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Qiu C, Chen J, Wu W, Liao B, Zheng X, Li Y, Huang J, Shi J, Hao Z. Genome-Wide Analysis and Abiotic Stress-Responsive Patterns of COBRA-like Gene Family in Liriodendron chinense. Plants (Basel) 2023; 12:1616. [PMID: 37111840 PMCID: PMC10143436 DOI: 10.3390/plants12081616] [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] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 06/19/2023]
Abstract
The COBRA gene encodes a plant-specific glycosylphosphatidylinositol (GPI)-anchored protein (GAP), which plays an important role in cell wall cellulose deposition. In this study, a total of 7 COBRA-like (COBL) genes were identified in the genome of the rare and endangered woody plant Liriodendron chinense (L. chinense). Phylogenetic analysis showed that these LcCOBL genes can be divided into two subfamilies, i.e., SF I and II. In the conserved motif analysis of two subfamilies, SF I contained 10 predicted motifs, while SF II contained 4-6 motifs. The tissue-specific expression patterns showed that LcCOBL5 was highly expressed in the phloem and xylem, indicating its potential role in cellulose biosynthesis. In addition, the cis-element analysis and abiotic stress transcriptomes showed that three LcCOBLs, LcCOBL3, LcCOBL4 and LcCOBL5, transcriptionally responded to abiotic stresses, including cold, drought and heat stress. In particular, the quantitative reverse-transcription PCR (qRT-PCR) analysis further confirmed that the LcCOBL3 gene was significantly upregulated in response to cold stress and peaked at 24-48 h, hinting at its potential role in the mechanism of cold resistance in L. chinense. Moreover, GFP-fused LcCOBL2, LcCOBL4 and LcCOBL5 were found to be localized in the cytomembrane. In summary, we expect these results to be beneficial for research on both the functions of LcCOBL genes and resistance breeding in L. chinense.
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Affiliation(s)
- Chen Qiu
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
| | - Jinhui Chen
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
| | - Weihuang Wu
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
| | - Bojun Liao
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
| | - Xueyan Zheng
- National Germplasm Bank of Chinese Fir at Fujian Yangkou Forest Farm, Nanping 353211, China
| | - Yong Li
- National Germplasm Bank of Chinese Fir at Fujian Yangkou Forest Farm, Nanping 353211, China
| | - Jing Huang
- Jinling Institute of Technology, Nanjing 211169, China
| | - Jisen Shi
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
- Key Laboratory of Forest Genetics and Biotechnology of Ministry of Education, Nanjing Forestry University, Nanjing 210037, China
| | - Zhaodong Hao
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
- Key Laboratory of Forest Genetics and Biotechnology of Ministry of Education, Nanjing Forestry University, Nanjing 210037, China
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Abdul Majeed S, Dunzendorfer H, Weiner J, Heiker JT, Kiess W, Körner A, Landgraf K. COBL, MKX and MYOC Are Potential Regulators of Brown Adipose Tissue Development Associated with Obesity-Related Metabolic Dysfunction in Children. Int J Mol Sci 2023; 24. [PMID: 36834493 DOI: 10.3390/ijms24043085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/27/2023] [Accepted: 02/01/2023] [Indexed: 02/08/2023] Open
Abstract
Obesity is already accompanied by adipose tissue (AT) dysfunction and metabolic disease in children and increases the risk of premature death. Due to its energy-dissipating function, brown AT (BAT) has been discussed as being protective against obesity and related metabolic dysfunction. To analyze the molecular processes associated with BAT development, we investigated genome-wide expression profiles in brown and white subcutaneous and perirenal AT samples of children. We identified 39 upregulated and 26 downregulated genes in uncoupling protein 1 (UCP1)-positive compared to UCP1-negative AT samples. We prioritized for genes that had not been characterized regarding a role in BAT biology before and selected cordon-bleu WH2 repeat protein (COBL), mohawk homeobox (MKX) and myocilin (MYOC) for further functional characterization. The siRNA-mediated knockdown of Cobl and Mkx during brown adipocyte differentiation in vitro resulted in decreased Ucp1 expression, while the inhibition of Myoc led to increased Ucp1 expression. Furthermore, COBL, MKX and MYOC expression in the subcutaneous AT of children is related to obesity and parameters of AT dysfunction and metabolic disease, such as adipocyte size, leptin levels and HOMA-IR. In conclusion, we identify COBL, MKX and MYOC as potential regulators of BAT development and show an association of these genes with early metabolic dysfunction in children.
