Serum peptidomic screening identified circulating peptide biomarkers predictive for preeclampsia

Large-scale prospective serum metabolomic profiling reveals candidate predictive biomarkers for suspected preeclampsia patients

Preeclampsia (PE) is a serious pregnancy complication that contributes to maternal and perinatal morbidity and mortality. Understanding its pathogenesis and revealing predictive biomarkers are essential for guiding treatment decisions. In order to explore the global changes of serum metabolites in PE patients and identify potential predictive biomarkers for suspected PE patients (pregnant women who had already shown PE-related symptoms in the middle to late stages of pregnancy, but were not yet confirmatively diagnosed as PE.), a large-scale serum metabolomic analysis was conducted in this study with a prospective cohort of 328 suspected PE patients in the middle or late pregnancy stages, as well as a retrospective cohort of 30 healthy pregnant women and 30 PE patients. Using liquid chromatography mass spectrometry (LC - MS), serum metabolomic profiling revealed that the development of PE was closely associated with disturbed amino acid metabolism. Moreover, a panel of seven predictive biomarkers including 2-methyl-3-hydroxy-5-formylpyridine-4-carboxylate, gamma-glutamyl-leucine, 2-hydroxyvaleric acid, LysoPC(16:1(9Z)/0:0), PC(DiMe(13,5)/MonoMe(13,5)), ADP-D-glycero-beta-D-manno-heptose and phenylalanyl-tryptophan were identified for PE development by performing multiple statistical analysis and LASSO regression analysis. The combination of these biomarkers showed promise in the prediction of PE development for suspected PE patients, with an AUC of 0.753 and 0.885 for the discovery and validation cohorts, respectively. These findings highlight the potential of large-scale prospective metabolomic studies combined with machine learning algorithms in identifying key biomarkers for predicting PE development, while retrospective metabolomics studies provide insights into the pathogenesis of PE.

The extent of postpartum cardiac reverse remodeling is reflected in urine proteome

The association of postpartum cardiac reverse remodeling (RR) with urinary proteome, particularly in pregnant women with cardiovascular (CV) risk factors who show long-term increased risk of cardiovascular disease and mortality is unknown. We aim to profile the urinary proteome in pregnant women with/without CV risk factors to identify proteins associated with postpartum RR. Our study included a prospective cohort of 32 healthy and 27 obese and/or hypertensive and/or diabetic pregnant women who underwent transthoracic echocardiography, pulse-wave-velocity, and urine collection at the 3rd trimester and 6 months postpartum. Shotgun HPLC-MS/MS profiled proteins. Generalized linear mixed-effects models were used to identify associations between urinary proteins and left ventricle mass (LVM), a surrogate of RR. An increase in arterial stiffness was documented from 3rd trimester to 6 months after delivery, being significantly elevated in women with CV risk factors. In addition, the presence of at least one CV risk factor was associated with worse LVM RR. We identified 6 and 11 proteins associated with high and low LVM regression, respectively. These proteins were functionally linked with insulin-like growth factor (IGF) transport and uptake regulation by IGF binding-proteins, platelet activation, signaling and aggregation and the immune system's activity. The concentration of IGF-1 in urine samples was associated with low LVM regression after delivery. Urinary proteome showed a predicting potential for identifying pregnant women with incomplete postpartum RR.

Noninvasive preeclampsia prediction using plasma cell-free RNA signatures

BACKGROUND

Preeclampsia, especially preterm preeclampsia and early-onset preeclampsia, is a life-threating pregnancy disorder, and the heterogeneity and complexity of preeclampsia make it difficult to predict risk and to develop treatments. Plasma cell-free RNA carries unique information from human tissue and may be useful for noninvasive monitoring of maternal, placental, and fetal dynamics during pregnancy.

OBJECTIVE

This study aimed to investigate various RNA biotypes associated with preeclampsia in plasma and to develop classifiers to predict preterm preeclampsia and early-onset preeclampsia before diagnosis.

STUDY DESIGN

We performed a novel, cell-free RNA sequencing method termed polyadenylation ligation-mediated sequencing to investigate the cell-free RNA characteristics of 715 healthy pregnancies and 202 pregnancies affected by preeclampsia before symptom onset. We explored differences in the abundance of different RNA biotypes in plasma between healthy and preeclampsia samples and built preterm preeclampsia and early-onset preeclampsia prediction classifiers using machine learning methods. Furthermore, we validated the performance of the classifiers using the external and internal validation cohorts and assessed the area under the curve and positive predictive value.

RESULTS

We detected 77 genes, including messenger RNA (44%) and microRNA (26%), that were differentially expressed in healthy mothers and mothers with preterm preeclampsia before symptom onset, which could separate participants with preterm preeclampsia from healthy samples and that played critical functional roles in preeclampsia physiology. We developed 2 classifiers for predicting preterm preeclampsia and early-onset preeclampsia before diagnosis based on 13 cell-free RNA signatures and 2 clinical features (in vitro fertilization and mean arterial pressure), respectively. Notably, both classifiers showed enhanced performance when compared with the existing methods. The preterm preeclampsia prediction model achieved 81% area under the curve and 68% positive predictive value in an independent validation cohort (preterm, n=46; control, n=151); the early-onset preeclampsia prediction model had an area under the curve of 88% and a positive predictive value of 73% in an external validation cohort (early-onset preeclampsia, n=28; control, n=234). Furthermore, we demonstrated that downregulation of microRNAs may play vital roles in preeclampsia through the upregulation of preeclampsia-relevant target genes.

CONCLUSION

In this cohort study, a comprehensive transcriptomic landscape of different RNA biotypes in preeclampsia was presented and 2 advanced classifiers with substantial clinical importance for preterm preeclampsia and early-onset preeclampsia prediction before symptom onset were developed. We demonstrated that messenger RNA, microRNA, and long noncoding RNA can simultaneously serve as potential biomarkers of preeclampsia, holding the promise of prevention of preeclampsia in the future. Abnormal cell-free messenger RNA, microRNA, and long noncoding RNA molecular changes may help to elucidate the pathogenic determinants of preeclampsia and open new therapeutic windows to effectively reduce pregnancy complications and fetal morbidity.

Circulating Peptidome Is Strongly Altered in COVID-19 Patients

Whilst the impact of coronavirus disease 2019 (COVID-19) on the host proteome, metabolome, and lipidome has been largely investigated in different bio-fluids, to date, the circulating peptidome remains unexplored. Thus, the present study aimed to apply an untargeted peptidomic approach to provide insight into alterations of circulating peptides in the development and severity of SARS-CoV-2 infection. The circulating peptidome from COVID-19 severe and mildly symptomatic patients and negative controls was characterized using LC-MS/MS analysis for identification and quantification purposes. Database search and statistical analysis allowed a complete characterization of the plasma peptidome and the detection of the most significant modulated peptides that were impacted by the infection. Our results highlighted not only that peptide abundance inversely correlates with disease severity, but also the involvement of biomolecules belonging to inflammatory, immune-response, and coagulation proteins/processes. Moreover, our data suggested a possible involvement of changes in protein degradation patterns. In the present research, for the first time, the untargeted peptidomic approach enabled the identification of circulating peptides potentially playing a crucial role in the progression of COVID-19.