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Ciora OA, Seegmüller T, Fischer JS, Wirth T, Häfner F, Stoecklein S, Flemmer AW, Förster K, Kindt A, Bassler D, Poets CF, Ahmidi N, Hilgendorff A. Delineating morbidity patterns in preterm infants at near-term age using a data-driven approach. BMC Pediatr 2024; 24:249. [PMID: 38605404 PMCID: PMC11010410 DOI: 10.1186/s12887-024-04702-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 03/11/2024] [Indexed: 04/13/2024] Open
Abstract
BACKGROUND Long-term survival after premature birth is significantly determined by development of morbidities, primarily affecting the cardio-respiratory or central nervous system. Existing studies are limited to pairwise morbidity associations, thereby lacking a holistic understanding of morbidity co-occurrence and respective risk profiles. METHODS Our study, for the first time, aimed at delineating and characterizing morbidity profiles at near-term age and investigated the most prevalent morbidities in preterm infants: bronchopulmonary dysplasia (BPD), pulmonary hypertension (PH), mild cardiac defects, perinatal brain pathology and retinopathy of prematurity (ROP). For analysis, we employed two independent, prospective cohorts, comprising a total of 530 very preterm infants: AIRR ("Attention to Infants at Respiratory Risks") and NEuroSIS ("Neonatal European Study of Inhaled Steroids"). Using a data-driven strategy, we successfully characterized morbidity profiles of preterm infants in a stepwise approach and (1) quantified pairwise morbidity correlations, (2) assessed the discriminatory power of BPD (complemented by imaging-based structural and functional lung phenotyping) in relation to these morbidities, (3) investigated collective co-occurrence patterns, and (4) identified infant subgroups who share similar morbidity profiles using machine learning techniques. RESULTS First, we showed that, in line with pathophysiologic understanding, BPD and ROP have the highest pairwise correlation, followed by BPD and PH as well as BPD and mild cardiac defects. Second, we revealed that BPD exhibits only limited capacity in discriminating morbidity occurrence, despite its prevalence and clinical indication as a driver of comorbidities. Further, we demonstrated that structural and functional lung phenotyping did not exhibit higher association with morbidity severity than BPD. Lastly, we identified patient clusters that share similar morbidity patterns using machine learning in AIRR (n=6 clusters) and NEuroSIS (n=8 clusters). CONCLUSIONS By capturing correlations as well as more complex morbidity relations, we provided a comprehensive characterization of morbidity profiles at discharge, linked to shared disease pathophysiology. Future studies could benefit from identifying risk profiles to thereby develop personalized monitoring strategies. TRIAL REGISTRATION AIRR: DRKS.de, DRKS00004600, 28/01/2013. NEuroSIS: ClinicalTrials.gov, NCT01035190, 18/12/2009.
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Affiliation(s)
| | - Tanja Seegmüller
- Center for Comprehensive Developmental Care (CDeC(LMU)) at the Social Pediatric Center (iSPZ Hauner), LMU University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany.
| | | | - Theresa Wirth
- Fraunhofer Institute for Cognitive Systems IKS, Munich, Germany
| | - Friederike Häfner
- Center for Comprehensive Developmental Care (CDeC(LMU)) at the Social Pediatric Center (iSPZ Hauner), LMU University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
- Institute for Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Zentrum München, Member of the German Lung Research Center (DZL), Munich, Germany
| | - Sophia Stoecklein
- Department of Radiology, LMU University Hospital, Ludwig-Maximilians-Universität München, Member of the German Lung Research Center (DZL), Munich, Germany
| | - Andreas W Flemmer
- Division of Neonatology, Department of Pediatrics, Dr. von Hauner Children's Hospital, LMU University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Kai Förster
- Center for Comprehensive Developmental Care (CDeC(LMU)) at the Social Pediatric Center (iSPZ Hauner), LMU University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
- Institute for Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Zentrum München, Member of the German Lung Research Center (DZL), Munich, Germany
- Division of Neonatology, Department of Pediatrics, Dr. von Hauner Children's Hospital, LMU University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Alida Kindt
- Metabolomics and Analytics Centre, LACDR, Leiden University, Leiden, Netherlands
| | - Dirk Bassler
- Department of Neonatology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Christian F Poets
- Department of Neonatology, University Children's Hospital Tübingen, Tübingen, Germany
| | - Narges Ahmidi
- Fraunhofer Institute for Cognitive Systems IKS, Munich, Germany
| | - Anne Hilgendorff
- Center for Comprehensive Developmental Care (CDeC(LMU)) at the Social Pediatric Center (iSPZ Hauner), LMU University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
- Institute for Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Zentrum München, Member of the German Lung Research Center (DZL), Munich, Germany
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Hoffmann M, Schwartz L, Ciora OA, Trummer N, Willruth LL, Jankowski J, Lee HK, Baumbach J, Furth PA, Hennighausen L, List M. circRNA-sponging: a pipeline for extensive analysis of circRNA expression and their role in miRNA sponging. Bioinform Adv 2023; 3:vbad093. [PMID: 37485422 PMCID: PMC10359604 DOI: 10.1093/bioadv/vbad093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 06/23/2023] [Accepted: 07/07/2023] [Indexed: 07/25/2023]
Abstract
