welcome to uw-madison, the wnprc, and o’connor lab! mhc genotyping workshop november 7 th – 11...

Welcome to UW-Madison, the WNPRC, and O’Connor Lab!

MHC Genotyping WorkshopNovember 7th – 11th, 2011

Madison, Wisconsin

Introductions

• Trainers (WNPRC Genetics Service)– Roger Wiseman– Julie Karl– Simon Lank– Gabe Starrett– Francesca Norante

• Participants– Wendy Garnica– Mark Garthwaite– Julie Holister-Smith– Suzanne Queen– Premeela Rajakumar– Yuko Yuki

Schedule of Events

• Monday– Welcome and Overview

Presentation– Begin bench work: cDNA

synthesis & PCR (run #1)

• Tuesday– PCR product purification,

quantification & pooling (run #1)

– Begin emulsion PCR (run #1)

– Begin bench work (run #2)

• Wednesday– Break & enrich DNA beads (run

#1)– Run Roche/454 GS Junior

instrument (run #1)– emPCR (run #2)

• Thursday– View run #1 results– Continue work on run #2– Informatics presentation– Data analysis

• Friday– Run #2 results– Continue Data Analysis & Wrap-up

Overview of Presentation

• Our lab & research focus• Evolution of DNA sequencing technology• Discussion of Roche/454 technology & sample

multiplexing• MHC genotyping method overview

– NHP immunogenetics– Genotyping strategy– Workflow

• Genotyping results

Welcome to Madison!

WNPRC

Welcome to Madison!

The Wisconsin National Primate Research Center (WNPRC)

• Only federally funded National Primate Research Center in the Midwest

• Center holds ~1,100 rhesus macaques, 200 marmosets, and 100 cynomolgus macaques

• Research strengths:– Immunogenetics & Virology– Aging & Metabolism– Reproductive & Regenerative Medicine

The O’Connor Laboratory

Genetics Services Members

The O’Connor Laboratory

Genetics Services Members

The O’Connor Laboratory: Research

• NHP immunogenetics (MHC class I, class II, KIR)– Cynomolgus Macaque (Mauritian, Indonesian, SE

Asian)– Rhesus Macaque (Indian & Chinese)– Japanese Macaque, Vervet, Sooty Mangaby

• SIV pathogenesis (immunology) and viral evolution

• Human immunogenetics (HLA) and HIV variation

The O’Connor Laboratory: Research

• NHP immunogenetics (MHC class I, class II, KIR)– Cynomolgus Macaque (Mauritian, Indonesian, SE

Asian)– Rhesus Macaque (Indian & Chinese)– Japanese Macaque, Vervet, Sooty Mangaby

• SIV pathogenesis (immunology) and viral evolution

• Human immunogenetics (HLA) and HIV variation

Sequencing Technology is Changing

• Micro sequencing reactions– Pyrosequencing– Single molecule sequencing

• Higher throughput– Millions of sequences per day

• Lower cost– $10,000 human genome

(original HGP = $3 billion)

Sequencing Technology: Overview

• 1st Generation (previous): Sanger sequencing

Applied Biosystems 3730xl: 1 x 103 reads / day- 500 to 1,000 bp read length

Sequencing Technology: Overview

• 2nd Generation (current): 454, Illumina, SoLID, Ion torrent

Roche / 454: 1 x 106 reads / day- 500 to 800 bp read length

Illumina: 2 x 109 reads / week- 100 or 200 bp read length

Sequencing Technology: Overview

• 3rd Generation (future): Pacific Biosciences, Nanopore sequencing, Complete Genomics

Pacific Biosciences: 1 x 105 sequences / hour- 1,000 to 10,000 bp reads (?) - Single molecule sequencing- Goal = $1,000 genome !

Sequencing Technology: Overview

• 1st Generation (previous): Sanger– Slow, Expensive, Not clonal, easy to analyze

• 2nd Generation (current): 454, Illumina, SoLID, Ion torrent– Faster, Cheaper, Clonal, hard to analyze

• 3rd Generation (future): Pacific Biosciences, Nanopore sequencing, Complete Genomics, Helicos– Very fast, Very cheap, Impossible to analyze

Roche / 454 Sequencing

How does it work?

