Manual 21320389

Manual 21320389
Factors determining the composition of a public cord blood stem cell
bank including HLA diversity
by
Juanita Mellet
A dissertation submitted in fulfilment of the requirements for the degree
of Magister Scientiae (M.Sc) Immunology
in
The Department of Immunology
Faculty of Health Sciences
University of Pretoria
2013
Supervisor: Prof. MS Pepper
© University of Pretoria
ABSTRACT
The human leukocyte antigen (HLA) is the most polymorphic region in the human genome
and accounts for more than 10% of human diversity. This region plays an important role in
matching donors and recipients for transplantation. The South African Bone Marrow
Registry (SABMR) does not reflect the demographics of the South African population. The
large number of polymorphisms resulting from HLA diversity in the Black South African
population and their limited representation in the SABMR reduce the chances of finding
adequate matches between donors and recipients in this group. Umbilical cord blood is an
alternative to bone marrow for the treatment of fatal diseases. Less strict HLA matching is
required due to the naive nature of the T cells in cord blood. A public umbilical cord blood
bank is a necessity in trying to cater for the diverse population in South Africa. However, the
ethnic diversity of the South African population poses a great challenge in constituting a
public umbilical cord blood bank that is representative of the entire population. The Roche
designed next generation sequencing (NGS) high resolution (HR) HLA typing kit enables
sequencing of additional HLA exons and could improve the degree of matching between
individuals to ultimately decrease adverse reactions. An extensive study of the literature
was performed to establish the demographics, linguistics, and HLA diversity of the South
African population to determine how a public cord blood bank should be constituted. In
addition, HLA genotyping was performed by 454 NGS on 20 samples that had previously
been HLA typed by conventional methods. The 454 NGS technique made use of a Roche
designed medium and high resolution HLA typing kit to genotype the samples. It was
possible to assign accurate genotypes to 95.5% of the loci of interest for the total number of
20 samples using the MR kit, compared with 98.5% using the HR kit. In conclusion, the
present study indicates the extreme HLA diversity in the South African population, and
therefore, recommends constituting the first public umbilical cord blood bank in Gauteng on
the basis of race or major ethnic groupings. A minimum number of 10 000 cord blood units
is needed to initiate the bank. Furthermore, the 454 NGS platform together with the HR HLA
typing kit display potential as an alternative method to be used in a public cord blood bank,
as well as routine clinical and diagnostic laboratories, to ultimately improve HLA matching
between donors and recipients.
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ACKNOWLEDGEMENTS
To God, my Saviour.
My Supervisor, Professor Michael Pepper, for the opportunity that you have given me and
for your continued guidance and assistance throughout this study period. I truly appreciate
the commitment and time that went into this project.
My immediate family and friends, your encouragement, support, and love throughout the
past few years have made me reach my goal. I could not have done this without you!
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TABLE OF CONTENTS
LIST OF ABBREVIATIONS AND SYMBOLS ................................................................................... vi
LIST OF FIGURES ......................................................................................................................... x
LIST OF TABLES ........................................................................................................................ xiii
CHAPTER ONE: INTRODUCTION ................................................................................................. 1
1.1. Problem Statement ........................................................................................................ 4
1.2. Aim ................................................................................................................................ 4
1.3. Study Objectives............................................................................................................. 5
CHAPTER TWO: REVIEW OF THE LITERATURE ........................................................................... 7
2.1. The Immune System....................................................................................................... 7
2.2. The Major Histocompatibility Complex ....................................................................... 10
2.3. Discovery of the MHC/HLA region ............................................................................... 11
2.4. HLA Structure and Function ......................................................................................... 13
2.4.1. Class I HLA molecules ............................................................................................. 14
2.4.2. The Endogenous Pathway ...................................................................................... 16
2.4.3. Class II HLA molecules ............................................................................................ 17
2.4.4. The Exogenous Pathway ......................................................................................... 19
2.5. HLA Gene Expression and Regulation .......................................................................... 20
2.6. HLA Diversity ................................................................................................................ 23
2.7. Evolution, Selection, and Linkage Disequilibrium across the HLA region .................... 25
2.7.1. Evolution and Balancing Selection ......................................................................... 27
2.7.1.1. Heterozygous Advantage (over-dominant selection)...................................... 28
2.7.1.2. Frequency-dependant Selection ...................................................................... 29
2.7.2. Non-synonymous versus Synonymous mutations ................................................. 29
2.7.3. Linkage Disequilibrium ........................................................................................... 30
2.8. HLA Nomenclature ....................................................................................................... 31
2.9. HLA Ambiguities ........................................................................................................... 33
2.10. HLA Typing Applications .............................................................................................. 34
2.10.1. Transplantations ................................................................................................... 34
2.10.1.1. Solid Organ Transplantation .......................................................................... 35
2.10.1.2. Stem Cell Transplantation .............................................................................. 36
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2.10.2. Forensic and Anthropological studies .................................................................. 40
2.10.3. Disease Associations ............................................................................................. 41
CHAPTER THREE: DEMOGRAPHICS, LINGUISTICS, AND HLA DIVERSITY OF THE SOUTH
AFRICAN POPULATION ............................................................................................................. 45
3.1. Demographics and Linguistics ...................................................................................... 46
3.2. HLA Diversity ................................................................................................................ 54
CHAPTER FOUR: MATERIALS AND METHODOLOGY ................................................................ 58
4.1. Sample Selection and Quantification ........................................................................... 59
4.2. Integrity Check by Agarose Gel Electrophoresis .......................................................... 61
4.3. GS GType HLA Typing Kit and Primers ......................................................................... 63
4.4. Polymerase Chain Reaction.......................................................................................... 64
4.5. Amplicon Purification ................................................................................................... 67
4.6. Amplicon Quantitation ................................................................................................. 69
4.7. Amplicon Normalisation and Sample Pooling.............................................................. 69
4.8. Emulsion PCR and Sequencing ..................................................................................... 71
4.9. Software Analysis ......................................................................................................... 73
4.10. Statistical Analysis ....................................................................................................... 74
CHAPTER FIVE: CONVENTIONAL TECHNIQUES VERSUS NEXT GENERATION SEQUENCING FOR
HLA TYPING .............................................................................................................................. 75
5.1. Introduction ................................................................................................................. 75
5.2. Results and Discussion ................................................................................................. 77
5.2.1. Software Analysis.................................................................................................... 77
5.2.2. Allelic and Genotypic Agreement ........................................................................... 80
5.2.3. Unambiguous and Ambiguous Genotype Assignment ........................................... 84
5.2.4. Low to High Resolution HLA Typing........................................................................ 87
5.2.5. Technical Limitations .............................................................................................. 88
5.3. Concluding Remarks ..................................................................................................... 89
CHAPTER SIX: CONCLUDING REMARKS ................................................................................... 91
REFERENCES ............................................................................................................................. 95
APPENDIX A ............................................................................................................................ 111
APPENDIX B ............................................................................................................................ 113
APPENDIX C ............................................................................................................................ 114
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LIST OF ABBREVIATIONS AND SYMBOLS
α
Alpha
β
Beta
γ
Gamma
μl
Microlitre
A
Adenine
Ad12
Adenovirus 12
AE
Elution buffer
AIDS
Acquired immune deficiency syndrome
APC
Antigen presenting cell
ATP
Adenosine triphosphate
B2M
B2-microglobulin
bp
Base pair
C
Cytosine
CIITA
Class II transcription activator
CD
Cluster of differentiation
CLIP
Class II-associated invariant-chain peptide
CMV
Cytomegalovirus
ddH2O
Double distilled water
dH2O
Distilled water
DNA
Deoxyribonucleic acid
dNTP
Deoxynucleoside triphosphate
E
Exon
emPCR
Emulsion PCR
ER
Endoplasmic reticulum
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EtOH
Ethanol
g
Gram
G
Guanine
G-CSF
Granulocyte colony-stimulating factor
Gly
Glycine
GVHD
Graft-versus-host disease
HBV
Hepatitis B virus
HGP
Human genome project
HIV
Human immunodeficiency virus
HLA
Human leukocyte antigen
HR
High resolution
HSC
Hematopoietic stem cell
IFN-γ
Interferon gamma
Ig
Immunoglobulin
IMGT
International immunogenetics information system
Kb
Kilobase
kg
Kilogram
LD
Linkage disequilibrium
M
Molar
Mb
Megabase
MgCl2
Magnesium chloride
MHC
Major histocompatibility complex
MID
Multiplex identifier
min
Minute
ml
Millilitre
mM
Millimolar
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MR
Medium resolution
MRC
Medical Research Council
MS
Multiple sclerosis
mtDNA
Mitochondrial DNA
ng
Nanogram
NGS
Next generation sequencing
NHLS
National Health Laboratory Service
NK
Natural killer
nm
Wavelength
PBSC
Peripheral blood stem cell
PCR
Polymerase chain reaction
PPi
Pyrophosphate
PTP
Picotiterplate
R2
Pearson coefficient of determination
RA
Rheumatoid arthritis
RER
Rough endoplasmic reticulum
RFLP
Restriction fragment length polymorphism
RFU
Relative fluorescence unit
RFX
Regulatory factor X
SABMR
South African Bone Marrow Registry
SBT
Sequence-based typing
sec
Second
SNP
Single-nucleotide polymorphism
SPRI
Solid phase reversible immobilisation
SSOP
Sequence-specific oligonucleotide probes
SSP
Sequence-specific primers
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STR
Short tandem repeats
T
Thiamine
TAP
Transporter associated with antigen processing
Taq
Thermus Aquaticus polymerase
TB
Tuberculosis
TBE
Tris-Borate-EDTA
T cell
T lymphocyte
TCR
T-cell receptor
TE
Tris-EDTA
TNF
Tumour necrosis factor
U
Unit
UCB
Umbilical cord blood
UCT
University of Cape Town
UP
University of Pretoria
USA
United States of America
UV
Ultraviolet
V
Volt
Val
Valine
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LIST OF FIGURES
Figure 1: Innate versus adaptive immune response. This figure is a schematic representation
of the innate and adaptive immune responses and the cell types involved (Image created by
Juanita Mellet, adapted from Figure 27-5 in Townsend et al., 2007). ...................................... 9
Figure 2: Humoral versus cell-mediated immune response. A schematic representation of
the differences between humoral and cell-mediated immune responses (Image created by
Juanita Mellet, adapted from Figure 43.14 in Campbell and Reece, 2005). ........................... 10
Figure 3: The HLA region on chromosome 6. The HLA region is located on the short arm (p)
of chromosome 6 and spans over 3.6 Mb. This region comprises three classes (I, II, and III)
that play an important role in immune responses (Image created by Juanita Mellet, adapted
from Figure 1 in Mehra and Kaur, 2003). ................................................................................ 11
Figure 4: Timeline of research in the MHC. The MHC research highlights, from 1936 to 2008
(Image created by Juanita Mellet). .......................................................................................... 13
Figure 5: HLA Class I gene structure (Image created by Juanita Mellet, adapted from Figure 2
in Blasczyk, 2003). .................................................................................................................... 14
Figure 6: Structure of the class I and II heterodimers. The green and pink structures
represent the α domain and β2M for class I molecules, respectively. The yellow and blue
structures represent the α and β domains for class II molecules, respectively (Image created
by Juanita Mellet, adapted from Figure 2 in Klein and Sato, 2000). ....................................... 16
Figure 7: The endogenous pathway. (1) Viruses enter cells through various methods;
following which viral peptides are tagged for degradation by ubiquitin. (2) Ubiquitin is
recognised by the proteosome and degrades peptides into small peptide fragments. (3) The
peptide fragments are transported via TAP molecules from the cytosol to the RER. (4) In the
RER the peptide and the class I molecule are assembled. (5) The assembled molecule is
transported from the RER to the cell surface via the Golgi complex. (6) Once it reaches the
cell membrane it is displayed on the cell surface. (7) Class I HLA molecules are recognised by
TCR on CD8+ T lymphocytes (Image created by Juanita Mellet, adapted from Figure 8-23 in
Kindt et al., 2006). .................................................................................................................... 17
Figure 8: HLA class II, DRB gene structure (Image created by Juanita Mellet, adapted from
Figure 3 in Blasczyk, 2003). ...................................................................................................... 18
Figure 9: The exogenous pathway. (1) Extracellular antigens enter the cell by endocytosis,
after which (2) antigens are degraded within an endosome, which later becomes a
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lysosome. (3) In the RER, an invariant chain binds to the binding groove of the MHC class II
molecule, which inhibits binding to peptides and also aids in exiting from the RER into the
cytosol. The MHC class II molecule leaves the RER in a vesicle. (4) The vesicle fuses with the
late endosome that still contains the endocytosed peptide. (5) The invariant chain is
degraded until only a small piece (CLIP) still blocks the peptide-binding groove. HLA-DM
facilitates the removal of CLIP and replaces it with a peptide. (6) A stable MHC molecule is
displayed on the cell surface for (7) presentation to TCR on CD4+ T lymphocytes (Image
created by Juanita Mellet, adapted from Figure 8-24 in Kindt et al., 2006). .......................... 20
Figure 10: HLA inheritance. This figure illustrates the inheritance of HLA alleles from parents
to their offspring. A (green) and B (pink) represents the father’s haplotypes, while C (yellow)
and D (blue) represents the mother’s haplotypes. Children inherit one haplotype from each
parent (Image created by Juanita Mellet, adapted from Figure 2 in Choo, 2007). ................. 21
Figure 11: The number of alleles named each year from 1987 to January 2013. The red bars
represent the number of class I alleles, while the yellow bars represent the number of class
II alleles from 1987 to present. Image courtesy of the HLA Informatics Group, Anthony Nolan
Research Institute, London, UK (Robinson et al., 2013). ......................................................... 24
Figure 12: Human migration out of Africa. This figure depicts the ‘Out of Africa’ theory and
the migration of humans from Africa to the rest of the world (Image created by Juanita
Mellet, adapted from Figure 2 in Fackenthal and Olopade, 2007). ........................................ 26
Figure 13: HLA nomenclature. This figure is a schematic representation of the HLA
nomenclature. This is important to consider with low, medium, and high resolution typing
(Image created by Juanita Mellet, adapted from Marsh et al., 2010). ................................... 32
Figure 14: The South African population. The figure depicts the percentages of the different
racial groups in South Africa (Data Source: Statistics South Africa, 2011). ............................. 47
Figure 15: The demographics and linguistics of the South African population. The graphs
depict the demographics and the distribution of linguistic groups across the nine different
provinces of South Africa (Statistics South Africa, 2001). ....................................................... 50
Figure 16: The Black South African languages. The different ethnic groups, languages and the
frequency of the language being spoken, and the distribution across provinces (Image
created by Juanita Mellet) (Data Source: Statistics South Africa, 2001). ................................ 51
Figure 17: The five most diverse provinces in South Africa. These figures indicate the
number of residents and the five most frequently spoken languages in each province (Image
created by Juanita Mellet) (Data Source: Statistics South Africa, 2007; 2011). ...................... 54
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Figure 18: Standard Curve. ...................................................................................................... 61
Figure 19: Electrophoretic separation of DNA. The movement of charged particles through
an electric field (Image created by Juanita Mellet) (Data Source: Russel, 2006). ................... 62
Figure 20: Medium and high resolution plate layout. The disposition of the exon specific
primers and samples on the 96-well plates for MR as well as HR primer sets. The letters
indicate the MHC target gene, while the digits indicate the exon of interest. Samples are
organised by column and the MID’s (grey area) are used to identify each sample and the
negative control. ...................................................................................................................... 64
Figure 21: The position of the MID tags and primers, relative to the DNA sequence of
interest (Image created by Juanita Mellet, adapted from Figure 1 in Bentley et al., 2009). .. 66
Figure 22: Amplicon purification after PCR. (1) Ampure beads were added to the sample and
attached to the nucleic acids for immobilisation, while contaminants remain in solution. (2)
A magnetic ring stand is used to produce a magnetic field that pulls the beads out of
solution. This separates contaminants and subsequent washing of nucleic acids produces
high quality DNA. (3) The addition of TE Buffer to the sample, eluted the nucleic acids from
the magnetic beads (Image created by Juanita Mellet, adapted from an image on the
Beckman Coulter website: www.beckmancoulter.com). ........................................................ 68
Figure 23: A representation of the GS Gtype HLA Assay Amplicon Dilution Calculator for MR
and HR runs (GS Gtype HLA Assay Manual, March 2011). ...................................................... 70
Figure 24: DNA is mixed with capture-beads. (a) Beads and PCR reagents get emulsified in
water-in-oil amplification microreactors. (b) DNA attaches to capture-beads by adaptors.