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Lopes BA, Meyer C, Barbosa TC, Zur Stadt U, Horstmann M, Venn NC, Heatley S, White DL, Sutton R, Pombo-de-Oliveira MS, Marschalek R, Emerenciano M. COBL is a novel hotspot for IKZF1 deletions in childhood acute lymphoblastic leukemia. Oncotarget 2018; 7:53064-53073. [PMID: 27419633 PMCID: PMC5288169 DOI: 10.18632/oncotarget.10590] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2016] [Accepted: 06/30/2016] [Indexed: 12/11/2022] Open
Abstract
IKZF1 deletion (ΔIKZF1) is an important predictor of relapse in childhood B-cell precursor acute lymphoblastic leukemia. Because of its clinical importance, we previously mapped breakpoints of intragenic deletions and developed a multiplex PCR assay to detect recurrent intragenic ΔIKZF1. Since the multiplex PCR was not able to detect complete deletions (IKZF1 Δ1-8), which account for ~30% of all ΔIKZF1, we aimed at investigating the genomic scenery of IKZF1 Δ1-8. Six samples of cases with IKZF1 Δ1-8 were analyzed by microarray assay, which identified monosomy 7, isochromosome 7q, and large interstitial deletions presenting breakpoints within COBL gene. Then, we established a multiplex ligation-probe amplification (MLPA) assay and screened copy number alterations within chromosome 7 in 43 diagnostic samples with IKZF1 Δ1-8. Our results revealed that monosomy and large interstitial deletions within chromosome 7 are the main causes of IKZF1 Δ1-8. Detailed analysis using long distance inverse PCR showed that six patients (16%) had large interstitial deletions starting within intronic regions of COBL at diagnosis, which is ~611 Kb downstream of IKZF1, suggesting that COBL is a hotspot for ΔIKZF1. We also investigated a series of 25 intragenic deletions (Δ2–8, Δ3–8 or Δ4–8) and 24 relapsed samples, and found one IKZF1-COBL tail-to-tail fusion, thus supporting that COBL is a novel hotspot for ΔIKZF1. Finally, using RIC score methodology, we show that breakpoint sequences of IKZF1 Δ1-8 are not analog to RAG-recognition sites, suggesting a different mechanism of error promotion than that suggested for intragenic ΔIKZF1.
