Sonja Suvakov1, Emma Bonner2, Valentina Nikolic3, Djurdja Jerotic4, Tatjana P Simic5, Vesna D Garovic6, Guillermo Lopez-Campos2, Lana McClements7. 1. Department of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA; Institute of Medical and Clinical Biochemistry, Faculty of Medicine, University of Belgrade, Belgrade, Serbia. 2. The Wellcome-Wolfson Institute for Experimental Medicine, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Northern Ireland, UK. 3. Department of Pharmacology and Toxicology, Medical Faculty, University of Nis, Nis, Serbia. 4. Institute of Medical and Clinical Biochemistry, Faculty of Medicine, University of Belgrade, Belgrade, Serbia. 5. Institute of Medical and Clinical Biochemistry, Faculty of Medicine, University of Belgrade, Belgrade, Serbia; Serbian Academy of Sciences and Arts, Belgrade, Serbia. 6. Department of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA. 7. The Wellcome-Wolfson Institute for Experimental Medicine, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Northern Ireland, UK; School of Life Sciences, Faculty of Science, University of Technology Sydney, NSW, Australia. Electronic address: lana.mcclements@uts.edu.au.
Abstract
OBJECTIVES: Preeclampsia is a cardiovascular pregnancy complication that occurs in 5-10% of pregnancies and it can lead to a number of pregnancy complications including maternal and foetal death. Long-term, preeclampsia is associated with up to 8-fold increased risk of cardiovascular disease (CVD) for both mothers and their offspring. The lack of mechanistic data in relation to the causes or consequences of preeclampsia has prevented the development of effective therapeutic and monitoring strategies. STUDY DESIGN: This study investigates common underlying mechanisms of preeclampsia and CVD, specifically hypertension and heart failure with preserved ejection fraction (HFpEF), using "in silico" approach of publicly available datasets. Integrated techniques were designed to mine data repositories and identify relevant biomarkers associated with these three conditions. MAIN OUTCOMES MEASURES: The knowledge base tools were employed that enabled the analysis of these biomarkers to discover potential molecular and biological links between these three conditions. RESULTS: Our bioinformatics "in silico" analyses of the publically available datasets identified 76 common biomarkers between preeclampsia, hypertension and HFpEF. These biomarkers were representative of 29 pathways commonly enriched across the three conditions which were largely related to inflammation, metabolism, angiogenesis, remodelling, haemostasis, apoptosis and the renin-angiotensin-aldosterone (RAAS) system. CONCLUSIONS: This bioinformatics approach uses the wealth of scientific data available in public repositories to gain a deeper understanding of the overlapping pathogenic mechanisms of associated diseases, which could be explored as biomarkers or targets to prevent long-term cardiovascular complications such as hypertension and HFpEF following preeclampsia.
OBJECTIVES: Preeclampsia is a cardiovascular pregnancy complication that occurs in 5-10% of pregnancies and it can lead to a number of pregnancy complications including maternal and foetal death. Long-term, preeclampsia is associated with up to 8-fold increased risk of cardiovascular disease (CVD) for both mothers and their offspring. The lack of mechanistic data in relation to the causes or consequences of preeclampsia has prevented the development of effective therapeutic and monitoring strategies. STUDY DESIGN: This study investigates common underlying mechanisms of preeclampsia and CVD, specifically hypertension and heart failure with preserved ejection fraction (HFpEF), using "in silico" approach of publicly available datasets. Integrated techniques were designed to mine data repositories and identify relevant biomarkers associated with these three conditions. MAIN OUTCOMES MEASURES: The knowledge base tools were employed that enabled the analysis of these biomarkers to discover potential molecular and biological links between these three conditions. RESULTS: Our bioinformatics "in silico" analyses of the publically available datasets identified 76 common biomarkers between preeclampsia, hypertension and HFpEF. These biomarkers were representative of 29 pathways commonly enriched across the three conditions which were largely related to inflammation, metabolism, angiogenesis, remodelling, haemostasis, apoptosis and the renin-angiotensin-aldosterone (RAAS) system. CONCLUSIONS: This bioinformatics approach uses the wealth of scientific data available in public repositories to gain a deeper understanding of the overlapping pathogenic mechanisms of associated diseases, which could be explored as biomarkers or targets to prevent long-term cardiovascular complications such as hypertension and HFpEF following preeclampsia.
Authors: Ingrid Aneman; Dillan Pienaar; Sonja Suvakov; Tatjana P Simic; Vesna D Garovic; Lana McClements Journal: Front Immunol Date: 2020-08-18 Impact factor: 7.561