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Ma C, Xie L. Prognostic model development and clinical correlation of eight key genes in skin cutaneous melanoma. Heliyon 2024; 10:e33930. [PMID: 39071565 PMCID: PMC11283098 DOI: 10.1016/j.heliyon.2024.e33930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 06/29/2024] [Accepted: 06/30/2024] [Indexed: 07/30/2024] Open
Abstract
Cutaneous melanoma (SKCM) is a challenging and increasingly prevalent cancer with limited effective treatments. In our extensive study of 342 SKCM samples, we developed a prognostic model identifying eight key genes-CASPASE7CLEAVEDD198, FOXO3A, Melanoma gp100, CD171, 1433ZETA, SRC, P21, and CABL-linked to SKCM prognosis. Statistical analysis indicated significant differences in clinical outcomes between low and high-risk groups, corroborated by principal component analysis (PCA). Survival analysis and receiver operating characteristic (ROC) curve analysis confirmed the model's predictive accuracy for SKCM prognosis. Additionally, we observed notable correlations between the expression levels of genes related to prognosis and clinical characteristics. Our research offers crucial insights into SKCM prognosis, suggesting potential diagnostic markers and personalized treatment targets.
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Affiliation(s)
- Chaoqun Ma
- Chengdu Badachu Medical Beauty Hospital, 1-5 Floors, No. 688, Middle Section of Tianfu Avenue, Chengdu High Tech Zone, Pilot Free Trade Zone, Sichuan, China
| | - Ling Xie
- Dermatology Department, Chengdu Second People's Hospital, No.10 Qingyun South Street, Jinjiang Zone, Chengdu, Sichuan, 610000, China
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Youssef L, Testa L, Crovetto F, Crispi F. 10. Role of high dimensional technology in preeclampsia (omics in preeclampsia). Best Pract Res Clin Obstet Gynaecol 2024; 92:102427. [PMID: 37995432 DOI: 10.1016/j.bpobgyn.2023.102427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/05/2023] [Accepted: 08/06/2023] [Indexed: 11/25/2023]
Abstract
Preeclampsia is a pregnancy-specific disease that has no known precise cause. Integrative biology approach based on multi-omics has been applied to identify upstream pathways and better understand the pathophysiology of preeclampsia. At DNA level, genomics and epigenomics studies have revealed numerous genetic variants associated with preeclampsia, including those involved in regulating blood pressure and immune response. Transcriptomics analyses have revealed altered expression of genes in preeclampsia, particularly those related to inflammation and angiogenesis. At protein level, proteomics studies have identified potential biomarkers for preeclampsia diagnosis and prediction in addition to revealing the main pathophysiological pathways involved in this disease. At metabolite level, metabolomics has highlighted altered lipid and amino acid metabolisms in preeclampsia. Finally, microbiomics studies have identified dysbiosis in the gut and vaginal microbiota in pregnant women with preeclampsia. Overall, omics technologies have improved our understanding of the complex molecular mechanisms underlying preeclampsia. However, further research is warranted to fully integrate and translate these omics findings into clinical practice.
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Affiliation(s)
- Lina Youssef
- BCNatal | Barcelona Center for Maternal Fetal and Neonatal Medicine, Hospital Clínic and Hospital Sant Joan de Déu, IDIBAPS, University of Barcelona, Barcelona, Spain; Institut de Recerca August Pi Sunyer (IDIBAPS), Barcelona, Spain; Josep Carreras Leukaemia Research Institute, Hospital Clinic/University of Barcelona Campus, Barcelona, Spain.
| | - Lea Testa
- BCNatal | Barcelona Center for Maternal Fetal and Neonatal Medicine, Hospital Clínic and Hospital Sant Joan de Déu, IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Francesca Crovetto
- BCNatal | Barcelona Center for Maternal Fetal and Neonatal Medicine, Hospital Clínic and Hospital Sant Joan de Déu, IDIBAPS, University of Barcelona, Barcelona, Spain; Institut de Recerca Sant Joan de Deu (IRSJD), Barcelona, Spain
| | - Fatima Crispi
- BCNatal | Barcelona Center for Maternal Fetal and Neonatal Medicine, Hospital Clínic and Hospital Sant Joan de Déu, IDIBAPS, University of Barcelona, Barcelona, Spain; Institut de Recerca August Pi Sunyer (IDIBAPS), Barcelona, Spain; Centre for Biomedical Research on Rare Diseases (CIBER-ER), Madrid, Spain
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3
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Selective Serotonin Reuptake Inhibitor Use in Pregnancy and Protective Mechanisms in Preeclampsia. Reprod Sci 2023; 30:701-712. [PMID: 35984571 PMCID: PMC9944568 DOI: 10.1007/s43032-022-01065-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 08/11/2022] [Indexed: 01/25/2023]
Abstract
Depression and preeclampsia share risk factors and are bi-directionally associated with increased risk for each other. Despite epidemiological evidence linking selective serotonin reuptake inhibitors (SSRIs) in pregnancy to preeclampsia, serotonin (5-HT) and vasopressin (AVP) secretion mechanisms suggest that SSRIs may attenuate preeclampsia risk. However, there is a need to clarify the relationship between SSRIs and preeclampsia in humans to determine therapeutic potential. This retrospective cohort study included clinical data from 9558 SSRI-untreated and 9046 SSRI-treated pregnancies. In a subcohort of 233 pregnancies, early pregnancy (< 20 weeks) maternal plasma copeptin, an inert and stable AVP prosegment secreted 1:1 with AVP, was measured by enzyme-linked immunosorbent assay. Diagnoses and depression symptoms (Patient Health Questionnaire-9 [PHQ-9]) were identified via medical records review. Descriptive, univariate, and multivariate regression analyses were conducted (α = 0.05). SSRI use was associated with decreased preeclampsia after controlling for clinical confounders (depression severity, chronic hypertension, diabetes, body mass index, age) (OR = 0.9 [0.7-1.0], p = 0.05). Moderate-to-severe depression symptoms were associated with significantly higher copeptin secretion than mild-to-no depression symptoms (240 ± 29 vs. 142 ± 10 ng/mL, p < 0.001). SSRIs significantly attenuated first trimester plasma copeptin (78 ± 22 users vs. 240 ± 29 ng/ml non-users, p < 0.001). In preeclampsia, SSRI treatment was associated with significantly lower copeptin levels (657 ± 164 vs. 175 ± 134 ng/mL, p = 0.04). Interaction between SSRI treatment and preeclampsia was also significant (p = 0.04). SSRIs may modulate preeclampsia risk and mechanisms, although further studies are needed to investigate the relationships between 5-HT and AVP in depression and preeclampsia.
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Blaauwendraad SM, Wahab RJ, van Rijn BB, Koletzko B, Jaddoe VWV, Gaillard R. Associations of Early Pregnancy Metabolite Profiles with Gestational Blood Pressure Development. Metabolites 2022; 12:metabo12121169. [PMID: 36557206 PMCID: PMC9785484 DOI: 10.3390/metabo12121169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/18/2022] [Accepted: 11/22/2022] [Indexed: 11/25/2022] Open
Abstract
Blood pressure development plays a major role in both the etiology and prediction of gestational hypertensive disorders. Metabolomics might serve as a tool to identify underlying metabolic mechanisms in the etiology of hypertension in pregnancy and lead to the identification of novel metabolites useful for the prediction of gestational hypertensive disorders. In a population-based, prospective cohort study among 803 pregnant women, liquid chromatography—mass spectrometry was used to determine serum concentrations of amino-acids, non-esterified fatty acids, phospholipids and carnitines in early pregnancy. Blood pressure was measured in each trimester of pregnancy. Information on gestational hypertensive disorders was obtained from medical records. Higher individual metabolite concentrations of the diacyl-phosphatidylcholines and acyl-lysophosphatidylcholines group were associated with higher systolic blood pressure throughout pregnancy (Federal Discovery Rate (FDR)-adjusted p-values < 0.05). Higher concentrations of one non-esterified fatty acid were associated with higher diastolic blood pressure throughout pregnancy (FDR-adjusted p-value < 0.05). Using penalized regression, we identified 12 individual early-pregnancy amino-acids, non-esterified fatty acids, diacyl-phosphatidylcholines and acyl-carnitines and the glutamine/glutamic acid ratio, that were jointly associated with larger changes in systolic and diastolic blood pressure from first to third trimester. These metabolites did not improve the prediction of gestational hypertensive disorders in addition to clinical markers. In conclusion, altered early pregnancy serum metabolite profiles mainly characterized by changes in non-esterified fatty acids and phospholipids metabolites are associated with higher gestational blood pressure throughout pregnancy within the physiological ranges. These findings are important from an etiological perspective and, after further replication, might improve the early identification of women at increased risk of gestational hypertensive disorders.