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Affiliation(s)
- Bruno Almeida Lopes
- Pediatric Hematology-Oncology Program, Research Center, Instituto Nacional de Câncer, Rio de Janeiro, RJ, Brazil
| | - Claus Meyer
- Diagnostic Center of Acute Leukemia/Institute of Pharmaceutical Biology/ZAFES, Goethe-University of Frankfurt, Biocenter, Germany
| | - Thayana Conceição Barbosa
- Pediatric Hematology-Oncology Program, Research Center, Instituto Nacional de Câncer, Rio de Janeiro, RJ, Brazil
| | - Udo Zur Stadt
- Center for Diagnostics, University Medical Center Hamburg Eppendorf, Hamburg, Germany
| | - Martin Horstmann
- Center for Diagnostics, University Medical Center Hamburg Eppendorf, Hamburg, Germany.,Research Institute Children's Cancer Center, Hamburg, Germany.,Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Nicola C Venn
- Children's Cancer Institute, Lowy Cancer Research Centre UNSW, Sydney, New South Wales, Australia
| | - Susan Heatley
- South Australian Health and Medical Research Institute (SAHMRI), Adelaide, South Australia, Australia.,Discipline of Medicine, University of Adelaide, Adelaide, South Australia, Australia
| | - Deborah L White
- South Australian Health and Medical Research Institute (SAHMRI), Adelaide, South Australia, Australia.,Discipline of Medicine, University of Adelaide, Adelaide, South Australia, Australia
| | - Rosemary Sutton
- Children's Cancer Institute, Lowy Cancer Research Centre UNSW, Sydney, New South Wales, Australia
| | - Maria S Pombo-de-Oliveira
- Pediatric Hematology-Oncology Program, Research Center, Instituto Nacional de Câncer, Rio de Janeiro, RJ, Brazil
| | - Rolf Marschalek
- Diagnostic Center of Acute Leukemia/Institute of Pharmaceutical Biology/ZAFES, Goethe-University of Frankfurt, Biocenter, Germany
| | - Mariana Emerenciano
- Pediatric Hematology-Oncology Program, Research Center, Instituto Nacional de Câncer, Rio de Janeiro, RJ, Brazil
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Mez J, Chung J, Jun G, Kriegel J, Bourlas AP, Sherva R, Logue MW, Barnes LL, Bennett DA, Buxbaum JD, Byrd GS, Crane PK, Ertekin-Taner N, Evans D, Fallin MD, Foroud T, Goate A, Graff-Radford NR, Hall KS, Kamboh MI, Kukull WA, Larson EB, Manly JJ, Haines JL, Mayeux R, Pericak-Vance MA, Schellenberg GD, Lunetta KL, Farrer LA. Two novel loci, COBL and SLC10A2, for Alzheimer's disease in African Americans. Alzheimers Dement 2017; 13:119-129. [PMID: 27770636 PMCID: PMC5318231 DOI: 10.1016/j.jalz.2016.09.002] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Revised: 08/24/2016] [Accepted: 09/09/2016] [Indexed: 11/28/2022]
Abstract
INTRODUCTION African Americans' (AAs) late-onset Alzheimer's disease (LOAD) genetic risk profile is incompletely understood. Including clinical covariates in genetic analyses using informed conditioning might improve study power. METHODS We conducted a genome-wide association study (GWAS) in AAs employing informed conditioning in 1825 LOAD cases and 3784 cognitively normal controls. We derived a posterior liability conditioned on age, sex, diabetes status, current smoking status, educational attainment, and affection status, with parameters informed by external prevalence information. We assessed association between the posterior liability and a genome-wide set of single-nucleotide polymorphisms (SNPs), controlling for APOE and ABCA7, identified previously in a LOAD GWAS of AAs. RESULTS Two SNPs at novel loci, rs112404845 (P = 3.8 × 10-8), upstream of COBL, and rs16961023 (P = 4.6 × 10-8), downstream of SLC10A2, obtained genome-wide significant evidence of association with the posterior liability. DISCUSSION An informed conditioning approach can detect LOAD genetic associations in AAs not identified by traditional GWAS.
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Affiliation(s)
- Jesse Mez
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA; Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, MA, USA.
