DOCSLIB.ORG

Supplementary Table 1: Clinical Evaluation of Patients. the Annotation of The

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Supplementary Table 1: Clinical Evaluation of Patients. the Annotation of The Supplementary Table 1: Clinical evaluation of patients. The annotation of the patients is according to Fig. 1A. Abbreviations: LVESD: left ventricular end systolic diameter, LVEDD: left ventricular end diastolic diameter (adult norm: 35-54mm), EF: ejection fraction. BSA: body surface area, NSR: normal sinus rhythm, LVH: left ventricular hypertrophy, SVT: supraventricular tachycardia. DCM patients are marked in red. LVEDD LVESD Posterior BSA patient sex age IVSEPTUM EF% ECG (mm) (mm) wall M2 51 NSR I-2 Male 63Y 30 11 10 65% 2.27 No LVH 45 NSR I-3 Female 56Y 26 11 10 60% 1.94 No LVH 50 NSR II-1 Male 36Y 33 10 9 60% 2.18 No LVH Female 49 29 NSR II-2 31Y 7 6 60% 1.88 No LVH 49 NSR II-3 Male 34Y 28 11 10 60% 2.29 No LVH NSR II-4 48 Female 33Y 27 9 8 62% 1.87 No LVH 66 60 SVT, severe III-1 Female 9Y Z-score: 7 7 28% 1.49 LVH +6.58 28 16 NSR III-2 Male 22M Z-score 6 6 67% 0.61 No LVH -1.4 NSR III-3 Female 6Y 33 18 6 5 63% 1.02 No LVH 35 Z-score 21 NSR III-4 Female 4Y 6 6 60% 0.92 +0.77 No LVH 60 SVT, 57 III-5 Male 12Y Z-score 7 7 5% 1.47 Severe +5 LVH 35 Z-score NSR III-6 Male 7Y 16 5 5 68% 1.04 +0.78 No LVH NSR III-7 Male 4M 24 12 4 4 70% 0.28 No LVH Supplementary Table 2: Number of variants in the exome sequence of patient III-5 with allele frequency of less than 1% in the public databases: gnomad browser, 1000 Genomes, ExAC and EVS and the reason for their negation as the causative mutation. Number of The cause of negation variants that were negated Common variants according to our internal laboratory 17 Exome database of the Bedouin population Common variants according to a database of healthy Saudi 18 individuals with LOF in varies genes (Alsalem et al. 2013) Low prediction for damage (Omicia score) 3 Weak splice site prediction impact according to the Berkeley 1 Drosophila Genome Project prediction program An homozygote individual/s in the genomAD browser data 5 Segregation (second patient not presenting variation in 9 homozygosity) Supplementary Table 3: List of variants that were negated by lack of segregation in patient III-1. Positions are according to GRCh37/hg19. Zygosity in Variants negated by segregation Patient III-1 1 Chromosome 1:207647230 G to A, c.2240G>A p.Gly747Glu Normal Complement C3d Receptor 2 protein (CR2) gene homozygote 2 Chromosome 10:27326228 G to C, c.2584C>G p.Gln862Glu Normal Ankyrin Repeat Domain 26 protein (ANKRD26) gene homozygote 3 Chromosome 10: 27337797 G to C, c.1747C>G p.Gln583Glu Normal Ankyrin Repeat Domain 26 protein (ANKRD26) gene homozygote 4 Chromosome 1:197887095 G to A, c.142G>A p.Ala48Thr Normal LIM Homeobox 9 protein (LHX9) gene homozygote 5 Chromosome 21: 46935943 C to A, c.1405G>T p.Ala469Ser Heterozygote Solute Carrier Family 19 