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Affiliation(s)
- Sophia M. Blaauwendraad
- The Generation R Study Group, Erasmus MC, University Medical Center, 3000 CA Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center, 3000 CA Rotterdam, The Netherlands
| | - Rama J. Wahab
- The Generation R Study Group, Erasmus MC, University Medical Center, 3000 CA Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center, 3000 CA Rotterdam, The Netherlands
| | - Bas B. van Rijn
- Department of Gynecology and Obstetrics, Erasmus MC, University Medical Center, 3000 CA Rotterdam, The Netherlands
| | - Berthold Koletzko
- Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children’s Hospital, LMU—Ludwig-Maximilians Universität München, 80337 Munich, Germany
| | - Vincent W. V. Jaddoe
- The Generation R Study Group, Erasmus MC, University Medical Center, 3000 CA Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center, 3000 CA Rotterdam, The Netherlands
| | - Romy Gaillard
- The Generation R Study Group, Erasmus MC, University Medical Center, 3000 CA Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center, 3000 CA Rotterdam, The Netherlands
- Correspondence:
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Cummins TD, Korte EA, Bhayana S, Merchant ML, Barati MT, Smoyer WE, Klein JB. Advances in proteomic profiling of pediatric kidney diseases. Pediatr Nephrol 2022; 37:2255-2265. [PMID: 35220505 PMCID: PMC9398920 DOI: 10.1007/s00467-022-05497-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 02/03/2022] [Accepted: 02/04/2022] [Indexed: 01/22/2023]
Abstract
Chronic kidney disease (CKD) can progress to kidney failure and require dialysis or transplantation, while early diagnosis can alter the course of disease and lead to better outcomes in both pediatric and adult patients. Significant CKD comorbidities include the manifestation of cardiovascular disease, heart failure, coronary disease, and hypertension. The pathogenesis of chronic kidney diseases can present as subtle and especially difficult to distinguish between different glomerular pathologies. Early detection of adult and pediatric CKD and detailed mechanistic understanding of the kidney damage can be helpful in delaying or curtailing disease progression via precise intervention toward diagnosis and prognosis. Clinically, serum creatinine and albumin levels can be indicative of CKD, but often are a lagging indicator only significantly affected once kidney function has severely diminished. The evolution of proteomics and mass spectrometry technologies has begun to provide a powerful research tool in defining these mechanisms and identifying novel biomarkers of CKD. Many of the same challenges and advances in proteomics apply to adult and pediatric patient populations. Additionally, proteomic analysis of adult CKD patients can be transferred directly toward advancing our knowledge of pediatric CKD as well. In this review, we highlight applications of proteomics that have yielded such biomarkers as PLA2R, SEMA3B, and other markers of membranous nephropathy as well as KIM-1, MCP-1, and NGAL in lupus nephritis among other potential diagnostic and prognostic markers. The potential for improving the clinical toolkit toward better treatment of pediatric kidney diseases is significantly aided by current and future development of proteomic applications.
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Affiliation(s)
- Timothy D Cummins
- Division of Nephrology and Hypertension, Clinical Proteomics Center, University of Louisville School of Medicine, 570 S. Preston St, Louisville, KY, 40202, USA.
| | - Erik A Korte
- Bluewater Diagnostics, Mount Washington, KY, USA
| | - Sagar Bhayana
- Nationwide Children's Hospital, The Ohio State University, Columbus, OH, USA
| | - Michael L Merchant
- Division of Nephrology and Hypertension, Clinical Proteomics Center, University of Louisville School of Medicine, 570 S. Preston St, Louisville, KY, 40202, USA
| | - Michelle T Barati
- Division of Nephrology and Hypertension, Clinical Proteomics Center, University of Louisville School of Medicine, 570 S. Preston St, Louisville, KY, 40202, USA
| | - William E Smoyer
- Nationwide Children's Hospital, The Ohio State University, Columbus, OH, USA
| | - Jon B Klein
- Division of Nephrology and Hypertension, Clinical Proteomics Center, University of Louisville School of Medicine, 570 S. Preston St, Louisville, KY, 40202, USA
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UV-B Filter Octylmethoxycinnamate Is a Modulator of the Serotonin and Histamine Receptors in Human Umbilical Arteries. Biomedicines 2022; 10:biomedicines10051054. [PMID: 35625791 PMCID: PMC9139053 DOI: 10.3390/biomedicines10051054] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 04/29/2022] [Accepted: 05/01/2022] [Indexed: 11/17/2022] Open
Abstract
Every day, people use personal care products containing UV filters. Although their use initially showed a protective role, toxicity is a concern for human health as several UV filters are endocrine-disrupting chemicals (EDCs). Exposure to EDCs may induce cardiovascular diseases and can affect the health of sensitive people, such as pregnant women. Currently, the world’s most widely used UV-B filter is octylmethoxycinnamate (OMC), an EDC. However, the disruptive effects on pregnant women are little known. The present work proposed to understand how long-term exposure to OMC affects vascular homeostasis. Endothelium-denuded human umbilical artery (HUA) rings were incubated in an organ bath system. Long-term effects of exposure to OMC (0.001–50 μmol/L) were evaluated on the contractile responses of HUA to the application of the contractile agents, serotonin (5-HT) and histamine (Hist). To investigate in more detail the vascular mode of action of OMC, through which it impairs the vascular homeostasis of HUA, the activity and expression of different 5-HT and Hist-receptors involved in contractility processes were studied. Our findings pointed out an increase in the reactivity of HUA to 5-HT and Hist due to OMC exposure. These alterations in reactivity may be precursors of preeclampsia development and/or gestational hypertension.
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Yao M, Xiao Y, Yang Z, Ge W, Liang F, Teng H, Gu Y, Yin J. Identification of Biomarkers for Preeclampsia Based on Metabolomics. Clin Epidemiol 2022; 14:337-360. [PMID: 35342309 PMCID: PMC8943653 DOI: 10.2147/clep.s353019] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 02/25/2022] [Indexed: 01/15/2023] Open
Abstract
Background Preeclampsia (PE) is a significant cause of maternal and neonatal morbidity and mortality worldwide. However, the pathogenesis of PE is unclear and reliable early diagnostic methods are still lacking. The purpose of this review is to summarize potential metabolic biomarkers and pathways of PE, which might facilitate risk prediction and clinical diagnosis, and obtain a better understanding of specific metabolic mechanisms of PE. Methods This review included human metabolomics studies related to PE in the PubMed, Google Scholar, and Web of Science databases from January 2000 to November 2021. The reported metabolic biomarkers were systematically examined and compared. Pathway analysis was conducted through the online software MetaboAnalyst 5.0. Results Forty-one human studies were included in this systematic review. Several metabolites, such as creatinine, glycine, L-isoleucine, and glucose and biomarkers with consistent trends (decanoylcarnitine, 3-hydroxyisovaleric acid, and octenoylcarnitine), were frequently reported. In addition, eight amino acid metabolism-related, three carbohydrate metabolism-related, one translation-related and one lipid metabolism-related pathways were identified. These biomarkers and pathways, closely related to renal dysfunction, insulin resistance, lipid metabolism disorder, activated inflammation, and impaired nitric oxide production, were very likely to contribute to the progression of PE. Conclusion This study summarized several metabolites and metabolic pathways, which may be associated with PE. These high-frequency differential metabolites are promising to be biomarkers of PE for early diagnosis, and the prominent metabolic pathway may provide new insights for the understanding of the pathogenesis of PE.
