Sequence Feature Variant Type (SFVT) Method: HLA Associations with Systemic Sclerosis

Genetic Determinants of Influenza Virus Host Range Restriction

Y. Megan Kong, Nishanth Marthandan, Paula Guidry, Jyothi Noronha, R. Burke Squires, Elizabeth McClellan, Mengya Liu, Yu Qian,

David Dougall, Jie Huang, Diane Xiang, Brett Pickett, Victoria Hunt, Young Kim, Jeff Wiser, Thomas Smith, Jonathan Dietrich, Edward Klem, Lindsay Cowell, Nancy Monson, David Karp, Richard H. Scheuermann

Laboratory of Molecular Pathology Retreat - 10 MAR 2011

Abstracts & Posters – Immunology

• HLA Research Data, Reference Data, Visualization Tools and Analysis Tools in ImmPort– Paula A. Guidry, Nishanth Marthandan, Thomas Smith, Patrick Dunn, Steven J. Mack, Glenys Thomson, Jeffrey

Wiser, David R. Karp, Richard H. Scheuermann

• Creating a Cell Detail Page for Hematopoietic Cells in ImmPort– David S. Dougall, Shai Shen-Orr, John Campbell, Yue Liu, Patrick Dunn, Y. Megan Kong, Mark M. Davis,

Richard H. Scheuermann

• Minimum Information about a Genotyping Experiment– Jie Huang, Nishanth Marthandan, Alexander Pertsemlidis, LiangHao Ding, Julia Kozlitina, Joseph Maher, Nancy

Olsen, Jonathan Rios, Michael Story, Chao Xing, Richard H. Scheuermann

• Translational Research in ImmPort– Y. Megan Kong, Carl Dalke, Diane Xiang, Max Y. Qian, David Dougall, David Karp, Richard H. Scheuermann

• Potential of a Unique Antibody Gene Signature to Predict Conversion to Clinically Definite Multiple Sclerosis

– A.J. Ligocki, L. Lovato, D. Xiang, P. Guidry, R.H. Scheuermann, S.N. Willis, S. Almendinger, M.K. Racke, E.M. Frohman, D.A. Hafler, K.C. O'Connor, N.L. Monson

• Analysis of DRB1 Sequence Feature Variant Type Associations with Systemic Sclerosis Autoantibodies Types and Racial Groups

– Nishanth Marthandan, Paula Guidry, Glenys Thomson, Frank Arnett, David R. Karp, Richard H. Scheuermann

• An automated analysis and visualization pipeline for identification and comparison of cell populations in high-dimensional flow cytometry data

– Yu Qian, David Dougall, Megan Kong, Paula Guidry, and Richard H. Scheuermann

Abstracts & Posters – Infectious Diseases

• Influenza Research Database (IRD): A Web-based Resource for Influenza Virus Data & Analysis – Victoria Hunt, R. Burke Squires, Jyothi Noronha, Ed Klem, Jon Dietrich, Chris Larsen, Richard H. Scheuermann

• Tool for Identifying Sequence Variations that Correlate with Virus Phenotypic Characteristics– Brett Pickett, Prabakaran Ponraj, Victoria Hunt, Mengya Liu, Liwei Zhou, Sanjeev Kumar, Jonathan Dietrich,

Sam Zaremba, Chris Larson, Edward B. Klem, Richard H. Scheuermann

• Conserved Epitope Regions (CER): Elucidation of Evolutionarily Stable, Immunologically Reactive Regions of Human H1N1 Influenza Viruses

– R. Burke Squires, Brett Pickett, Jyothi Noronha, Victoria Hunt, Richard H. Scheuermann

• Influenza NS1-dependent Host Range Restriction Demonstrated By Sequence Feature Variant Type Analysis

– Jyothi M. Noronha, R. Burke Squires, Mengya Liu, Victoria Hunt, Brett Pickett and Richard H. Scheuermann

MHC-mediated antigen presentation

HLA allele counts

HLA-A HLA-B HLA-C

1519 (1119) 2069 (1601) 1016 (750)

HLA-DRB HLA-DQA1 HLA-DQB1 HLA-DPA1 HLA-DPB1

966 (738) 35 (26) 144 (103) 28 (16) 145 (127)

MICA MICB TAP

73 (60) 31 (20) 11 (9)

Figures in parenthesis indicate the number of unique proteins encoded by thevarious alleles at each locus.1634 new alleles were described in 2010 alone.