| | - Jaeyoon Chung
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA
| | - Gyungah Jun
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA; Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA; Department of Ophthalmology, Boston University School of Medicine, Boston, MA, USA
| | - Joshua Kriegel
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA; Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, MA, USA
| | - Alexandra P Bourlas
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA; Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, MA, USA
| | - Richard Sherva
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA
| | - Mark W Logue
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA; Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Lisa L Barnes
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - David A Bennett
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Joseph D Buxbaum
- Departments of Neuroscience, Mount Sinai School of Medicine, New York, NY, USA; Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY, USA
| | - Goldie S Byrd
- Department of Biology, North Carolina A & T State University, Greensboro, NC, USA
| | - Paul K Crane
- Department of Medicine, University of Washington, Seattle, WA, USA
| | | | - Denis Evans
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - M Daniele Fallin
- Department of Mental Health, Johns Hopkins School of Public Health, Baltimore, MD, USA; Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD, USA; Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Alison Goate
- Departments of Neuroscience, Mount Sinai School of Medicine, New York, NY, USA; Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY, USA
| | | | - Kathleen S Hall
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
| | - M Ilyas Kamboh
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Walter A Kukull
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Eric B Larson
- Group Health, Group Health Research Institute, Seattle, WA, USA
| | - Jennifer J Manly
- Department of Neurology and the Taub Institute, Columbia University, New York, NY, USA
| | - Jonathan L Haines
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA
| | - Richard Mayeux
- Department of Neurology and the Taub Institute, Columbia University, New York, NY, USA
| | | | - Gerard D Schellenberg
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn L Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Lindsay A Farrer
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA; Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, MA, USA; Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA; Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA; Department of Ophthalmology, Boston University School of Medicine, Boston, MA, USA; Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
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Roberts JL, Hovanes K, Dasouki M, Manzardo AM, Butler MG. Chromosomal microarray analysis of consecutive individuals with autism spectrum disorders or learning disability presenting for genetic services. Gene 2014; 535:70-8. [PMID: 24188901 PMCID: PMC4423794 DOI: 10.1016/j.gene.2013.10.020] [Citation(s) in RCA: 76] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2013] [Revised: 09/26/2013] [Accepted: 10/10/2013] [Indexed: 01/15/2023]
Abstract
Chromosomal microarray analysis is now commonly used in clinical practice to identify copy number variants (CNVs) in the human genome. We report our experience with the use of the 105 K and 180K oligonucleotide microarrays in 215 consecutive patients referred with either autism or autism spectrum disorders (ASD) or developmental delay/learning disability for genetic services at the University of Kansas Medical Center during the past 4 years (2009-2012). Of the 215 patients [140 males and 75 females (male/female ratio=1.87); 65 with ASD and 150 with learning disability], abnormal microarray results were seen in 45 individuals (21%) with a total of 49 CNVs. Of these findings, 32 represented a known diagnostic CNV contributing to the clinical presentation and 17 represented non-diagnostic CNVs (variants of unknown significance). Thirteen patients with ASD had a total of 14 CNVs, 6 CNVs recognized as diagnostic and 8 as non-diagnostic. The most common chromosome involved in the ASD group was chromosome 15. For those with a learning disability, 32 patients had a total of 35 CNVs. Twenty-six of the 35 CNVs were classified as a known diagnostic CNV, usually a deletion (n=20). Nine CNVs were classified as an unknown non-diagnostic CNV, usually a duplication (n=8). For the learning disability subgroup, chromosomes 2 and 22 were most involved. Thirteen out of 65 patients (20%) with ASD had a CNV compared with 32 out of 150 patients (21%) with a learning disability. The frequency of chromosomal microarray abnormalities compared by subject group or gender was not statistically different. A higher percentage of individuals with a learning disability had clinical findings of seizures, dysmorphic features and microcephaly, but not statistically significant. While both groups contained more males than females, a significantly higher percentage of males were present in the ASD group.
