Metabolomics Applied to Cord Serum in Preeclampsia Newborns: Implications for Neonatal Outcomes

Maternal-offspring brain and tissue cross-talk in preeclampsia: insights from a rat model

This study aimed to investigate the differential metabolic profiles across maternal and offspring brains, serum, and placental tissues in preeclampsia (PE), with a particular focus on elucidating the maternal-offspring brain and tissue cross-talk that may contribute to the complex pathophysiology of PE. PE was induced in rats using the nitric oxide synthase inhibitor N-nitro-L-arginine methyl ester (L-NAME) to simulate both early-onset PE (EOPE) and late-onset PE (LOPE). We utilized non-targeted proton nuclear magnetic resonance (NMR) metabolomics to characterize the metabolic profiles of serum, placental tissue extracts, and brain tissues from both mothers and offspring. Multivariate analysis, Spearman correlation, Density-Based Spatial Clustering of Applications with Noise algorithm, Data-Driven Statistical Predictive Correlation network analysis and Tissue heterogeneity analysis were employed to explore tissue-specific metabolic signatures and their interactions. Following L-NAME induction, both EOPE and LOPE presented significant metabolic differences and shared traits across tissues, with distinct tissue-specific responses characterizing the metabolic profile of PE. Serum from both PE groups showed a decrease in tryptophan, isobutyrate, and lactate, with an increase in betaine. Lactate was upregulated in placental tissues, highlighting its metabolic role. Extensive intra-tissue metabolic correlations and inter-tissue metabolite exchanges were detected among the maternal brain, serum, placenta, and offspring brain across all three experimental groups. EOPE and LOPE exhibited distinctly different metabolic characteristics and trajectories of differential metabolites, along with diverse interaction patterns between the maternal/offspring brain and the placenta. This study uncovers the multi-tissue metabolic remodeling in response to preeclampsia, implying that addressing pathophysiological stress is crucial and may have potential implications for neurological outcomes. The comprehensive analysis highlights the pivotal role of the brain-placenta axis in preeclampsia, advocating for a classified diagnostic and management approach.

Disrupted fetal carbohydrate metabolism in children with autism spectrum disorder

BACKGROUND

Despite the power and promise of early detection and treatment in autism spectrum disorder (ASD), early-life biomarkers are limited. An early-life risk biosignature would advance the field's understanding of ASD pathogenies and targets for early diagnosis and intervention. We therefore sought to add to the growing ASD biomarker literature and evaluate whether fetal metabolomics are altered in idiopathic ASD.

METHODS

Banked cord blood plasma samples (N = 36 control, 16 ASD) were analyzed via gas chromatography and mass spectrometry (GC-MS). Samples were from babies later diagnosed with idiopathic ASD (non-familial, non-syndromic) or matched, neurotypical controls. Metabolite set enrichment analysis (MSEA) and biomarker prediction were performed (MetaboAnalyst).

RESULTS

We detected 76 metabolites in all samples. Of these, 20 metabolites differed significantly between groups: 10 increased and 10 decreased in ASD samples relative to neurotypical controls (p < 0.05). MSEA revealed significant changes in metabolic pathways related to carbohydrate metabolism and glycemic control. Untargeted principle components analysis of all metabolites did not reveal group differences, while targeted biomarker assessment (using only Fructose 6-phosphate, D-Mannose, and D-Fructose) by a Random Forest algorithm generated an area under the curve (AUC) = 0.766 (95% CI: 0.612-0.896) for ASD prediction.

CONCLUSIONS

Despite a high and increasing prevalence, ASD has no definitive biomarkers or available treatments for its core symptoms. ASD's earliest developmental antecedents remain unclear. We find that fetal plasma metabolomics differ with child ASD status, in particular invoking altered carbohydrate metabolism. While prior clinical and preclinical work has linked carbohydrate metabolism to ASD, no prior fetal studies have reported these disruptions in neonates or fetuses who go on to be diagnosed with ASD. Future work will investigate concordance with maternal metabolomics to determine maternal-fetal mechanisms.

