Women with preterm birth have a distinct cervicovaginal metabolome

Ghartey, Jeny; Bastek, Jamie A.; Brown, Amy G.; Anglim, Laura; Elovitz, Michal A.

doi:10.1016/j.ajog.2015.03.052

Cited by 61 publications

(86 citation statements)

References 42 publications

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“…High-dimensional biology techniques allow characterization of the genome (54,55,125,126), transcriptome (55,(127)(128)(129)(130)(131)(132)(133)(134)(135)(136)(137), proteome (55,125,127,(138)(139)(140)(141)(142)(143)(144)(145)(146)(147)(148), lipidome (149)(150)(151)(152)(153)(154)(155), glucome (156,157), metabolome (6,55,128,(158)(159)(160)(161)(162)(163), and cytome (164)(165)(166)(167)(168)(169). Major trust of the new biology and medicine has been to gather the information derived from these platforms to optimize diagnosis, prognosi...…”

Section: Introductionmentioning

confidence: 99%

The maternal plasma proteome changes as a function of gestational age in normal pregnancy: a longitudinal study

Romero

Erez

Maymon

et al. 2017

American Journal of Obstetrics and Gynecology

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Objective Pregnancy is accompanied by dramatic physiologic changes in maternal plasma proteins. Characterization of the maternal plasma proteome in normal pregnancy is an essential step for understanding changes to predict pregnancy outcome. The objective of this study was to describe maternal plasma proteins that change in abundance with advancing gestational age, and determine biological processes that are perturbed in normal pregnancy. Materials and methods A longitudinal study included 43 normal pregnancies that had a term delivery of an infant who was appropriate for gestational age (AGA) without maternal or neonatal complications. For each pregnancy, 3 to 6 maternal plasma samples (median=5,) were profiled to measure the abundance of 1,125 proteins using multiplex assays. Linear mixed effects models with polynomial splines were used to model protein abundance as a function of gestational age, and significance of the association was inferred via likelihood ratio tests. Proteins considered to be significantly changed were defined as having: 1) more than 1.5 fold change between 8 and 40 weeks of gestation; and 2) a false discovery rate (FDR) adjusted p-value <0.1. Gene ontology enrichment analysis was employed to identify biological processes over-represented among the proteins that changed with advancing gestation. Results 1) Ten percent (112/1,125) of the profiled proteins changed in abundance as a function of gestational age; 2) of the 1,125 proteins analyzed Glypican-3, sialic acid-binding immunoglobulin-type lectins (Siglec)-6, placental growth factor (PlGF), C-C motif (CCL)-28, carbonic anhydrase 6, Prolactin (PRL), interleukin-1 receptor 4 (IL-1 R4), dual specificity mitogen-activated protein kinase 4 (MP2K4) and pregnancy-associated plasma protein-A (PAPP-A) had more than 5 fold change in abundance across gestation. These 9 proteins are known to be involved in a wide range of both physiologic and pathologic processes, such as growth regulation, embryogenesis, angiogenesis immunoregulation, inflammation etc.; and 3) biological processes associated with protein changes in normal pregnancy included defense response, defense response to bacteria, proteolysis and leukocyte migration (FDR=10%). Conclusions The plasma proteome of normal pregnancy demonstrates dramatic changes in both magnitude of changes and the fraction of the proteins involved. Such information is important to understand the physiology of pregnancy, development of biomarkers to differentiate normal vs. abnormal pregnancy, and determine the response to interventions.

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Section: Introductionmentioning

confidence: 99%

The maternal plasma proteome changes as a function of gestational age in normal pregnancy: a longitudinal study

Romero

Erez

Maymon

et al. 2017

American Journal of Obstetrics and Gynecology

View full text Add to dashboard Cite

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“…As a result, the alterations in metabolic levels in biofluids indicate the important information in a disease‐specific manner for use as diagnostic markers or therapeutic targets . The above fact has made metabolomics a useful tool in the identification of disease biomarkers, revelation of pathogenesis, and the treatment of disease . So far, little is known about the pathogenesis of CML and the impact of TKI treatment on the metabolome of CML patients.…”