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Affiliation(s)
- Mengxin Yao
- Department of Epidemiology and Health Statistics, Medical College of Soochow University, Suzhou, People’s Republic of China
| | - Yue Xiao
- Department of Epidemiology and Health Statistics, Medical College of Soochow University, Suzhou, People’s Republic of China
| | - Zhuoqiao Yang
- Department of Epidemiology and Health Statistics, Medical College of Soochow University, Suzhou, People’s Republic of China
| | - Wenxin Ge
- Department of Epidemiology and Health Statistics, Medical College of Soochow University, Suzhou, People’s Republic of China
| | - Fei Liang
- Department of Epidemiology and Health Statistics, Medical College of Soochow University, Suzhou, People’s Republic of China
| | - Haoyue Teng
- Department of Epidemiology and Health Statistics, Medical College of Soochow University, Suzhou, People’s Republic of China
| | - Yingjie Gu
- Department of Obstetrics and Gynecology, International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People’s Republic of China
| | - Jieyun Yin
- Department of Epidemiology and Health Statistics, Medical College of Soochow University, Suzhou, People’s Republic of China
- Correspondence: Jieyun Yin, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou, Jiangsu, People’s Republic of China, Tel/Fax +86 0512 6588036, Email
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Impact of combined consumption of fish oil and probiotics on the serum metabolome in pregnant women with overweight or obesity. EBioMedicine 2021; 73:103655. [PMID: 34740110 PMCID: PMC8577343 DOI: 10.1016/j.ebiom.2021.103655] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 10/04/2021] [Accepted: 10/13/2021] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND If a pregnant woman is overweight, this can evoke metabolic alterations that may have health consequences for both mother and child. METHODS Pregnant women with overweight/obesity (n = 358) received fish oil+placebo, probiotics+placebo, fish oil+probiotics or placebo+placebo from early pregnancy onwards. The serum metabolome was analysed from fasting samples with a targeted NMR-approach in early and late pregnancy. GDM was diagnosed by OGTT. FINDINGS The intervention changed the metabolic profile of the women, but the effect was influenced by their GDM status. In women without GDM, the changes in nine lipids (FDR<0.05) in the fish oil+placebo-group differed when compared to the placebo+placebo-group. The combination of fish oil and probiotics induced changes in more metabolites, 46 of the lipid metabolites differed in women without GDM when compared to placebo+placebo-group; these included reduced increases in the concentrations and lipid constituents of VLDL-particles and less pronounced alterations in the ratios of various lipids in several lipoproteins. In women with GDM, no differences were detected in the changes of any metabolites due to any of the interventions when compared to the placebo+placebo-group (FDR<0.05). INTERPRETATION Fish oil and particularly the combination of fish oil and probiotics modified serum lipids in pregnant women with overweight or obesity, while no such effects were seen with probiotics alone. The effects were most evident in the lipid contents of VLDL and LDL only in women without GDM. FUNDING State Research Funding for university-level health research in the Turku University Hospital Expert Responsibility Area, Academy of Finland, the Diabetes Research Foundation, the Juho Vainio Foundation, Janssen Research & Development, LLC.
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Mohammad S, Bhattacharjee J, Vasanthan T, Harris CS, Bainbridge SA, Adamo KB. Metabolomics to understand placental biology: Where are we now? Tissue Cell 2021; 73:101663. [PMID: 34653888 DOI: 10.1016/j.tice.2021.101663] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/30/2021] [Accepted: 10/04/2021] [Indexed: 12/16/2022]
Abstract
Metabolomics, the application of analytical chemistry methodologies to survey the chemical composition of a biological system, is used to globally profile and compare metabolites in one or more groups of samples. Given that metabolites are the terminal end-products of cellular metabolic processes, or 'phenotype' of a cell, tissue, or organism, metabolomics is valuable to the study of the maternal-fetal interface as it has the potential to reveal nuanced complexities of a biological system as well as differences over time or between individuals. The placenta acts as the primary site of maternal-fetal exchange, the success of which is paramount to growth and development of offspring during pregnancy and beyond. Although the study of metabolomics has proven moderately useful for the screening, diagnosis, and understanding of the pathophysiology of pregnancy complications, the placental metabolome in the context of a healthy pregnancy remains poorly characterized and understood. Herein, we discuss the technical aspects of metabolomics and review the current literature describing the placental metabolome in human and animal models, in the context of health and disease. Finally, we highlight areas for future opportunities in the emerging field of placental metabolomics.
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Affiliation(s)
- S Mohammad
- School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada
| | - J Bhattacharjee
- School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada
| | - T Vasanthan
- School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada
| | - C S Harris
- Department of Biology & Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, ON, Canada
| | - S A Bainbridge
- Interdisciplinary School of Health Sciences, Faculty of Health Sciences, University of Ottawa, ON, Canada; Department of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa, ON, Canada
| | - K B Adamo
- School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada.
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C1431T Variant of PPARγ Is Associated with Preeclampsia in Pregnant Women. Life (Basel) 2021; 11:life11101052. [PMID: 34685423 PMCID: PMC8540421 DOI: 10.3390/life11101052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 10/01/2021] [Accepted: 10/02/2021] [Indexed: 12/16/2022] Open
Abstract
Peroxisome proliferator-activated receptor γ (PPARγ) is essential for placental development, whose SNPs have shown increased susceptibility to pregnancy-related diseases, such as preeclampsia. Our aim was to investigate the association between preeclampsia and three PPARγ SNPs (Pro12Ala, C1431T, and C681G), which together with nine clinical factors were used to build a pragmatic model for preeclampsia prediction. Data were collected from 1648 women from the EDEN cohort, of which 35 women had preeclamptic pregnancies, and the remaining 1613 women had normal pregnancies. Univariate analysis comparing preeclamptic patients to the control resulted in the SNP C1431T being the only factor significantly associated with preeclampsia (p < 0.05), with a confidence interval of 95% and odds ratio ranging from 4.90 to 8.75. On the other hand, three methods of multivariate feature selection highlighted seven features that could be potential predictors of preeclampsia: maternal C1431T and C681G variants, obesity, body mass index, number of pregnancies, primiparity, cigarette use, and education. These seven features were further used as input into eight different machine-learning algorithms to create predictive models, whose performances were evaluated based on metrics of accuracy and the area under the receiver operating characteristic curve (AUC). The boost tree-based model performed the best, with respective accuracy and AUC values of 0.971 ± 0.002 and 0.991 ± 0.001 in the training set and 0.951 and 0.701 in the testing set. A flowchart based on the boost tree model was constructed to depict the procedure for preeclampsia prediction. This final decision tree showed that the C1431T variant of PPARγ is significantly associated with susceptibility to preeclampsia. We believe that this final decision tree could be applied in the clinical prediction of preeclampsia in the very early stages of pregnancy.
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McBride N, Yousefi P, Sovio U, Taylor K, Vafai Y, Yang T, Hou B, Suderman M, Relton C, Smith GCS, Lawlor DA. Do Mass Spectrometry-Derived Metabolomics Improve the Prediction of Pregnancy-Related Disorders? Findings from a UK Birth Cohort with Independent Validation. Metabolites 2021; 11:530. [PMID: 34436471 PMCID: PMC8399752 DOI: 10.3390/metabo11080530] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 07/23/2021] [Accepted: 07/30/2021] [Indexed: 12/01/2022] Open
Abstract
Many women who experience gestational diabetes (GDM), gestational hypertension (GHT), pre-eclampsia (PE), have a spontaneous preterm birth (sPTB) or have an offspring born small/large for gestational age (SGA/LGA) do not meet the criteria for high-risk pregnancies based upon certain maternal risk factors. Tools that better predict these outcomes are needed to tailor antenatal care to risk. Recent studies have suggested that metabolomics may improve the prediction of these pregnancy-related disorders. These have largely been based on targeted platforms or focused on a single pregnancy outcome. The aim of this study was to assess the predictive ability of an untargeted platform of over 700 metabolites to predict the above pregnancy-related disorders in two cohorts. We used data collected from women in the Born in Bradford study (BiB; two sub-samples, n = 2000 and n = 1000) and the Pregnancy Outcome Prediction study (POPs; n = 827) to train, test and validate prediction models for GDM, PE, GHT, SGA, LGA and sPTB. We compared the predictive performance of three models: (1) risk factors (maternal age, pregnancy smoking, BMI, ethnicity and parity) (2) mass spectrometry (MS)-derived metabolites (n = 718 quantified metabolites, collected at 26-28 weeks' gestation) and (3) combined risk factors and metabolites. We used BiB for the training and testing of the models and POPs for independent validation. In both cohorts, discrimination for GDM, PE, LGA and SGA improved with the addition of metabolites to the risk factor model. The models' area under the curve (AUC) were similar for both cohorts, with good discrimination for GDM (AUC (95% CI) BiB 0.76 (0.71, 0.81) and POPs 0.76 (0.72, 0.81)) and LGA (BiB 0.86 (0.80, 0.91) and POPs 0.76 (0.60, 0.92)). Discrimination was improved for the combined models (compared to the risk factors models) for PE and SGA, with modest discrimination in both studies (PE-BiB 0.68 (0.58, 0.78) and POPs 0.66 (0.60, 0.71); SGA-BiB 0.68 (0.63, 0.74) and POPs 0.64 (0.59, 0.69)). Prediction for sPTB was poor in BiB and POPs for all models. In BiB, calibration for the combined models was good for GDM, LGA and SGA. Retained predictors include 4-hydroxyglutamate for GDM, LGA and PE and glycerol for GDM and PE. MS-derived metabolomics combined with maternal risk factors improves the prediction of GDM, PE, LGA and SGA, with good discrimination for GDM and LGA. Validation across two very different cohorts supports further investigation on whether the metabolites reflect novel causal paths to GDM and LGA.
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Affiliation(s)
- Nancy McBride
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; (P.Y.); (K.T.); (M.S.); (C.R.); (D.A.L.)
- NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol BS8 2BN, UK
- Department of Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
| | - Paul Yousefi
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; (P.Y.); (K.T.); (M.S.); (C.R.); (D.A.L.)