Flowgram (instead of chromat)

O’Connor Laboratory Sequencing

20072006 2008 2009 20102005

Sanger sequencing

NHP MHC class I genotyping with E. coli based cloning and Sanger sequencing: Throughput of ~ 8 animals per week.

O’Connor Laboratory Sequencing

20072006 2008 2009 20102005

Sanger sequencing

Pilot with Roche sequencing

center

MHC class I genotyping pilot project: ~24 samples per week

O’Connor Laboratory Sequencing

20072006 2008 2009 20102005

Sanger sequencing

GS FLX at UIUC

Pilot with Roche sequencing

center

MHC class I genotyping at UIUC, ~ 48 samples per week

O’Connor Laboratory Sequencing

20072006 2008 2009 20102005

Sanger sequencing

GS FLX at UIUC

Pilot with Roche sequencing

center

Titanium pilot with Roche sequencing center

MHC class I full-length sequencing project with Roche using Titanium chemistry

O’Connor Laboratory Sequencing

20072006 2008 2009 20102005

Sanger sequencing

GS Junior in lab

GS FLX at UIUC

Pilot with Roche sequencing

center

Titanium pilot with Roche sequencing center

MHC class I and viral sequencing projects run in-house ( > 48 samples per week )

Roche/454 Sequencing Advantages

• Inherently clonal (no bacterial cloning needed)• Far cheaper per base than Sanger (3 – 4 orders

of magnitude)• Reliable read number and data regularity• Easy protocol: many people trained

GS Junior 5 Month Run Summary

MHC Class I 568bp Amplicon – 9 runs

Average 70,848 HQ reads 523 bp median length

Highest 101,711 526

Lowest 33,552 521

SIV Whole Genome – 16 runs

Average 101,846 HQ reads 360 bp median length

Highest 177,642 494

Lowest 42,949 147

SIV Epitope Amplicons (Various Sizes) – 5 runs

Average 80,244 HQ reads 369 bp median length

Highest 107,605 388

Lowest 37,066 356

Ease of Use Access to instrument since Jan 2010 34 different fully-trained operators to date 7 additional people have begun training, but

have not yet completed a solo run

Ease of Use Access to instrument since Jan 2010 34 different fully-trained operators to date 7 additional people have begun training, but

have not yet completed a solo run

Ultra-Deep vs. Ultra-Wide Sequencing

• 2nd & 3rd Generation = thousands / millions of sequences per run

• Cost per run is high ($1000s)• Can examine polymorphic target at high depth

(ultra-deep)– expensive

• Can sequence many samples sequenced at the same time (ultra-wide)– cheap

Ultra-Deep vs. Ultra-Wide Sequencing

• Significantly improves sensitivity over traditional Sanger-based sequencing (500x vs 2x coverage)

Ultra-Deep vs. Ultra-Wide Sequencing

Ultra-deep Ultra-wide

• Low frequency ARV resistance• TCR sequencing• Antibody sequencing

• HLA Typing• Allele frequencies• SNP detection

Multiplexed (Ultra-wide) Amplicon Sequencing

MultiplexIdentifier

MID Tag

Methods to increase multiplexing1. Physically subdividing plate (gasket)2. Sample specific MID sequence tags3. Uniquely mixing 5’ & 3’ MID tags

Patient MID1 ATCGTAGTCA2 TCCGATCGA3 GTGTAACGT4 CCATGGATC5 TGGATGCAG6 TAGTAGCCA7 GTAGTCTAA8 AACGATGCA9 GCGCTAGCA

Patient 5' MID 3' MID1 1 12 1 23 1 34 2 15 2 26 2 37 3 18 3 29 3 3

1.

3.2.

