1. Laboratory of Molecular Pathology Retreat - 10 MAR 2011
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

2. 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

3. 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

4. MHC-mediated antigen presentation

5. HLA allele counts IMGT HLA – March 2011 HLA-A HLA-B HLA-C
1519 (1119) (1601) (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 the
various alleles at each locus.
1634 new alleles were described in 2010 alone.

6. HLA and autoimmune disease
HLA Allele
Relative Risk
Ankylosing spondylitis
B27
87.4
Postgonococcal arthritis
14.0
Acute anterior uveitis
14.6
Rheumatoid arthritis
DR4
5.8
Chronic active hepatitis
DR3
13.9
Sjogren syndrome
9.7
Insulin-dependent diabetes
DR3/DR4
14.3
21-Hydroxylase deficiency
BW47
15.0
Robbins Pathologic Basis of Disease 6th Edition (1999)

7. 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: (2002)
Figure4 Associations of HLA class 1 types with progression to AIDS. Relative hazard (RH) values were determined for each of the 54 HLA types used. Bars represent 95% confidence intervals (CI) for each RH. Class I types with CI that cross the line representing a RH of 1 are not significant, whereas all others are significant at p < All B*35 alleles combined are significantly associated with progression to AIDS, as is B*53. However, B*35PY, a subset of the B*35 group of alleles, is not significant, whereas the B*35Px subgroup is strongly associated with AIDS progression.

8. HLA and adverse drug reaction
HLA allele
Drug sensitivity
Association
Prevalence
B*1502
cabamazepine (epilepsy)
p = 3 x 10-27
high Chinese
absent Caucasians
B*5701
abacavir (HIV)
p = 5 x 10-20
high Caucasians
absent Africans, Hispanics
B*5801
allopurinol (gout)
p = 5 x 10-24
P. Parham

9. HLA Allele Nomenclature
Locus
Asterisk
Allele family
(serological
where
possible)
Amino
acid
difference
Non-coding
(silent)
polymorphism
Intron,
3’ or 5’
polymorphism
N = null
L = low
S = Sec.
A = Abr.
Q = Quest.
The hierarchical relationships between alleles that are implied in the allele nomenclature only partially captures the complex sequence relationships between HLA alleles. For example, although all subtypes of the A*24 family share some level of sequence similarity, the relationships between subtypes are not further captured in the four digit nomenclature.

10. DRB1 phylogeny
DRB1*15
DRB1*16
DRB1*04
DRB1*10
DRB1*09
DRB1*07

11. DRB1 phylogeny
DRB1*13
DRB1*13
DRB1*13
DRB1*13
DRB1*13
DRB1*13

12. DRB1 phylogeny
DRB1*15
DRB1*16
DRB1*04
DRB1*10
DRB1*09
DRB1*07

13. DRB1 alignment
07/15
07/09
09/15

14. 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
Focused regions in the HLA genes/proteins that effect gene expression and/or protein function are responsible for enhanced disease risk

15. 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

16. 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

20. Summary of SFs defined
1775 total

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

22. Representative Sequence Features Variant Types

23. 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

24. DRB1*0101 Visualization

25. Composite SF- Risk and Protective Variants

26. DRB1*0101 Visualization protective risk 67F 70D 71R 86V 26F 37Y 30Y
86G
37F
30L
28E
protective
risk

27. Publication

28. 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

29. 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

30. Overlap of top 100 SFVTs 72 28 75 Anti-centromere SFVTs
Anti-topoisomerase SFVTs
Purpose of or the points made by this slide:
Comparison of SFVT analysis results of SSc patients based on presence/absence of different auto-antibodies: Anti-centromere vs Anti-topoisomerase
To introduce the data considered (the top 100 ranked SFVTs) in comparing the significant SFVTs between the anti-centromere and anti-topoisomerase SFVT results (please note that the number of SFVTs are different because of the tied rankings)
Caveats:
The counts of SFVTs have not taken into account the duplicity of SFVTs (due to the sharing of exactly same variant positions in the dataset). But when I cursorily looked, it doesn’t seem to change the distribution as the duplicity will lower the counts across the board for both the groups. (I am working on getting those numbers but was taking long. But I can get that to you by today if necessary)

31. 28 common SFVTs 10 18 18 10 Anti-centromere SFVTs
Anti-topoisomerase SFVTs
Risky
Protective
Purpose of or the points made by this slide:
Distribution of the 28 overlapping SFVTs between the anti-centromere and anti-topoisomerase data with respect to risky and protective SFVTs
Risky SFVTs defined by odds ratio > 1.0
Protective SFVTs defined by odds ratio < 1.0
To show that the significant SFVTs overlap across the two dataset but the overlapping SFVTs do not share any SFVTs in the same direction of risk or protection indicating that those SFVTs are all in opposite direction (i.e. risky in one while protective on the other) 18 10 10 18
Anti-centromere
Anti-topoisomerase
Anti-centromere
Anti-topoisomerase