Each bead carries a unique single-stranded DNA fragment. (c) Beads were loaded into the
picotiterplate and (d) the wells were filled with beads and sequencing enzymes. (e) A
Scanning Electron micrograph of the picotiterplate. (f) A micrograph of the tiny beads that
were added to the wells (Reproduced with permission from Roche)..................................... 72
Figure 25: A schematic representation of the process of 454 sequencing (Reproduced with
permission from Roche). .......................................................................................................... 73
Figure 26: JSI SeqHLA 454 software results for HLA-B. The screenshots (cropped) display the
typing results at the B locus for samples 1 and 8. (a) The result window (red rectangular
boxes) for sample 1 indicates a heterozygote with a genotype ambiguity string. (b) The
result window for sample 8 indicates a heterozygote with unambiguous typing results,
genotype assignment B*15:10:01/B*57:02:01. ...................................................................... 79
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LIST OF TABLES
Table 1: Class I exons and their coding peptides. The class I genes consist of eight exons that
each code for a specific part of the HLA molecule. ................................................................. 15
Table 2: Suffixes in HLA nomenclature. Summary of what the different possible suffixes
mean when used in HLA nomenclature. .................................................................................. 33
Table 3: HLA class I and II allele susceptibility and protection to various autoimmune
diseases. ................................................................................................................................... 42
Table 4: HLA class I and II allele susceptibility and protection to various infectious diseases.
.................................................................................................................................................. 43
Table 5: The HLA allele associations with adverse drug reactions. ......................................... 44
Table 6: Frequent HLA alleles in the different South African population groups by low
resolution typing techniques. .................................................................................................. 55
Table 7: Frequent HLA alleles observed in the South African population by high resolution
typing techniques..................................................................................................................... 56
Table 8: Low to high resolution HLA typing techniques. ......................................................... 59
Table 9: DNA concentrations of the 8-point standard curve................................................... 61
Table 10: The primer sequences and amplicons’ fragment length (post-PCR). ...................... 65
Table 11: The MID sequence tags for the GS GType HLA typing kit. This table indicates the
position of the tag on the plate, MID name, and sequence. ................................................... 66
Table 12: The reagents for making up the PCR master mix. ................................................... 67
Table 13: PCR protocol............................................................................................................. 67
Table 14: GS Gtype HLA primer sets for medium and high resolution typing. ........................ 77
Table 15: HLA typing results for samples 1-10 by conventional and NGS typing techniques. 81
Table 16: HLA typing results for samples 11-20 by conventional and NGS typing techniques.
.................................................................................................................................................. 82
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Table 17: Unambiguous and ambiguous typing results by 454 sequencing. .......................... 85
Table 18: Unambiguous and ambiguous typing results produced by different software
programs. ................................................................................................................................. 86
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CHAPTER 1
INTRODUCTION
The South African population is known as ‘the rainbow nation’, which is appropriate for a
country with a cultural variety emphasised by 11 official languages. South Africa is one of
the most diverse and complex countries on the planet with regard to genetic heterogeneity.
The high degree of genetic heterogeneity in Africans and South Africans is a result of both
inter- and intra-population variation. Inter-population variation occurs between
populations, whereas intra-population variation is between members of a population.
Variations within populations are mainly due to bottlenecks, which can be explained on the
basis of the ‘Out of Africa’ theory that suggests that humans migrated in isolated groups
from Africa to the rest of the world. Genetic diversity decreases with increased distance
from the original population. Therefore, when a population migrates, they will be less
diverse than the original population.
The South African Bone Marrow Registry (SABMR) does not reflect the demographics of the
South African population (SABMR, 2008), since the majority of donors are Caucasian (72%),
9% Coloured, 5% Asian, and 4% Black South Africans. The increased diversity observed in the
Black South African population and the under-represented number of these individuals
belonging to the SABMR poses a significant challenge in obtaining human leukocyte antigen
(HLA) matching donors for these individuals. A public cord blood bank in South Africa could
increase the chances of obtaining HLA matching donors for African individuals, locally and
globally. Bone marrow transplantation requires matching at 10 HLA alleles (five from each
parent) with a 9/10 match between donors and recipients. Umbilical cord blood on the
other hand involves matching of six HLA alleles (three from each parent) and a 4/6 match
between donors and recipients is considered to be acceptable for transplantation. The naive
nature of the T cells in cord blood results in higher tolerance to HLA mismatches. For this
reason, there is a greater probability of obtaining an HLA matching donor for cord blood
than with bone marrow.
The characteristics of umbilical cord blood render it a suitable alternative to bone marrowand peripheral blood-derived stem cells for cell-based therapies (e.g. bone marrow
transplantation), which has led to the establishment of umbilical cord blood stem cell banks
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Chapter 1
Introduction
across the globe. Public banks involve altruistic donation in which fetal blood is harvested
from the placenta via the umbilical vein and stored in a general facility for use in unrelated
(allogeneic) cord blood transplantations. Private cord blood banks were established for
autologous use by the donor, either in childhood, or later in life, in the case of disease
development. The likelihood that a child will be able to make use of autologous cord blood
is very small. The probability of possessing an HLA identical sibling is 25%, compared with
40% for a 4/6 and 75% for a 3/6 match (Beatty et al., 2000). Directed cord blood banking is
done for the purpose of donating to siblings that are known to have a disease that can be
treated by bone marrow transplantation.
Umbilical cord blood is valuable for the reconstitution of hematopoiesis in children with
malignant as well as non-malignant disorders, such as in the treatment of hematological
malignancies, non malignant blood disorders, and metabolic disorders. Umbilical cord blood
is a rich source of hematopoietic stem cells (Rocha et al., 2004; Kurtzberg et al., 1996) which
undergoes engraftment following transplantation (Broxmeyer et al., 1989). Clinical
observations have indicated that acute graft-versus-host disease (GVHD) occurs less
frequently in patients who receive cord blood for transplantation than in individuals who
receive bone marrow (Cairo and Wagner, 1997). Stem cells from cord blood are known to be
“immune naive” and differ from stem cells from the bone marrow due to minimal previous
exposure to antigens (Chalmers et al., 1998) and also contain fewer helper T cells (Loetscher
et al., 1998). Although cord blood-derived stem cells have certain advantages, the number
of cells obtained from a cord blood sample is limited, which also limits the age/size of the
patient that can be transplanted. A minimum of 2.5-3.0 x 107 nucleated cells are required
per kilogram body weight (Migliaccio et al., 2000; Zhang et al., 2012). Suboptimal cell
numbers reduce the efficacy of engraftment following transplantation.
There are several advantages to using cord blood instead of bone marrow; (1) cord blood is
readily available following birth and does not require an invasive procedure for harvesting;
(2) it is possible to cater for a greater diversity due to the ability of cord blood stem cells to
tolerate a greater degree of HLA disparity; (3) cells have a high proliferative capacity; and (4)
there is a decreased rate of acute GVHD following transplantation.
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Chapter 1
Introduction
There are also a number of disadvantages in making use of cord blood for transplantations:
(1) there are no additional donor cells available, whereas in the case of bone marrow, it is
possible to obtain more blood from the donor; (2) the low number of cells available due to
the small volumes of cord blood readily available; (3) increased risk of infection, since cord
blood cells are immune naive; and (4) in the case where more than one unit is required for
transplantation, all units need to match the recipient as well as each other.
South Africa is a multicultural country where a multiracial population of approximately
52 million currently resides. These include: 39 million Black South Africans (80%); 5 million
White South Africans (9%); 4 million Mixed ancestry South Africans (8%), and 1 million Asian
South Africans (3%) (Statistics South Africa, 2011). In support of the constitution of a public
cord blood bank in South Africa, the bank would have to be representative of the entire
South African population. It has been estimated that a minimum number of 10 000 cord
blood units would be needed to initiate the bank, of which 8 000 (80%) would have to be
representative of Black, 900 (9%) of White, 900 (9%) of Mixed ancestry, and 200 (2%) of
Asian South Africans.
The majority of the White South African population originally descended from Europe and
speaks Afrikaans and English. The Mixed ancestry population mainly speaks Afrikaans, while
the Indian population is English-speaking. The Black South African population has four broad
groupings, namely: Nguni (which include Zulu, Xhosa, Ndebele, and Swazi), Sotho-Tswana
(which include Southern-, Northern-, and Western Sothos), Tsonga, and Venda. The diversity
observed renders the Black population the most diverse population in South Africa. The
language that one speaks is often an indication of the ethnicity of an individual. South Africa
has 11 official languages; however, there are several other languages being spoken in
addition to the official languages. Language is often a reflection of the geographic HLA
distribution, which reflects cultural groupings and intermarrying. It is evident that
intra-population variation is often more diverse than inter-population variation. It is,
therefore, important to determine the diversity within the subgroups of a given population
in addition to the diversity between the different populations.
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Chapter 1
Introduction
Several studies have considered certain population groups to determine the HLA diversity in
the Black South African population (du Toit et al., 1988; du Toit et al., 1988, p. 42;
du Toit et al., 1990a; 1990b). These studies primarily focused on either the Xhosa or the Zulu
populations, since they make up a large proportion of the Black South African population in
this country. These studies have established that various HLA alleles appear to be restricted
to the Xhosa population, while others are exclusive to the Zulu population. Some alleles
were observed to occur at a higher frequency in Black South African individuals, while
others are rare or completely absent. All of the above-mentioned studies made use of
serological methods for HLA determination. A more recent study by Paximadis and
co-workers also determined the HLA diversity in South African individuals; however, this
study made use of a sequence-based method in order to determine the HLA genotypes of
individuals. This study included individuals from all the different linguistic groups residing in
South Africa (Paximadis et al., 2011).