O’Connor lab sequencing projects

• NHP comprehensive MHC genotyping & allele discovery (amplicons)

Importance of MHC Class I

MHC class I molecules dictate immunity to disease

High degree of polymorphism within the MHC class I peptide-binding domain

Specific MHC alleles associated with superior control of HIV infection

Source: modified from Yewdell et al., Nature Reviews Immunology 2003

Host Immune Genetics

NHP MHC Class I Allele LibrariesTo

tal #

Alle

les

in G

enBa

nk

Rhesus Macaque

Cynomolgus Macaque

Pig-tailed Macaque

Vervet Sooty Mangabey

0

100

200

300

400

500

600

700 663

09

156

460

NHP MHC Class I Allele LibrariesTo

tal #

Alle

les

in G

enBa

nk

Rhesus Macaque

Cynomolgus Macaque

Pig-tailed Macaque

Vervet Sooty Mangabey

0

100

200

300

400

500

600

700 663

09

156

460

Human HLA class I = 5,400 alleles

Human HLA vs NHP MHC Class I

A C

A C

B

B

Human HLA class I

Human HLA vs NHP MHC Class I

A C

A C

B

B

Human HLA class I

A1 A2 A4 A3 B1 B2 B3 B4 BN

A1 A2 A3 A4 B1 B2 B3 B4 BN

Nonhuman primate MHC class I

MHC Genotyping Design

• 568bp amplicon captures highly variable peptide binding region flanked by conserved sequences

• Amplifies in multiple primate species• Longer reads provide better resolution of

alleles

% M

HC

Clas

s I V

aria

bilit

y

100

80

60

40

20

0

Leader Peptide α1 Domain α2 Domain α3 Domain

Cyto-plasmic

Trans-membrane

1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181 191 201 211 221 231 241 251 261 271 281 291 301 311 321 331 341 351 361

Amino Acid Position

F R

568bp Amplicon

MHC Genotyping Design

568bp Amplicon

Primer = Adapter (A or B) + MID + sequence-specific

MHC Genotyping Design

568bp Amplicon

Primer = Adapter (A or B) + MID + sequence-specific

Within a single nonhuman primate sample:

MHC Genotyping Design

568bp Amplicon

Primer = Adapter (A or B) + MID + sequence-specific

Within an MHC class I amplicon genotyping pool:

Roche/454 MHC Workflow• Total RNA isolation and cDNA

synthesis– RNA isolation ~4 hrs; cDNA synthesis ~2

hrs

• Primary PCR amplification– plus SPRI purification, quantification,

pooling ~3 hrs

• emPCR– set-up ~1 hr, run ~5.5 hrs

• Breaking and enrichment– ~3 hrs

• GS Junior run– set-up ~1.5 hrs; run time ~10 hrs

• Data processing and analysis– run processing ~2 hrs;– analysis time varies

www.454.com

GS Junior Run Metrics – MHC

Reads per SampleSample MID Read Count Sample MID Read Count

Monkey001 1 525 Monkey049 49 585Monkey002 2 392 Monkey050 50 504Monkey003 3 1,023 Monkey051 51 673Monkey004 4 504 Monkey052 52 565Monkey005 5 450 Monkey053 53 893Monkey006 6 722 Monkey054 54 581Monkey007 7 622 Monkey055 55 623Monkey008 8 489 Monkey056 56 955Monkey009 9 344 Monkey057 57 698Monkey010 10 635 Monkey058 58 792Monkey011 11 660 Monkey059 59 655Monkey012 12 796 Monkey060 60 1,203Monkey013 13 653 Monkey061 61 428Monkey014 14 731 Monkey062 62 8Monkey015 15 1,342 Monkey063 63 391Monkey016 16 628 Monkey064 64 663Monkey017 17 76 Monkey065 65 411Monkey018 18 481 Monkey066 66 386Monkey019 19 503 Monkey067 67 625Monkey020 20 633 Monkey068 68 637Monkey021 21 573 Monkey069 69 367Monkey022 22 463 Monkey070 70 391Monkey023 23 390 Monkey071 71 585Monkey024 24 723 Monkey072 72 808Monkey025 25 739 Monkey073 73 594Monkey026 26 560 Monkey074 74 391Monkey027 27 1,672 Monkey075 75 578Monkey028 28 559 Monkey076 76 728Monkey029 29 801 Monkey077 77 612Monkey030 30 590 Monkey078 78 283Monkey031 31 548 Monkey079 79 475Monkey032 32 748 Monkey080 80 527Monkey033 33 583 Monkey081 81 27Monkey034 34 374 Monkey082 82 226Monkey035 35 226 Monkey083 83 113Monkey036 36 791 Monkey084 84 481Monkey037 37 618 Monkey085 85 52Monkey038 38 558 Monkey086 86 612Monkey039 39 438 Monkey087 87 733Monkey040 40 666 Monkey088 88 800Monkey041 41 250 Monkey089 89 647Monkey042 42 451 Monkey090 90 1,094Monkey043 43 612 Monkey091 91 522Monkey044 44 673 Monkey092 92 756Monkey045 45 570 Monkey093 93 624Monkey046 46 207 Monkey094 94 912Monkey047 47 604 Monkey095 95 610Monkey048 48 180 Monkey096 96 514