- Department of Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
| | - Ulla Sovio
- NIHR Cambridge Biomedical Research Centre, Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge CB2 0QQ, UK; (U.S.); (G.C.S.S.)
| | - Kurt Taylor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; (P.Y.); (K.T.); (M.S.); (C.R.); (D.A.L.)
| | - Yassaman Vafai
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford BD9 6DA, UK; (Y.V.); (T.Y.); (B.H.)
| | - Tiffany Yang
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford BD9 6DA, UK; (Y.V.); (T.Y.); (B.H.)
| | - Bo Hou
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford BD9 6DA, UK; (Y.V.); (T.Y.); (B.H.)
| | - Matthew Suderman
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; (P.Y.); (K.T.); (M.S.); (C.R.); (D.A.L.)
- Department of Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
| | - Caroline Relton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; (P.Y.); (K.T.); (M.S.); (C.R.); (D.A.L.)
- Department of Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
| | - Gordon C. S. Smith
- NIHR Cambridge Biomedical Research Centre, Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge CB2 0QQ, UK; (U.S.); (G.C.S.S.)
| | - Deborah A. Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; (P.Y.); (K.T.); (M.S.); (C.R.); (D.A.L.)
- NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol BS8 2BN, UK
- Department of Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
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12
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Lorigo M, Quintaneiro C, Maia CJ, Breitenfeld L, Cairrao E. UV-B filter octylmethoxycinnamate impaired the main vasorelaxant mechanism of human umbilical artery. CHEMOSPHERE 2021; 277:130302. [PMID: 33789217 DOI: 10.1016/j.chemosphere.2021.130302] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 03/06/2021] [Accepted: 03/12/2021] [Indexed: 05/24/2023]
Abstract
Personal care products (PCPs) are a group of diverse substances widely used daily for health, beauty, and cleanliness. More than 90% of all PCPs contain the UV-B filter octylmethoxycinnamate (OMC) as a protective function, however, their safety has recently been questioned. The purpose of the present work was to understand how the long-term exposure of UV-filter OMC, used daily by pregnant women, disrupts their vascular homeostasis, altering vascular responses of proteins and channels involved in contractile processes. The long-term effects of 24 h of exposure to OMC (1, 10, and 50 μmol/L) were evaluated on contractile responses of human umbilical arteries (HUA) to serotonin and potassium chloride. Since OMC altered vascular homeostasis of arteries, its vascular mode of action was explored in more detail through the analysis of the activity of cGMP and Ca2+-channels, two pathways involved in their relaxation and contraction, respectively. Our findings showed that long-term exposure of UV-filter OMC impaired the main vasorelaxant mechanism of HUA, once OMC altered the vasorelaxant response pattern of sodium nitroprusside and nifedipine. Results also showed that long-term exposure to OMC induced a decreased vasorelaxation response on HUA due to an interference with the NO/sGC/cGMP/PKG pathway. Moreover, OMC seems to modulate the L-type Ca2+ channels, the BKCa 1.1 α-subunit channels, and the PKG. Overall, since OMC compromises the vascular homeostasis of pregnant women it can be an inductor of pregnancy hypertensive disorders.
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Affiliation(s)
- Margarida Lorigo
- CICS-UBI, Health Sciences Research Centre, University of Beira Interior, 6200-506, Covilhã, Portugal; FCS - UBI, Faculty of Health Sciences, University of Beira Interior, Covilhã, Portugal.
| | - Carla Quintaneiro
- Department of Biology & CESAM, University of Aveiro, 3810-193, Aveiro, Portugal.
| | - Cláudio J Maia
- CICS-UBI, Health Sciences Research Centre, University of Beira Interior, 6200-506, Covilhã, Portugal; FCS - UBI, Faculty of Health Sciences, University of Beira Interior, Covilhã, Portugal.
| | - Luiza Breitenfeld
- CICS-UBI, Health Sciences Research Centre, University of Beira Interior, 6200-506, Covilhã, Portugal; FCS - UBI, Faculty of Health Sciences, University of Beira Interior, Covilhã, Portugal.
| | - Elisa Cairrao
- CICS-UBI, Health Sciences Research Centre, University of Beira Interior, 6200-506, Covilhã, Portugal; FCS - UBI, Faculty of Health Sciences, University of Beira Interior, Covilhã, Portugal.
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13
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Ramirez-Hincapie S, Giri V, Keller J, Kamp H, Haake V, Richling E, van Ravenzwaay B. Influence of pregnancy and non-fasting conditions on the plasma metabolome in a rat prenatal toxicity study. Arch Toxicol 2021; 95:2941-2959. [PMID: 34327559 DOI: 10.1007/s00204-021-03105-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 06/17/2021] [Indexed: 11/25/2022]
Abstract
The current parameters for determining maternal toxicity (e.g. clinical signs, food consumption, body weight development) lack specificity and may underestimate the extent of effects of test compounds on the dams. Previous reports have highlighted the use of plasma metabolomics for an improved and mechanism-based identification of maternal toxicity. To establish metabolite profiles of healthy pregnancies and evaluate the influence of food consumption as a confounding factor, metabolite profiling of rat plasma was performed by gas- and liquid-chromatography-tandem mass spectrometry techniques. Metabolite changes in response to pregnancy, food consumption prior to blood sampling (non-fasting) as well as the interaction of both conditions were studied. In dams, both conditions, non-fasting and pregnancy, had a marked influence on the plasma metabolome and resulted in distinct individual patterns of changed metabolites. Non-fasting was characterized by increased plasma concentrations of amino acids and diet related compounds and lower levels of ketone bodies. The metabolic profile of pregnant rats was characterized by lower amino acids and glucose levels and higher concentrations of plasma fatty acids, triglycerides and hormones, capturing the normal biochemical changes undergone during pregnancy. The establishment of metabolic profiles of pregnant non-fasted rats serves as a baseline to create metabolic fingerprints for prenatal and maternal toxicity studies.
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Affiliation(s)
- S Ramirez-Hincapie
- Experimental Toxicology and Ecology, BASF SE, 67056, Ludwigshafen, Germany
| | - V Giri
- Experimental Toxicology and Ecology, BASF SE, 67056, Ludwigshafen, Germany
| | - J Keller
- Experimental Toxicology and Ecology, BASF SE, 67056, Ludwigshafen, Germany
| | - H Kamp
- Experimental Toxicology and Ecology, BASF SE, 67056, Ludwigshafen, Germany
| | - V Haake
- BASF Metabolome Solution GmbH, Berlin, Germany
| | - E Richling
- Food Chemistry and Toxicology, Department of Chemistry, University of Kaiserslautern, Kaiserslautern, Germany
| | - B van Ravenzwaay
- Experimental Toxicology and Ecology, BASF SE, 67056, Ludwigshafen, Germany.
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14
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Monni G, Atzori L, Corda V, Dessolis F, Iuculano A, Hurt KJ, Murgia F. Metabolomics in Prenatal Medicine: A Review. Front Med (Lausanne) 2021; 8:645118. [PMID: 34249959 PMCID: PMC8267865 DOI: 10.3389/fmed.2021.645118] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 05/04/2021] [Indexed: 11/13/2022] Open
Abstract
Pregnancy is a complicated and insidious state with various aspects to consider, including the well-being of the mother and child. Developing better non-invasive tests that cover a broader range of disorders with lower false-positive rates is a fundamental necessity in the prenatal medicine field, and, in this sense, the application of metabolomics could be extremely useful. Metabolomics measures and analyses the products of cellular biochemistry. As a biomarker discovery tool, the integrated holistic approach of metabolomics can yield new diagnostic or therapeutic approaches. In this review, we identify and summarize prenatal metabolomics studies and identify themes and controversies. We conducted a comprehensive search of PubMed and Google Scholar for all publications through January 2020 using combinations of the following keywords: nuclear magnetic resonance, mass spectrometry, metabolic profiling, prenatal diagnosis, pregnancy, chromosomal or aneuploidy, pre-eclampsia, fetal growth restriction, pre-term labor, and congenital defect. Metabolite detection with high throughput systems aided by advanced bioinformatics and network analysis allowed for the identification of new potential prenatal biomarkers and therapeutic targets. We took into consideration the scientific papers issued between the years 2000-2020, thus observing that the larger number of them were mainly published in the last 10 years. Initial small metabolomics studies in perinatology suggest that previously unidentified biochemical pathways and predictive biomarkers may be clinically useful. Although the scientific community is considering metabolomics with increasing attention for the study of prenatal medicine as well, more in-depth studies would be useful in order to advance toward the clinic world as the obtained results appear to be still preliminary. Employing metabolomics approaches to understand fetal and perinatal pathophysiology requires further research with larger sample sizes and rigorous testing of pilot studies using various omics and traditional hypothesis-driven experimental approaches.
