Helena Persson1, Andrew T Kwon2, Jordan A Ramilowski2, Gilad Silberberg3, Cilla Söderhäll4, Christina Orsmark-Pietras1, Björn Nordlund5, Jon R Konradsen5, Michiel J L de Hoon2, Erik Melén6, Yoshihide Hayashizaki7, Gunilla Hedlin5, Juha Kere8, Carsten O Daub9. 1. Department of Biosciences and Nutrition and Center for Innovative Medicine (CIMED), Karolinska Institutet, Stockholm, Sweden. 2. Omics Science Center,§ RIKEN Yokohama Institute, Yokohama, Japan; Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Japan. 3. Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Sweden. 4. Department of Biosciences and Nutrition and Center for Innovative Medicine (CIMED), Karolinska Institutet, Stockholm, Sweden; Centre for Allergy Research, Karolinska Institutet, Stockholm, Sweden. 5. Centre for Allergy Research, Karolinska Institutet, Stockholm, Sweden; Astrid Lindgren Children's Hospital, Karolinska University Hospital, Stockholm, Sweden; Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden. 6. Centre for Allergy Research, Karolinska Institutet, Stockholm, Sweden; Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; Sachs' Children's Hospital, Stockholm, Sweden. 7. Omics Science Center,§ RIKEN Yokohama Institute, Yokohama, Japan; Preventive Medicine and Diagnosis Innovation Program, RIKEN Research Cluster for Innovation, Wako, Japan. 8. Department of Biosciences and Nutrition and Center for Innovative Medicine (CIMED), Karolinska Institutet, Stockholm, Sweden; Centre for Allergy Research, Karolinska Institutet, Stockholm, Sweden; Folkhälsan Institute of Genetics, Helsinki, Finland; Research Programs Unit, University of Helsinki, Helsinki, Finland. Electronic address: juha.kere@ki.se. 9. Department of Biosciences and Nutrition and Center for Innovative Medicine (CIMED), Karolinska Institutet, Stockholm, Sweden; Omics Science Center,§ RIKEN Yokohama Institute, Yokohama, Japan; Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Japan.
Abstract
BACKGROUND: Children with problematic severe asthma have poor disease control despite high doses of inhaled corticosteroids and additional therapy, leading to personal suffering, early deterioration of lung function, and significant consumption of health care resources. If no exacerbating factors, such as smoking or allergies, are found after extensive investigation, these children are given a diagnosis of therapy-resistant (or therapy-refractory) asthma (SA). OBJECTIVE: We sought to deepen our understanding of childhood SA by analyzing gene expression and modeling the underlying regulatory transcription factor networks in peripheral blood leukocytes. METHODS: Gene expression was analyzed by using Cap Analysis of Gene Expression in children with SA (n = 13), children with controlled persistent asthma (n = 15), and age-matched healthy control subjects (n = 9). Cap Analysis of Gene Expression sequencing detects the transcription start sites of known and novel mRNAs and noncoding RNAs. RESULTS: Sample groups could be separated by hierarchical clustering on 1305 differentially expressed transcription start sites, including 816 known genes and several novel transcripts. Ten of 13 tested novel transcripts were validated by means of RT-PCR and Sanger sequencing. Expression of RAR-related orphan receptor A (RORA), which has been linked to asthma in genome-wide association studies, was significantly upregulated in patients with SA. Gene network modeling revealed decreased glucocorticoid receptor signaling and increased activity of the mitogen-activated protein kinase and Jun kinase cascades in patients with SA. CONCLUSION: Circulating leukocytes from children with controlled asthma and those with SA have distinct gene expression profiles, demonstrating the possible development of specific molecular biomarkers and supporting the need for novel therapeutic approaches.
BACKGROUND:Children with problematic severe asthma have poor disease control despite high doses of inhaled corticosteroids and additional therapy, leading to personal suffering, early deterioration of lung function, and significant consumption of health care resources. If no exacerbating factors, such as smoking or allergies, are found after extensive investigation, these children are given a diagnosis of therapy-resistant (or therapy-refractory) asthma (SA). OBJECTIVE: We sought to deepen our understanding of childhood SA by analyzing gene expression and modeling the underlying regulatory transcription factor networks in peripheral blood leukocytes. METHODS: Gene expression was analyzed by using Cap Analysis of Gene Expression in children with SA (n = 13), children with controlled persistent asthma (n = 15), and age-matched healthy control subjects (n = 9). Cap Analysis of Gene Expression sequencing detects the transcription start sites of known and novel mRNAs and noncoding RNAs. RESULTS: Sample groups could be separated by hierarchical clustering on 1305 differentially expressed transcription start sites, including 816 known genes and several novel transcripts. Ten of 13 tested novel transcripts were validated by means of RT-PCR and Sanger sequencing. Expression of RAR-related orphan receptor A (RORA), which has been linked to asthma in genome-wide association studies, was significantly upregulated in patients with SA. Gene network modeling revealed decreased glucocorticoid receptor signaling and increased activity of the mitogen-activated protein kinase and Jun kinase cascades in patients with SA. CONCLUSION: Circulating leukocytes from children with controlled asthma and those with SA have distinct gene expression profiles, demonstrating the possible development of specific molecular biomarkers and supporting the need for novel therapeutic approaches.
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