Genetic Analysis of DBA Gareth Gerrard Imperial Molecular Pathology / Centre for Haematology Hammersmith Hospital Molecular Diagnostics Begins With… DNA, Codons and the Amino Acid Code Genes: Exons, Introns & Splicing DNA, RNA & Proteins (& Cake) Amino acids The Ribosome (80S) 60S (L) unit: 5S RNA, 28S RNA, 5.8S RNA + ~49 proteins 40S (S) unit: 18S RNA + 33 proteins A cake making machine that uses mRNA as the recipe and amino acids as the ingredients DBA is a ribosomopathy* DBA Mutations affecting ribosomal protein (RP) genes *probably Mutations affecting: 25-35% RPL5, RPL11, RPS26, RPS24, RPS17, RPS10, RPL35a,RPS7, RPL26, RPL15 25% RPS19 40-50% ?? Heterozygous, autosomal dominant • ~80 RP genes in total • 10 are known to be affected in DBA • GATA1 may also have a role Leading to RP haploinsufficiency Types of Mutations in DBA – 1) Missense Change in recipe – use salt instead of sugar = cake no good! Types of Mutations – 2) Nonsense Change in recipe – leave out half of ingredients = cake no good! Types of Mutations – 3) Frameshift Change in recipe – words become unreadable = cake no good! Types of Mutations – 4) Splice Site Change in recipe – pages left out or go blank = cake no good! Types of Mutations – 5) Copy Number Variation (CNV) Change in recipe – pages torn out = cake no good! Why Screen? • Accurate diagnosis • Donor selection for allogeneic haematopoietic stem cell transplantation • Reproductive choices • Linking genotype to phenotype 10 Commonly Identified DBA associated RP Genes Mutations are mostly SNVs and indels, but large deletions & insertion are also seen RPS26 RPS24 RPS7 RPS17 RPL26 RPS35a RPL11 RPS10 Unknown RPL5 RPS19 = 7 genes in conventional molecular screen Mutation Detection Technology – Sanger Sequencing ABI 3130 ABI 3500xl 1 Sample / 1 Gene / day 5 Samples / 1 Gene / day Standard DBA Screening Pipeline Measure & QC Peripheral Blood Extract DNA RPS19 RPL5 RPL11 RPS24 RPS17 RPL35a RPS7 Sanger Sequence PCR target gene exons Next Generation Sequencers V1 - Pilot Roche 454 Getting on a bit / Expensive Illumina MiSeq Highest throughput V2 - Current Ion Torrent PGM Fastest / most flexible Why Next Generation Seq (NGS)? • • • • Very high throughput (fast) Can look at all 80+ RP genes at once Can multiplex many samples at once Potential to pick up allele-loss deletions & insertions (CNV) • Cost effective per-gene / per-sample • Once identified, family members can be screened by conventional sequencing RP Gene loci used for V1 Gene Capture Small SA S2 S3 S3A S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S15A S16 S17 S18 S19 S20 S21 S23 S24 S25 S26 S27 S27A S28 S29 S30 RPSA RPS2 RPS3 RPS3A RPS4X RPS4Y RPS5 RPS6 RPS7 RPS8 RPS9 RPS10 RPS11 RPS12 RPS13 RPS14 RPS15 RPS15A RPS16 RPS17 RPS18 RPS19 RPS20 RPS21 RPS23 RPS24 RPS25 RPS26 RPS27 RPS27A RPS28 RPS29 FAU Location (for capture) chr3:39448180-39453929 chr16:2012061-2014861 ch11:75110530-75133324 chr4:152020725-152025804 chrX:71475529-71497150 chrY:2709527-2734997 chr19:58898636-58906170 chr9:19375713-19380252 chr2:3622795-3628509 chr1:45240923-45244451 chr19:54704610-54752862 chr6:34385231-34393902 chr19:49999634-50002944 chr6:133135580-133138703 chr11:17095936-17099334 chr5:149822753-149829319 chr19:1438363-1440492 chr16:18792617-18801656 chr19:39923852-39926618 chr15:82821158-82824972 chr6:33239787-33244287 chr19:42363988-42375482 chr8:56979854-56987069 chr20:60962105-60963576 chr5:81569177-81574396 chr10:79793518-79816570 chr11:118886422-118889401 chr12:56435637-56438116 chr1:153963235-153964626 chr2:55459039-55462989 chr19:8386384-8387809 chr14:50043390-50053094 chr11:64888100-64889945 Large Subunit L3 RPL3 L4 RPL4 L5 RPL5 L6 RPL6 L7 RPL7 L7A RPL7A L8 RPL8 L9 RPL9 L10 RPL10 L10A RPL10A L11 RPL11 L12 RPL12 L13 RPL13 L13A RPL13A L14 RPL14 L15 RPL15 L17 RPL17 L18 RPL18 L18A RPL18A L19 RPL19 L21 RPL21 L22 RPL22 L23 RPL23 L23A RPL23A L24 RPL24 L26 RPL26 L27 RPL27 L27A RPL27A L28 RPL28 L29 RPL29 L30 RPL30 L31 RPL31 L32 RPL32 chr22:39708887-39716394 chr15:66790801-66797221 chr1:93297597-93307481 chr12:112842994-112856642 chr8:74202506-74208024 chr9:136215069-136218281 chr8:146015150-146017972 chr4:39455744-39460568 chrX:153618315-153637504 chr6:35436185-35438562 chr1:24018269-24022915 