Allele Calls & Transcript Profiles%

Tot

al R

eads

MHC Class I AllelesMamu-A

1*026:01

Mamu-A4*14g

Mamu-B*065:03

Mamu-B*090:01

Mamu-B*151:nov:01

Mamu-B*142:nov:01

Mamu-I*01g

Mamu-B*013:nov:01

Mamu-B*046g

Mamu-B*046:06

Mamu-E*03:01:01

Mamu-E*01:12

0

2

4

6

8

10

12

14

16

ChRh10

ChRh11

ChRh12

Lymphocyte Specific Expression

% T

otal

Rea

ds

MHC Class I AllelesMafa-A

1*063:01

Mafa-A2*05g

Mafa-A4*01:01

Mafa-B*104:01:01

Mafa-B*134:02:01

Mafa-B*144:02:01

Mafa-B*064:01:01

Mafa-B*057:01:01

Mafa-B*046:01:01

Mafa-B*131:02

Mafa-B*152:01N

Mafa-B*060:05:02

Mafa-E*01g

Mafa-E*01:nov:09

0

5

10

15

20

25

30

35

40

45

50CD16

CD20

CD4

CD8

CD14

ROGER: INSERT ADDITIONAL DATA SLIDES?

Same methods applicable to HLA typing

• We have developed a similar assay to genotype human samples: HLA Class I and DRB loci

• Cheaper, higher-resolution, and higher-throughput than existing methods

• Can genotype up to 96 individuals per GS-Jr run

High Resolution HLA Genotyping

3 45 87 129 171 213 255 297 339 381 423 465 507 549 591 633 675 717 759 801 843 885 927 969 1011 1053 10950

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1kb-F / 581-R (Amplicon 1)

LP α1 Domain α2 Domain α3 Domain CTTM

581-F / 1kb-R bp SBT (Amplicon 2)