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Affiliation(s)
- Giovanni Monni
- Department of Prenatal and Preimplantation Genetic Diagnosis and Fetal Therapy, Ospedale Pediatrico Microcitemico “A.Cao,”Cagliari, Italy
| | - Luigi Atzori
- Clinical Metabolomics Unit, Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy
| | - Valentina Corda
- Department of Prenatal and Preimplantation Genetic Diagnosis and Fetal Therapy, Ospedale Pediatrico Microcitemico “A.Cao,”Cagliari, Italy
| | - Francesca Dessolis
- Department of Prenatal and Preimplantation Genetic Diagnosis and Fetal Therapy, Ospedale Pediatrico Microcitemico “A.Cao,”Cagliari, Italy
| | - Ambra Iuculano
- Department of Prenatal and Preimplantation Genetic Diagnosis and Fetal Therapy, Ospedale Pediatrico Microcitemico “A.Cao,”Cagliari, Italy
| | - K. Joseph Hurt
- Divisions of Maternal Fetal Medicine and Reproductive Sciences, Department of Obstetrics and Gynecology, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Federica Murgia
- Department of Prenatal and Preimplantation Genetic Diagnosis and Fetal Therapy, Ospedale Pediatrico Microcitemico “A.Cao,”Cagliari, Italy
- Clinical Metabolomics Unit, Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy
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15
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Rohde PD, Kristensen TN, Sarup P, Muñoz J, Malmendal A. Prediction of complex phenotypes using the Drosophila melanogaster metabolome. Heredity (Edinb) 2021; 126:717-732. [PMID: 33510469 PMCID: PMC8102504 DOI: 10.1038/s41437-021-00404-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 01/04/2021] [Accepted: 01/04/2021] [Indexed: 01/30/2023] Open
Abstract
Understanding the genotype-phenotype map and how variation at different levels of biological organization is associated are central topics in modern biology. Fast developments in sequencing technologies and other molecular omic tools enable researchers to obtain detailed information on variation at DNA level and on intermediate endophenotypes, such as RNA, proteins and metabolites. This can facilitate our understanding of the link between genotypes and molecular and functional organismal phenotypes. Here, we use the Drosophila melanogaster Genetic Reference Panel and nuclear magnetic resonance (NMR) metabolomics to investigate the ability of the metabolome to predict organismal phenotypes. We performed NMR metabolomics on four replicate pools of male flies from each of 170 different isogenic lines. Our results show that metabolite profiles are variable among the investigated lines and that this variation is highly heritable. Second, we identify genes associated with metabolome variation. Third, using the metabolome gave better prediction accuracies than genomic information for four of five quantitative traits analyzed. Our comprehensive characterization of population-scale diversity of metabolomes and its genetic basis illustrates that metabolites have large potential as predictors of organismal phenotypes. This finding is of great importance, e.g., in human medicine, evolutionary biology and animal and plant breeding.
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Affiliation(s)
- Palle Duun Rohde
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark.
| | - Torsten Nygaard Kristensen
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
- Department of Animal Science, Aarhus University, Tjele, Denmark
| | - Pernille Sarup
- Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
- Nordic Seed A/S, Odder, Denmark
| | - Joaquin Muñoz
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Anders Malmendal
- Department of Science and Environment, Roskilde University, Roskilde, Denmark.
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17
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Harville EW, Li YY, Pan K, McRitchie S, Pathmasiri W, Sumner S. Untargeted analysis of first trimester serum to reveal biomarkers of pregnancy complications: a case-control discovery phase study. Sci Rep 2021; 11:3468. [PMID: 33568690 PMCID: PMC7876105 DOI: 10.1038/s41598-021-82804-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 01/15/2021] [Indexed: 11/23/2022] Open
Abstract
Understanding of causal biology and predictive biomarkers are lacking for hypertensive disorders of pregnancy (HDP) and preterm birth (PTB). First-trimester serum specimens from 51 cases of HDP, including 18 cases of pre-eclampsia (PE) and 33 cases of gestational hypertension (GH); 53 cases of PTB; and 109 controls were obtained from the Global Alliance to Prevent Prematurity and Stillbirth repository. Metabotyping was conducted using liquid chromatography high resolution mass spectroscopy and nuclear magnetic resonance spectroscopy. Multivariable logistic regression was used to identify signals that differed between groups after controlling for confounders. Signals important to predicting HDP and PTB were matched to an in-house physical standards library and public databases. Pathway analysis was conducted using GeneGo MetaCore. Over 400 signals for endogenous and exogenous metabolites that differentiated cases and controls were identified or annotated, and models that included these signals produced substantial improvements in predictive power beyond models that only included known risk factors. Perturbations of the aminoacyl-tRNA biosynthesis, L-threonine, and renal secretion of organic electrolytes pathways were associated with both HDP and PTB, while pathways related to cholesterol transport and metabolism were associated with HDP. This untargeted metabolomics analysis identified signals and common pathways associated with pregnancy complications.
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Affiliation(s)
- E W Harville
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, Epidemiology #8318, 1440 Canal St. Ste. 2001, New Orleans, LA, 70112, USA.
| | - Y-Y Li
- Department of Nutrition, Nutrition Research Institute, University of North Carolina at Chapel Hill School of Public Health, CB#74612, Chapel Hill, NC, 27599-7461, USA
| | - K Pan
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, Epidemiology #8318, 1440 Canal St. Ste. 2001, New Orleans, LA, 70112, USA
| | - S McRitchie
- Department of Nutrition, Nutrition Research Institute, University of North Carolina at Chapel Hill School of Public Health, CB#74612, Chapel Hill, NC, 27599-7461, USA
| | - W Pathmasiri
- Department of Nutrition, Nutrition Research Institute, University of North Carolina at Chapel Hill School of Public Health, CB#74612, Chapel Hill, NC, 27599-7461, USA
| | - S Sumner
- Department of Nutrition, Nutrition Research Institute, University of North Carolina at Chapel Hill School of Public Health, CB#74612, Chapel Hill, NC, 27599-7461, USA.
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18
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Kenny LC, Thomas G, Poston L, Myers JE, Simpson NAB, McCarthy FP, Brown LW, Bond AE, Tuytten R, Baker PN. Prediction of preeclampsia risk in first time pregnant women: Metabolite biomarkers for a clinical test. PLoS One 2020; 15:e0244369. [PMID: 33370367 PMCID: PMC7769282 DOI: 10.1371/journal.pone.0244369] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 12/08/2020] [Indexed: 11/19/2022] Open
Abstract
Preeclampsia remains a leading cause of maternal and perinatal morbidity and mortality. Accurate prediction of preeclampsia risk would enable more effective, risk-based prenatal care pathways. Current risk assessment algorithms depend on clinical risk factors largely unavailable for first-time pregnant women. Delivering accurate preeclampsia risk assessment to this cohort of women, therefore requires for novel biomarkers. Here, we evaluated the relevance of metabolite biomarker candidates for their selection into a prototype rapid, quantitative Liquid Chromatography-tandem Mass Spectrometry (LC-MS/MS) based clinical screening assay. First, a library of targeted LC-MS/MS assays for metabolite biomarker candidates was developed, using a medium-throughput translational metabolomics workflow, to verify biomarker potential in the Screening-for-Pregnancy-Endpoints (SCOPE, European branch) study. A variable pre-selection step was followed by the development of multivariable prediction models for pre-defined clinical use cases, i.e., prediction of preterm preeclampsia risk and of any preeclampsia risk. Within a large set of metabolite biomarker candidates, we confirmed the potential of dilinoleoyl-glycerol and heptadecanoyl-2-hydroxy-sn-glycero-3-phosphocholine to effectively complement Placental Growth Factor, an established preeclampsia biomarker, for the prediction of preeclampsia risk in first-time pregnancies without overt risk factors. These metabolites will be considered for integration in a prototype rapid, quantitative LC-MS/MS assay, and subsequent validation in an independent cohort.