Member 1 (SLC19A1) gene 6 Chromosome 16: 81301741 G to A, c.843+5G>A Normal homozygote beta-carotene oxygenase 1 (BCMO1) gene 7 Chromosome 10: 43127813 G to T, c.84C>A p.His28Gln Normal Zinc Finger Protein 33B (ZNF33B) gene homozygote 8 Chromosome 10: 43127814 G to T, c.83A>T p.His28Leu Normal Zinc Finger Protein 33B (ZNF33B) gene homozygote 9 Chromosome 10: 62038628 T to C, c.318A>G p.Lys106Lys Normal Ankyrin 3 (ANK3) gene homozygote Supplementary Table 4: Full list of the 54 homozygous variants that were negated and the cause of negation Negated by common in internal laboratory Exome database of the Bedouin population Position Omicia No Gene Change Effect dbSNP Score C → G chr1:148023129 1 NBPF14 c.380G>C missense 0.446 rs202128472 p.Glu127Asp G → C missense chr1:152189055 2 HRNR c.5050C>G splice site 0.049 rs4845749 p.Arg1684Gly impact T → G chr7:45123906 0.043 3 NACAD c.1873A>C missense rs61740887 p.Ile625Leu chr7:148106477 C → G 4 CNTNAP2 splice region 0.243 rs77025884 c.3716-6C>G CTCA → C chr9:95237024 5 ASPN c.153_155delTGA inframe deletion 0.5 rs878929025 p.Asp51del A → C chr10:48029324 6 ASAH2C c.562T>G missense 0.087 rs993869 p.Ser188Ala ACAGCAGCAGCAGC AGCAGCAG → A 7 ATN1 chr12:7045891 c.1488_1508delGCAGC inframe deletion 0.5 AGC... p.Gln496_Gln502del A → AGAGGAG chr14:24769849 inframe 8 NOP9 c.504_509dupGGAGGA 0.5 rs113258190 insertion p.Glu168_Glu169dup C → CCTGCTGCTGCTG chr14:92537354 inframe 9 ATXN3 c.904_915dupCAGCAG 0.5 insertion rs763541221 CAGCAG p.Gln302_Gln305dup A → C 10 GOLGA6L6 chr15:20740040 c.1710T>G missense 0.07 p.Asp570Glu C → G chr15:23685206 11 GOLGA6L2 c.2416G>C missense 0.258 rs112738749 p.Ala806Pro A → C chr16:88599697 12 ZFPM1 c.1331A>C missense 0.044 rs368520732 p.Glu444Ala G → C chr16:88599698 13 ZFPM1 c.1332G>C missense 0.045 rs201915453 p.Glu444Asp CCTCTGG → C chr16:88599699 c.1335_1340delTCTGG 14 ZFPM1 inframe deletion 0.5 rs149145771 C p.Leu446_Ala447del C → T chr18:25727748 15 CDH2 c.61G>A missense 0.781 rs17495042 p.Ala21Thr G → A chr18:29116284 16 DSG2 c.1543G>A missense 0.899 rs2230235 p.Val515Ile C → G chr18:46284585 17 CTIF c.880C>G missense 0.317 rs145237824 p.Leu294Val Negated by common variants according to a database of healthy Saudi individuals with LOF in varies genes (Alsalem et al. 2013) Position Omicia No Gene Change Effect dbSNP Score A → C chr1:198723439 1 PTPRC c.3551A>C missense 0.65 rs762923302 p.Asn1184Thr C → A chr1:212970533 2 TATDN3 c.253C>A missense 0.275 rs148528899 p.Leu85Ile T → A chr1:228526578 3 OBSCN c.19980T>A missense 0.245 rs144372515 p.Ser6660Arg C → T chr3:75786415 4 ZNF717 c.2359G>A missense 0.649 rs77511709 p.Glu787Lys C → T chr3:75786453 5 ZNF717 c.2321G>A missense 0.074 rs77961859 p.Ser774Asn A → C chr3:75788076 6 ZNF717 c.698T>G missense 0.442 rs62250111 p.Val233Gly A → G chr3:75788085 7 ZNF717 c.689T>C missense 0.144 rs201605431 p.Val230Ala C → T chr3:75788105 8 ZNF717 c.669G>A missense 0.073 rs796745611 p.Met223Ile G → A missense chr3:75788109 9 ZNF717 c.665C>T splice site 0.076 rs796849627 p.Ala222Val impact C → T chr3:75788130 10 ZNF717 c.644G>A missense 0.088 rs113708852 p.Gly215Glu C → T ZNF717 chr3:75788137 11 c.637G>A missense 0.204 rs199883677 p.Glu213Lys C → A chr7:144101728 12 NOBOX c.131G>T missense 0.052 rs115206969 p.Arg44Leu chr7:149476781 G → C 13 SSPO splice region 0.249 rs745841041 n.1224+7G>C G → C chr7:155301717 14 CNPY1 c.16C>G missense 0.927 rs73163379 p.Leu6Val C → T chr10:26856281 15 APBB1IP c.1865C>T missense 0.156 rs200114349 p.Ala622Val C → T chr16:74678537 16 RFWD3 c.889G>A missense 0.832 rs138454127 p.Ala297Thr G → T chr16:82033586 17 SDR42E1 c.312C>A missense 0.044 rs769262424 p.Asp104Glu G → A chr19:48305586 18 TPRX1 c.682C>T missense 0.1 rs145255760 p.Pro228Ser Negated by low prediction for damage (Omicia score) Position Omicia No Gene Change Effect dbSNP Score chr14:74823778 G → A 1 VRTN missense 0.061 rs140827396 c.292G>A p.Ala98Thr A → G chr11:56237609 2 OR5M3 c.365T>C missense 0.118 rs200070203 p.Met122Thr C → A chr7:150747236 3 ASIC3 c.578C>A missense 0.156 rs141910870 p.Ala193Asp Negated by weak splice site prediction impact of Berkeley Drosophila Genome Project prediction program (http://www.fruitfly.org/seq_tools/splice.html) Position Omicia No Gene Change Effect dbSNP Score chr1:232626815 T → G 1 SIPA1L2 splice region 0.245 rs776937773 c.1618-7A>C Negated by an homozygote individual/s in the genomAD browser data Position Omicia No Gene Change Effect dbSNP Score chr7:143096736 G → A 1 EPHA1 splice region 0.243 rs149923216 c.835+8C>T G → A NO chr7:144094553 2 c.1856C>T missense 0.19 NOBOX rs146227301 p.Pro619Leu chr7:144101781 G → A 3 NOBOX splice region 0.243 rs189548165 c.86-8C>T T → C chr16:2070066 4 NPW c.164T>C missense 0.715 rs573754213 p.Leu55Pro G → A chr18:48256003 5 MAPK4 c.1543G>A missense 0.646 rs573296530 p.Glu515Lys Negated by segregation (second patient not presenting variation in homozygosity) Position Omicia No Gene Change Effect dbSNP Score C → A missense chr21:46935943 1 SLC19A1 c.1405G>T splice site 0.68 rs560827689 p.Ala469Ser impact splice region G → A 2 BCMO1 chr16:81301741 splice site 0.564 c.843+5G>A impact T → C 3 ANK3 chr10:62038628 c.318A>G splice region 0.808 p.Lys106Lys T → A 4 ZNF33B chr10:43127814 c.83A>T missense 0.314 p.His28Leu G → T 5 ZNF33B chr10:43127813 c.84C>A missense 0.17 p.His28Gln G → C chr10:27326228 6 ANKRD26 c.2584C>G missense 0.686 rs74128547 p.Gln862Glu G → C chr10:27337797 7 ANKRD26 c.1747C>G missense 0.193 rs56151272 p.Gln583Glu G → A 8 CR2 chr1:207647230 c.2240G>A missense 0.855 p.Gly747Glu G → A chr1:197887095 9 LHX9 c.142G>A missense 0.605 rs113693840 p.Ala48Thr Supplementary Figure 1: Western blot analyses results for CAP1. Fibroblast cell lysates of patient III-1 and 2 controls (C1, C2) , the bands observed with each of the antibodies are marked: mouse anti-CAP1 antibody (SC-376191), mouse anti GAPDH (Millipore MAB374).