High-resolution Typing for 40 Reference Cell Lines

UW ID# A* B* C*HLA-Ref01 A*31:01:02   B*51:01:01   C*15:02:01  HLA-Ref02 A*32:01:01   B*38:01:01   C*12:03:01:01/02  HLA-Ref03 A*02:16 A*03:01:01:01/03 B*51:01:01   C*07:04:01 C*15:02:01HLA-Ref04 A*24:02:01:01/02L A*26:02 B*40:06:01:01/02 B*51:01:01 C*08:01:01 C*14:02:01HLA-Ref05 A*30:01:01   B*13:02:01   C*06:02:01:01/02  HLA-Ref06 A*02:01:01:01/02L/03 A*02:07 B*46:01:01   C*01:02:01  HLA-Ref07 A*33:03:01   B*44:03:01   C*14:03  HLA-Ref08 A*30:01:01 A*68:02:01:01/02/03 B*42:01:01   C*1701  HLA-Ref09 A*02:06:01 A*11:01:01 B*15:01:01:01 B*35:01:01:01/02 C*03:03:01 C*04:01:01:01/02/03HLA-Ref10 A*26:01:01   B*08:01:01   C*07:01:01  HLA-Ref11 A*02:04   B*51:01:01   C*15:02:01  HLA-Ref12 A*03:01:01:01/03   B*47:01:01:01/02   C*06:02:01:01/02  HLA-Ref13 A*01:01:01:01   B*57:01:01   C*06:02  HLA-Ref14 A*02:01:01:01/02L/03   B*35:03:01   C*12:03:01:01/02  HLA-Ref15 A*02:01:01:01/02L/03   B*35:01:01:01/02   C*04:01:01:01/02/03  HLA-Ref16 A*34:01:01   B*15:21 B*15:35 C*04:03 C*07:02:01:01/02/03HLA-Ref17 A*02:01:01:01/02L/03   B*15:01:01:01   C*03:04:01:01/02  HLA-Ref18 A*01:01:01:01   B*49:01:01   C*07:01:01  HLA-Ref19 A*25:01   B*51:01:01   C*01:02  HLA-Ref20 A*30:02:01   B*18:01:01:01   C*05:01:01:01/02  HLA-Ref21 A*01:01:01:01 A*02:05:01 B*08:01:01 B*50:01:01 C*06:02:01:01/02 C*07:01:01HLA-Ref22 A*01:01:01:01 A*03:01:01:01/03 B*07:02:01 B*58:01:01 C*07:01:01 C*07:02:01:01/02/03HLA-Ref23 A*01:01:01 A*02:01 B*05:801 B*07:02 C*07:01 C*07:02HLA-Ref24 A*01:01:01:01 A*24:02:01:01/02L B*39:06:02 B*58:01:01 C*07:01:01 C*07:02:01:01/02/03HLA-Ref25 A*01:01:01:01 A*01:37 B*35:01:01:01/02 B*58:01:01    HLA-Ref26 A*03:01:01:01/03   B*07:02:01 B*35:01:01:01/02 C*04:01:01:01/02/03 C*07:02:01:01/02/03HLA-Ref27 A*03:01:01:01/03   B*07:02:01 B*35:01:01:01/02 C*04:01:01:01/02/03 C*07:02:01:01/02/03HLA-Ref28 A*01:01:01:01 A*03:01:01:01/03 B*35:01:01:01/02 B*58:01:01 C*04:01:01:01/02/03 C*07:18 (701?)HLA-Ref29 A*03:01:01:01/03 A*24:02:01:01/02L B*35:01:01:01/02 B*51:01:04 C*04:01:01:01/02/03 C*07:04:01HLA-Ref30 A*02:01:01:01/02L/03 A*03:01:01:01/03 B*07:02:01 B*37:01:01 C*06:02:01:01/02 C*07:02:01:01/02/03HLA-Ref31 A*01:01:01:01 A*24:02:01:01/02L B*39:06:02 B*58:01:01 C*07:01:01 C*07:02:01:01/02/03HLA-Ref32 A*24:02:01:01/02L   B*07:02:01 B*51:01:01 C*07:117  HLA-Ref33 A*03:01:01:01/03   B*07:02:01 B*35:01:01:01/02 C*04:01:01:01/02/03 C*07:02:01:01/02/03HLA-Ref34 A*03:01:01:01/03 A*24:02:01:01/02L B*35:01:01:01/02 B*39:06:02 C*04:01:01:01/02/03 C*07:02:01:01/02/03HLA-Ref35 A*02:01:01:01/02L/03 A*24:02:01:01/02L B*07:02:01 B*13:02:01 C*06:02:01:01/02 C*07:02:01:01/02/03HLA-Ref36 A*24:02:01:01/02L A*31:01:02 B*07:02:01 B*40:01:02 C*03:04:01:01/02 C*07:02:01:01/02/03HLA-Ref37 A*02:01:01:01/02L/03 A*24:02:01:01/02L B*15:01:01:01 B*39:06:02 C*03:03:01 C*07:02:01:01/02/03HLA-Ref38 A*3402 A*7401 B*801 B*1503 C*02:10 C*701HLA-Ref39 A*2308N A*301 B*440301 B*5129 C*02:02:02 C*04HLA-Ref40 A*02:01:01:01/02L/03 A*29:02:01 B*35:01:01:01/02 B*44:03:01 C*04:01:01:01/02/03 C*16:01:01