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Affiliation(s)
- Louise C. Kenny
- Faculty of Health & Life Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Grégoire Thomas
- SQU4RE, Lokeren, Belgium
- Metabolomic Diagnostics, Cork, Ireland
| | - Lucilla Poston
- Department of Women and Children’s Health, King’s College London, London, United Kingdom
| | - Jenny E. Myers
- Maternal & Fetal Health Research Centre, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom
| | - Nigel A. B. Simpson
- Department of Women’s and Children’s Health, University of Leeds, Leeds, United Kingdom
| | - Fergus P. McCarthy
- Irish Centre for Fetal and Neonatal Translational Research, University College Cork, Cork, Ireland
| | | | | | | | - Philip N. Baker
- College of Life Sciences, University of Leicester, Leicester, United Kingdom
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Odenkirk MT, Stratton KG, Gritsenko MA, Bramer LM, Webb-Robertson BJM, Bloodsworth KJ, Weitz KK, Lipton AK, Monroe ME, Ash JR, Fourches D, Taylor BD, Burnum-Johnson KE, Baker ES. Unveiling molecular signatures of preeclampsia and gestational diabetes mellitus with multi-omics and innovative cheminformatics visualization tools. Mol Omics 2020; 16:521-532. [PMID: 32966491 PMCID: PMC7736332 DOI: 10.1039/d0mo00074d] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
To fully enable the development of diagnostic tools and progressive pharmaceutical drugs, it is imperative to understand the molecular changes occurring before and during disease onset and progression. Systems biology assessments utilizing multi-omic analyses (e.g. the combination of proteomics, lipidomics, genomics, etc.) have shown enormous value in determining molecules prevalent in diseases and their associated mechanisms. Herein, we utilized multi-omic evaluations, multi-dimensional analysis methods, and new cheminformatics-based visualization tools to provide an in depth understanding of the molecular changes taking place in preeclampsia (PRE) and gestational diabetes mellitus (GDM) patients. Since PRE and GDM are two prevalent pregnancy complications that result in adverse health effects for both the mother and fetus during pregnancy and later in life, a better understanding of each is essential. The multi-omic evaluations performed here provide new insight into the end-stage molecular profiles of each disease, thereby supplying information potentially crucial for earlier diagnosis and treatments.
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Affiliation(s)
- Melanie T Odenkirk
- Department of Chemistry, North Carolina State University, Raleigh, NC 27695, USA.
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20
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Yilmaz A, Ustun I, Ugur Z, Akyol S, Hu WT, Fiandaca MS, Mapstone M, Federoff H, Maddens M, Graham SF. A Community-Based Study Identifying Metabolic Biomarkers of Mild Cognitive Impairment and Alzheimer's Disease Using Artificial Intelligence and Machine Learning. J Alzheimers Dis 2020; 78:1381-1392. [PMID: 33164929 DOI: 10.3233/jad-200305] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
BACKGROUND Currently, there is no objective, clinically available tool for the accurate diagnosis of Alzheimer's disease (AD). There is a pressing need for a novel, minimally invasive, cost friendly, and easily accessible tool to diagnose AD, assess disease severity, and prognosticate course. Metabolomics is a promising tool for discovery of new, biologically, and clinically relevant biomarkers for AD detection and classification. OBJECTIVE Utilizing artificial intelligence and machine learning, we aim to assess whether a panel of metabolites as detected in plasma can be used as an objective and clinically feasible tool for the diagnosis of mild cognitive impairment (MCI) and AD. METHODS Using a community-based sample cohort acquired from different sites across the US, we adopted an approach combining Proton Nuclear Magnetic Resonance Spectroscopy (1H NMR), Liquid Chromatography coupled with Mass Spectrometry (LC-MS) and various machine learning statistical approaches to identify a biomarker panel capable of identifying those patients with AD and MCI from healthy controls. RESULTS Of the 212 measured metabolites, 5 were identified as optimal to discriminate between controls, and individuals with MCI or AD. Our models performed with AUC values in the range of 0.72-0.76, with the sensitivity and specificity values ranging from 0.75-0.85 and 0.69-0.81, respectively. Univariate and pathway analysis identified lipid metabolism as the most perturbed biochemical pathway in MCI and AD. CONCLUSION A comprehensive method of acquiring metabolomics data, coupled with machine learning techniques, has identified a strong panel of diagnostic biomarkers capable of identifying individuals with MCI and AD. Further, our data confirm what other groups have reported, that lipid metabolism is significantly perturbed in those individuals suffering with dementia. This work may provide additional insight into AD pathogenesis and encourage more in-depth analysis of the AD lipidome.
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Affiliation(s)
- Ali Yilmaz
- Department of Obstetrics and Gynecology, Department of Internal Medicine, Oakland University-William Beaumont School of Medicine, Rochester, MI, USA.,Metabolomics Division, Beaumont Research Institute, Royal Oak, MI USA
| | - Ilyas Ustun
- Wayne State University, Civil and Environmental Engineering, Detroit, MI, USA
| | - Zafer Ugur
- Metabolomics Division, Beaumont Research Institute, Royal Oak, MI USA
| | - Sumeyya Akyol
- Metabolomics Division, Beaumont Research Institute, Royal Oak, MI USA
| | - William T Hu
- Department of Neurology, Emory University, Atlanta, GA, USA
| | - Massimo S Fiandaca
- Department of Neurology, University of California Irvine, Irvine, CA, USA
| | - Mark Mapstone
- Department of Neurology, University of California Irvine, Irvine, CA, USA
| | - Howard Federoff
- Department of Neurology, University of California Irvine, Irvine, CA, USA
| | - Michael Maddens
- Department of Obstetrics and Gynecology, Department of Internal Medicine, Oakland University-William Beaumont School of Medicine, Rochester, MI, USA
| | - Stewart F Graham
- Department of Obstetrics and Gynecology, Department of Internal Medicine, Oakland University-William Beaumont School of Medicine, Rochester, MI, USA.,Metabolomics Division, Beaumont Research Institute, Royal Oak, MI USA
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21
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Thomford NE, Bope CD, Agamah FE, Dzobo K, Owusu Ateko R, Chimusa E, Mazandu GK, Ntumba SB, Dandara C, Wonkam A. Implementing Artificial Intelligence and Digital Health in Resource-Limited Settings? Top 10 Lessons We Learned in Congenital Heart Defects and Cardiology. ACTA ACUST UNITED AC 2020; 24:264-277. [DOI: 10.1089/omi.2019.0142] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Nicholas Ekow Thomford
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- School of Medical Sciences, Department of Medical Biochemistry, University of Cape Coast, Cape Coast, Ghana
| | - Christian Domilongo Bope
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- School of Medical Sciences, Department of Medical Biochemistry, University of Cape Coast, Cape Coast, Ghana
- Department of Mathematics and Computer Sciences, Faculty of Sciences, University of Kinshasa, Kinshasa, D.R. Congo
| | - Francis Edem Agamah
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Kevin Dzobo
- Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Division of Medical Biochemistry, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Richmond Owusu Ateko
- University of Ghana Medical School, Department of Chemical Pathology, University of Ghana, Accra, Ghana
| | - Emile Chimusa
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Gaston Kuzamunu Mazandu
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Simon Badibanga Ntumba
- Department of Mathematics and Computer Sciences, Faculty of Sciences, University of Kinshasa, Kinshasa, D.R. Congo
| | - Collet Dandara
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Ambroise Wonkam
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
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22
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Graham SF, Turkoglu O, Yilmaz A, Ustun I, Ugur Z, Bjorndhal T, Han B, Mandal R, Wishart D, Bahado-Singh RO. Targeted metabolomics highlights perturbed metabolism in the brain of autism spectrum disorder sufferers. Metabolomics 2020; 16:59. [PMID: 32333121 DOI: 10.1007/s11306-020-01685-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Accepted: 04/17/2020] [Indexed: 12/17/2022]
Abstract
INTRODUCTION Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders characterized by deficiencies in social interactions and communication, combined with restricted and repetitive behavioral issues. OBJECTIVES As little is known about the etiopathophysiology of ASD and early diagnosis is relatively subjective, we aim to employ a targeted, fully quantitative metabolomics approach to biochemically profile post-mortem human brain with the overall goal of identifying metabolic pathways that may have been perturbed as a result of the disease while uncovering potential central diagnostic biomarkers. METHODS Using a combination of 1H NMR and DI/LC-MS/MS we quantitatively profiled the metabolome of the posterolateral cerebellum from post-mortem human brain harvested from people who suffered with ASD (n = 11) and compared them with age-matched controls (n = 10). RESULTS We accurately identified and quantified 203 metabolites in post-mortem brain extracts and performed a metabolite set enrichment analyses identifying 3 metabolic pathways as significantly perturbed (p < 0.05). These include Pyrimidine, Ubiquinone and Vitamin K metabolism. Further, using a variety of machine-based learning algorithms, we identified a panel of central biomarkers (9-hexadecenoylcarnitine (C16:1) and the phosphatidylcholine PC ae C36:1) capable of discriminating between ASD and controls with an AUC = 0.855 with a sensitivity and specificity equal to 0.80 and 0.818, respectively. CONCLUSION For the first time, we report the use of a multi-platform metabolomics approach to biochemically profile brain from people with ASD and report several metabolic pathways which are perturbed in the diseased brain of ASD sufferers. Further, we identified a panel of biomarkers capable of distinguishing ASD from control brains. We believe that these central biomarkers may be useful for diagnosing ASD in more accessible biomatrices.
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Affiliation(s)
- Stewart F Graham
- Oakland University-William Beaumont School of Medicine, Rochester, MI, 48309, USA.