IMGT HLA – March 2011

HLA and autoimmune disease

Disease HLA Allele Relative Risk

Ankylosing spondylitis B27 87.4

Postgonococcal arthritis B27 14.0

Acute anterior uveitis B27 14.6

Rheumatoid arthritis DR4 5.8

Chronic active hepatitis DR3 13.9

Sjogren syndrome DR3 9.7

Insulin-dependent diabetes DR3/DR4 14.3

21-Hydroxylase deficiency BW47 15.0

Robbins Pathologic Basis of Disease 6th Edition (1999)

HLA and infectious disease

• Correlation between HLA genotype and HIV viral burden and progression to AIDS

• M Dean, M Carrington and SJ O'Brien Annual Review of Genomics and Human Genetics Vol. 3: 263-292 (2002)

HLA and adverse drug reaction

HLA allele Drug sensitivity Association Prevalence

B*1502 cabamazepine (epilepsy) p = 3 x 10-27 high Chineseabsent Caucasians

B*5701 abacavir (HIV) p = 5 x 10-20 high Caucasiansabsent Africans, Hispanics

B*5801 allopurinol (gout) p = 5 x 10-24 high Chinese

P. Parham

HLA Allele Nomenclature

HLA - A * 24 02 01 01

Locus Asterisk Allele family(serological

where possible)

Aminoacid

difference

Non-coding(silent)

polymorphism

Intron, 3’ or 5’

polymorphism

N = nullL = low

S = Sec.A = Abr.

Q = Quest.

HLA - A * 24 02 01 02 L

10

DRB1 phylogeny

DRB1*07

DRB1*09

DRB1*10

DRB1*04

DRB1*16

DRB1*15

DRB1 phylogenyDRB1*13

DRB1*13

DRB1*13

DRB1*13

DRB1*13

DRB1*13

12

DRB1 phylogeny

DRB1*07

DRB1*09

DRB1*10

DRB1*04

DRB1*16

DRB1*15

DRB1 alignment07/15 07/09 09/15

HLA–mediated disease predisposition

• Hypothesis: – While the allelic/haplotypic structures reflect evolutionary history

of the locus, it is the focused regions in the HLA genes/proteins that effect gene expression, protein structure and/or protein function that are responsible for enhanced disease risk

Summary of SFVT approach

• Define individual sequence features (SF) in HLA proteins (genes)

• Determine the extent of polymorphism for each sequence feature by defining the observed variant types (VT)

• Re-annotate HLA typing information with complete list of VT for each SF

• Examine the association between every sequence feature variant type and disease or other phenotype

Representative Sequence Features

17

A*0201 - ‘peptide binding’ SF

18

A*0201 - ‘peptide binding pocket B’ SF

19

A*0201 - ‘CD8 binding’ & ‘TCR binding’ SF

CD8 Binding

TC

R B

inding

Summary of SFs defined

1775 total

Variant Types for Hsa_HLA-DRB1_beta-strand 2_peptide antigen binding

Representative Sequence Features Variant Types

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HLA SFVT Association with Systemic Sclerosis

• Summary of data set– Systemic sclerosis (SSc, scleroderma) is a chronic condition characterized by

altered immune reactivity, thickened skin, endothelial dysfunction, interstitial fibrosis, gangrene, pulmonary hypertension, gastrointestinal tract dysmotility, and renal arteriolar dysfunction.

– A large cohort of ~1300 SSc patients and ~1000 healthy controls has been assembled by Drs. Frank C. Arnett, John Reveille and colleagues at the University of Texas Health Science Center at Houston.

– Information on autoantibody reactivity for over 15 nuclear antigens is available.– 4-digit typing has been done for DRB1, DQA1, and DQB1 in all individuals.