Load more

Recommended publications
  • Supplementary Data
    Figure 2S 4 7 A - C 080125 CSCs 080418 CSCs - + IFN-a 48 h + IFN-a 48 h + IFN-a 72 h 6 + IFN-a 72 h 3 5 MRFI 4 2 3 2 1 1 0 0 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 7 B 13 080125 FBS - D 080418 FBS - + IFN-a 48 h 12 + IFN-a 48 h + IFN-a 72 h + IFN-a 72 h 6 080125 FBS 11 10 5 9 8 4 7 6 3 MRFI 5 4 2 3 2 1 1 0 0 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 Molecule Molecule FIGURE 4S FIGURE 5S Panel A Panel B FIGURE 6S A B C D Supplemental Results Table 1S. Modulation by IFN-α of APM in GBM CSC and FBS tumor cell lines. Molecule * Cell line IFN-α‡ HLA β2-m# HLA LMP TAP1 TAP2 class II A A HC§ 2 7 10 080125 CSCs - 1∞ (1) 3 (65) 2 (91) 1 (2) 6 (47) 2 (61) 1 (3) 1 (2) 1 (3) + 2 (81) 11 (80) 13 (99) 1 (3) 8 (88) 4 (91) 1 (2) 1 (3) 2 (68) 080125 FBS - 2 (81) 4 (63) 4 (83) 1 (3) 6 (80) 3 (67) 2 (86) 1 (3) 2 (75) + 2 (99) 14 (90) 7 (97) 5 (75) 7 (100) 6 (98) 2 (90) 1 (4) 3 (87) 080418 CSCs - 2 (51) 1 (1) 1 (3) 2 (47) 2 (83) 2 (54) 1 (4) 1 (2) 1 (3) + 2 (81) 3 (76) 5 (75) 2 (50) 2 (83) 3 (71) 1 (3) 2 (87) 1 (2) 080418 FBS - 1 (3) 3 (70) 2 (88) 1 (4) 3 (87) 2 (76) 1 (3) 1 (3) 1 (2) + 2 (78) 7 (98) 5 (99) 2 (94) 5 (100) 3 (100) 1 (4) 2 (100) 1 (2) 070104 CSCs - 1 (2) 1 (3) 1 (3) 2 (78) 1 (3) 1 (2) 1 (3) 1 (3) 1 (2) + 2 (98) 8 (100) 10 (88) 4 (89) 3 (98) 3 (94) 1 (4) 2 (86) 2 (79) * expression of APM molecules was evaluated by intracellular staining and cytofluorimetric analysis; ‡ cells were treatead or not (+/-) for 72 h with 1000 IU/ml of IFN-α; # β-2 microglobulin; § β-2 microglobulin-free HLA-A heavy chain; ∞ values are indicated as ratio between the mean of fluorescence intensity of cells stained with the selected mAb and that of the negative control; bold values indicate significant MRFI (≥ 2).
    [Show full text]
  • Analyses of Allele-Specific Gene Expression in Highly Divergent
    ARTICLES Analyses of allele-specific gene expression in highly divergent mouse crosses identifies pervasive allelic imbalance James J Crowley1,10, Vasyl Zhabotynsky1,10, Wei Sun1,2,10, Shunping Huang3, Isa Kemal Pakatci3, Yunjung Kim1, Jeremy R Wang3, Andrew P Morgan1,4,5, John D Calaway1,4,5, David L Aylor1,9, Zaining Yun1, Timothy A Bell1,4,5, Ryan J Buus1,4,5, Mark E Calaway1,4,5, John P Didion1,4,5, Terry J Gooch1,4,5, Stephanie D Hansen1,4,5, Nashiya N Robinson1,4,5, Ginger D Shaw1,4,5, Jason S Spence1, Corey R Quackenbush1, Cordelia J Barrick1, Randal J Nonneman1, Kyungsu Kim2, James Xenakis2, Yuying Xie1, William Valdar1,4, Alan B Lenarcic1, Wei Wang3,9, Catherine E Welsh3, Chen-Ping Fu3, Zhaojun Zhang3, James Holt3, Zhishan Guo3, David W Threadgill6, Lisa M Tarantino7, Darla R Miller1,4,5, Fei Zou2,11, Leonard McMillan3,11, Patrick F Sullivan1,5,7,8,11 & Fernando Pardo-Manuel de Villena1,4,5,11 Complex human traits are influenced by variation in regulatory DNA through mechanisms that are not fully understood. Because regulatory elements are conserved between humans and mice, a thorough annotation of cis regulatory variants in mice could aid in further characterizing these mechanisms. Here we provide a detailed portrait of mouse gene expression across multiple tissues in a three-way diallel. Greater than 80% of mouse genes have cis regulatory variation. Effects from these variants influence complex traits and usually extend to the human ortholog. Further, we estimate that at least one in every thousand SNPs creates a cis regulatory effect.
