Search for other papers by Xu-Ting Song in
Google Scholar
PubMed
Search for other papers by Jia-Nan Zhang in
Google Scholar
PubMed
Search for other papers by Duo-Wei Zhao in
Google Scholar
PubMed
Search for other papers by Yu-Fei Zhai in
Google Scholar
PubMed
Search for other papers by Qi Lu in
Google Scholar
PubMed
Search for other papers by Mei-Yu Qi in
Google Scholar
PubMed
Search for other papers by Ming-Hai Lu in
Google Scholar
PubMed
Search for other papers by Shou-Long Deng in
Google Scholar
PubMed
Search for other papers by Hong-Bing Han in
Google Scholar
PubMed
Search for other papers by Xiu-Qin Yang in
Google Scholar
PubMed
Search for other papers by Yu-Chang Yao in
Google Scholar
PubMed
bioinformatics analysis of sheep IGF1 splice variants Based on GenBank and Ensemble sequences of IGF1 splice variants in different species, a conserved region was found by aligning sequences from multiple species. Considering sequence specificity and
Search for other papers by Nathalia Liberatoscioli Menezes Andrade in
Google Scholar
PubMed
Search for other papers by Mariana Ferreira de Assis Funari in
Google Scholar
PubMed
Search for other papers by Alexsandra Christianne Malaquias in
Google Scholar
PubMed
Search for other papers by Paulo Ferrez Collett-Solberg in
Google Scholar
PubMed
Search for other papers by Nathalia L R A Gomes in
Google Scholar
PubMed
Departamento de Medicina, Faculdade de Ciencias Medicas da Santa Casa de Sao Paulo, Sao Paulo, Brasil
Search for other papers by Renata Scalco in
Google Scholar
PubMed
Search for other papers by Naiara Castelo Branco Dantas in
Google Scholar
PubMed
Search for other papers by Raissa C Rezende in
Google Scholar
PubMed
Search for other papers by Angelica M F P Tiburcio in
Google Scholar
PubMed
Search for other papers by Micheline A R Souza in
Google Scholar
PubMed
Unidade de Endocrinologia do Desenvolvimento, Laboratorio de Hormonios e Genetica Molecular (LIM42), Hospital das Clinicas da Faculdade de Medicina, Universidade de Sao Paulo (USP), Sao Paulo, Brasil
Search for other papers by Bruna L Freire in
Google Scholar
PubMed
Search for other papers by Ana C V Krepischi in
Google Scholar
PubMed
Search for other papers by Carlos Alberto Longui in
Google Scholar
PubMed
Search for other papers by Antonio Marcondes Lerario in
Google Scholar
PubMed
Search for other papers by Ivo J P Arnhold in
Google Scholar
PubMed
Unidade de Endocrinologia do Desenvolvimento, Laboratorio de Hormonios e Genetica Molecular (LIM42), Hospital das Clinicas da Faculdade de Medicina, Universidade de Sao Paulo (USP), Sao Paulo, Brasil
Search for other papers by Alexander A L Jorge in
Google Scholar
PubMed
Unidade de Endocrinologia do Desenvolvimento, Laboratorio de Hormonios e Genetica Molecular (LIM42), Hospital das Clinicas da Faculdade de Medicina, Universidade de Sao Paulo (USP), Sao Paulo, Brasil
Search for other papers by Gabriela Andrade Vasques in
Google Scholar
PubMed
SHOX defects ( 27 ). In-house bioinformatic analysis was performed as previously reported ( 12 ). The sequences were aligned to the human reference genome sequence (GRCh37/hg19). Copy number variation (CNVs) analysis was also performed for all patients
Search for other papers by Yao Su in
Google Scholar