• Initial re-annotation of 4 digit DRB1 typing data– DRB1*1104 => SF1_VT43; SF2_VT4; SF3_VT12 ………

• Statistical analysis– Split data set into two - pseudo-replicates– 2 x n contingency table for every SF (286), where n = number of VT– Chi-squared or Fisher’s Exact Test analysis – Select SF with adjusted p-value <0.01 (83/286)– 2 x 2 contingency table (type vs non-type) for every VT (418 total)– Merge results of pseudo-replicates

DRB1*0101 Visualization

Composite SF- Risk and Protective Variants

DRB1*0101 Visualization

67F 70D

71R

86V

26F

37Y

30Y28D

67I 70D

71R

86G

26F

37F

30L28E

protective risk

Publication

Limitations to initial study

• Did not take into account difference in allele frequency distributions in different racial populations

• Treated SSc as a single disease– limited cutaneous involvement associated with pulmonary

hypertension; 60-70% are anti-centromere positive

– diffuse cutaneous involvement associated with more interstitial lung disease and kidney involvement; 30% are anti-topo positive

– the two antibodies tend to be mutually exclusive

Auto-antibody SFVT associations

• Separated SSc participants based on presence of anti-topoisomerase or anti-centromere auto-antibody (cases only)– 231 anti-topoisomerase

– 318 anti-centromere

– 3 both

– 752 neither

• SSc with anti-topo vs SSc without anti-topo

• SSc with anti-cent vs SSc without anti-cent

2872 75

Anti-centromere SFVTs

Anti-topoisomerase SFVTs

Overlap of top 100 SFVTs

018 10

Risky

Anti-centromere Anti-topoisomerase

Anti-centromere SFVTs Anti-topoisomerase SFVTs

010 18

Protective

Anti-centromere Anti-topoisomerase

28 common SFVTs

039 40

Risky vs Risky

Anti-centromere risky SFVTs

Anti-topoisomerase risky SFVTs

1821 22

Risky vs Protective

Anti-centromere risky SFVTs

Anti-topoisomerase risky SFVTs

102 30

Protective vs Risky

Anti-centromere protective SFVTs

Anti-topoisomerase risky SFVTs

012 40

Protective vs Protective

Anti-centromere protective SFVTs

Anti-topoisomerase protective SFVTs

Table 7. Some of the SFVTs significantly associated with the presence of anti-centromere autoantibody

Table 8. Some of the SFVTs significantly associated with presence of anti-topoisomerase autoantibody

Sequence Feature Variant Type (SFVT)

Variant Type Definition Odds ratio

No. of case alleles

No. of control alleles

Corrected p-value

DRB1*0101 2.96 100 116 4.55 e-13

DRB1*0401 2.09 62 96 4.91 e-04

DRB1*0801 2.64 30 36 3.29 e-04

Hsa_HLA-DRB1_SF163_VT1 67L_70Q_71R 2.52 194 296 1.28 e-17

Hsa_HLA-DRB1_SF137_VT128E_30C_47Y_61W_67L_71R

2.96 120 145 6.76 e-16

Hsa_HLA-DRB1_SF142_VT19W_56P_57D_60Y_61W_67L

2.87 124 155 1.10 e-15

Hsa_HLA-DRB1_SF130_VT160Y_67L_70Q_71R_77T_78Y_81H_82N_85V 2.44 174 267 4.38 e-15

Hsa_HLA-DRB1_SF98_VT1 67L 2.06 343 727 1.16 e-14

Sequence Feature Variant Type (SFVT)

Variant Type Definition Odds ratio

No. of case alleles

No. of control alleles

Corrected p-value

DRB1*1501 1.78 65 180 8.17 e-03

DRB1*1104 3.70 74 105 1.67 e-06

Hsa_HLA-DRB1_SF163_VT8 67F_70D_71R 2.22 149 375 1.72 e-11

Hsa_HLA-DRB1_SF137_VT25 28D_30Y_47F_61W_67F_71R 2.71 105 208 2.56 e-13

Hsa_HLA-DRB1_SF142_VT11 9E_56P_57D_60Y_61W_67F 2.72 137 285 1.85 e-16

Hsa_HLA-DRB1_SF130_VT1560Y_67F_70D_71R_77T_78Y_81H_82N_85V

2.26 149 370 9.93 e-12

Hsa_HLA-DRB1_SF98_VT3 67F 2.11 156 413 3.72 e-11

Anti-centr 9W_28E_30C_47Y_67LAnti-topo 9E_28D_30Y_47F_67F

Hsa_HLA-DRB1_SF137_VT25 (all SSc) 1.85 1.38 e-07

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ImmPort HLA SFVT Workflow

Table of subject vs. HLA 4-digit typing data Table of subject vs. SFVT feature vector