    [Show full text]
  • Accepted Version
    Article Genetic Control of Gonadal Sex Determination and Development STEVANT, Isabelle, NEF, Serge Abstract Sex determination is the process by which the bipotential gonads develop as either testes or ovaries. With two distinct potential outcomes, the gonadal primordium offers a unique model for the study of cell fate specification and how distinct cell populations diverge from multipotent progenitors. This review focuses on recent advances in our understanding of the genetic programs and epigenetic mechanisms that regulate gonadal sex determination and the regulation of cell fate commitment in the bipotential gonads. We rely primarily on mouse data to illuminate the complex and dynamic genetic programs controlling cell fate decision and sex-specific cell differentiation during gonadal formation and gonadal sex determination. Reference STEVANT, Isabelle, NEF, Serge. Genetic Control of Gonadal Sex Determination and Development. Trends in Genetics, 2019 PMID : 30902461 DOI : 10.1016/j.tig.2019.02.004 Available at: http://archive-ouverte.unige.ch/unige:115790 Disclaimer: layout of this document may differ from the published version. 1 / 1 Trends in Genetics Genetic control of sex determination and gonad development --Manuscript Draft-- Manuscript Number: TIGS-D-18-00173R1 Article Type: Review Keywords: sex determination; ovary; testis; lineage specification; gene expression; epigenetic regulation Corresponding Author: Serge Nef geneva, SWITZERLAND First Author: Isabelle Stévant Order of Authors: Isabelle Stévant Serge Nef Abstract: Sex determination is the process by which the bipotential gonads develop as either testes or ovaries. With two distinct potential outomes, the gonadal primordium offers a unique model for the study of cell fate specification and how distinct cell populations diverge from multipotent progenitors.
    [Show full text]
  • Architecture of a Lymphomyeloid Developmental Switch Controlled by PU.1, Notch and Gata3 Marissa Morales Del Real and Ellen V
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Caltech Authors DEVELOPMENT AND STEM CELLS RESEARCH ARTICLE 1207 Development 140, 1207-1219 (2013) doi:10.1242/dev.088559 © 2013. Published by The Company of Biologists Ltd Architecture of a lymphomyeloid developmental switch controlled by PU.1, Notch and Gata3 Marissa Morales Del Real and Ellen V. Rothenberg* SUMMARY Hematopoiesis is a classic system with which to study developmental potentials and to investigate gene regulatory networks that control choices among alternate lineages. T-cell progenitors seeding the thymus retain several lineage potentials. The transcription factor PU.1 is involved in the decision to become a T cell or a myeloid cell, and the developmental outcome of expressing PU.1 is dependent on exposure to Notch signaling. PU.1-expressing T-cell progenitors without Notch signaling often adopt a myeloid program, whereas those exposed to Notch signals remain in a T-lineage pathway. Here, we show that Notch signaling does not alter PU.1 transcriptional activity by degradation/alteration of PU.1 protein. Instead, Notch signaling protects against the downregulation of T-cell factors so that a T-cell transcriptional network is maintained. Using an early T-cell line, we describe two branches of this network. The first involves inhibition of E-proteins by PU.1 and the resulting inhibition of Notch signaling target genes. Effects of E- protein inhibition can be reversed by exposure to Notch signaling. The second network is dependent on the ability of PU.1 to inhibit important T-cell transcription factor genes such as Myb, Tcf7 and Gata3 in the absence of Notch signaling.