Example High-Resolution HLA Genotypes with DRB

Sample AlleleReads 1kbF 581F 581R 1kbR DRB-F DRB-R

HIV_114 A*36:01 122 35 41 23 23    HIV_114 A*68:01:01 150 50 45 50 5    HIV_114 B*41:02:01 74 16 24 25 9    HIV_114 B*53:01:01 223 36 87 61 39    HIV_114 C*04:01:01 99 14 52 13 20    HIV_114 C*17:01:01 (primer) 45 2 32 2 9    HIV_114 DRB1*01:02:01 163         83 80HIV_114 DRB1*16:02:01 127         65 62HIV_114 DRB5*02-novel? 60         60 .

HIV_115 A*03:01:01 60 24 16 7 13    HIV_115 A*11:01:01 70 32 16 9 13    HIV_115 B*07:02:01 120 28 48 12 32    HIV_115 B*51:01:01 177 53 53 35 36    HIV_115 C*07:02:01 62 30 15 16 1    HIV_115 C*15:02:01 109 60 20 19 10    HIV_115 DRB1*04:04:01 165         86 79HIV_115 DRB1*07:01:01 228         114 114HIV_115 DRB4*01:01:01:01 93         75 18HIV_115 DRB4*01:03:01:01 99         75 24

HIV_116 A*01:01:01 122 37 31 49 5    HIV_116 A*02:01:01 97 40 17 31 9    HIV_116 B*08:01:01 213 57 71 63 22    HIV_116 B*15:01:01 129 21 58 32 18    HIV_116 C*03:04:01 103 27 43 21 12    HIV_116 C*07:01:01 114 46 22 41 5    HIV_116 DRB1*03:01:01 471         244 227HIV_116 DRB1*04:01:01 429         221 208HIV_116 DRB3*01:01:02 137         74 63HIV_116 DRB4*01:03:01:01 176         101 75

Sample Allele Reads 1kbF 581F 581R 1kbR DRB-F DRB-RHIV_117 A*26:01:01 167 24 74 40 29    HIV_117 A*29:02:01 96 24 31 24 17    HIV_117 B*44:03:01 (putative) 286 112 53 59 62    HIV_117 B*44:10 (putative) 210 113 51 46 .    HIV_117 C*04:01:01 245 38 130 26 51    HIV_117                HIV_117 DRB1*03:01:01 173         94 79HIV_117 DRB1*07:01:01 171         81 90HIV_117 DRB3*02:02:01 50         25 25HIV_117 DRB4*01:03:01:01 44         29 15

HIV_118 A*02:01:01 117 33 46 24 14    HIV_118 A*23:01:01 156 42 61 39 14    HIV_118 B*40:01:02 113 13 50 35 15    HIV_118 B*44:03:01 206 51 81 63 11    HIV_118 C*03:04:01 84 7 47 15 15    HIV_118 C*14:03 142 28 61 31 22    HIV_118 DRB1*04:01:01 151         80 71HIV_118 DRB1*10:01:01 195         96 99HIV_118 DRB4*01:03:01:01 57         33 24

HIV_119 A*29:01:01:01 36 13 7 10 6    HIV_119 A*68:01:02 73 36 12 20 5    HIV_119 B*07:05:01 48 12 11 7 18    HIV_119 B*44:02:01:01 86 41 15 26 4    HIV_119 C*05:01:01 47 25 5 10 7    HIV_119 C*15:05:01/02 63 26 15 11 11    HIV_119 DRB1*04:04:01 233         89 144HIV_119 DRB1*07:01:01 250         105 145HIV_119 DRB4*01:03:01:01 77         33 44