- Research Institute, Metabolomics Division, Beaumont Health, Royal Oak, MI, 48073, USA.
| | - Onur Turkoglu
- Oakland University-William Beaumont School of Medicine, Rochester, MI, 48309, USA
| | - Ali Yilmaz
- Oakland University-William Beaumont School of Medicine, Rochester, MI, 48309, USA
- Research Institute, Metabolomics Division, Beaumont Health, Royal Oak, MI, 48073, USA
| | - Ilyas Ustun
- Wayne State University, Civil and Environmental Engineering, Detroit, MI, USA
| | - Zafer Ugur
- Oakland University-William Beaumont School of Medicine, Rochester, MI, 48309, USA
- Research Institute, Metabolomics Division, Beaumont Health, Royal Oak, MI, 48073, USA
| | - Trent Bjorndhal
- Department of Biological and Computing Sciences, University of Alberta, Edmonton, AB, Canada
| | - BeomSoo Han
- Department of Biological and Computing Sciences, University of Alberta, Edmonton, AB, Canada
| | - Rupa Mandal
- Department of Biological and Computing Sciences, University of Alberta, Edmonton, AB, Canada
| | - David Wishart
- Department of Biological and Computing Sciences, University of Alberta, Edmonton, AB, Canada
| | - Ray O Bahado-Singh
- Oakland University-William Beaumont School of Medicine, Rochester, MI, 48309, USA
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23
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Abstract
Preeclampsia is a medical condition affecting 5-10% of pregnancies. It has serious effects on the health of the pregnant mother and developing fetus. While possible causes of preeclampsia are speculated, there is no consensus on its etiology. The advancement of big data and high-throughput technologies enables to study preeclampsia at the new and systematic level. In this review, we first highlight the recent progress made in the field of preeclampsia research using various omics technology platforms, including epigenetics, genome-wide association studies (GWAS), transcriptomics, proteomics and metabolomics. Next, we integrate the results in individual omic level studies, and show that despite the lack of coherent biomarkers in all omics studies, inhibin is a potential preeclamptic biomarker supported by GWAS, transcriptomics and DNA methylation evidence. Using network analysis on the biomarkers of all the literature reviewed here, we identify four striking sub-networks with clear biological functions supported by previous molecular-biology and clinical observations. In summary, omics integration approach offers the promise to understand molecular mechanisms in preeclampsia.
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Taylor K, Ferreira DLS, West J, Yang T, Caputo M, Lawlor DA. Differences in Pregnancy Metabolic Profiles and Their Determinants between White European and South Asian Women: Findings from the Born in Bradford Cohort. Metabolites 2019; 9:metabo9090190. [PMID: 31540515 PMCID: PMC6780545 DOI: 10.3390/metabo9090190] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 09/16/2019] [Accepted: 09/17/2019] [Indexed: 12/12/2022] Open
Abstract
There is widespread metabolic disruption in women upon becoming pregnant. South Asians (SA) compared to White Europeans (WE) have more fat mass and are more insulin-resistant at a given body mass index (BMI). Whether these are reflected in other gestational metabolomic differences is unclear. Our aim was to compare gestational metabolic profiles and their determinants between WE and SA women. We used data from a United Kingdom (UK) cohort to compare metabolic profiles and associations of maternal age, education, parity, height, BMI, tricep skinfold thickness, gestational diabetes (GD), pre-eclampsia, and gestational hypertension with 156 metabolic measurements in WE (n = 4072) and SA (n = 4702) women. Metabolic profiles, measured in fasting serum taken between 26–28 weeks gestation, were quantified by nuclear magnetic resonance. Distributions of most metabolic measures differed by ethnicity. WE women had higher levels of most lipoprotein subclasses, cholesterol, glycerides and phospholipids, monosaturated fatty acids, and creatinine but lower levels of glucose, linoleic acid, omega-6 and polyunsaturated fatty acids, and most amino acids. Higher BMI and having GD were associated with higher levels of several lipoprotein subclasses, triglycerides, and other metabolites, mostly with stronger associations in WEs. We have shown differences in gestational metabolic profiles between WE and SA women and demonstrated that associations of exposures with these metabolites differ by ethnicity.
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Affiliation(s)
- Kurt Taylor
- Population Health Science, Bristol Medical School, Bristol BS8 2BN, UK.
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2PS, UK.
| | - Diana L Santos Ferreira
- Population Health Science, Bristol Medical School, Bristol BS8 2BN, UK.
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2PS, UK.
| | - Jane West
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford BD9 6RJ, UK.
| | - Tiffany Yang
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford BD9 6RJ, UK.
| | - Massimo Caputo
- Translational Science, Bristol Medical School, Bristol BS2 8DZ, UK.
- Bristol NIHR Biomedical Research Center, Bristol BS1 2NT, UK.
| | - Deborah A Lawlor
- Population Health Science, Bristol Medical School, Bristol BS8 2BN, UK.
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2PS, UK.
- Bristol NIHR Biomedical Research Center, Bristol BS1 2NT, UK.
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25
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Suhail Y, Cain MP, Vanaja K, Kurywchak PA, Levchenko A, Kalluri R, Kshitiz. Systems Biology of Cancer Metastasis. Cell Syst 2019; 9:109-127. [PMID: 31465728 PMCID: PMC6716621 DOI: 10.1016/j.cels.2019.07.003] [Citation(s) in RCA: 229] [Impact Index Per Article: 45.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 04/29/2019] [Accepted: 06/28/2019] [Indexed: 12/12/2022]
Abstract
Cancer metastasis is no longer viewed as a linear cascade of events but rather as a series of concurrent, partially overlapping processes, as successfully metastasizing cells assume new phenotypes while jettisoning older behaviors. The lack of a systemic understanding of this complex phenomenon has limited progress in developing treatments for metastatic disease. Because metastasis has traditionally been investigated in distinct physiological compartments, the integration of these complex and interlinked aspects remains a challenge for both systems-level experimental and computational modeling of metastasis. Here, we present some of the current perspectives on the complexity of cancer metastasis, the multiscale nature of its progression, and a systems-level view of the processes underlying the invasive spread of cancer cells. We also highlight the gaps in our current understanding of cancer metastasis as well as insights emerging from interdisciplinary systems biology approaches to understand this complex phenomenon.
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Affiliation(s)
- Yasir Suhail
- Department of Biomedical Engineering, University of Connecticut Health Center, Farmington, CT, USA; Cancer Systems Biology @ Yale (CaSB@Yale), Yale University, West Haven, CT, USA
| | - Margo P Cain
- Department of Cancer Biology, MD Anderson Cancer Center, Houston, TX, USA
| | - Kiran Vanaja
- Cancer Systems Biology @ Yale (CaSB@Yale), Yale University, West Haven, CT, USA
| | - Paul A Kurywchak
- Department of Cancer Biology, MD Anderson Cancer Center, Houston, TX, USA
| | - Andre Levchenko
- Cancer Systems Biology @ Yale (CaSB@Yale), Yale University, West Haven, CT, USA
| | - Raghu Kalluri
- Department of Cancer Biology, MD Anderson Cancer Center, Houston, TX, USA
| | - Kshitiz
- Department of Biomedical Engineering, University of Connecticut Health Center, Farmington, CT, USA; Cancer Systems Biology @ Yale (CaSB@Yale), Yale University, West Haven, CT, USA.
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26
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Nguyen TPH, Patrick CJ, Parry LJ, Familari M. Using proteomics to advance the search for potential biomarkers for preeclampsia: A systematic review and meta-analysis. PLoS One 2019; 14:e0214671. [PMID: 30951540 PMCID: PMC6450632 DOI: 10.1371/journal.pone.0214671] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Accepted: 03/18/2019] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Preeclampsia (PE) is a leading cause of maternal and perinatal morbidity and mortality worldwide. Although predictive multiparametric screening is being developed, it is not applicable to nulliparous women, and is not applied to low-risk women. As PE is considered a heterogenous disorder, it is unlikely that any single multiparametric screening protocol containing a small group of biomarkers could have the required accuracy to predict all PE subgroups. Given the etiology of PE is complex and not fully understood, it begs the question, whether the search for biomarkers based on the predominant view of impaired placentation involving factors predominately implicated in angiogenesis and inflammation, has been too limiting. Here we highlight the enormous potential of state-of-the-art, high-throughput proteomics, to provide a comprehensive and unbiased approach to biomarker identification. METHODS AND FINDINGS Our literature search identified 1336 articles; after review, 45 studies with proteomic data from PE women that were eligible for inclusion. From 710 proteins with altered abundance, we identified 13 common circulating proteins, some of which had not been previously considered as prospective biomarkers of PE. An additional search of the literature for original publications testing any of the 13 common proteins using non-proteomic techniques was also undertaken. Strikingly, 9 of these common proteins had been independently evaluated in PE studies as potential biomarkers. CONCLUSION This study highlights the potential of using high-throughput data sets, which are comprehensive and without bias, to identify a profile of proteins that may improve predictions of PE and understanding of its etiology. We bring to the attention of the medical and research communities that the strengths and advantages of using data from high-throughput studies for biomarker discovery would be increased dramatically, if first and second trimester samples were collected for proteomics, and if standardized guidelines for patient reporting and data collection were implemented.