PubMed
Search for other papers by Li Chen in
Google Scholar
PubMed
Search for other papers by Dong-Yao Zhang in
Google Scholar
PubMed
Search for other papers by Xu-Pei Gan in
Google Scholar
PubMed
Search for other papers by Yan-Nan Cao in
Google Scholar
PubMed
Search for other papers by De-Cui Cheng in
Google Scholar
PubMed
Search for other papers by Wen-Yu Liu in
Google Scholar
PubMed
Search for other papers by Fei-Fei Li in
Google Scholar
PubMed
Search for other papers by Xian-Ming Xu in
Google Scholar
PubMed
Search for other papers by Hong-Kun Wang in
Google Scholar
PubMed
.5. Collection of fecal samples and the extraction, amplification, database construction, and bioinformatics analysis of fecal DNA On the same day as the OGTT was conducted, 2–3 g (or four scoops) of fresh feces were collected with a sampler, soaked in a
Search for other papers by Aneta Gawlik in
Google Scholar
PubMed
Search for other papers by Michael Shmoish in
Google Scholar
PubMed
Search for other papers by Michaela F Hartmann in
Google Scholar
PubMed
Search for other papers by Stefan A Wudy in
Google Scholar
PubMed
Search for other papers by Zbigniew Olczak in
Google Scholar
PubMed
Search for other papers by Katarzyna Gruszczynska in
Google Scholar
PubMed
Search for other papers by Ze’ev Hochberg in
Google Scholar
PubMed
Objective
Analysis of steroids by gas chromatography-mass spectrometry (GC-MS) defines a subject’s steroidal fingerprint. Here, we compare the steroidal fingerprints of obese children with or without liver disease to identify the ‘steroid metabolomic signature’ of childhood nonalcoholic fatty liver disease.
Methods
Urinary samples of 85 children aged 8.5–18.0 years with BMI >97% were quantified for 31 steroid metabolites by GC-MS. The fingerprints of 21 children with liver disease (L1) as assessed by sonographic steatosis (L1L), elevated alanine aminotransferases (L1A) or both (L1AL), were compared to 64 children without markers of liver disease (L0). The steroidal signature of the liver disease was generated as the difference in profiles of L1 against L0 groups.
Results
L1 comparing to L0 presented higher fasting triglycerides (P = 0.004), insulin (P = 0.002), INS/GLU (P = 0.003), HOMA-IR (P = 0.002), GGTP (P = 0.006), AST/SGOT (P = 0.002), postprandial glucose (P = 0.001) and insulin (P = 0.011). L1AL showed highest level of T-cholesterol and triglycerides (P = 0.029; P = 0.044). Fasting insulin, postprandial glucose, INS/GLU and HOMA-IR were highest in L1L and L1AL (P = 0.001; P = 0.017; P = 0.001; P = 0.001). The liver disease steroidal signature was marked by lower DHEA and its metabolites, higher glucocorticoids (mostly tetrahydrocortisone) and lower mineralocorticoid metabolites than L0. L1 patients showed higher 5α-reductase and 21-hydroxylase activity (the highest in L1A and L1AL) and lower activity of 11βHSD1 than L0 (P = 0.041, P = 0.009, P = 0.019).
Conclusions
The ‘steroid metabolomic signature’ of liver disease in childhood obesity provides a new approach to the diagnosis and further understanding of its metabolic consequences. It reflects the derangements of steroid metabolism in NAFLD that includes enhanced glucocorticoids and deranged androgens and mineralocorticoids.