1.1. Problem Statement
The ethnic diversity of the South African population poses a great challenge in constituting a
public umbilical cord blood bank that is representative of the entire population. The large
number of polymorphisms resulting from the HLA diversity within the South African
population will reduce the probability of finding adequate matches between donors and
recipients.
1.2. Aim
The aim of this study was to determine how a public cord blood bank should be constituted
in order to cater for the diverse South African population, which includes race, ethnicity,
and HLA diversity (conventional techniques vs. next generation sequencing).
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Introduction
1.3. Study Objectives
The present study involved an analysis at four levels:
1. Population demographics
Population statistics were obtained from Statistics South Africa (www.statssa.gov.za)
regarding the different population groups in the country. The last census was conducted in
2011 and conveys the most recent population statistics. HLA statistics for the South African
population were gathered from research that was conducted during the 1980’s and a paper
published in 2011, which are the most recent papers published concerning the diversity of
HLA in South African individuals. There are also some HLA statistics available on the allele
frequency website.
2. Linguistic distribution
Statistics on the linguistic distribution were also gathered from Statistics South Africa,
concerning the different languages and the frequency at which they are spoken in the nine
provinces. This was helpful in determining where the population groups and subgroups are
mainly distributed.
3. Conventional (low to high resolution) techniques for HLA typing
Twenty samples were obtained from the National Health Laboratory Service (NHLS),
Department of Immunology, Diagnostic Section, at the University of Cape Town (UCT),
courtesy of Professor Clive Gray. Low to high resolution typing had previously been
performed on the 20 samples prior to the commencement of this study and the different
HLA types were blinded for the purpose of this study. This study has made use of a next
generation sequencing (NGS) technology to determine the HLA genotypes of the 20 samples
at a high resolution.
4. Next generation sequencing at Medium and High Resolution
This part of the analysis involved the use of the GS GHLA typing kit. DNA amplification was
performed in a multiplex manner by making use of specifically designed primers for the
various HLA regions (HLA-A, B, C, DQB1, and DRB1) that were investigated during this study.
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Introduction
The kit targets the most hypervariable regions of the HLA genes that include exons 2 and 3
for class I genes, and exon 2 for class II genes. This is defined as medium resolution (MR)
genotyping by the manufacturer of the kit. High resolution (HR) genotyping includes, in
addition, exon 4 for the class I genes and supplementary regions of the class II genes.
Medium and high resolution genotyping kits made use of different PCR plates. This study
made use of two different primer sets for the different genotyping resolutions. The MR and
HR resolution, as described by the manufacturers of the plate, has caused some confusion in
that MR and HR are not referring to the different exons being sequenced. It is generally
accepted that low to medium resolution techniques (SSOP and SSP) identifies two (A*02) to
four
digits
but
often
with
multiple
possibilities
at
the
four-digit
level
(A*02:02/02:05/02:09/02:40) due to the ambiguous nature of the HLA genes. High
resolution techniques identify four digits and higher (A*02:01:01:01), since it reveals the
entire nucleotide sequence. Therefore, it is the highest level of resolution. The ‘medium’
and ‘high’ resolution terms, as mentioned by the manufacturers of this product, are
therefore incorrect.
HLA analysis was performed at the DNA level using the 454 NGS Roche platform. The
amplified DNA regions were sequenced by Inqaba Biotec™ with a GS Junior sequencer, in
order to determine the HLA genotypes of the various samples. High resolution HLA
genotyping (as described by the manufacturers of this product) was performed for the
20 samples.
The objectives for the NGS aspect of the study include:
i.
Validation of the HLA kit on selected samples of the South African population.
ii.
Determination of the degree of complexity revealed by NGS.
iii.
Assessment of the value of NGS in determining the composition of the cord blood
bank.
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2.1. The Immune System
The immune system is an ever evolving collection of organs, cells, and tissues that function
together to protect the body against invading pathogens, yet recognise and tolerate the
body’s own cells. It is the body’s natural form of defence and is found exclusively in
vertebrates, which have developed and evolved into an adaptable host defence system. It
has the ability to protect the body through a variety of cells and molecules, capable of
recognising and eliminating foreign pathogens in a dynamic network. Viruses, bacteria, and
parasites are all classified as pathogenic organisms. These organisms display different
molecular patterns that distinguish one pathogen from another. These changes are
recognised by the immune cells as ‘foreign’, after which the invader will be eliminated and
neutralised by the immune system. However, pathogens have evolved to such an extent
that they have the ability to evade the host’s immune system. The immune system of
vertebrates is highly complex and consists of two different types of responses that both play
a vital role in protection against pathogens. These include the innate and adaptive immune
responses which both generate an immune response to pathogens and microbes.
The innate immune system is regarded as the body’s first line of defence, also referred to as
an individual’s natural immunity against an extensive range of microbes. The innate immune
system consists of molecular and chemical mechanisms and acts in a non-specific manner to
eliminate pathogens. This part of the immune system involves external as well as internal
mechanisms of defence. External defence includes the skin, mucus membranes, and
secretions, while internal refers to phagocytic cells, antimicrobial proteins, inflammatory
responses, and also natural killer (NK) cells. Innate immunity includes the involvement of a
variety of white blood cells, while excluding B and T lymphocytes. The cells of the innate
immune system are non-specific and, are therefore, able to initiate an immune response to
various pathogens that may enter the body. This type of immune response can determine
and distinguish ‘self’ from ‘non-self’ but is not capable of distinguishing small differences in
foreign molecules, which is rather a feature of the adaptive immune response.
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The adaptive immune response, in contrast to the innate immune response, is the second
line of defence and is characterised as a slow response initially, but on subsequent
exposures to a pathogen it acts more specifically and rapidly. The prime characteristics of
the adaptive immune system are to develop a response and adapt to, recognise, eliminate,
and memorise the pathogen. B and T lymphocytes are restricted to the adaptive immunity
together with immunoglobulins, which are products of assembled gene segments. This
allows for the increased variability observed in the adaptive immune recognition. The
adaptive immune response is much more sensitive and antigen-specific in order to allow
T lymphocytes to become activated. Once the T cells are activated, it takes over from the
innate immune response for the destruction of pathogens. The ability of the adaptive
immune response to recognise small changes is maintained by ‘memory cells’ of the
immune system. Although the adaptive immune response is a well developed system,
capable of protecting our bodies against pathogens, it is also capable of initiating
unfortunate outcomes, such as autoimmune diseases, allergies, and rejection of transplants.
Figure 1 portrays the differences between the innate and adaptive immune responses.
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INNATE IMMUNITY
ADAPTIVE IMMUNITY
B-lymphocytes
Antibodies
Epithelial
Barriers
T-lymphocytes
Effector T-cells
phagocytes
NK Cells
Complement
Hours
1
6
Days
12
1
Time after infection
3
5
Figure 1: Innate versus adaptive immune response. This figure is a schematic representation of the
innate and adaptive immune responses and the cell types involved (Image created by Juanita Mellet,
adapted from Figure 27-5 in Townsend et al., 2007).
There are two components of the adaptive immune response that are crucial in
differentiation and proliferation of B and T lymphocytes, known as the humoral and
cell-mediated immune responses (Figure 2). The humoral part of the immune system
involves the interaction of B-cells with antigens and their proliferation and differentiation
into antibody-secreting plasma cells and memory B-cells. The immunoglobulins (IgG, IgM,
IgE, IgA) on the surfaces of these cells bind to antigens in order to eliminate them from the
system. T lymphocytes are activated and generated in response to antigens to form part of
the cell-mediated immune response. T lymphocytes proliferate to form T helper and
cytotoxic T lymphocytes for the destruction of infected cells. The HLA molecules play a
major role in the cell-mediated immune response, where they are displayed on the surfaces
of cells for presentation to T lymphocytes.
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Humoral Immune Response
Cell-mediated Immune Response
First antigenic exposure
Dendritic cells display antigens
Intact Antigens
Infected cells display
antigens
Activate
B-cell
TH2
Helper T-cell
TH1
Cytotoxic
T-cell
TH1
Plasma cells
Memory B-cells
TH1
Activates
NK-cells
Activates
macrophages
Antibodies are secreted that defend against
pathogens and toxins
Kills bacteria, tumour cells, and virus infected
cells
Figure 2: Humoral versus cell-mediated immune response. A schematic representation of the
differences between humoral and cell-mediated immune responses (Image created by Juanita Mellet,
adapted from Figure 43.14 in Campbell and Reece, 2005).
2.2. The Major Histocompatibility Complex
The major histocompatibility complex (MHC) is a multigene family present within the
genomes of all vertebrates. Genes within this region encode glycoproteins that bind
peptides of intra- and extracellular origin, that are presented to T lymphocytes. These
regions vary significantly between different species due to the differences in gene number,
composition, and organisation (Kelley et al., 2005; Belov et al., 2006). The complete MHC
sequence is available for various species, which include mouse and human. The MHC region
evolved about 500 million years ago, where the most primitive form is believed to still be
present in nurse sharks (Kasahara et al., 1992). There are various other factors, such as
selection pressures and pathogens that have acted on these regions and have introduced
the variability and diversity in the form of gene gain and loss, observed currently in different
animals.
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The MHC in humans is referred to as the HLA complex, located on the short arm of
chromosome 6 (Figure 3). This region encodes cell surface proteins that recognise and bind
peptides. These peptides are displayed on the cell surface, where they are presented to
T lymphocytes. In the case where peptides are recognised as ‘foreign’, an immune response
is initiated. The HLA region consists of over 128 functional genes, that play a critical role in
the innate as well as adaptive immune responses, since 40% of the HLA genes have an
immune function (MHC Sequencing Consortium, 1999). These molecules affect the response
to antigens of organisms and, are therefore, also linked to disease susceptibility and the
development of autoimmune diseases.
Chromosome 6
p-arm
q-arm
Class II
Class III
Class I
Figure 3: The HLA region on chromosome 6. The HLA region is located on the short arm (p) of
chromosome 6 and spans over 3.6 Mb. This region comprises three classes (I, II, and III) that play an
important role in immune responses (Image created by Juanita Mellet, adapted from Figure 1 in
Mehra and Kaur, 2003).
2.3. Discovery of the MHC/HLA region
The MHC was initially described in 1936 by Peter Gorer after the observation of
agglutination of mice erythrocytes by rabbit sera (Gorer, 1937). Research on the MHC was
further advanced by George Snell, who discovered that it influences the ability of an
organism to either accept of reject transplanted tissue from another member of the same
species. This is due to the incompatibility of certain antigens that causes graft rejection. It
was only after this particular discovery that Gorer named it the H-2 complex in mice
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(Snell, 1986). Jean Dausset established the HLA complex in 1952 when he detected an
alloantigen present on human leukocytes, which he called MAC (the initials of the three
individuals who played a significant role in the detection of this molecule), today known as
HLA-A2. This was confirmed by three independent studies in 1958 by Jean Dausset, Jon van
Rood, and Rose Payne (Dausset, 1958; van Rood et al., 1958; Payne and Rolfs, 1958), who
defined the presence of various other antigens on human leukocytes. These three reports
laid the foundation of the information that is currently available for, and known as, the HLA
complex. Following his investigation in 1958, Dausset stated that these antigens will still
become of great importance in tissue transplantation. This has lead to the ongoing studies
by George Snell and the discovery of the histocompatibility locus. However, it was only later
in the 1970’s, after a publication by Zinkernagel and Doherty on T cell restriction, that the
complete function of this complex became apparent. This study revealed that T lymphocytes
only recognise viral peptides when displayed on MHC molecules and, will therefore, only be
activated in the presence of a combined signal of a MHC molecule together with a
pathogenic peptide (Zinkernagel and Doherty, 1974). In 1980, the Nobel Prize in Physiology
or Medicine was awarded for the discoveries concerning genetically determined structures
on the cell surface that regulate immunological reactions. The laureates, Baruj Benacerraf,
Jean Dausset, and George Snell jointly held this prestigious award for their work in
discovering the HLA molecules (The Nobel Prize in Physiology or Medicine, 1980). Figure 4
depicts the HLA research highlights, from 1936 to 2008.
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1968 First bone marrow
transplantation in a patient with SCID
2003 Integrated haplotype
map of the human MHC
1984 HLA-DQ locus identified
1964 First International
Histocompatibility Workshop
1977 HLA-DR locus
discovered
1952 Discovery of the
first Human Leukocyte
Antigen
1950
1980
1936 MHC first
described in mice
2005 Haplotype map of the
human genome
1992 Genetic
linkage map of the
human genome
2000
2008
1978 Concept of
Linkage Disequilibrium
applied to MHC
1963 First successful
kidney transplant from
an HLA identical sibling
2008 Eight common MHC
haplotypes resequenced
1987 Discovery of the
molecular structure of
HLA
1967 Human MHC named HLA
2001 Sperm typing to
define recombination
hotspots and LD in MHC
1999 Sequencing of the MHC
1975 HLA-C locus discovered
Figure 4: Timeline of research in the MHC. The MHC research highlights, from 1936 to 2008 (Image
created by Juanita Mellet).
Over the years, the discovery of the different HLA genes and their importance in immune
responses has led to the classification of class I and class II HLA genes. The HLA class I
molecules are found on all somatic cells in the body and is involved in CD8-mediated
immunity. Class II HLA molecules have a more limited distribution and is involved in
CD4-mediated immunity. The main function of the HLA molecules is to present processed
peptides (epitopes) to either CD8+ T cells (class I) or CD4+ T cells (class II).