    [Show full text]
  • Supplementary Figure 1. Qrt-PCR Analyses for Measuring the Knockdown Efficiency of Transcription Factors. A549 and U937 Cells Kn
    Supplementary Figure 1. qRT-PCR analyses for measuring the knockdown efficiency of transcription factors. A549 and U937 cells knocking down or overexpressing different transcription factors including NF-κB subunits p65 and p50 (A), HIF1 (B), TCF4 (C), AP-1 subunits c-Jun and c-FOS (D), or FOXP3 (E) were subjected to RNA isolation, followed by qRT-PCR analyses to examine the expression of these transcription factors. **P<0.01 and ***P<0.001. 1 Supplementary Figure 2. Effects of FOXP3 downregulation and overexpression on the expression of NLRP1, IL1B and IL18. A549 and U937 cells were transfected with si-FOXP3 or pCDNA3-2×Flag-FOXP3. After 24 h, cells were subjected to RNA isolation, followed by qRT-PCR analyses to examine the expression of NLRP1 (A), IL1B (B) and IL18 (C). ***P<0.001. 2 Supplementary Figure 3. Effects of CtBP2 downregulation and overexpression on the expression of miR-199a-3p, NLRP1, IL1B and IL18. A549 and U937 cells were transfected with si-CtBP2 or pCDNA3-2×Flag-CtBP2. After 24 h, cells were subjected to RNA isolation, followed by qRT-PCR analyses to examine the expression of miR-199a-3p (A), NLRP1 (B), IL1B (C) and IL18 (D). ***P<0.001. 3 Supplementary Figure 4. CHFTC specifically bond to the promoter of miR-199a-3p. (A and B) Effects of CtBP2 knockdown and overexpression on HDAC1 and FOXP3 protein levels. A549 cells were transfected with two CtBP2-specific shRNAs and pCDNA3-2×Flag-CtBP2 to obtain two CtBP2-knockdown cell lines (KD-1 and KD-2) (A) and two CtBP2-overexpression cells lines (OE-1 and OE-2) (B), respectively.
    [Show full text]
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
    Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated.
    [Show full text]
  • A Private 16Q24.2Q24.3 Microduplication in a Boy with Intellectual Disability, Speech Delay and Mild Dysmorphic Features
    G C A T T A C G G C A T genes Article A Private 16q24.2q24.3 Microduplication in a Boy with Intellectual Disability, Speech Delay and Mild Dysmorphic Features Orazio Palumbo * , Pietro Palumbo , Ester Di Muro, Luigia Cinque, Antonio Petracca, Massimo Carella and Marco Castori Division of Medical Genetics, Fondazione IRCCS-Casa Sollievo della Sofferenza, San Giovanni Rotondo, 71013 Foggia, Italy; [email protected] (P.P.); [email protected] (E.D.M.); [email protected] (L.C.); [email protected] (A.P.); [email protected] (M.C.); [email protected] (M.C.) * Correspondence: [email protected]; Tel.: +39-088-241-6350 Received: 5 June 2020; Accepted: 24 June 2020; Published: 26 June 2020 Abstract: No data on interstitial microduplications of the 16q24.2q24.3 chromosome region are available in the medical literature and remain extraordinarily rare in public databases. Here, we describe a boy with a de novo 16q24.2q24.3 microduplication at the Single Nucleotide Polymorphism (SNP)-array analysis spanning ~2.2 Mb and encompassing 38 genes. The patient showed mild-to-moderate intellectual disability, speech delay and mild dysmorphic features. In DECIPHER, we found six individuals carrying a “pure” overlapping microduplication. Although available data are very limited, genomic and phenotype comparison of our and previously annotated patients suggested a potential clinical relevance for 16q24.2q24.3 microduplication with a variable and not (yet) recognizable phenotype predominantly affecting cognition. Comparing the cytogenomic data of available individuals allowed us to delineate the smallest region of overlap involving 14 genes. Accordingly, we propose ANKRD11, CDH15, and CTU2 as candidate genes for explaining the related neurodevelopmental manifestations shared by these patients.