chr9:130209953-130213684 chr16:89627056-89630950 chr19:49990811-49995565 chr3:40498783-40506549 chr3:23958036-23965183 chr18:47014858-47018906 chr19:49118585-49122793 chr19:17970730-17974962 chr17:37356536-37360980 chr13:27825446-27830828 chr1:6241329-6269449 chr17:37004118-37010064 chr17:27046411-27051377 chr3:101399935-101405626 chr17:8280838-8286568 chr17:41150446-41154956 chr11:8703958-8736306 chr19:55897300-55903449 chr3:52027644-52029958 chr8:99037079-99058697 chr2:101618177-101640494 chr3:12875984-12883087 L34 L35 L35A L36 L36A L37 L37A L38 L39 L40 L41 LP0 LP1 LP2 RPL34 RPL35 RPL35A RPL36 RPL36A RPL37 RPL37A RPL38 RPL39 UBA52 RPL41 RPLP0 RPLP1 RPLP2 chr4:109541722-109551568 chr9:127620159-127624260 chr3:197676858-197683481 chr19:5690272-5691674 chrX:100645812-100651105 chr5:40825364-40835437 chr2:217362912-217443903 chr17:72199721-72206676 chrX:118920467-118925606 chr19:18682614-18688269 chr12:56510370-56511727 chr12:120634489-120639038 chr15:69745123-69748172 chr11:809647-812880 Latest Version adds GATA1, but loses RPS17 http:// ribosome.med.miyazaki-u.ac.jp NGS Workflow – v1 3µg Genomic DNA 20 probands Target Enrichment Fragment DNA: Covaris e220 Hybridise and capture Ribosomal Protein Gene DNA including exons, introns, & regulatory regions Total Time = 2 weeks Data analysis Sanger seq validation High-throughput Sequencing Library quant, pool, clean up and cluster generation DBA – NGS v1 – Results from Initial 20 Samples Gene n= (17) RPL5 5(4) British Journal of Haematology, 2013, 162,530–536 RPS26 3 RPL11 2 RPS17 2(1) RPS7 1 RPS10 1 RPS24 1 RPS19 0 Tot Mut 15(13) NoMut 2 % 29.4% 17.6% 11.8% 11.8% 5.9% 5.9% 5.9% 0.0% 88.2% 11.8% Type 3(2) SG/2 FSD SG/FSI/SL FSD/FSI 2(1) SG SSD SG SG SG= Stop Gain SNV (Nonsense); FSD= Frame-shift Deletion; FSI= Frame-shift Insertion; SL= Start Loss SNV (Missense); SSD= Splice Site Defect DBA – NGS – v2 Workflow: Days 1 - 3 20ng gDNA Allows screening of 16 samples for 80+ Genes per run AmpliSeq Library Prep (1-2 days) qPCR quant & pool KAPA Quant Kit Day 1 Template & Enrich OneTouch2 & ES Day 2 PGM Sequence 2 x 8 barcode Day 3 DBA NGS – Day 4: Analysis... Variant Caller TSv3.6.2 DON’T PANIC! VCF Files VEP IGV SHOW ME THE KITTEHS Ensembl v72 Virtualbox 4.2 NextGene / SeqNext Ion Reporter v1.6 CONDEL / Mutation Assessor Human Splicing Finder v2.4.1 MolDiag team for Sanger validation & reporting DBA – NGS - Analysis DBA Mutation - IGV PileUp showing RPS26 Nonsense TTC (Phenylalanine) -> TAA (STOP) DBA-NGS v2 – Initial Results DBA-HALO Barcode 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Results Gene RPL15 RPS26 RPL13A RPS7 RPL29 RPS19 RPL7 RPL15 RPL17 RPS10 Consequence Stop-Gain splice donor variant missense_variant missense_variant inframe_insertion frameshift_insertion splice_region_variant missense_variant Exon Base Codon LOVD? LOF? dbSNP MAF Sanger Valid? 4 3:23960737G>A p120W>* No Yes(?) n/a n/a Yes 1 n.30+1G>AINTRON=1/2 Yes Yes rs148622862 n/a Yes 7 c.481C>A p.Ala161Asp No ? rs150697570 n/a 6 c.562T>C p.133L>S No ??? n/a n/a Yes 4 c.386_391dupCCAAGG p.Ala129_Lys130dup n/a ??? rs141201675 n/a * 4 c.199-200_insG 67 No ??? n/a n/a * 1+8 c.107+8A>GINTRON=1/3 n/a ??? rs74460527 0.0096 * 5 c.466T>G p.141S>A n/a ??Splicing n/a n/a * splice_region_variant,5_prime_UTR_variant 1 c.87G>A Exon1/6 (5'UTR) n/a Stop-Gain c.337C>T p.113R>* Yes ??? Yes rs140522052 <1% rs267607022 * Yes - c 3 definite hits (1 novel); 2 very likely; 5 interesting Only 1 DBA had no mutation (9); 10-13 non-affected family members Summary • Screening for mutations in DBA is now an established technology • We now use NGS technology to screen all 80+ Ribosomal protein genes • Family members screened by conventional sequencing (for known mutation) • Will introduce screening for CNV in near future Thank You! IPML Hammersmith Letizia Foroni Kikkeri Naresh MRD Pierre Foskett Thet Myint Faisal Abdillah Mol Diag Mikel Valganon Alex Foong Natalie Killeen Sarmad Toma R&D Mary Alikian George Nteliopoulos Students Clinical Team Aysha Patel Sakuntala Ale Robin Ferrari Josu de la Fuente Anastasios Karadimitris Deena Iskander ACHS CGL Tim Aitman Michael Müller Dalia Kasperaviciute Laurence Game Jane Apperley David Marin Dragana Milojkovic Jiri Pavlu John Goldman