2.4. HLA Structure and Function
The T lymphocytes of the immune system are constantly binding to the HLA molecules on
the surfaces of cells in order to determine whether a cell’s normal function has been
disrupted by a viral infection. Under normal circumstances, HLA molecules display peptides
of proteins synthesized and degraded for presentation on the cell surface. In healthy
individuals, the immune system will recognise those peptides as ‘self’. In the case of
infection or disruption of function, an HLA molecule displays foreign peptides in its binding
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groove, which activates the T lymphocytes. The immune system reacts by lysing the cell in
an attempt to halt further infection by a pathogen or replication of cancer cells.
2.4.1. Class I HLA molecules
The class I HLA region spans over 2 000 Kb and consists of approximately 20 genes. There
are three classical HLA genes within the class I region, HLA-A, -B, and -C. The non-classical
genes include HLA-E, -F, -G, -J, -X, and various others, some known as pseudogenes, since
they do not encode protein products. The non-classical genes within this region are not as
polymorphic as the classical genes and also not necessarily peptide-presenting antigens. The
HLA-B locus is the most polymorphic of the class I genes (Mungall et al., 2003). The HLA-B
locus has 2 862 alleles currently documented in the HLA database, while HLA-A has 2 188,
and HLA-C has 1 746 different alleles (Robinson et al., 2013). The classical HLA genes consist
of eight exons, while the polymorphisms reside in gene regions that encode the
peptide-binding groove. These regions include exons 2 and 3 (Figure 5), and consists of 270
base pairs (bp) and 276 bp, respectively. These two exons encode the α1 and α2 domains,
respectively, while exon 4 encodes the α3 domain and consists of 276 bp. Exons 2 and 3
represent the most variable region of the gene (peptide-binding groove), while the rest of
the gene is more conserved. Exon 4 also contains some variability but not as much as exons
2 and 3, therefore, it is also regarded as being conserved. All eight exons of the class I HLA
genes encode particular components of the cell surface molecule (Table 1) that, in unison,
create a fully functional HLA structure.
5’
E1
α1
α2
α3
270bp
276bp
276bp
E2
E3
E4
Variable Region
3’
E5
E6
E7
E8
Conserved Region
Figure 5: HLA Class I gene structure (Image created by Juanita Mellet, adapted from Figure 2 in
Blasczyk, 2003).
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Table 1: Class I exons and their coding peptides. The class I genes consist of eight exons that each
code for a specific part of the HLA molecule.
Exon
Encodes
1
Leader peptide
2 and 3
Functionally important α1 and α2 domains
4
α3 domain
5
Transmembrane domain
6 and 7
Cytoplasmic tail
8
Contributes last two nucleotides to the C-terminal and the 3’
untranslated region (exon 8 is completely untranslated in HLA-B)
Class I molecules consist of two chains, the α chain and non-covalently bound
β2-microglobulin (Figure 6). The α1 and α2 chains are the variable regions within the class I
genes. These variable regions form the peptide-binding groove designed to bind
endogenously-derived peptides. Class I HLA molecules present peptides within the binding
groove as a complex with the α1 and α2 domains, which are then spatially recognized by the
T-cell receptor (TCR) of CD8+ T lymphocytes. Engagement of the CD8+ TCR, along with
adhesion molecules, creates an immunological synapse between the two cell types. The
class I endogenous pathway is associated with defence against intracellular pathogens such
as viruses.
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Peptide-binding cleft
α2
α3
α1
β2M
α1
β1
α2
β2
Plasma Membrane
Class II
Class I
Figure 6: Structure of the class I and II heterodimers. The green and pink structures represent the α
domain and β2M for class I molecules, respectively. The yellow and blue structures represent the α
and β domains for class II molecules, respectively (Image created by Juanita Mellet, adapted from
Figure 2 in Klein and Sato, 2000).
2.4.2. The Endogenous Pathway
The endogenous pathway includes the degradation of intracellular proteins by a proteolytic
pathway present in all cells (Figure 7). The proteasome is a large protein complex involved in
the degradation of misfolded or unwanted proteins (including pathogens) into smaller
peptides. Most proteins that are targeted for degradation have a small protein, called
ubiquitin, attached to them. The immune system utilises this proteolytic pathway to
produce small peptides for presentation with class I HLA molecules.
Peptides that are generated by the proteasome are transported into the rough endoplasmic
reticulum (RER) by a transporter associated with antigen processing (TAP), a membranebound heterodimer with an affinity for peptides consisting of eight to 14 amino acids. The
class I molecules generally present peptides of approximately nine amino acids. The
preferred peptide length is achieved by aminopeptidases in the endoplasmic reticulum (ER).
It is optimal for TAP to favour peptides for class I MHC molecules. The α domain and the
β2-microglobulin of the class I MHC molecules is synthesised in polysomes. The assembly of
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a stable class I molecule requires a peptide in the binding groove of the HLA molecule. These
molecules then proceed to the cell surface via the Golgi complex.
T cell
CD8
Virus
MHC Class I
peptide
7
TCR
1
6
Virus peptide
2
Proteosome
Peptide
fragment
5
Vesicle
3
Cytosol
Endoplasmic
Reticulum
4
MHC Class I
TAP
Nucleus
Class I
Figure 7: The endogenous pathway. (1) Viruses enter cells through various methods; following which
viral peptides are tagged for degradation by ubiquitin. (2) Ubiquitin is recognised by the proteosome
and degrades peptides into small peptide fragments. (3) The peptide fragments are transported via
TAP molecules from the cytosol to the RER. (4) In the RER the peptide and the class I molecule are
assembled. (5) The assembled molecule is transported from the RER to the cell surface via the Golgi
complex. (6) Once it reaches the cell membrane it is displayed on the cell surface. (7) Class I HLA
molecules are recognised by TCR on CD8+ T lymphocytes (Image created by Juanita Mellet, adapted
from Figure 8-23 in Kindt et al., 2006).
2.4.3. Class II HLA molecules
Class II molecules include the HLA-D genes, that are subdivided into DQ, DP, and DR, and are
restricted to immune competent cells (B-lymphocytes, macrophages, and endothelial cells
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of T lymphocytes). The class II genes encode proteins that are expressed on the cell surface
of antigen-presenting cells (APCs), where they present to the helper T cells. All
class II molecules consist of two chains, an α chain and a β chain (Figure 8). Recent studies
have identified the presence of multiple forms of β, as well as α chain genes in humans. The
DR gene only contains one α domain, while DP and DQ both contain two α domains. DM and
DO have been identified as non-classical genes within the class II HLA region. These HLA
molecules have slightly different functions compared to other genes within this region. The
DM gene encodes a class II-like molecule that assists in loading the antigenic peptide into
the HLA molecule. The DO gene encodes molecules expressed solely in the thymus and on
the surface of mature B cells, where it acts as a class II regulator by negatively modulating
HLA-DM. Within the class II genes, exon 2 is the most variable region and also forms the
peptide-binding groove (β1).
5’
E1
β1
β2
270bp
282bp
E2
E3
Variable Region
3’
E4
E5
E6
Conserved Region
Figure 8: HLA class II, DRB gene structure (Image created by Juanita Mellet, adapted from Figure 3 in
Blasczyk, 2003).
Class II molecules are involved in the exogenous pathway and are associated with defence
against extracellular pathogens such as bacteria. The TCR, which binds to the exogenously
derived peptide-class II HLA complex, is found on helper T cells (CD4 cells). The helper
function of these cells involves the activation of the general immune response. This includes
cytokines and the cellular and humoral immune responses, which is why these molecules
are only present on immunologically active cells (Browning and McMichael, 1996).
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2.4.4. The Exogenous Pathway
Antigen-presenting cells have the ability to internalise antigens by means of endocytosis or
phagocytosis. Once these antigens are inside the cell they are degraded by various chemical
pathways, which include the early and late endosome and finally the lysosome, called the
exogenous pathway (Figure 9). The different stages of this pathway occur at a range of pH
concentrations and are believed to be transported by small transport vesicles between
compartments. The lysosome has acid-dependant hydrolases, such as proteases, nucleases,
and various others. The hydrolases degrade the antigen into oligopeptides (13-18 amino
acids), which then binds to the class II HLA molecule and acquires protection against any
further degradation.
Due to the presence of both class I and II molecules on the surface of a cell, it is essential
that these molecules do not bind to the same antigenic peptides within a cell. During the
process of HLA class II synthesis, an invariant protein chain is assembled with the α and β
domains of the class II HLA molecule. This chain interacts with the peptide-binding groove of
the molecule and also aids in folding and exiting from the RER. The invariant chain is
degraded in successive stages, however, a small segment of the chain, called CLIP
(class II-associated invariant-chain peptide) remains bound to the molecule. The CLIP
prevents binding of any premature peptides to the binding-groove of the molecule. HLA-DM
is a non-classical class II HLA molecule that assists in the exchange of CLIP with an antigenic
peptide, after which the HLA molecule is transported to the plasma membrane and
displayed on the cell surface.
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T cell
CD4
MHC Class II
peptide
TCR
1
7
Endocytosed
antigens
6
Antigenic
peptides
2
5
Endosome
CLIP
4
Cytosol
Endoplasmic
Reticulum
MHC
Class II
3
Invariant chain
Class II
Nucleus
Figure 9: The exogenous pathway. (1) Extracellular antigens enter the cell by endocytosis, after which
(2) antigens are degraded within an endosome, which later becomes a lysosome. (3) In the RER, an
invariant chain binds to the binding groove of the MHC class II molecule, which inhibits binding to
peptides and also aids in exiting from the RER into the cytosol. The MHC class II molecule leaves the
RER in a vesicle. (4) The vesicle fuses with the late endosome that still contains the endocytosed
peptide. (5) The invariant chain is degraded until only a small piece (CLIP) still blocks the
peptide-binding groove. HLA-DM facilitates the removal of CLIP and replaces it with a peptide. (6) A
stable MHC molecule is displayed on the cell surface for (7) presentation to TCR on CD4+
T lymphocytes (Image created by Juanita Mellet, adapted from Figure 8-24 in Kindt et al., 2006).
2.5. HLA Gene Expression and Regulation
The HLA molecules are co-dominantly expressed, which implies that heterozygous
individuals will express the gene products of both alleles on the surface of their cells. The
inheritance of HLA molecules was first demonstrated in 10 infants by Payne and Rolfs in
1958. They observed three successive generations and determined that these molecules are
generally inherited in a heterozygous state (Payne and Rolfs, 1958). The HLA alleles within a
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family are inherited in a Mendelian fashion, known as an HLA haplotype (Figure 10). A
haplotype is defined as a set of alleles inherited on homologous chromosomes. These alleles
are closely linked to one another with recombination occurring at low frequencies, and
therefore, they are inherited as a block. Children have a one in eight (12.5%) chance of
inheriting an identical haplotype to their parents’, while siblings have a one in four (25%)
chance of inheriting the identical haplotype to their siblings’ (Choo, 2007). In a family where
parents have more than four children, it is expected that at least two children will be HLA
identical, called haplo-identical siblings. Haplo-identical siblings can also be referred to as an
‘HLA identical match’.
Father
Mother
A23
A1
A11
A3
B55
B8
B16
B27
DR17
DR4
DR7
DR15
C
A B
D
Children
A23
A3
A1
A3
A11
A1
A23
A11
B55
B27
B8
B27
B16
B8
B55
B16
DR17
DR15
DR4
DR15
DR7
DR4
DR17
DR7
A D
B
D
C
B
A C
Figure 10: HLA inheritance. This figure illustrates the inheritance of HLA alleles from parents to their
offspring. A (green) and B (pink) represents the father’s haplotypes, while C (yellow) and D (blue)
represents the mother’s haplotypes. Children inherit one haplotype from each parent (Image created
by Juanita Mellet, adapted from Figure 2 in Choo, 2007).
Normal healthy cells display self peptides in their HLA binding grooves as a result of normal
turnover of self proteins. The high degree of variability and diversity within populations
enables binding of different peptides due to differences within the peptide-binding groove.
Over the last decade it has been determined that peptides that are bound by certain HLA
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molecules share specific amino acids (or ones with similar properties) at a few positions,
called anchor residues (Falk et al., 1991). The anchor region aids in specificity of binding,
where it interacts with the surface of the HLA binding groove. Therefore, a specific HLA
molecule can bind various peptides that possess specific amino acids in the anchor positions
even though the remainder of the amino acid sequence may differ.
As previously mentioned, the classical class I molecules are more commonly expressed on
nucleated cells even though the level of expression differs between different cell types.
Lymphocytes express the highest level of HLA molecules compared to all other cells in the
human body and constitute about 1% (5 x 10 5 molecules) of the plasma membrane proteins
per cell. The level of HLA expression varies among different cell types in the body. Unlike
class I molecules, class II molecules are constitutively expressed on the surfaces of APCs.