Indolelactic acid as a potential metabolic biomarker for diagnosing gout

Gout is a heterogeneous disease caused by the deposition of monosodium urate crystals in joints, but its pathogenesis is currently poorly understood. The discovery of novel biomarkers is necessary for the early detection and diagnosis of gout. The present study aimed to characterize the metabolic profile of patients with gout using metabolomics, and to uncover the underlying pathological mechanisms leading to gout development. Serum samples were collected from 49 healthy participants and 47 patients with gout. Using ultra-high-performance liquid chromatography Orbitrap Exploris mass spectrometer non-target metabolomics technology, with a variable importance in the projection >1 and a false discovery rate adjusted P<0.05 was used, while a biomarker panel was screened using receiver operating characteristic (ROC) analysis. The potential differentially expressed markers related to gout were identified by ROC analysis, and the erythrocyte sedimentation rate, uric acid, alanine transaminase, aspartate aminotransferase, creatinine, triglyceride, total cholesterol, high-density lipoprotein and low-density lipoprotein levels were significantly different in the group of patients with gout compared with those in healthy individuals. A total of 186 differentially expressed metabolites were identified, with 156 differential metabolites upregulated and 30 downregulated in the patients with gout compared with healthy individuals. Pathway analysis demonstrated that D-glutamine and D-glutamate metabolism may serve key roles in gout. Compared with healthy people, the indolelactic acid (ILA) level of patients with gout was significantly higher. ILA may serve as a potential biomarker for the diagnosis of gout and could be used to detect or predict gout progression in the future.

Decoding the 'Fifth' Nucleotide: Impact of RNA Pseudouridylation on Gene Expression and Human Disease

Cellular RNAs, both coding and noncoding are adorned by > 100 chemical modifications, which impact various facets of RNA metabolism and gene expression. Very often derailments in these modifications are associated with a plethora of human diseases. One of the most oldest of such modification is pseudouridylation of RNA, wherein uridine is converted to a pseudouridine (Ψ) via an isomerization reaction. When discovered, Ψ was referred to as the 'fifth nucleotide' and is chemically distinct from uridine and any other known nucleotides. Experimental evidence accumulated over the past six decades, coupled together with the recent technological advances in pseudouridine detection, suggest the presence of pseudouridine on messenger RNA, as well as on diverse classes of non-coding RNA in human cells. RNA pseudouridylation has widespread effects on cellular RNA metabolism and gene expression, primarily via stabilizing RNA conformations and destabilizing interactions with RNA-binding proteins. However, much remains to be understood about the RNA targets and their recognition by the pseudouridylation machinery, the regulation of RNA pseudouridylation, and its crosstalk with other RNA modifications and gene regulatory processes. In this review, we summarize the mechanism and molecular machinery involved in depositing pseudouridine on target RNAs, molecular functions of RNA pseudouridylation, tools to detect pseudouridines, the role of RNA pseudouridylation in human diseases like cancer, and finally, the potential of pseudouridine to serve as a biomarker and as an attractive therapeutic target.

Decreased Fatty Acid Oxidation Gene Expression in Pre-Eclampsia According to the Onset and Presence of Intrauterine Growth Restriction

Mitochondrial fatty acid oxidation (FAO) is lower in placentas with pre-eclampsia. The aim of our study was to compare the placental mRNA expression of FAO enzymes in healthy pregnancies vs. different subgroups of pre-eclampsia according to the severity, time of onset, and the presence of intrauterine growth restriction (IUGR). By using real-time qPCR, we measured the mRNA levels of long-chain 3-hydroxyacyl-CoA dehydrogenase (LCHAD), medium-chain acyl-CoA dehydrogenase (MCAD), and carnitine palmitoyltransferases 1A and 2 (CPT1A, CPT2) on the maternal side (anchoring villi in the basal decidua) and on the fetal side (chorionic plate) of the placenta (n = 56). When compared to the controls, LCHAD, MCAD, and CPT2 mRNA had decreased in all pre-eclampsia subgroups globally and on the fetal side. On the maternal side, LCHAD mRNA was also lower in all pre-eclampsia subgroups; however, MCAD and CPT2 mRNA were only reduced in severe and early-onset disease, as well as CPT2 in IUGR (p < 0.05). There were no differences in CPT1A mRNA expression. We conclude that the FAO enzymes mRNA in the placenta was lower in pre-eclampsia, with higher reductions observed in severe, early-onset, and IUGR cases and more striking reductions on the fetal side.

Biomarkers of cardiovascular disease risk in the neonatal population

The consistently high prevalence of cardiovascular disease (CVD) has urged the need for punctual and effective prevention. Extended research on this specific area has demonstrated the influence of fetal and neonatal periods on the risk of developing CVD in adulthood. Thus, the role of traditional and novel biological markers to the effective screening of CVD among the neonatal population is widely investigated. The objective of the present narrative review is to examine those neonatal biomarkers that may play a role in the development of CVD, to exhibit scientific data that appertain to their association with various perinatal conditions leading to CVD predisposition, and their potential role on prediction and prevention strategies. Multiple biomarkers, traditional and novel, have been mined across the studied literature. Adiposity, insulin resistance, altered lipid profile, inflammation, and endothelial dysfunction seem among the headliners of CVD. Even though various novel molecules have been studied, their clinical utility remains controversial. Therefore, it is quite important for the scientific community to find elements with strong predictive value and practical clinical use.