Motivation Circular RNAs (circRNAs) are long noncoding RNAs (lncRNAs) often associated with diseases and considered potential biomarkers for diagnosis and treatment. Among other functions, circRNAs have been shown to act as microRNA (miRNA) sponges, preventing the role of miRNAs that repress their targets. However, there is no pipeline to systematically assess the sponging potential of circRNAs. Results We developed circRNA-sponging, a nextflow pipeline that (i) identifies circRNAs via backsplicing junctions detected in RNA-seq data, (ii) quantifies their expression values in relation to their linear counterparts spliced from the same gene, (iii) performs differential expression analysis, (iv) identifies and quantifies miRNA expression from miRNA-sequencing (miRNA-seq) data, (v) predicts miRNA binding sites on circRNAs, (vi) systematically investigates potential circRNA-miRNA sponging events, (vii) creates a network of competing endogenous RNAs and (viii) identifies potential circRNA biomarkers. We showed the functionality of the circRNA-sponging pipeline using RNA sequencing data from brain tissues, where we identified two distinct types of circRNAs characterized by a specific ratio of the number of the binding site to the length of the transcript. The circRNA-sponging pipeline is the first end-to-end pipeline to identify circRNAs and their sponging systematically with raw total RNA-seq and miRNA-seq files, allowing us to better indicate the functional impact of circRNAs as a routine aspect in transcriptomic research. Availability and implementation https://github.com/biomedbigdata/circRNA-sponging. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
| | | | | | - Nico Trummer
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising D-85354, Germany
| | - Lina-Liv Willruth
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising D-85354, Germany
| | - Jakub Jankowski
- Laboratory of Genetics and Physiology, National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Hye Kyung Lee
- Laboratory of Genetics and Physiology, National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Jan Baumbach
- Computational Systems Biology, University of Hamburg, Hamburg, Germany
- Computational BioMedicine Lab, University of Southern Denmark, Odense, Denmark
| | - Priscilla A Furth
- Laboratory of Genetics and Physiology, National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA
- Departments of Oncology & Medicine, Georgetown University, Washington, DC, USA
| | - Lothar Hennighausen
- Institute for Advanced Study, Technical University of Munich, Garching D-85748, Germany
- Laboratory of Genetics and Physiology, National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Markus List
- To whom correspondence should be addressed. or
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Hoffmann M, Schwartz L, Ciora OA, Trummer N, Willruth LL, Jankowski J, Lee HK, Baumbach J, Furth P, Hennighausen L, List M. circRNA-sponging: a pipeline for extensive analysis of circRNA expression and their role in miRNA sponging. bioRxiv 2023:2023.01.19.524495. [PMID: 36789427 PMCID: PMC9928029 DOI: 10.1101/2023.01.19.524495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Motivation Circular RNAs (circRNAs) are long non-coding RNAs (lncRNAs) often associated with diseases and considered potential biomarkers for diagnosis and treatment. Among other functions, circRNAs have been shown to act as microRNA (miRNA) sponges, preventing the role of miRNAs that repress their targets. However, there is no pipeline to systematically assess the sponging potential of circRNAs. Results We developed circRNA-sponging, a nextflow pipeline that (1) identifies circRNAs via back-splicing junctions detected in RNA-seq data, (2) quantifies their expression values in relation to their linear counterparts spliced from the same gene, (3) performs differential expression analysis, (4) identifies and quantifies miRNA expression from miRNA-sequencing (miRNA-seq) data, (5) predicts miRNA binding sites on circRNAs, (6) systematically investigates potential circRNA-miRNA sponging events, (7) creates a network of competing endogenous RNAs, and (8) identifies potential circRNA biomarkers. We showed the functionality of the circRNA-sponging pipeline using RNA sequencing data from brain tissues where we identified two distinct types of circRNAs characterized by a distinct ratio of the binding site length. The circRNA-sponging pipeline is the first end-to-end pipeline to identify circRNAs and their sponging systematically with raw total RNA-seq and miRNA-seq files, allowing us to better indicate the functional impact of circRNAs as a routine aspect in transcriptomic research. Availability https://github.com/biomedbigdata/circRNA-sponging. Contact markus.daniel.hoffmann@tum.de ; markus.list@tum.de. Supplementary Material Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Markus Hoffmann
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany,Institute for Advanced Study (Lichtenbergstrasse 2a, D-85748 Garching, Germany), Technical University of Munich, Germany,National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD 20892, United States of America,corresponding authors Contact:;
| | - Leon Schwartz
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Octavia-Andreea Ciora
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Nico Trummer
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Lina-Liv Willruth
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Jakub Jankowski
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD 20892, United States of America
| | - Hye Kyung Lee
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD 20892, United States of America
| | - Jan Baumbach
- Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany,Computational BioMedicine Lab, University of Southern Denmark, Odense, Denmark
| | - Priscilla Furth
- Departments of Oncology & Medicine, Georgetown University, Washington, DC, USA
| | - Lothar Hennighausen
- Institute for Advanced Study (Lichtenbergstrasse 2a, D-85748 Garching, Germany), Technical University of Munich, Germany,National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD 20892, United States of America
| | - Markus List
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany,corresponding authors Contact:;
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