Class II expression depends on the differentiation stage of a cell. This is confirmed by the
expression of class II molecules on mature B cells, but the lack thereof on pre-B cells. This is
also observed on monocytes and macrophages that express low levels of class II molecules
until they are activated by interaction with antigens, after which the level of expression
increases significantly (Kindt et al., 2006). There are three classical class II genes (DP, DQ,
and DR) and each has two forms. Therefore, an individual expresses six class II molecules
from either parent. This number can further increase with the presence of multiple β chains.
The diversity generated by the presence of multiple chains increases the number of
antigenic peptides to which the molecules can bind, and is therefore, considered to be a
great advantage to an organism.
All HLA class I and II genes have promoter sequences at the 5’ end where sequence-specific
transcription factors are able to bind. Various transcription factors and motifs have already
been identified for the various HLA genes. Two transcription factors, class II transcription
activator (CIITA) and regulatory factor X (RFX), have been identified for class II genes. In the
case where these transcription factors are unable to bind, individuals suffer from severe
immunodeficiency due to the lack of HLA class II on their surfaces. The expression of HLA
molecules is also largely controlled by cytokines. The interferons and tumour necrosis factor
(TNF) have been shown to play a significant role in increased expression of HLA class I
molecules on the surfaces of cells. Interferon gamma (IFN-γ) binds to a specific transcription
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factor that binds to the promoter sequence which up-regulates the expression of the class I
genes. IFN-γ also seems to be involved in inducing CIITA, which in turn also increases the
expression of class II molecules. There are also other cell-type specific cytokines that play a
role. The expression of these molecules can also be down-regulated by certain factors, such
as prostaglandins and corticosteroids, which are known to decrease the expression of class
II molecules. IFN-γ also decreases the expression of class II molecules on the surfaces of
B cells. The expression of HLA molecules can also be decreased by certain viral infections,
which include hepatitis B virus (HBV), human cytomegalovirus (CMV), and adenovirus 12
(Ad12).
2.6. HLA Diversity
The HLA region is the most polymorphic in the human genome (International HapMap
Consortium, 2005) and accounts for more than 10% of human diversity, with heterozygosity
ranging from between 80-90% (Hughes and Nei, 1988). These allelic variants mostly arise
within the nine classical genes of the HLA region. Since the role of HLA is to present peptides
from invading organisms that may enter the body, it is likely that HLA has evolved to
manage a vast number of peptides. There are currently 8 794 HLA alleles listed in the
IMGT/HLA database, of which 6 919 are class I alleles and 1 875 are class II alleles (Robinson
et al., 2013). Figure 11 below represents the increase in the number of alleles identified to
date. This high number of HLA alleles can result in a synonymous or a non-synonymous
change of the amino acid sequence. The most dramatic is a non-synonymous change, which
causes a slight difference in the amino acid sequence of the HLA molecules, which can also
explain the elevated number of HLA antigens.
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Figure 11: The number of alleles named each year from 1987 to January 2013. The red bars represent
the number of class I alleles, while the yellow bars represent the number of class II alleles from 1987
to present. Image courtesy of the HLA Informatics Group, Anthony Nolan Research Institute, London,
UK (Robinson et al., 2013).
The reason for the diversity of this highly complex system is the fact that these molecules
need to present an enormous array of antigenic peptides to T cells in order to elicit a unique
immune response to a wide variety of peptides. The sequence variation of these molecules
is not randomly distributed across the gene, instead it is denser in short stretches of the
gene (exons 2 and 3) that code for the peptide-binding groove. These genes evolve by
processes that take place over many years, which include the accumulation of mutations,
gene conversion/interlocus genetic exchange, over-dominant balancing selection, and
frequency-dependant selection. The accumulation of mutations can be attributed to the
euchromatic state of the DNA over the HLA region, since these genes are frequently
transcribed and translated into protein molecules expressed on the cell surfaces of almost
all cells. Therefore, the HLA genes are exposed to many mutagens that cause the build-up of
mutations across this particular region. The mutations within these genes mostly arise in the
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form of single-nucleotide polymorphisms (SNPs), while the SNPs within the coding region
are the most informative, and have directed the allelic diversity observed today. The
diversity of these molecules has occurred due to the presence of different alleles at a
specific locus within a species. The alleles can differ from one another by an alteration at a
minimum of one SNP. Several thousand allelic variants of the HLA system have already been
described. The presence of alleles differs from one individual to another by 5-10%. The
diversity can also be the result of the presence of duplicated genes, with similar or
overlapping functions. It is plausible to make this assumption, since the HLA complex
consists of genes with similar but not completely identical structure and function (HLA-A,
-B, and -C).
The enormous variation observed is highly specific and accounts for the diversity amongst
populations (Jin and Wang, 2003). The divergence rate of the HLA genes is due to the long
history of independent haplotype evolution, where Africans are genetically more diverse
compared to other populations (Wainscoat et al., 1986). This suggests, together with other
data, that Africans were the founder population of Homo sapiens (Wainscoat et al., 1986;
Cann et al., 1987). Smaller isolated populations will have a decreased number of alleles
compared to larger admixed populations, which once again indicates that the high degree of
diversity within the HLA region is the result of population demographics as well as selection
pressures.
2.7. Evolution, Selection, and Linkage Disequilibrium across the HLA region
Modern humans are thought to have replaced all the archaic human species since their
evolution in Africa approximately 200 000 years ago. The ‘Out of Africa’ theory suggests that
humans originated in East Africa and evolved into modern humans that migrated from
Africa, approximately 60 000 years ago, to the rest of the world (Figure 12). The theory
further suggests that the migration event caused modern humans to replace ancient
humans, such as Neanderthals.
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Figure 12: Human migration out of Africa. This figure depicts the ‘Out of Africa’ theory and the
migration of humans from Africa to the rest of the world (Image created by Juanita Mellet, adapted
from Figure 2 in Fackenthal and Olopade, 2007).
In 2010, the first Neanderthal genome was sequenced and has shed some light on ancient
genomics. This has aided to some extent to answer various questions on human evolution
and history. Neanderthals lived in western Eurasia from about 400 000 years ago until
30 000 years ago and are classified as a subspecies of Homo sapiens. In December 2010,
another population of ancient humans was discovered in a cave in Denisova (Southern
Siberia) and are said to be more diverse than Neanderthals (Gibbons, 2011). The Denisovans
and the Neanderthals are referred to as archaic humans, since their evolutionary story
began to split from those of humans about 500 000 years ago.
The human distribution patterns observed today are due to the migration of populations
that started as a result of fluctuating climate changes, which has led to the distribution of
populations across continents. There is an alleged theory that interbreeding between
modern and archaic humans occurred early after migration from Africa, which has led to an
enhanced immune system in modern humans. According to Ferrer-Admetlla and
co-workers, these archaic humans had a better adapted immune system to local pathogens
(Ferrer-Admetlla et al., 2008). This was confirmed by a study completed by Professor John
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Hawks and co-workers at the University of Wisconsin, where they discovered that these
groups lacked particular forms of genes, which made it possible to fend off certain diseases
(John Hawks Weblog, 2012). There are ongoing studies to determine whether these genes
are linked to autoimmune diseases.
Professor Peter Parham’s team at Stanford University compared the diverse immune genes
amongst Neanderthals and Denisovans, and reported at a Royal Society Symposium in
London (2011) that they carried HLA genes that are abundant in modern humans from
Europe and Asia, respectively (Callaway, 2011). This supports the notion that Denisovans
had once lived across Asia. It is estimated that 50% of Europeans owe the variant of a
specific HLA gene to Neanderthals. The variants observed in these two groups are either at
low frequency in Africans, or entirely absent. It is believed that the most diverse populations
are those still residing in Africa, since populations become less diverse the further they have
migrated from their origin. An article published in 2004, by Cao and co-workers, suggests
that the genetic diversity between all African populations is greater than the diversity
observed between European populations (Cao et al., 2004). This is also observed for the HLA
regions between distinct populations, which could partly be due to interbreeding with
archaic humans, but may be the result of selection pressures that have shaped and
increased the presently observed diversity.
2.7.1. Evolution and Balancing Selection
The strong balancing selection at the HLA genes together with recombination, instils the
human population with a multitude of HLA alleles and haplotypes. ‘Balancing selection’
refers to the maintenance of multiple alleles in the gene pool of a given population at a
frequency higher than that of gene mutations. Immune defence is strongly dependant on
the pivotal role of the HLA genes; therefore, it is of utmost importance to maintain a variety
of surface molecules for the long-term survival of humans. An article published in 2011 by
Abi-Rached and co-workers (Abi-Rached et al., 2011), compared modern and archaic
humans by investigating the HLA genes, since these genes are generally conserved across
generations. They discovered similarities, which suggest that interbreeding took place
between these groups and modern humans.
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Natural selection acts on several aspects, such as morphology, physiology, and also life
history traits. These traits exhibit heritable genetic variation, which could explain the vast
number of alleles observed within the HLA region (Takahata and Nei, 1990). Various studies
have investigated the impact of natural selection on the HLA genes and its critical role in
HLA frequencies observed throughout populations. Although previous studies have reported
that HLA genes are subjected to selection, it is still a mystery as to how these genes
maintain their immense degree of variation. A study by Ferrer-Admetlla and co-workers
concluded that there is a significantly higher level of balancing selection in Europeans than
in African individuals (Ferrer-Admetlla et al., 2008). There are several selection pressures
that are believed to have contributed significantly to the diversity observed within the HLA
region today. These include heterozygous advantage (over-dominant selection and
frequency-dependant selection). It is well-known that multiple HLA alleles confer resistance
or susceptibility to various infectious diseases. However, this is not constant across
populations and has led researchers to believe that the selection on these genes is also
pathogen-driven. These selection pressures are called ‘pathogen driven selection’, which
operates on specific alleles and are favoured due to their ability to provide protection
against certain pathogens.
2.7.1.1. Heterozygous Advantage (over-dominant selection)
In 1975, Doherty and Zinkernagel suggested that heterozygotes have a higher fitness
compared to homozygotes (Doherty and Zinkernagel, 1975). Heterozygotes are capable of
fending off an increased range of pathogens and will, therefore, survive for prolonged
periods of time, which increases their fitness and also the chance to reproduce. This
suggestion has been proven in various studies, which indicates that a single heterozygote
advantage has an increased effect on the polymorphisms observed throughout this region.
A heterozygous individual has an increased number of antigens displayed on the cell
surface ,and will therefore, recognise additional pathogenic peptides and respond more
efficiently when compared to a homozygous individual, which is a result of heterozygous
advantage (over-dominant selection). Over-dominant selection increases the proportion of
heterozygous individuals within a given population.
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Sanchez-Mazas and co-workers investigated the HLA region and observed lower
homozygosity for several loci than would have been expected under normal circumstances
(Sanchez-Mazas et al., 2000). An additional study, published in 2012 observed a positive
correlation between genetic diversity in HLA-B and pathogen-driven selection, while a
negative correlation was observed for DQB1. This has also shown to correlate with genetics
and the geographical migration of populations (Sanchez-Mazas et al., 2012).
2.7.1.2. Frequency-dependant Selection
Frequency-dependant selection was first proposed prior to the knowledge on MHC
functioning (Bodmer, 1972), and suggested that fitness can vary depending on the allele
frequencies present in a population. Pathogens also evolve and acquire new mutations that
enable them to increase their own fitness. When organisms evolve they adapt according to
their environment. In the case where certain HLA molecules are frequent within a
population and capable of destroying a specific organism, organisms will evolve to avoid
destruction by such molecules, therefore, rendering themselves resistant to binding by such
molecules. A rare or novel HLA molecule, however, will still have the ability to bind to such a
pathogen due to minimal exposure of pathogens to such a molecule. Once such a molecule
becomes frequent, pathogens will again start adapting and will reduce the fitness of the
molecules. Therefore, alleles fluctuate in frequency driven by pathogen adaptation.
2.7.2. Non-synonymous versus Synonymous mutations
It is generally believed that synonymous changes occur more frequently within the genome
than non-synonymous changes. Synonymous changes can be explained as a substitution
that does not alter the amino acid sequence, whereas a non-synonymous change refers to a
substitution that alters the amino acid sequence. The occurrence of non-synonymous
changes has been determined to be favoured by selection in the binding groove of the HLA
molecules. A study performed by Hughes and Nei established that the codons that form the
peptide-binding groove of these molecules accumulate a higher rate of non-synonymous
changes compared with synonymous changes, whereas regions outside the binding groove
showed a higher rate of synonymous changes (Hughes and Nei, 1988; 1989). This has led
scientists to believe that selection plays a strong role in maintaining these non-synonymous
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substitutions within the HLA region in order to increase the binding capacity to antigenic
peptides.
Mutations typically occur randomly throughout the genome. This, however, is not the case
within the HLA region. The high number of alleles observed is predominantly across the
classical HLA genes. Studies have determined that as the distance from the classical genes
increases there is a decrease in the number of polymorphisms within genes located in the
HLA region. The genes that are further away have decreased variation, since recombination
removes those genes from the selected regions, while it is maintained within the closely
linked genes. This suggests and confirms that the classical genes are under strong selection.
The selection is, therefore, stronger on genes that are closely linked to the classical genes.