Table of p-values, adj. p-values, odds ratio, confidence intervals

CD8 Binding

TC

R B

inding

Summary

• SFVT Approach– Proposed a novel approach for HLA disease associations based on sequence feature variant

type analysis (SFVT)– Defined structural and functional protein sequence features (SF) for all classical human MHC

class I and II proteins– Determined variant types (VT) for all SF in known alleles– Available in ImmPort www.immport.org, IMGT-HLA and dbMHC

• Systemic Sclerosis Analysis– Based on the SFVT approach, identified a region of the HLA-DRB1 protein centered around

peptide-binding pocket 7 that appears to be associated with disease risk– Sequences found in HLA-DRB1*1104 at positions 28, 30, 37, 67 and 86, especially with

aromatic amino acids, were associated with increase disease risk– Sequences found in this region of HLA-DRB1*0302 appear to be protective– Different alleles are associated with altered risk in different racial/ethnic populations, but

they share common SFVTs– SFVTs associated with risk of developing SSc are different in patients with anti-topo versus

anti-cent antibodies, supporting the idea that these are distinct disease– However, the risk-associated SFVTs are from the same SFs suggesting a common

mechanism of disease pathogenesis

Public Health Impact of Influenza

• Seasonal flu epidemics occur yearly during the fall/ winter months and result in 3-5 million cases of severe illness worldwide.

• More than 200,000 people are hospitalized each year with seasonal flu-related complications in the U.S.

• Approximately 36,000 deaths occur due to seasonal flu each year in the U.S.

• Populations at highest risk are children under age 2, adults age 65 and older, and groups with other comorbidities.

Source: World Health Organization - http://www.who.int/mediacentre/factsheets/fs211/en/index.html

Flu pandemics of the 20th and 21st centuries

• 1918 flu pandemic (Spanish flu)– H1N1 subtype

– The most severe pandemic

– Estimated to claim 2.5% - 5% of world’s population (20 – 100 million deaths)

• Asian flu (1957 – 1958)– H2N2 subtype

– 1 – 1.5 million deaths

• Hong Kong flu (1968 – 1969)– H3N2 subtype

– 750,000 - 1 million deaths

• 2009 pandemic – H1N1

– >16,000 deaths as of March 2010

Influenza Virus

Orthomyxoviridae familyNegative-strand RNASegmentedEnveloped

8 RNA segments encode11 proteinsClassified based on serology of HA and NA

SFVT approach

VT-1 I F D R L E T L I LVT-2 I F N R L E T L I LVT-3 I F D R L E T I V LVT-4 L F D Q L E T L V SVT-5 I F D R L E N L T LVT-6 I F N R L E A L I LVT-7 I Y D R L E T L I LVT-8 I F D R L E T L V LVT-9 I F D R L E N I V LVT-10 I F E R L E T L I LVT-11 L F D Q M E T L V S

Influenza A_NS1_nuclear-export-signal_137(10)

• Identify regions of protein/gene with known structural or functional properties – Sequence Features (SF)• an alpha-helical region, the binding site for another protein, an enzyme active site, an

immune epitope• Determine the extent of sequence variation for each SF by defining each unique sequence as

a Variant Type (VT)• High-level, comprehensive grouping of all virus strains by VT membership for each SF

independently• Genotype-phenotype association statistical analysis (virulence, pathogenesis, host range,

immune evasion, drug resistance)

Influenza A_NS1_alpha-helix_171(17)

Protein Subtype Functional Structural Immune Epitopes Total Count

PB2 - 7 10 564 585

PB1-F2 - 2 2 - 6

PB1 - 6 5 733 744

PA - 1 29 534 565

NS2 - 2 3 78 83

NS1 - 21 15 458 494

NP - 10 25 472 512

NA N1 10 26 113 153

NA N2 9 59 106 180

M2 - 4 10 96 116

M1 - 12 14 286 312

HA H1 4 37 335 376

HA H2 4 10 20 34

HA H3 2 59 390 481

HA H5 3 14 40 65

HA H7 - 1 2 3

Total 97 319 4227 4709

Influenza A Sequence Features as of January 2011

NS1 Sequence Features

VT for SF8 (nuclear export signal)

VT-1 strains

DO VARIATIONS IN NS1 SEQUENCE FEATURES INFLUENCE INFLUENZA VIRUS HOST RANGE?

