSIONS

Our preliminary findings suggested that SEZ6, NRAD1, and LPAR4 might represent the useful genetic biomarkers for predicting APO risk during early and mid-pregnancy in women with SLE, and enhanced our understanding of the origins of pregnancy complications in pregnant women with SLE. However, further validation was required.

Biochemical and clinical predictors in pregnant women with antiphospholipid syndrome and systemic lupus erythematosus: comprehensive update

BACKGROUND

Autoimmune diseases as antiphospholipid syndrome (APS) and systemic lupus erythematosus (SLE) could cause many maternal complications. The most common maternal complications of autoimmune diseases are lupus flare, hypertension, nephritis, preeclampsia (PE), eclampsia, and poor pregnancy outcomes which including preterm delivery and pregnancy loss. Only the lupus anticoagulant in the greatest prospective multicenter study has been associated with adverse pregnancy outcomes of the APS.

PURPOSE

This review aims to provide a comprehensive update for predictors in pregnant women with APS/SLE.

METHODS

These data have been collected from clinical and pathological studies, systematic reviews, and meta-analysis.

RESULTS

In recent years the SLE and APS demonstrated to have different and valuable clinical and biomarker predictors for the pregnancy outcome. Treatment of pregnant women with APS is low molecular weight heparin (LMWH) and aspirin; however, around 75% of this management is considered successful.

CONCLUSION

This review summarizes recent research that focuses on biochemical and clinical predictors of adverse pregnancy outcomes (APOs) of pregnant women with SLE and APS. Furthermore, we have collected more evidence that confirms the safety and efficacy of hydroxychloroquine (HCQ) preventing APOs.

Metabolomic biomarkers in midtrimester maternal plasma can accurately predict the development of preeclampsia

Early identification of patients at risk of developing preeclampsia (PE) would allow providers to tailor their prenatal management and adopt preventive strategies, such as low-dose aspirin. Nevertheless, no mid-trimester biomarkers have as yet been proven useful for prediction of PE. This study investigates the ability of metabolomic biomarkers in mid-trimester maternal plasma to predict PE. A case–control study was conducted including 33 pregnant women with mid-trimester maternal plasma (gestational age [GA], 16–24 weeks) who subsequently developed PE and 66 GA-matched controls with normal outcomes (mid-trimester cohort). Plasma samples were comprehensively profiled for primary metabolic and lipidomic signatures based on gas chromatography time-of-flight mass spectrometry (GC-TOF MS) and liquid chromatography Orbitrap mass spectrometry (LC-Orbitrap MS). A potential biomarker panel was computed based on binary logistic regression and evaluated using receiver operating characteristic (ROC) analysis. To evaluate whether this panel can be also used in late pregnancy, a retrospective cohort study was conducted using plasma collected from women who delivered in the late preterm period because of PE (n = 13) or other causes (n = 21) (at-delivery cohort). Metabolomic biomarkers were compared according to the indication for delivery. Performance of the metabolomic panel to identify patients with PE was compared also to a commonly used standard, the plasma soluble fms-like tyrosine kinase-1/placental growth factor (sFlt-1/PlGF) ratio. In the mid-trimester cohort, a total of 329 metabolites were identified and semi-quantified in maternal plasma using GC-TOF MS and LC-Orbitrap-MS. Binary logistic regression analysis proposed a mid-trimester biomarker panel for the prediction of PE with five metabolites (SM C28:1, SM C30:1, LysoPC C19:0, LysoPE C20:0, propane-1,3-diol). This metabolomic model predicted PE better than PlGF (AUC [95% CI]: 0.868 [0.844–0.891] vs 0.604 [0.485–0.723]) and sFlt-1/PlGF ratio. Analysis of plasma from the at-delivery cohort confirmed the ability of this biomarker panel to distinguish PE from non-PE, with comparable discrimination power to that of the sFlt-1/PlGF ratio. In conclusion, an integrative metabolomic biomarker panel in mid-trimester maternal plasma can accurately predict the development of PE and showed good discriminatory power in patients with PE at delivery.

Lysophosphatidic Acid Receptor 5 Contributes to Imiquimod-Induced Psoriasis-Like Lesions through NLRP3 Inflammasome Activation in Macrophages

The pathogenesis of psoriasis, an immune-mediated chronic skin barrier disease, is not fully understood yet. Here, we identified lysophosphatidic acid (LPA) receptor 5 (LPA₅)-mediated signaling as a novel pathogenic factor in psoriasis using an imiquimod-induced psoriasis mouse model. Amounts of most LPA species were markedly elevated in injured skin of psoriasis mice, along with LPA₅ upregulation in injured skin. Suppressing the activity of LPA₅ with TCLPA5, a selective LPA₅ antagonist, improved psoriasis symptoms, including ear thickening, skin erythema, and skin scaling in imiquimod-challenged mice. TCLPA5 administration attenuated dermal infiltration of macrophages that were found as the major cell type for LPA₅ upregulation in psoriasis lesions. Notably, TCLPA5 administration attenuated the upregulation of macrophage NLRP3 in injured skin of mice with imiquimod-induced psoriasis. This critical role of LPA₅ in macrophage NLRP3 was further addressed using lipopolysaccharide-primed bone marrow-derived macrophages. LPA exposure activated NLRP3 inflammasome in lipopolysaccharide-primed cells, which was evidenced by NLRP3 upregulation, caspase-1 activation, and IL-1β maturation/secretion. This LPA-driven NLRP3 inflammasome activation in lipopolysaccharide-primed cells was significantly attenuated upon LPA₅ knockdown. Overall, our findings establish a pathogenic role of LPA₅ in psoriasis along with an underlying mechanism, further suggesting LPA₅ antagonism as a potential strategy to treat psoriasis.

Caution in studying and interpreting the lupus metabolome

Several metabolomics studies have shed substantial light on the pathophysiological pathways underlying multiple diseases including systemic lupus erythematosus (SLE). This review takes stock of our current understanding of this field. We compare, collate, and investigate the metabolites in SLE patients and healthy volunteers, as gleaned from published metabolomics studies on SLE. In the surveyed primary reports, serum or plasma samples from SLE patients and healthy controls were assayed using mass spectrometry or nuclear magnetic resonance spectroscopy, and metabolites differentiating SLE from controls were identified. Collectively, the circulating metabolome in SLE is characterized by reduced energy substrates from glycolysis, Krebs cycle, fatty acid β oxidation, and glucogenic and ketogenic amino acid metabolism; enhanced activity of the urea cycle; decreased long-chain fatty acids; increased medium-chain and free fatty acids; and augmented peroxidation and inflammation. However, these findings should be interpreted with caution because several of the same metabolic pathways are also significantly influenced by the medications commonly used in SLE patients, common co-morbidities, and other factors including smoking and diet. In particular, whereas the metabolic alterations relating to inflammation, oxidative stress, lipid peroxidation, and glutathione generation do not appear to be steroid-dependent, the other metabolic changes may in part be influenced by steroids. To conclude, metabolomics studies of SLE and other rheumatic diseases ought to factor in the potential contributions of confounders such as medications, co-morbidities, smoking, and diet.