2.7.3. Linkage Disequilibrium
Closely linked genes are referred to as being in linkage disequilibrium (LD). This is a notion
that has been studied for many years and has also been a topic of interest within the HLA
region. The term linkage disequilibrium describes a setting in which two or more alleles
occur together more frequently than would be expected under normal circumstances, and is
termed a haplotype. Haplotypes are population-specific, and therefore, their frequencies
may differ between populations. Scientists believe that these patterns are conserved over
generations, which has led to the concept of the ‘ancestral haplotype’. There was always
believed to be a relationship between LD and genetic distance, which a study in 2000 failed
to prove due to the strong linkage between class I loci considering the increased distance
between them (Sanchez-Mazas et al., 2000).
Investigations across the HLA region have discovered strong LD patterns between the class I
and class II genes. It was indicated that HLA-B and -C are in strong LD, and HLA-DQ and -DR
are also in strong LD with one another. The presence of recombination hotspots between
DPB1 and DQB1 loci could be the reason for the weak linkage observed across these genes.
Linkage across DP seems to be weak in numerous European populations. Linkage
disequilibrium may vary between populations due to the differences in the creation,
maintenance and decay in LD patterns. These patterns of LD are dependant on various
contributing factors that include migration, admixture, and genetic drift. According to
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Sanchez-Mazas and co-workers, the HLA region is subject to a range of selection pressures
and strong linkage, which accounts for the high degree of variation observed within this
region.
2.8. HLA Nomenclature
HLA typing is performed to determine an individual’s molecular genotype for the various
HLA loci; however, it is not always as straightforward as it sounds. The continually increasing
number of HLA alleles makes it a priority to have a structured way of designating names,
while also taking into account the ambiguities in dealing with these genes.
The HLA nomenclature system utilises sets of digits to designate HLA names. Each allele
present in the IMGT/HLA database has a unique name consisting of four sets of digits
(Figure 13). The name is dependant on the sequence of the allele, and can therefore, be
longer than four sets of digits when necessary. The first set of digits indicates the type,
which refers to the specific allele group. The next set of digits indicates the subtype; this
number is assigned in order of the discovery of the sequences and refers to the specific
protein. In the case where the two sets of digits differ, it is an indication that there are one
or more nucleotide substitutions that alter the amino acid sequence. Synonymous changes
within the coding region of the genes are indicated by the third set of digits. Sequence
polymorphisms in intronic, 5’- and 3’-untranslated, and flanking regions are indicated by the
fourth set of digits. There are also various suffixes that are used for HLA nomenclature.
These suffixes indicate the expression of a particular antigen/allele (Table 2).
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Specific HLA gene
Specific HLA
protein
DNA changes in
non-coding region
HLA-A*02:101:01:02N
Human
Leukocyte
Antigen
Allele
Group
Expression
changes
Synonymous DNA
substitution in coding
region
Figure 13: HLA nomenclature. This figure is a schematic representation of the HLA nomenclature. This
is important to consider with low, medium, and high resolution typing (Image created by Juanita
Mellet, adapted from Marsh et al., 2010).
HLA typing is performed at different resolutions depending on the level of information
required for the various applications. The resolution of HLA typing refers to the amount of
information that one obtains from the different typing methods, where low resolution is the
minimal amount of information needed, while high resolution is the most information that
one is able to obtain. Low resolution typing refers to HLA typing at the antigen level and
includes information on the first two digits, which can be determined by serological
methods. Medium resolution typing makes use of the PCR, as well as DNA-based methods
for determination of the HLA types of individuals. However, medium resolution does not
determine the exact sequence of the HLA genes. It rather makes use of a more general
approach of using DNA probes to determine the HLA type. Medium resolution typing is
faster and more cost effective than sequencing, and is therefore, used to screen individuals
as potential donors. DNA of donors that match at medium resolution is further sequenced
to determine the exact sequence of the specific HLA genes. High resolution molecular HLA
typing refers to allelic typing. DNA- or sequence-based methods are generally performed for
high resolution typing, which determines the exact sequence of the genes of interest. Allelic
typing is the process of using typing methods that provide information on four or more
digits. Depending on the method used for typing, it is possible to observe ambiguous results.
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Table 2: Suffixes in HLA nomenclature. Summary of what the different possible suffixes mean when
used in HLA nomenclature.
HLA Nomenclature
Indication
HLA-A*23:01N
Null allele
HLA-A*30:14L
An allele encoding a protein that expresses ‘low’ surface antigens
HLA-A*24:02:01:02L
An allele encoding a protein that expresses ‘low’ surface antigens
due to a mutation outside of the coding region
HLA-B*44:02:01:02S
An allele outside the coding region that expresses a protein as a
‘secreted’ molecule only
HLA-A*32:11Q
An allele with a mutation that has previously been shown to have a
significant effect on cell surface expression, but this was not
confirmed and remains ‘questionable’
Information reproduced with permission from the HLA Informatics Group, Anthony Nolan Research
Institute, London, UK (Robinson et al., 2013).
The HLA nomenclature is important in obtaining and conveying the correct information
regarding HLA types. It has taken many years and continuous HLA workshops to come up
with the WHO nomenclature system, currently in use. The HLA nomenclature is updated on
a monthly basis to include newly discovered alleles and also to correct previously
encountered errors.
2.9. HLA Ambiguities
The vast number of HLA alleles that have already been identified, together with the
escalating discovery of alleles in this highly polymorphic region, has led to the problem of
HLA ambiguity, which is still a matter of concern in HLA typing. Ambiguous typing refers to
more than one interpretation of the results, which arises since the majority of the typing
techniques focus on exons 2 and 3 to assign a genotype. The reason for this is due to the
fact that these two exons are the most polymorphic within these genes. However, it is
possible that the diversity is located within a region that is not sequenced, and therefore,
the diversity cannot be determined due to identical sequences over exons 2 and 3. Although
the change outside the sequenced region may alter one’s genotype, it might be that these
regions are less important due to the fact that the variation is not within the binding groove
of the molecules. Even though this could be a possibility, it is not known whether a change
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in another part of the molecule will alter the whole molecule. New typing techniques
involve the sequencing of additional exons, which will resolve some of the ambiguity.
Previous studies have mentioned that in order to resolve the ambiguities, it is necessary to
sequence the entire gene and not just particular regions of it. Like that, it will be possible to
determine the complete genotype/haplotype of an individual at a high resolution for various
applications.
2.10. HLA Typing Applications
The purpose of HLA typing is to obtain detailed information on the diverse HLA genes for
various applications. Various disciplines require specific information regarding these genes.
The most important and most frequently used application is typing for transplantation,
which requires information on the HLA loci in donors and recipients. Forensic,
anthropological, and disease association studies also make use of HLA typing in order to
identify criminals, determine migration patterns, ancestry, and also identifying predisposing
HLA alleles to specific diseases, e.g. those that are immune associated.
2.10.1. Transplantations
A primary application of HLA typing is the matching between donors and recipients for
transplantation, which has been a routine procedure for many years. The increased
mismatching between individuals results in a higher risk of rejection and occurrence of
GVHD in recipients. Graft rejection is the process in which the recipient’s immune system
attacks the transplanted tissue (Porth, 2010). This occurs due to the natural function of the
immune system to detect ‘foreign’ material present in the body, and to effectively destroy
it, as is the case with viruses and bacteria. Transplant rejections can be avoided by
serotyping or DNA-based methods to determine the most appropriate donor-recipient HLA
match, or through the administration of immunosuppressive drugs. In the case of HLA
mismatching, GVHD can occur when the donor cells recognise the host cells as foreign and
start attacking them. HLA mismatching between individuals can promote chimaerism in
recipients. Chimaerism refers to the fusion of two genetically distinct cell lines, which could
occur in the case of transplants. HLA matching has contributed in a large extent to the
success of engraftment in transplantations. There are different transplantations and the
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degree of matching is different for each one. These include the transplantation of solid
organs as well as stem cells (bone marrow transplantation).
2.10.1.1. Solid Organ Transplantation
Solid organ transplantation was inaugurated in the 1950’s, after the performance of the first
successful kidney transplant between identical twins in 1954 (Merrill et al., 1956). Since
then, numerous solid organ transplants have been performed on a regular basis, that
include transplants of the liver, kidney, heart, lung, cornea, and many more. The outcome of
organ transplants has improved significantly since the introduction of HLA matching and
immunosuppressive drugs in 1979 (Calne et al., 1979), the latter of which limits immune
reactions by suppression of the immune system. The degree of HLA typing for solid organ
transplants is different when considering the organ for transplantation. It has been shown
that HLA typing has had a remarkable impact in kidney transplants and still remains a
priority, while it is less considered in heart transplants. Although studies have indicated the
benefit of HLA matching in heart and lung transplantations, there are some contradictions.
A study performed in 2005 did not demonstrate a positive correlation with a higher degree
of matching at HLA-A, -B, and -DR in heart transplants (Almenar et al., 2005). Various other
risk factors have rendered HLA compatibility less concerning in heart transplants, while age,
body size, and blood group compatibility between donors and recipients are primarily
considered. HLA compatibility is also not the primary consideration for lung transplants. The
only reason for HLA typing is in the case of sensitisation.
There are, however, concerns regarding HLA compatibility in liver transplants. A study
performed in the 1990’s indicated that complete class I matching for liver transplants may
have a deleterious effect, even though some matching is desirable for graft survival
(Donaldson et al., 1993). Cornea transplants do not require an HLA matched donor, since
the cornea is not transplanted to an immunologically privileged site. Mismatches for solid
organ transplants are tolerated depending on the organ for transplant; however,
cross-matching is a requirement, since it could lead to organ rejection. Cross-matching
includes testing for already existing antibodies that could cause an immune reaction to a
transplanted organ. If an individual is positive for a cross-match, it indicates a response was
generated and that the organ cannot be transplanted, while a negative result indicates the
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absence of an immune response. This, however, is not the situation in stem cell
transplantations, since a higher degree of matching is essential for engraftment and the
prevention of GVHD.
2.10.1.2. Stem Cell Transplantation
The high prevalence of various blood disorders globally necessitates thousands of stem cell
transplantations yearly. The fact is that only 25% of these individuals have an adequate
HLA-matching donor. There are two types of stem cell transplants, autologous and
allogeneic. Autologous transplantations involve the transfer of an individual’s own stem
cells back into the individual, while allogeneic involves the use of donor stem cells, which
has to be a genetic HLA match to the recipient. A study by Jean Dausset showed that grafts
between siblings were more successful than grafts between unrelated individuals (Dausset,
1958). The preferred donor, is therefore, a matched HLA relative which is not always
possible since siblings are not necessarily HLA-identical. As has been mentioned earlier, the
possibility of finding an HLA-identical match between relatives is 25%. It has, therefore,
become more common to make use of unrelated donors. The mortality rate is higher in
unrelated transplants that mismatch at one or two alleles compared to a fully matched
transplant (Flomenberg et al., 2004; Petersdorf et al., 2004; Lee et al., 2007). The difficulty
in finding a compatible stem cell donor is attributable to the fact that stem cell
transplantations require allelic matching between certain loci, which decreases the
possibility of obtaining a fully matched donor. Two distinct classes of stem cells are currently
utilised in transplantation for the purpose of assorted therapeutic applications. These
include bone marrow and umbilical cord blood (UCB) stem cells.
Transplantations utilising Stem Cells from Bone Marrow
The bone marrow is located within the marrow of long and flat bones and is the site in
which virtually all blood stem cells reside, constituting what is defined as the stem cell
niche. All blood cells are derived from hematopoietic stem cells (HSCs) and transplantation
of these cells involves the transfer of immunocompetent cells from donors to recipients for
immune restoration. The ultimate result is for these stem cells to migrate to the bone
marrow, a process known as stem cell homing, for complete reconstitution of the
damaged/destroyed bone marrow. A new technique enables the collection of peripheral
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blood stem cells (PBSCs) through a process known as apheresis. The growth factor,
granulocyte colony-stimulating factor (G-CSF), facilitates the mobilisation of stem cells into
the bloodstream. The mobilised stem cells are obtained from peripheral blood, which is a
less invasive procedure when compared to acquiring it from within the bone. Bone marrow
stem cell transplants can be used for myeloablative and non-myeloablative treatment.
Myeloablative treatment involves the administration of high doses of chemotherapy in
order to deplete the recipient’s entire bone marrow of cells prior to transplantation, while
non-myeloablative treatment does not involve chemotherapy. When donating bone marrow
stem cells, HLA typing is performed for 10 HLA loci, five from each parent (HLA-A, -B, -C,
-DRB1, and -DQB1), and a 9/10 or 10/10 match between donors and recipients is a
prerequisite. Unrelated donors may be found through a national bone marrow registry. In
1990, the SABMR was introduced and established as an organisation that assists in finding
an adequate HLA match for those individuals not fortunate enough to possess an
HLA-identical relative. As a result of the high degree of matching necessary for bone marrow
transplantation, in order to prevent the rejection of the cells, umbilical cord blood has
become an alternative source of stem cells, due to the immature nature of these cells.