    [Show full text]
  • Long, Noncoding RNA Dysregulation in Glioblastoma
    cancers Review Long, Noncoding RNA Dysregulation in Glioblastoma Patrick A. DeSouza 1,2 , Xuan Qu 1, Hao Chen 1,3, Bhuvic Patel 1 , Christopher A. Maher 2,4,5,6 and Albert H. Kim 1,6,* 1 Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA; [email protected] (P.A.D.); [email protected] (X.Q.); [email protected] (H.C.); [email protected] (B.P.) 2 Department of Internal Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA; [email protected] 3 Department of Neuroscience, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA 4 Department of Biomedical Engineering, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA 5 McDonnell Genome Institute, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA 6 Siteman Cancer Center, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA * Correspondence: [email protected] Simple Summary: Developing effective therapies for glioblastoma (GBM), the most common primary brain cancer, remains challenging due to the heterogeneity within tumors and therapeutic resistance that drives recurrence. Noncoding RNAs are transcribed from a large proportion of the genome and remain largely unexplored in their contribution to the evolution of GBM tumors. Here, we will review the general mechanisms of long, noncoding RNAs and the current knowledge of how these impact heterogeneity and therapeutic resistance in GBM. A better understanding of the molecular drivers required for these aggressive tumors is necessary to improve the management and outcomes Citation: DeSouza, P.A.; Qu, X.; of this challenging disease.
    [Show full text]
  • Early Pregnancy Vitamin D Status and Risk of Preeclampsia
    Early pregnancy vitamin D status and risk of preeclampsia Hooman Mirzakhani, … , Joseph Loscalzo, Scott T. Weiss J Clin Invest. 2016;126(12):4702-4715. https://doi.org/10.1172/JCI89031. Clinical Medicine Clinical trials Reproductive biology BACKGROUND. Low vitamin D status in pregnancy was proposed as a risk factor of preeclampsia. METHODS. We assessed the effect of vitamin D supplementation (4,400 vs. 400 IU/day), initiated early in pregnancy (10–18 weeks), on the development of preeclampsia. The effects of serum vitamin D (25-hydroxyvitamin D [25OHD]) levels on preeclampsia incidence at trial entry and in the third trimester (32–38 weeks) were studied. We also conducted a nested case-control study of 157 women to investigate peripheral blood vitamin D–associated gene expression profiles at 10 to 18 weeks in 47 participants who developed preeclampsia. RESULTS. Of 881 women randomized, outcome data were available for 816, with 67 (8.2%) developing preeclampsia. There was no significant difference between treatment (N = 408) or control (N = 408) groups in the incidence of preeclampsia (8.08% vs. 8.33%, respectively; relative risk: 0.97; 95% CI, 0.61–1.53). However, in a cohort analysis and after adjustment for confounders, a significant effect of sufficient vitamin D status (25OHD ≥30 ng/ml) was observed in both early and late pregnancy compared with insufficient levels (25OHD <30 ng/ml) (adjusted odds ratio, 0.28; 95% CI, 0.10–0.96). Differential expression of 348 vitamin D–associated […] Find the latest version: https://jci.me/89031/pdf CLINICAL MEDICINE The Journal of Clinical Investigation Early pregnancy vitamin D status and risk of preeclampsia Hooman Mirzakhani,1 Augusto A.