Search for other papers by Veronica Astro in
Google Scholar
PubMed
Search for other papers by Elisabetta Fiacco in
Google Scholar
PubMed
Search for other papers by Kelly Johanna Cardona-Londoño in
Google Scholar
PubMed
Search for other papers by Ilario De Toma in
Google Scholar
PubMed
Search for other papers by Hams Saeed Alzahrani in
Google Scholar
PubMed
Search for other papers by Jumana Alama in
Google Scholar
PubMed
Search for other papers by Amal Kokandi in
Google Scholar
PubMed
Search for other papers by Taha Abo-Almagd Abdel-Meguid Hamoda in
Google Scholar
PubMed
Center of Innovation in Personalized Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
Search for other papers by Majed Felemban in
Google Scholar
PubMed
Search for other papers by Antonio Adamo in
Google Scholar
PubMed
expanded the iPSCs. E F characterized the iPSCs, by immunofluorescence, Taqman assay, and STR analysis. E F performed Teratoma assays. V A, K J C L, and I D T performed the bioinformatic analysis. A A designed the study. V A and A A drafted the manuscript
Department of Endocrinology and Metabolic Diseases, IRCCS Istituto Auxologico Italiano, Milan, Italy
Search for other papers by Luca Persani in
Google Scholar
PubMed
Search for other papers by Martine Cools in
Google Scholar
PubMed
Search for other papers by Stamatina Ioakim in
Google Scholar
PubMed
Search for other papers by S Faisal Ahmed in
Google Scholar
PubMed
Search for other papers by Silvia Andonova in
Google Scholar
PubMed
Search for other papers by Magdalena Avbelj-Stefanija in
Google Scholar
PubMed
Search for other papers by Federico Baronio in
Google Scholar
PubMed
Search for other papers by Jerome Bouligand in
Google Scholar
PubMed
Search for other papers by Hennie T Bruggenwirth in
Google Scholar
PubMed
Search for other papers by Justin H Davies in
Google Scholar
PubMed
Search for other papers by Elfride De Baere in
Google Scholar
PubMed
Search for other papers by Iveta Dzivite-Krisane in
Google Scholar
PubMed
Search for other papers by Paula Fernandez-Alvarez in
Google Scholar
PubMed
Search for other papers by Alexander Gheldof in
Google Scholar
PubMed
Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
Search for other papers by Claudia Giavoli in
Google Scholar
PubMed
Search for other papers by Claus H Gravholt in
Google Scholar
PubMed
Search for other papers by Olaf Hiort in
Google Scholar
PubMed
Search for other papers by Paul-Martin Holterhus in
Google Scholar
PubMed
Search for other papers by Anders Juul in
Google Scholar
PubMed
Search for other papers by Csilla Krausz in
Google Scholar
PubMed
Search for other papers by Kristina Lagerstedt-Robinson in
Google Scholar
PubMed
West of Scotland Centre for Genomic Medicine, Queen Elizabeth University Hospital, Glasgow, United Kingdom
Search for other papers by Ruth McGowan in
Google Scholar
PubMed
Search for other papers by Uta Neumann in
Google Scholar
PubMed
Search for other papers by Antonio Novelli in
Google Scholar
PubMed
Search for other papers by Xavier Peyrassol in
Google Scholar
PubMed
Search for other papers by Leonidas A Phylactou in
Google Scholar
PubMed
Search for other papers by Julia Rohayem in
Google Scholar
PubMed
Search for other papers by Philippe Touraine in
Google Scholar
PubMed
Search for other papers by Dineke Westra in
Google Scholar
PubMed
Search for other papers by Valeria Vezzoli in
Google Scholar
PubMed
Search for other papers by Raffaella Rossetti in
Google Scholar
PubMed
CHH), (iii) the percentage of patients with positive NGS reports (i.e. cases with at least one rare non-synonymous variant in the candidate genes), (iv) quality criteria used for reporting, including bioinformatic support (i.e. reports including class
Search for other papers by Catherine Cardot Bauters in
Google Scholar
PubMed
Search for other papers by Emmanuelle Leteurtre in
Google Scholar
PubMed
Search for other papers by Bruno Carnaille in
Google Scholar