Umbilical Cord Blood Transplantation
The use of UCB is a recent development in bone marrow transplantation and is obtained
from a newborn baby’s placenta through the umbilical vein. The blood from the umbilical
cord/placenta is a rich source of stem cells (Rocha et al., 2004) and due to the immaturity of
the immune cells in cord blood, it is only necessary to perform HLA typing for HLA-A, -B, and
-DRB1. The outcome of various studies has suggested that a 4/6 to a 6/6 match is adequate
for unrelated donors (Wagner et al., 2002; Eapen et al., 2007; Barker et al., 2010). However,
a recent study by Eapen and co-workers suggests that typing of HLA-C should be performed
in addition to the three loci that are presently being typed for UCB to minimise the risk of
mortality after UCB transplantations (Eapen et al., 2011). A previous study by Petersdorf and
co-workers have indicated that matching of the class I alleles is vital, since mismatching at
these alleles can cause graft failure (Petersdorf et al., 2001). It has previously been reported
that the time required to search for a matching cord blood donor is less than the time it
takes to find a matching bone marrow donor (Rocha et al., 2000; Barker et al., 2002), which
makes UCB a good option for transplantation.
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The potential use of UCB was first proposed in 1982 by Edward Boyse, whereafter the first
successful HLA-identical UCB transplant was performed in 1988 (Gluckman et al., 1989) by
Gluckman and co-workers on a 5 year-old Fanconi’s Anaemia patient. The first unrelated
UCB transplant was performed in 1993 by Krutzberg and Wagner (Kurtzberg et al., 1996;
Wagner et al., 1996). UCB is important in transplantations, since it has enough
hematopoietic progenitor cells to support engraftment (Broxmeyer et al., 1989). The rapid
proliferative capacity of these cells makes it possible to reconstitute the entire bone marrow
(Gluckman et al., 1997). UCB is valuable for the reconstitution of hematopoiesis in children
with malignant, as well as non-malignant disorders. There are sufficient cells within cord
blood to assure long term engraftment and the risk of GVHD is low even in HLA mismatched
transplants. Clinical observations have shown that the GVHD is decreased following
transplantation, in patients who receive cord blood compared to individuals receiving bone
marrow (Cairo and Wagner, 1997). Cord blood stem cells differ from bone marrow stem
cells, since cord blood stem cells are “immune naive” due to the minimal previous exposure
to antigens (Chalmers et al., 1998) and also containing fewer helper T cells (Loetscher et al.,
1998). According to two papers published by Bensussan et al. (1994) and Berthou et al.
(1995), cord blood contains increased numbers of NK cells and less cytotoxic T cell activity.
Stem cells from cord blood mature into anti-inflammatory interleukin-10 producing cells,
which induce tolerance, and could therefore, be responsible for reduced occurrence of
GVHD in cord blood transplantations (Bacchetta et al., 1994; Eskdale et al., 1998).
There are several advantages of using cord blood instead of bone marrow. These include: (1)
cord blood is readily available following birth, and does not require an invasive procedure
for harvesting; (2) it is possible to cater for a greater diversity due to the ability of cord
blood stem cells to tolerate a greater degree of HLA disparity; (3) cells have a high
proliferative capacity; and (4) there is a decreased rate of acute GVHD following
transplantation. Although these cells have a number of advantages and are excellent for
transplants due to their immunological characteristics, there are also some disadvantages
that need to be considered in making use of cord blood for transplantations: (1) there are
no additional donor cells available, whereas in the case of bone marrow, it is possible to
obtain more blood from the donor; (2) the low number of cells available due to the small
volumes of cord blood readily available, makes it best suited for young individuals under the
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age of 30, with a body weight of less the 50 kg; (3) higher risk of infection, since cord blood
cells are immune-naive; and (4) in the case where more than one unit is required, which is
referred to as pooled or sequential UCB transplantation, all of the units need to be matched
to the recipient as well as to each other. The characteristics of umbilical cord blood make it
an alternative source to bone marrow, which has led to the establishment of public and
private cord blood banks across the globe.
Commercial entities provide an opportunity to store a newborn baby’s umbilical cord blood
in a private bank for a prolonged period of time in the case of disease development later in
life. The blood is only available for personal and family use. The storing of these units is a
matter of concern at the moment, since it is still unsure whether viable cells will be retained
from the UCB units after storing samples for many years. A major concern is the facilities in
which these units are stored and whether they are stored at the satisfactory conditions. It
has been estimated that the chances of a child needing their own cord blood ranges from
1 in 10 000 to 1 in 200 000 (Kline, 2001). A 2009 study reported that only nine autologous
transplantations were performed compared to 41 allogeneic transplantations from privately
banked UCB. The participants of this study do not recommend private storage of UCB for
individuals of Northern European descent. However, 11% of the participants recommend
private storage in the case where parents have different ethnic minorities (Thornley et al.,
2009). There is also the option of donation to a public bank facility. Public banks are
generally owned by the government and involve allogeneic donations, where the blood is
taken from the placenta and stored in a general facility for the use of unrelated cord blood
transplantations. In the already existing public cord blood banks across the globe, there is
no association between the cord blood samples and a donor or the family. As soon as the
cord blood is taken it is government property, and can therefore, be used by any matching
individual in need of transplantation. There is, however, a risk which involves infection of
the donor’s cells that may only become apparent months or years after the cord blood was
donated. This underlies the importance of comprehensive screening methods at public cord
blood facilities for the detection of genetic and/or infectious diseases. Hybrid models are
another possibility for storing umbilical cord blood, which entails merging of private and
public cord blood banks. This model provides a facility for the preservation of public, as well
as private, umbilical cord blood unit storage.
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2.10.2. Forensic and Anthropological studies
Forensic science makes use of various techniques to distinguish one individual from another
by particularly targeting the hypervariable regions within the genome. This is due to the fact
that it is believed that the genomes between individuals vary in 0.1% (The International SNP
Map Working Group, 2001). This analysis can be performed by analysing restriction
fragment length polymorphisms (RFLP), mitochondrial DNA (mtDNA), gender typing,
Y-chromosome typing, HLA typing, and a few others. Y-chromosome and HLA typing can also
be performed for the purpose of paternity exclusion testing, a major aspect in forensic
medicine. Paternity testing typically makes use of classical blood tests and HLA typing, due
to the high degree of variability of the HLA loci. By determining the HLA genotype, 80% of
the male population will be eliminated from being an alleged father. In the case where the
father possesses an identical haplotype to that of the child, he could be considered as being
a potential father. Although paternity testing has been performed for many years in South
Africa, the emergence of DNA evidence in crime investigations has only recently become a
reality. The enormous frequency of crime offences is a continuous concern and due to lack
of education and training, DNA analysis has not yet been implemented to fulfil its full
potential in the area of criminal investigations. A DNA database with the genetic profile of
each individual will assist in identifying potential suspects in criminal investigations.
However, some regions might be identical between different individuals by pure chance. For
ethical, as well as practical reasons, only non-coding regions will be used for identification,
since genes are not likely to contain such a high degree of variability to distinguish one
individual from another. These non-coding regions contain long stretches of DNA, which
makes it the preferred choice in detecting differences between individuals.
Anthropology is the study of human behaviour, origin, physical, and cultural development to
determine the extent of diversity between populations. Anthropological studies also analyse
variable DNA regions within the genome, such as short tandem repeats (STR), mtDNA, and
HLA. A topic of great interest in anthropology, is the discovery of the Neanderthal and
Denisovan remains and the ongoing process of identifying how they are genetically related
to modern humans. It has been indicated that HLA may not be the best choice for
comparison with modern humans, since natural selection causes changes within the HLA
genes. However, they could provide valuable information about the short-term history of
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population events. As has been mentioned earlier in this chapter, the HLA haplotypes
present in Africans are different from the haplotypes present in Caucasians and Asians,
which is due to strong natural selection that influences the frequency of HLA alleles
observed in populations over time.
2.10.3. Disease Associations
The genetic diversity within the HLA region has been identified to be associated with
diseases more than any other region within the human genome. Several HLA alleles are
present at an increased frequency in individuals with specific diseases compared to the
general population. These HLA alleles can either have a positive or a negative genetic
association with a particular disease. This includes autoimmune diseases, infectious
diseases, and drug-induced hypersensitivity. Various studies are investigating the HLA
antigens and alleles for susceptibility to diseases.
Susceptibility to autoimmune diseases include rheumatoid arthritis (RA), multiple sclerosis
(MS), serum lupus erythromatosus, diabetes mellitus, and various other diseases (Table 3),
while infectious diseases include human immunodeficiency virus/acquired immune
deficiency syndrome (HIV/AIDS), hepatitis, tuberculosis (TB), malaria, and many more
(Table 4). Studies have indicated that individuals heterozygous at the class II classical HLA
genes are more likely to clear hepatitis B viruses than homozygous individuals
(Thursz et al., 1997). A similar concept was observed in HIV-infected individuals. HLA
heterozygosity delays the onset of AIDS in HIV-infected individuals, while the presence of
the HLA-B*35-C*04 haplotype is associated with rapid progressionn to AIDS in Caucasians
(Carrington et al., 1999). Even though heterozygosity and certain HLA alleles confer
protection, there are other alleles that increase susceptibility to these diseases, such as
HLA-B*35, that is associated with increased progression to AIDS. An awareness of increased
susceptibility to certain diseases might encourage lifestyle changes or early treatment that
will afford an enhanced quality of life.
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Table 3: HLA class I and II allele susceptibility and protection to various autoimmune diseases.
Autoimmune Diseases
HLA Class I Effect
Susceptive
Protective
Addison’s
Disease
-
B15
Ankylosing
Spondylitis
B*27:01
B*27:04
B*27:05
B*27:06
B*27:09
Celiac Disease
-
Grave’s
Disease
-
Multiple
Sclerosis
-
HLA Class II Effect
Susceptive
Protective
References
-
(Gombos et al., 2007;
Baker et al., 2010; 2011)
-
-
(Gonzalez-Roces et al.,
1997)
-
DQ2
DQ8
-
(Karell et al., 2003)
-
DRB1*03
DRB1*08
DRB1*07
(Chen et al., 1999)
-
DRB1*15:01
DRB1*13:03
-
(Lang et al., 2002; IMSGC
and Wellcome Trust Case
Control Consortium 2,
2011)
-
(Fries et al., 2002; Hughes
et al., 2008)
DRB1*12
(Graham et al., 2002;
Shankarkumar et al.,
2003; Pan et al., 2009;
Farouk et al., 2011)
DR15
DR14
(Nejentsev et al., 2007)
DQ2
DR3
DRB1*01
Rheumatoid
Arthritis
-
-
DRB1*04
DRB1*10
DRB1*14
Systemic Lupus
Erythromatosus
-
-
DRB1*03
DRB1*08
DRB1*15
Type I Diabetes
A*24
B*18
B*39
A*01
A*11
A*31
B*27
DR3
DR4
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Table 4: HLA class I and II allele susceptibility and protection to various infectious diseases.
Infectious Diseases
HLA Class I Effect
HLA Class II Effect
Susceptive
Protective
Susceptive
Susceptive
References
Hepatitis B
-
-
DR*03
DR*07
DR*04
DR*13
(Yan et al., 2012)
Hepatitis C
B*08
B*18
A*03
B*07
B*27
C*01
DRB1*03
HIV
B*35
B*27
B*57
Leprosy
-
Malaria
Tuberculosis
(TB)
DQB1*03
DRB1*01
DRB1*11
(Tripathy et al., 2009;
Fanning et al., 2000
Harris et al., 2008; Thio
et al., 2001)
-
-
(Gao et al., 2005)
-
DR2
DR3
DRB1*04
(reviewed in Mira,
2006; da Silva et al.,
2009)
-
B*53
-
DRB1*13:02
DQB1*05:01
(Hill et al., 1991)
B*51
B*52
DR2
-
(Vijaya Lakshmi et al.,
2006; Kettaneh et al.,
2006)
Drug induced hypersensitivity reactions are also a major concern that adds to the already
existing healthcare burden, due to the often severe and sometimes fatal adverse effects.
Different HLA alleles have been identified to be associated with drug hypersensitivity, which
include antiretroviral (ARV) agents, such as Abacavir, Nevirapine, and various other drugs
(Table 5). HLA typing is valuable in determining whether an individual possesses certain
alleles that might predispose them to having an adverse reaction to various drugs.
Knowledge of predisposing alleles will result in alternative drug administration to avoid
deleterious effects. This concept ties in with personalised medicine, which attempts to
predict the outcome of certain genetic variants. The ultimate purpose of personalised
medicine is to prescribe the correct medicine at the correct dosage, depending on an
individual’s genotype. However, accurate diagnostic tests are required for identification of
patients that will be able to benefit from such new therapies.
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Table 5: The HLA allele associations with adverse drug reactions.
Adverse Drug
Reactions
Drugs
HLA Class I
HLA Class II
References
Abacavir
B*57:01
-
(Mallal et al., 2002)
Nevirapine
B*35:05
C*04
C*08
DRB1*01:01
DQB1
(Gatanaga et al., 2007;
Chantarangsu et al., 2009;
Carr et al., 2013)
Carbamazepine
A*31:01
B*15:02
-
(Mehta et al., 2009;
McCormack et al., 2011)
Allopurinol
B*58:01
-
(Somkrua et al., 2011)
Ximelagatran
-
DRB1*07:01
(Kindmark et al., 2008)
Although many studies have indicated an association of certain HLA antigens and alleles
with various diseases, there is still a possibility that multiple alleles combined are
responsible for the results observed in these studies. This is not just an indication of the
involvement of HLA alleles to disease susceptibility, but could also include the association
with other genes that, together, create these deleterious effects.