    [Show full text]
  • Expression of the POTE Gene Family in Human Ovarian Cancer Carter J Barger1,2, Wa Zhang1,2, Ashok Sharma 1,2, Linda Chee1,2, Smitha R
    www.nature.com/scientificreports OPEN Expression of the POTE gene family in human ovarian cancer Carter J Barger1,2, Wa Zhang1,2, Ashok Sharma 1,2, Linda Chee1,2, Smitha R. James3, Christina N. Kufel3, Austin Miller 4, Jane Meza5, Ronny Drapkin 6, Kunle Odunsi7,8,9, 2,10 1,2,3 Received: 5 July 2018 David Klinkebiel & Adam R. Karpf Accepted: 7 November 2018 The POTE family includes 14 genes in three phylogenetic groups. We determined POTE mRNA Published: xx xx xxxx expression in normal tissues, epithelial ovarian and high-grade serous ovarian cancer (EOC, HGSC), and pan-cancer, and determined the relationship of POTE expression to ovarian cancer clinicopathology. Groups 1 & 2 POTEs showed testis-specifc expression in normal tissues, consistent with assignment as cancer-testis antigens (CTAs), while Group 3 POTEs were expressed in several normal tissues, indicating they are not CTAs. Pan-POTE and individual POTEs showed signifcantly elevated expression in EOC and HGSC compared to normal controls. Pan-POTE correlated with increased stage, grade, and the HGSC subtype. Select individual POTEs showed increased expression in recurrent HGSC, and POTEE specifcally associated with reduced HGSC OS. Consistent with tumors, EOC cell lines had signifcantly elevated Pan-POTE compared to OSE and FTE cells. Notably, Group 1 & 2 POTEs (POTEs A/B/B2/C/D), Group 3 POTE-actin genes (POTEs E/F/I/J/KP), and other Group 3 POTEs (POTEs G/H/M) show within-group correlated expression, and pan-cancer analyses of tumors and cell lines confrmed this relationship. Based on their restricted expression in normal tissues and increased expression and association with poor prognosis in ovarian cancer, POTEs are potential oncogenes and therapeutic targets in this malignancy.
    [Show full text]
  • Oncorequisite Role of an Aldehyde Dehydrogenase in the Pathogenesis of T-Cell Acute Lymphoblastic Leukemia
    Acute Lymphoblastic Leukemia SUPPLEMENTARY APPENDIX Oncorequisite role of an aldehyde dehydrogenase in the pathogenesis of T-cell acute lymphoblastic leukemia Chujing Zhang, 1 Stella Amanda, 1 Cheng Wang, 2 Tze King Tan, 1 Muhammad Zulfaqar Ali, 1 Wei Zhong Leong, 1 Ley Moy Ng, 1 Shojiro Kitajima, 3 Zhenhua Li, 4 Allen Eng Juh Yeoh, 1,4 Shi Hao Tan 1 and Takaomi Sanda 1,5 1Cancer Science Institute of Singapore, National University of Singapore, Singapore; 2Department of Anatomy, National University of Singapore, Singapore; 3Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan; 4VIVA-NUS CenTRAL, Department of Pedi - atrics, National University of Singapore, Singapore and 5Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore. ©2021 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol. 2019.245639 Received: December 18, 2019. Accepted: May 14, 2020. Pre-published: May 15, 2020. Correspondence: TAKAOMI SANDA - [email protected] Zhang et al Roles of ALDH in T-ALL Supplementary Information Supplementary Materials and Methods Cell culture and reagents All human leukemia cell lines were cultured in RPMI-1640 medium (BioWest) supplemented with 10% FBS (BioWest). 293T cells were maintained in DMEM medium (BioWest) supplemented with 10% FBS and penicillin/streptomycin (Life Technologies). All-trans retinaldehyde was purchased from Sigma-Aldrich. Glucose free and Glutamine free RPMI- 1640 media were purchased from ThermoFisher. Patient derived xenograft (PDX) sample T-ALL patient-derived PDX sample (DFCI15) was kindly provided by Alejandro Gutierrez (Boston Children’s Hospital, Boston). Mouse studies were conducted according to the recommendations from the Institutional Animal Care and Use Committee (IACUC) and all protocols were approved by the Committee at the National University of Singapore (NUS).
    [Show full text]
  • Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
    Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase
    [Show full text]