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Affiliation(s)
| | | | - Laura Jean Parry
- School of BioSciences, University of Melbourne, Parkville, Australia
| | - Mary Familari
- School of BioSciences, University of Melbourne, Parkville, Australia
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27
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Affiliation(s)
- Sarosh Rana
- From the Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, University of Chicago, IL (S.R.)
| | - Elizabeth Lemoine
- Harvard Medical School, Boston, MA (E.L.)
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA (E.L., S.A.K.)
| | - Joey P. Granger
- Department of Physiology, University of Mississippi Medical Center, Jackson (J.P.G.)
| | - S. Ananth Karumanchi
- Departments of Medicine, Obstetrics and Gynecology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA (S.A.K.)
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA (E.L., S.A.K.)
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28
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Grabowski P, Rappsilber J. A Primer on Data Analytics in Functional Genomics: How to Move from Data to Insight? Trends Biochem Sci 2019; 44:21-32. [PMID: 30522862 PMCID: PMC6318833 DOI: 10.1016/j.tibs.2018.10.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Revised: 10/19/2018] [Accepted: 10/25/2018] [Indexed: 02/06/2023]
Abstract
High-throughput methodologies and machine learning have been central in developing systems-level perspectives in molecular biology. Unfortunately, performing such integrative analyses has traditionally been reserved for bioinformaticians. This is now changing with the appearance of resources to help bench-side biologists become skilled at computational data analysis and handling large omics data sets. Here, we show an entry route into the field of omics data analytics. We provide information about easily accessible data sources and suggest some first steps for aspiring computational data analysts. Moreover, we highlight how machine learning is transforming the field and how it can help make sense of biological data. Finally, we suggest good starting points for self-learning and hope to convince readers that computational data analysis and programming are not intimidating.
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Affiliation(s)
- Piotr Grabowski
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 13355 Berlin, Germany
| | - Juri Rappsilber
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 13355 Berlin, Germany; Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh EH9 3BF, UK.
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29
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Mayrink J, Costa ML, Cecatti JG. Preeclampsia in 2018: Revisiting Concepts, Physiopathology, and Prediction. ScientificWorldJournal 2018; 2018:6268276. [PMID: 30622442 PMCID: PMC6304478 DOI: 10.1155/2018/6268276] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Revised: 11/05/2018] [Accepted: 11/22/2018] [Indexed: 12/13/2022] Open
Abstract
Preeclampsia currently remains one of the leading causes of death and severe maternal morbidity. Although its prevalence is still underestimated in some places due to underreporting, preeclampsia is a disease that health professionals need to know how to deal with and take action. For this reason, the studies about the theme remain along with the advances in their understanding that often implies improvement and change of concepts and conducts. The complexity of its etiology is a challenge and requires further studies for its full understanding. Apparently, poor adaptation of the maternal organism to the conceptus, marked by the nonoccurrence of changes in the uterine spiral arteries, determines a series of systemic repercussions that compound the various forms of preeclampsia presentation. In recent years, the use of acetylsalicylic acid to prevent cases of early onset of the disease has been consolidated and, alongside, studies have advanced the development of accessible and effective methods of identifying women at risk of preeclampsia. The aim of this review is to discuss updates on the occurrence, concept, pathophysiology, repercussion, prevention, and prediction of preeclampsia.
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Affiliation(s)
- J. Mayrink
- Obstetric Unit, Department of Obstetrics and Gynecology, School of Medical Sciences, University of Campinas, Campinas, Brazil
| | - M. L. Costa
- Obstetric Unit, Department of Obstetrics and Gynecology, School of Medical Sciences, University of Campinas, Campinas, Brazil
| | - J. G. Cecatti
- Obstetric Unit, Department of Obstetrics and Gynecology, School of Medical Sciences, University of Campinas, Campinas, Brazil
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30
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Abstract
Preeclampsia is a multifactorial disorder defined by hypertension and increased urinary protein excretion during pregnancy. It is a significant cause of maternal and neonatal deaths worldwide. Despite various research efforts to clarify pathogenies of preeclampsia and predict this disease before beginning of symptoms, the pathogenesis of preeclampsia is unclear. Early prediction and diagnosis of women at risk of preeclampsia has not markedly improved. Therefore, the objective of this study was to perform a review on metabolomic articles assessing predictive and diagnostic biomarkers of preeclampsia. Four electronic databases including PubMed/Medline, Web of Science, Sciencedirect, and Scopus were searched to identify studies of preeclampsia in humans using metabolomics from inception to March 2018. Twenty-one articles in a variety of biological specimens and analytical platforms were included in the present review. Metabolite profiles may assist in the diagnosis of preeclampsia and discrimination of its subtypes. Lipids and their related metabolites were the most generally detected metabolites. Although metabolomic biomarkers of preeclampsia are not routinely used, this review suggests that metabolomics has the potential to be developed into a clinical tool for preeclampsia diagnosis and could contribute to an improved understanding of disease mechanisms. ABBREVIATIONS PE: preeclampsia; sFlt-1: soluble FMS-like tyrosine kinase-1; PlGF: placental growth factor; GC-MS: gas chromatography-mass spectrometry; LC-MS: liquid chromatography-mass spectrometry; NMR: nuclear magnetic resonance spectroscopy; HMDB: human metabolome database; RCT: randomized control trial; e-PE: early-onset PE; l-PE: late-onset PE; PLS-DA: partial least-squares-discriminant analysis; CRL: crown-rump length; UtPI: uterine artery Doppler pulsatility index; BMI: body mass index; MAP: mean arterial pressure; OS: oxidative stress; PAPPA: plasma protein A; FTIR: Fourier transform infrared; BCAA: branched chain amino acids; Arg: arginine; NO: nitric oxide.
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Affiliation(s)
- B Fatemeh Nobakht M Gh
- a Department of Basic Medical Sciences , Neyshabur University of Medical Sciences , Neyshabur , Iran
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31
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Li CR, Li MN, Yang H, Li P, Gao W. Rapid characterization of chemical markers for discrimination of Moutan Cortex and its processed products by direct injection-based mass spectrometry profiling and metabolomic method. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2018; 45:76-83. [PMID: 29685367 DOI: 10.1016/j.phymed.2018.04.003] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Revised: 02/28/2018] [Accepted: 04/02/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND Processing of herbal medicines is a characteristic pharmaceutical technique in Traditional Chinese Medicine, which can reduce toxicity and side effect, improve the flavor and efficacy, and even change the pharmacological action entirely. It is significant and crucial to perform a method to find chemical markers for differentiating herbal medicines in different processed degrees. PURPOSE The aim of this study was to perform a rapid and reasonable method to discriminate Moutan Cortex and its processed products, and to reveal the characteristics of chemical components depend on chemical markers. METHODS Thirty batches of Moutan Cortex and its processed products, including 11 batches of Raw Moutan Cortex (RMC), 9 batches of Moutan Cortex Tostus (MCT) and 10 batches of Moutan Cortex Carbonisatus (MCC), were directly injected in electrospray ionization quadrupole time-of-flight mass spectrometry (ESI-QTOF MS) for rapid analysis in positive and negative mode. Without chromatographic separation, each run was completed within 3 min. The raw MS data were automatically extracted by background deduction and molecular feature (MF) extraction algorithm. In negative mode, a total of 452 MFs were obtained and then pretreated by data filtration and differential analysis. After that, the filtered 85 MFs were treated by principal component analysis (PCA) to reduce the dimensions. Subsequently, a partial least squares discrimination analysis (PLS-DA) model was constructed for differentiation and chemical markers detection of Moutan Cortex in different processed degrees. The positive mode data were treated as same as those in negative mode. RESULTS RMC, MCT and MCC were successfully classified. Moreover, 14 and 3 chemical markers from negative and positive mode respectively, were screened by the combination of their relative peak areas and the parameter variable importance in the projection (VIP) values in PLS-DA model. The content changes of these chemical markers were employed in order to illustrate chemical changes of Moutan Cortex after processed. CONCLUSION These results showed that the proposed method which combined non-targeted metabolomics analysis with multivariate statistics analysis is reasonable and effective. It could not only be applied to discriminate herbal medicines and their processing products, but also to reveal the characteristics of chemical components during processing.
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Affiliation(s)
- Chao-Ran Li
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Meng-Ning Li
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Hua Yang
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Ping Li
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 210009, China.
| | - Wen Gao
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 210009, China.
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