Section: Introductionmentioning

confidence: 99%

Monitoring tyrosine kinase inhibitor therapeutic responses with a panel of metabolic biomarkers in chronic myeloid leukemia patients

et al. 2018

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The aim of this study is to investigate the potential biomarkers associated with chronic myeloid leukemia (CML), reveal the metabolite changes related to the continuous phases of tyrosine kinase inhibitors (TKIs), and find the potential biomarkers associated with treatment effects. Fifty‐two patients with CML and 26 matched healthy people were enrolled as the discovery set. Another 194 randomly selected CML patients treated with TKI were chosen as the external validation set. Plasma samples from the patients and controls were profiled using the gas chromatography‐mass spectrometry‐based metabonomic approach. Multivariate and univariate statistical analyses were combined to select the differential metabolic features. The gas chromatography‐mass spectrometry‐based metabolomics showed a clear clustering and separation of metabolic patterns from healthy controls and pre‐ and post‐TKI treatment CML patients in the discovery set. We identified 9 metabolites that differentiated CML patients from healthy controls, including lactic acid, isoleucine, glycerol, glycine, myristic acid, d‐sorbitol, d‐galactose, d‐glucose, and myo‐inositol. Among the 9 markers, glycerol and myristic acid had the most significant association with TKI treatment effects in both discovery and external validation sets. In the receiver operating characteristic analysis, the combination of glycerol and myristic acid showed a better discrimination performance compared to a single biomarker. The results indicated that metabolic profiling has the potential for diagnosis of CML and the panel of biomarkers including myristic acid and glycerol could be useful in monitoring TKI therapeutic responses.

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“…Infection/inflammation was a significant risk factor for preterm delivery. Ghartey et al . identified differences in the vaginal metabolome in patients with premature rupture of the membranes who subsequently delivered preterm.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence and amniotic fluid multiomics: prediction of perinatal outcome in asymptomatic women with short cervix

Bahado‐Singh¹,

Sonek

McKenna

et al. 2019

Ultrasound in Obstet & Gyne

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Objective To evaluate the application of artificial intelligence (AI), i.e. deep learning and other machine‐learning techniques, to amniotic fluid (AF) metabolomics and proteomics, alone and in combination with sonographic, clinical and demographic factors, in the prediction of perinatal outcome in asymptomatic pregnant women with short cervical length (CL). Methods AF samples, which had been obtained in the second trimester from asymptomatic women with short CL (< 15 mm) identified on transvaginal ultrasound, were analyzed. CL, funneling and the presence of AF ‘sludge’ were assessed in all cases close to the time of amniocentesis. A combination of liquid chromatography coupled with mass spectrometry and proton nuclear magnetic resonance spectroscopy‐based metabolomics, as well as targeted proteomics analysis, including chemokines, cytokines and growth factors, was performed on the AF samples. To determine the robustness of the markers, we used six different machine‐learning techniques, including deep learning, to predict preterm delivery < 34 weeks, latency period prior to delivery < 28 days after amniocentesis and requirement for admission to a neonatal intensive care unit (NICU). Omics biomarkers were evaluated alone and in combination with standard sonographic, clinical and demographic factors to predict outcome. Predictive accuracy was assessed using the area under the receiver–operating characteristics curve (AUC) with 95% CI, sensitivity and specificity. Results Of the 32 patients included in the study, complete omics, demographic and clinical data and outcome information were available for 26. Of these, 11 (42.3%) patients delivered ≥ 34 weeks, while 15 (57.7%) delivered < 34 weeks. There was no statistically significant difference in CL between these two groups (mean ± SD, 11.2 ± 4.4 mm vs 8.9 ± 5.3 mm, P = 0.31). Using combined omics, demographic and clinical data, deep learning displayed good to excellent performance, with an AUC (95% CI) of 0.890 (0.810–0.970) for delivery < 34 weeks' gestation, 0.890 (0.790–0.990) for delivery < 28 days post‐amniocentesis and 0.792 (0.689–0.894) for NICU admission. These values were higher overall than for the other five machine‐learning methods, although each individual machine‐learning technique yielded statistically significant prediction of the different perinatal outcomes. Conclusions This is the first study to report use of AI with AF proteomics and metabolomics and ultrasound assessment in pregnancy. Machine learning, particularly deep learning, achieved good to excellent prediction of perinatal outcome in asymptomatic pregnant women with short CL in the second trimester. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.

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Women with preterm birth have a distinct cervicovaginal metabolome

Cited by 61 publications

References 42 publications

The maternal plasma proteome changes as a function of gestational age in normal pregnancy: a longitudinal study

The maternal plasma proteome changes as a function of gestational age in normal pregnancy: a longitudinal study

Monitoring tyrosine kinase inhibitor therapeutic responses with a panel of metabolic biomarkers in chronic myeloid leukemia patients

Artificial intelligence and amniotic fluid multiomics: prediction of perinatal outcome in asymptomatic women with short cervix

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