VT for SF8 (nuclear export signal)

Causes of apparent NS1 VT-associated host range restriction

• Virus spread = capability + opportunity– Phenotypic property of the virus – limited capacity

– Restricted founder effect – limited opportunity• Restricted spatial-temporal distribution

• Sampling bias – assumption of random sampling– Oversampling – avian H5N1 in Asia; 2009 H1N1

– Undersampling – large and domestic cats

• Linkage to causative variant

VT-10 strains

VT for SF8 (nuclear export signal)

VT lineages

VT-10 lineage

VT-4 lineage

VT-4 strains

VT-4 lineage = B allele/group

VT-15 & VT-8 lineages

VT-5 strains

Summary• Compiling list of all known influenza protein sequence features (SFs) in

IRD• Observed dramatic skewing in NS1 SFVT host distributions• In some cases, attributable to sampling biases

– VT-1 and Avian H5N1 due to Asian sampling in mid-2000's– VT-2 and human due to 2009 pandemic H1N1– VT-11 and Other (Environment) in Delaware Bay

• Performing multivariate statistical analysis to control for confounding variables

• In other cases, attributable to founder effects– VT-13 and -14 and Viet Nam 2003

• However, in other cases these explanations do not appears to be consistent with the data, suggesting that these may indeed be NS1-mediated host range restrictions– Equine VT-10 lineage– Avian VT-4 lineage (B allele/group)– Human VT-8 lineage– Human VT-15 lineage

• Nuclear export vs linkage disequilibrium?

HLA SFVT Acknowledgements

BISC ImmPort Team

• David Karp (UTSW)

• Nishanth Marthandan (UTSW)

• Paula Guidry (UTSW)

• Frank C. Arnett (UTH)

• John Reveille (UTH)

• Chul Ahn (UTSW)

• Glenys Thompson (Berkeley)

• Tom Smith (NG)

• Jeff Wiser (NG)

DAIT HLA Working Group• David DeLuca (Hannover)• Raymond Dunivin (NCBI)• Michael Feolo (NCBI)• Wolfgang Helmberg (Graz)• Steven G. E. Marsh

(ANRI)• David Parrish (ITN)• Bjoern Peters (LIAI)• Effie Petersdorf (FHCRC)• Matthew J. Waller (ANRI)

Sequence Ontology WG• Michael Ashburner

(Cambridge)• Lindsay Cowell (UTSW)• Alexander D. Diehl

(Buffalo) • Karen Eilbeck (Utah)• Suzanna Lewis (LBNL)• Chris Mungall (LBNL)• Darren A. Natale

(Georgetown)• Barry Smith (Buffalo)

With support from NIAID N01AI40076

59

• U.T. Southwestern– Richard Scheuermann– Burke Squires– Jyothi Noronha– Mengya Liu– Victoria Hunt– Shubhada Godbole– Brett Pickett– Ayman Al-Rawashdeh

• MSSM– Adolfo Garcia-Sastre– Eric Bortz– Gina Conenello– Peter Palese

• Vecna– Chris Larsen– Al Ramsey

• LANL– Catherine Macken– Mira Dimitrijevic

• U.C. Davis– Nicole Baumgarth

• Northrop Grumman– Ed Klem– Mike Atassi– Kevin Biersack– Jon Dietrich– Wenjie Hua– Wei Jen– Sanjeev Kumar– Xiaomei Li– Zaigang Liu– Jason Lucas– Michelle Lu– Bruce Quesenberry– Barbara Rotchford– Hongbo Su– Bryan Walters– Jianjun Wang– Sam Zaremba– Liwei Zhou

• IRD SWG– Gillian Air, OMRF– Carol Cardona, Univ. Minnesota– Adolfo Garcia-Sastre, Mt Sinai– Elodie Ghedin, Univ. Pittsburgh– Martha Nelson, Fogarty– Daniel Perez, Univ. Maryland– Gavin Smith, Duke Singapore– David Spiro, JCVI– Dave Stallknecht, Univ. Georgia– David Topham, Rochester– Richard Webby, St Jude

• SFVT experts– Gillian Air, OMRF– Toru Takimoto, Rochester– Summer Galloway, Emory– Robert Lamb, Northwestern– Benjamin Hale, Mt. Sinai

• USDA– David Suarez

• Sage Analytica– Robert Taylor– Lone Simonsen

• CEIRS Centers

Influenza SFVT Acknowledgments