32. 39 40 21 18 22 2 10 30 12 40 Risky vs Risky Risky vs Protective40 21 18 22
Anti-centromere risky SFVTs
Anti-topoisomerase risky SFVTs
Anti-centromere risky SFVTs
Anti-topoisomerase risky SFVTs
Protective vs Risky
Protective vs Protective
Purpose of or the points made by this slide:
Comparison of risky and protective SFVTs in the overlapping Sequence features
This again illustrates the overlap of the SFVTs between the datasets and the direction of risk and protection shared by those SFVTs between the two auto-antibody dataset
This also shows that the though there is significant overlap of the SFs as shown in slide 7 and slide 8 (divided by risky and protective SFVTs), there are different SFVTs belonging to the same SFs that are significantly associated with risk (and protection) between the different autoantibody SFVT results 2 10 30 12 40
Anti-centromere protective SFVTs
Anti-topoisomerase risky SFVTs
Anti-centromere protective SFVTs
Anti-topoisomerase protective SFVTs

33. Anti-centr 9W_28E_30C_47Y_67L Anti-topo 9E_28D_30Y_47F_67F
Table 7. Some of the SFVTs significantly associated with the presence of anti-centromere 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_VT1
28E_30C_47Y_61W_67L_71R
120
145
6.76 e-16
Hsa_HLA-DRB1_SF142_VT1
9W_56P_57D_60Y_61W_67L
2.87
124
155
1.10 e-15
Hsa_HLA-DRB1_SF130_VT1
60Y_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
Anti-centr 9W_28E_30C_47Y_67L
Anti-topo 9E_28D_30Y_47F_67F
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*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
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_VT15
60Y_67F_70D_71R_77T_78Y_81H_82N_85V
2.26
370
9.93 e-12
Hsa_HLA-DRB1_SF98_VT3
67F
2.11
156
413
3.72 e-11
Purpose of or the points made by this slide:
To show examples of some of the alleles and the SFVTs that are significant with risk
The p-values of the SFVTs are much lower than that of the alleles
Hsa_HLA-DRB1_SF137_VT (all SSc) e-07

34. 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
TCR Binding

35. Summary SFVT Approach Systemic Sclerosis Analysis
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 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

36. 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 -
Source: World Health Organization -

37. 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, million deaths
2009 pandemic
H1N1
>16,000 deaths as of March 2010

38. Influenza Virus Orthomyxoviridae family Negative-strand RNA Segmented
Enveloped
8 RNA segments encode
11 proteins
Classified based on serology of HA and NA

39. Influenza A_NS1_nuclear-export-signal_137(10)
SFVT approach
Influenza A_NS1_alpha-helix_171(17)
VT-1 I F D R L E T L I L
VT-2 I F N R L E T L I L
VT-3 I F D R L E T I V L
VT-4 L F D Q L E T L V S
VT-5 I F D R L E N L T L
VT-6 I F N R L E A L I L
VT-7 I Y D R L E T L I L
VT-8 I F D R L E T L V L
VT-9 I F D R L E N I V L
VT-10 I F E R L E T L I L
VT-11 L F D Q M E T L V S
Influenza A NS1 protein (PDB 2GX9) crystal structure showing
Nuclear Export Signal “Sequence Feature” (SF) highlighted in Red
Alpha-helix SF highlighted in green
Amino acid alignment with colors showing variation within nuclear export signal region
Each sequence with 1+ substitutions comprises a unique fingerprint or “Variant Type” (VT)
A set of unique sequence substitutions existing within any defined region is a sequence feature variant type (SFVT)
Statistical analyses on SFVTs can identify genotype-phenotype relationships
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)

40. Influenza A Sequence Features as of January 2011
Protein
Subtype
Functional
Structural
Immune Epitopes
Total Count
PB2 - 7 10
564
585
PB1-F2 2 6
PB1 5
733
744 PA 1 29
534
565
NS2 3 78 83
NS1 21 15
458
494 NP 25
472
512 NA N1 26
113
153 N2 9 59
106
180 M2 4 96
116 M1 12 14
286
312 HA H1 37
335
376 H2 20 34 H3
390
481 H5 40 65 H7
Total 97
319
4227
4709

41. NS1 Sequence Features

42. VT for SF8 (nuclear export signal)

43. VT-1 strains

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

45. VT for SF8 (nuclear export signal)

47. 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

48. VT-10 strains

49. VT for SF8 (nuclear export signal)

50. VT lineages

51. VT-10 lineage

52. VT-4 lineage

53. VT-4 strains

54. VT-4 lineage = B allele/group

55. VT-15 & VT-8 lineages

56. VT-5 strains

57. 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?

58. 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. Influenza SFVT Acknowledgments
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
Toru Takimoto, Rochester
Summer Galloway, Emory
Robert Lamb, Northwestern
Benjamin Hale, Mt. Sinai
USDA
David Suarez
Sage Analytica
Robert Taylor
Lone Simonsen
CEIRS Centers