PubMed
Search for other papers by Christine Do Cao in
Google Scholar
PubMed
Search for other papers by Stéphanie Espiard in
Google Scholar
PubMed
Search for other papers by Malo Penven in
Google Scholar
PubMed
Search for other papers by Evelyne Destailleur in
Google Scholar
PubMed
Search for other papers by Isabelle Szuster in
Google Scholar
PubMed
Search for other papers by Tonio Lovecchio in
Google Scholar
PubMed
CHU Lille, Service de Biochimie Hormonologie, Métabolisme, Nutrition-Oncologie, Centre de Biologie Pathologie Génétique, Lille, France
Search for other papers by Julie Leclerc in
Google Scholar
PubMed
Search for other papers by Fredéric Frénois in
Google Scholar
PubMed
Search for other papers by Emmanuel Esquivel in
Google Scholar
PubMed
Search for other papers by Patricia L M Dahia in
Google Scholar
PubMed
Search for other papers by Emilie Ait-Yahya in
Google Scholar
PubMed
Search for other papers by Michel Crépin in
Google Scholar
PubMed
Search for other papers by Pascal Pigny in
Google Scholar
PubMed
of the libraries. After quantification of enriched target DNA, samples were pooled for multiplexed sequencing. NGS sequencing data were aligned to hg19 human reference and annotated using two independent bioinformatic pipelines (alignment using bwa v0
Search for other papers by June Young Choi in
Google Scholar
PubMed
Search for other papers by Jin Wook Yi in
Google Scholar
PubMed
Search for other papers by Jun Hyup Lee in
Google Scholar
PubMed
Search for other papers by Ra-Yeong Song in
Google Scholar
PubMed
Search for other papers by Hyeongwon Yu in
Google Scholar
PubMed
Search for other papers by Hyungju Kwon in
Google Scholar
PubMed
Search for other papers by Young Jun Chai in
Google Scholar
PubMed
Search for other papers by Su-jin Kim in
Google Scholar
PubMed
Search for other papers by Kyu Eun Lee in
Google Scholar
PubMed
–Meier estimator with log-rank test was used for survival analysis. Differentially expressed genes (DEG) and gene ontology (GO) tests between two VDR groups were performed using ‘EdgeR’ package, which is one of the bioinformatics tools in Bioconductor ( https
Department of General Surgery, Tangdu Hospital, Air Force Medical University, Xi’an, Shaanxi, People’s Republic of China
Search for other papers by Lijuan Yuan in
Google Scholar
PubMed
Search for other papers by Xihui Chen in
Google Scholar
PubMed
Search for other papers by Ziyu Liu in
Google Scholar
PubMed
Search for other papers by Dan Wu in
Google Scholar
PubMed
Search for other papers by Jianguo Lu in
Google Scholar
PubMed
Search for other papers by Guoqiang Bao in
Google Scholar
PubMed
Search for other papers by Sijia Zhang in
Google Scholar
PubMed
Search for other papers by Lifeng Wang in
Google Scholar
PubMed
Search for other papers by Yuanming Wu in
Google Scholar
PubMed
for short read alignment . Bioinformatics 2009 25 1966 – 1967 . ( https://doi.org/10.1093/bioinformatics/btp336 ) 19497933 10.1093/bioinformatics/btp336 20 Li H Durbin R. Fast and accurate long-read alignment with Burrows-Wheeler transform
Search for other papers by Huifei Sophia Zheng in
Google Scholar
PubMed
Search for other papers by Jeffrey G Daniel in
Google Scholar
PubMed
Search for other papers by Julia M Salamat in
Google Scholar
PubMed
Search for other papers by Laci Mackay in
Google Scholar
PubMed
Search for other papers by Chad D Foradori in
Google Scholar
PubMed
Search for other papers by Robert J Kemppainen in
Google Scholar
PubMed
Search for other papers by Satyanarayana R Pondugula in
Google Scholar
PubMed
Search for other papers by Ya-Xiong Tao in
Google Scholar
PubMed
Search for other papers by Chen-Che Jeff Huang in
Google Scholar
PubMed
is not completely elucidated at the genome-wide level. In this study, RNA sequencing (RNA-seq) and downstream bioinformatics analysis were performed to explore the early transcriptional response to Dex exposure in vivo and in vitro . This study