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Key Words
- A2BP1
- ACADL
- ACOXL
- ADIPOQ
- ALS2 chromosome region gene 8
- ALS2CR8
- ANKRD11
- ANOVA
- ASD
- Autism spectrum disorders (ASD)
- BAC
- BCL2-like 11 gene
- BCL2L11
- CACNA1C
- CHRNA7
- CNV
- COBL
- CT
- Chromosomal microarray analysis
- Copy number variant (CNV)
- DLG1
- DLG4
- DNA
- Developmental delay
- EEF1B2
- EEG
- F-box only 45 gene
- FAM117B
- FAT tumor suppressor 1 gene
- FAT1
- FBXO45
- FISH
- FXR2
- FZD5
- GALR1
- GATA zinc finger domain-containing protein 2B gene
- GATAD2B
- GDNF-inducible zinc finger protein 1 gene
- GZF1
- HAX1
- HCLS1-associated protein X1 gene
- HDAC
- IDH1
- IL1RAPL1
- ITPR1
- KLF7
- KNG1
- LINS
- LMNA
- Learning disability
- MAP2
- MBP
- MRPL19
- MYL1
- NADH-ubiquinone oxidoreductase Fe-S protein 1 gene
- NDUFS1
- NLGN2
- NPHP1
- NRXN1
- PAK2
- PARK2
- PMP22
- POLG
- PRPF8
- PTEN
- PTH2R
- RPE
- SACS
- SD
- SH2B adaptor protein 1 gene
- SH2B1
- SH3 and multiple ankyrin repeat domains 3 gene
- SHANK3
- SHOX
- SMARCA4
- STAG2
- SUMF1
- SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily A, member gene
- TRAPPC2
- UCSC
- USP6
- University of California, Santa Cruz
- X-linked inhibitor of apoptosis gene
- XIAP
- YWHAE
- ZNF407
- aCGH
- acyl-coA dehydrogenase, long chain gene
- acyl-coA oxidase-like gene
- adipocyte-, C1q-, and collagen domain containing gene
- analysis of variance
- ankyrin repeat domain-containing protein 11 gene
- array comparative genomic hybridization
- ataxin 2-binding protein 1 gene
- autism spectrum disorder
- bacterial artificial chromosome
- calcium channel, voltage dependent, L-type, alpha 1C subunit gene
- cholinergic receptor, neuronal nicotinic, alpha polypeptide 7 gene
- computed tomography
- copy number variant
- cordon-bleu gene
- deoxyribonucleic acid
- discs, large homolog 1 gene
- discs, large homolog 4 gene
- electroencephalogram
- eukaryotic translation elongation factor 1, beta-2 gene
- family with sequence similarity 117, member B gene
- fluorescence in situ hybridization
- fragile X mental retardation, autosomal homolog 2 gene
- frizzled 5 gene
- galanin receptor 1 gene
- histone deacetylase gene
- inositol 1,4,5-triphosphate receptor, type 1 gene
- interleukin 1 receptor accessory protein-like 1 gene
- isocitrate dehydrogenase 1 gene
- kininogen 1 gene
- kruppel-like factor 7 gene
- lamin A gene
- lines homolog gene
- microtubule-associated protein 2 gene
- mitochondrial ribosomal protein L19 gene
- myelin basic protein gene
- myosin, light peptide 1 gene
- nephrocystin 1 gene
- neurexin 1 gene
- neuroligin 2 gene
- parathyroid hormone receptor 2 gene
- parkin gene
- peripheral myelin protein 22 gene
- phosphatase and tensin homolog gene
- polymerase gamma gene
- precursor mRNA-processing factor 8 gene
- protein-activated kinase 2 gene
- ribulose 5-phosphate 3-epimerase gene
- sacsin gene
- short stature homeobox gene
- standard deviation
- stromal antigen 2 gene
- sulfatase-modifying factor 1 gene
- tracking protein particle complex, subunit 2 gene
- tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, epsilon isoform gene
- ubiquitin-specific protease 6 gene
- zinc finger protein 407 gene
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Affiliation(s)
- Jennifer L Roberts
- Departments of Psychiatry, Behavioral Sciences and Pediatrics, The University of Kansas, Medical Center, Kansas City, KS, USA
| | | | - Majed Dasouki
- Department of Neurology, The University of Kansas Medical Center, Kansas City, KS, USA; King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Ann M Manzardo
- Departments of Psychiatry, Behavioral Sciences and Pediatrics, The University of Kansas, Medical Center, Kansas City, KS, USA
| | - Merlin G Butler
- Departments of Psychiatry, Behavioral Sciences and Pediatrics, The University of Kansas, Medical Center, Kansas City, KS, USA.
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