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DEMOGRAPHICS, LINGUISTICS, AND HLA DIVERSITY OF THE SOUTH
AFRICAN POPULATION
The African continent is the homeland for all modern humans and consists of immense
cultural, linguistic, and genetic diversity compared to other continents, which makes it an
excellent region for population variation studies. The fluctuations in climate, diet, and
exposure to different pathogens over time have contributed significantly to the genotypic
and phenotypic diversity observed in African populations. Ancient humans originated in
Africa and were subjected to the largest migration events (Tishkoff and Williams, 2002).
Africa is the second largest continent of which the majority of the countries are
underdeveloped, and therefore, also under sampled. The majority of studies have focused
on the more developed countries and the study subjects, were therefore, not a true
representation of the African population. A study conducted by Jorde and co-workers have
indicated, together with previous studies, that the majority of variation is observed within
populations and not necessarily between different populations. This study also observed the
effect of migration on variation, since less differentiation was observed between individuals
residing on the same continent (Jorde et al., 2000). Common genetic variants often do not
differ significantly between populations, especially within the same continent. Rare genetic
variants, however, are often population specific (Casals and Bertranpetit, 2012). Migration
out of Africa has led to the emergence of various founder populations. Population
bottlenecks reduce the amount of diversity of haplotypes in founder populations, and
therefore, the diversity observed in other populations is less when compared to African
individuals.
The South African population is known as ‘the rainbow nation’, which is appropriate for a
country with a cultural diversity emphasized by 11 official languages, mostly indigenous to
South Africa. The large number of population groups with diverse gene pools currently
residing in South Africa contributes to the diversity observed. The SABMR is mostly
comprised of Caucasian individuals, and is therefore, not representative of the entire South
African population (SABMR, 2008). Due to the high degree of diversity, and the small
number of South African black individuals belonging to the SABMR, it is challenging to
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obtain donors for these individuals. It is, therefore, believed that a public cord blood bank
could contribute to the resolution of this problem in the short-to-medium term in South
Africa. The bank will need to be representative of the South African population and may
possibly provide black African individuals, locally and in the global diaspora, with a better
chance in obtaining an HLA matching donor. Umbilical cord blood only requires matching at
six HLA alleles (three from each parent) and a 4/6 match between donors and recipients is
considered to be acceptable for transplantation. Bone marrow transplantations require
matching at 10 HLA alleles, with a 9/10 match between donors and recipients. The greater
degree of HLA disparity allowed for UCB further increases the chances of obtaining an HLA
matching donor, while it might be more difficult for bone marrow.
The diversity of the South African population can be considered at three levels, which
include: racial/ethnic groups, linguistic groups, and HLA diversity. In order for a cord blood
bank to accommodate the diversity of the South Africans, the aforementioned levels require
an in-depth study of the literature to determine the basis on which this bank should be
constituted.
3.1. Demographics and Linguistics
South Africa is a multiracial as well as multicultural country that comprises of White, Black,
Indian/Asian, and Mixed ancestry individuals that together make up this diverse country of
residence. The Census conducted in 2011 indicated that South Africa is presently populated
by more than 50 million individuals; 40 million Black (79.5%); 5 million White (8.9%);
4 million Mixed ancestry (8.9%); and 1 million Indian/Asian (2.5%) South African individuals
(Figure 14) (Statistics South Africa, 2011).
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South African population
(51 770 560)
Indian/Asian Other
0.5%
2.5%
Mixed-ancestry
8.9%
Caucasian
8.9%
Black African
79.2%
Figure 14: The South African population. The figure depicts the percentages of the different racial
groups in South Africa (Data Source: Statistics South Africa, 2011).
South Africa consists of nine provinces that is each comprised of distinct population groups
residing within each province. The different population groups are distributed across all of
the nine provinces, although the frequency of the linguistic groups differs between the
different provinces (Figure 15). The Black South African population consists of four broad
groupings, namely: Nguni (Includes Zulu, Xhosa, Ndebele, and Swazi); Sotho-Tswana
(Southern, Northern, and Western Sotho population); Tsonga; and Venda; which results in
them being the most diverse population group in South Africa (Figure 16). A study by Sonja
Bosch and co-workers has indicated the linguistic similarities of the Nguni languages
(Bosch et al., 2008). A study by Lane and co-workers also indicated that individuals
belonging to the Nguni group are genetically more related, which could suggest expansion
from a common ancestor. Another cluster is formed by the Sotho-speaking individuals and it
has also been shown that the Venda group shows close similarity to the Tsonga group
(Lane et al., 2001). Xhosa and Zulu tribes represent the majority of the South African black
population. Xhosa and Zulu are classified as Bantu (Nguni) languages. South Africa is known
to be the native land of Xhosa-speaking individuals, where they reside mainly in the Eastern
Cape.
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The language that one speaks is often an indication of the ethnicity of an individual, which
could reflect cultural groupings and cross-cultural marriages. The strong religious
backgrounds of the majority of South Africans contribute to a large extent in selecting a
partner. Populations belonging to the same linguistic group generally tend to cluster
together. Even though there are distinct ethnic and cultural groups residing in South Africa,
there is a high degree of intermixing between individuals from different linguistic groups
(Coetzee et al., 2009). The lowest rate of intermixing is amongst Southern Sotho and Tswana
individuals, which is 20% of women and 13% of men; while the highest rate is amongst
Xhosa, Northern Sotho, and Tsonga individuals, which is 35-40% of women and 20-23% of
men (Statistics South Africa, 2001).
The majority of the White South African population is of European descent and mainly speak
Afrikaans and English. Dutch and English were the first official languages of South Africa,
since 1910 to 1925, which originated from founder individuals of the colonising population.
These individuals came from different nations and different ethnical backgrounds. In 1961,
Dutch was replaced by Afrikaans when South Africa became a republic. Nowadays, Afrikaans
is spoken by 60% of the White population as a first language, while the other populations
speak Afrikaans as a second or a third language. The Mixed ancestry South African
population is comprised of individuals of mixed ancestral lineages, of which the majority
speak Afrikaans and are distributed mostly in the Western Cape. They are also observed in
high numbers in the Eastern and Northern Cape provinces. The Indian and Asian South
Africans speak English and mainly live in KwaZulu Natal and Gauteng provinces. There are
also a significant number of Asian individuals residing in South Africa, although the majority
is due to recent immigrations.
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Figure 15: The demographics and linguistics of the South African population. The graphs depict the
demographics and the distribution of linguistic groups across the nine different provinces of South
Africa (Statistics South Africa, 2001).
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Group
Language
Distribution
Ndebele
1.6%
Mpumalanga
Siswati
2.7%
Swaziland and
border
IsiXhosa
17.6%
Ciskei & Transkei
(Eastern Cape)
IsiZulu
23.8%
Majority: KZN
Minority: Gauteng
Sepedi
9.4%
Limpopo;
Mpumalanga;
Gauteng
Sesotho
7.9%
Lesotho; Free State;
Gauteng
Setswana
8.2%
North West;
Gauteng; Free
State; Northern
Cape
Venda
Tshivenda
2.3%
Limpopo;
Mpumalanga;
Gauteng
Tsonga
Tsonga
4.4%
Limpopo;
Gauteng
Nguni
Bantu
Sotho-Tswana
Figure 16: The Black South African languages. The different ethnic groups, languages and the
frequency of the language being spoken, and the distribution across provinces (Image created by
Juanita Mellet) (Data Source: Statistics South Africa, 2001).
The nine provinces are quite diverse in size, landscape, climate, and economy. Therefore, it
is not surprising that it is also diverse in terms of distribution of the different population
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groups. Figure 17 illustrates the five most diverse provinces in the country (Free State,
Limpopo, Mpumalanga, and North West), where individuals of at least five linguistic groups
reside (Statistics South Africa, 2011). Gauteng, however, is far more diverse than the other
four provinces in Figure 17. Gauteng is the residence for individuals from all the linguistic
groups present in South Africa. This also makes Gauteng best suitable for setting up the first
public umbilical cord blood bank, since it will be possible to collect blood from all the
different linguistic groups until other units can be set-up in some of the other provinces.
Free State (2 745 590)
Tswana
6.8%
Zulu
5.1%
Column1
Xhosa
9.1%
Afrikaans
11.9%
Sotho
64.4%
Limpopo (5 404 868)
Afrikaans Tswana
2.1%
2.6%
Column1
Venda
11.8%
Tsonga
25.1%
Sotho
60.8%
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Mpumalanga (4 039 939)
Mpumalanga
Sotho
10.2%
Swati
29.4%
Ndebele
10.3%
Tsonga
11.6%
Zulu
24.1%
North West (3 509 953)
Sotho
6.8%
Column1
Tsonga
3.4%
Xhosa
7.6%
Afrikaans
9.2%
Tswana
64.8%
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Gauteng (12 272 263)
Tsonga
Tshivenda
5%
4%
Setswana
8%
Column2
Afrikaans
15%
English
13%
Sesotho
13%
Sepedi
11%
Siswati 1%
IsiNdebele 2% IsiXhosa 7%
IsiZulu
21%
Figure 17: The five most diverse provinces in South Africa. These figures indicate the number of
residents and the five most frequently spoken languages in each province (Image created by Juanita
Mellet) (Data Source: Statistics South Africa, 2007; 2011).
3.2.
HLA Diversity
Although various studies have investigated the distribution of HLA on the African continent,
very few have looked at HLA diversity in the South African population. Professor Ernette du
Toit from UCT performed several studies in the 1980’s, which indicated the distribution and
diversity of the HLA alleles in South African populations and especially Black Africans. These
studies have focused mainly on the Xhosa, Mixed ancestry, and White/Caucasian
populations and HLA typing was performed at low resolution. Table 6 displays the combined
results from four different studies performed from 1985-1990 by Ernette du Toit and
co-workers (du Toit et al., 1988; du Toit et al., 1988, p. 42; du Toit et al., 1990a; 1990b).
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Table 6: Frequent HLA alleles in the different South African population groups by low resolution
typing techniques.
HLA-A
Black South
Africans (Xhosa)
A2
a
A28
a
HLA-C
B7
C2
B42
a
B58
B70
Caucasian South
Africans
A1
A2
A3
a
a
C6
a
a
a
a
A2
A28
A30
A33
DR2
a
DR3
(DR18; DR17)
a
DR5
C7
DR6
a
C3
C6
C7
B7
B48 (Mongoloid)
C4
C6
C7
DR2
DR3
DR4
DR5
DR6
DR7
B53
B70
C4
C6
C7
DR2
DR5
DR6
A43 (Khoisan)
Nigeriansb
a
C4
b
A2
A3
A30
HLA-D
B7
B8
a
A11
Mixed ancestry
South Africans
a
B45
A29
A30
HLA-B
DR1
DR2
DR4
DR5
DR7
a
a
Observed in all four studies for a specific population.
b
Okoye et al., 1985.
The combined results indicate the antigens observed at a frequency of more than 10% in
the different population groups that were studied. The HLA typing performed for all of
these studies were serological in nature and able to distinguish major structural and
functional variation between antigens. South African Blacks had an increased number of
antigens compared to the other population groups. Various antigens have been shown to
be frequent in Black South Africans, while other antigens, such as B*27 is either rare or
absent in this group. The Mixed ancestry South Africans share antigens with Black South
Africans as well as with White South Africans. They also possess A*43 that is commonly
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observed in the Khoisan population and B*48 that has only previously been observed in
Mongoloids and could indicate possible admixture.
The results obtained for the Black South African population (Xhosa) were also compared
with results from a study performed by Okoye and co-workers (Okoye et al., 1985).
Nigerians and Black South Africans share some common antigens, while other antigens
were particularly observed in each group. By making use of newly developed high
resolution techniques, it would be possible to determine the in depth variation that might
go undetected in serological or low resolution typing methods.
A recently performed study by Paximadis and co-workers has also investigated HLA diversity
in the South African population (Table 7) (Paximadis et al., 2011). This study included
individuals from all the linguistic groups in South Africa, of which the highest numbers were
Zulu and Xhosa individuals. This study made use of more recent techniques that performed
HLA typing at high resolution (four-digit level).
Table 7: Frequent HLA alleles observed in the South African population by high resolution typing
techniques.
HLA-A
HLA-B
HLA-C
HLA-D
Black South
Africans
A*30:01
A*30:02
B*58:02
B*42:01
C*04:01
C*06:02
C*17:01
DRB1*03:01
DRB1*03:02
DRB1*11:01
DRB1*13:01
Caucasian
South Africans
A*01:01:01
A*02:01:01
A*03:01:01
B*07:02:01
B*08:01
C*07:01
C*07:02:01
DRB1*03:01
DRB1*04:01
DRB1*15:01
The results from Paximadis and co-workers were consistent with the results from the
previous studies and various new alleles were discovered in the different population groups.
Even though these alleles were not present at high frequencies, this highlights the diversity
of the HLA genes and the increasing discovery of new alleles. It is difficult to compare the
different studies that have been performed on the South Africa populations due to
differences in population groups, sample sizes, and the different typing techniques used.
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There are many challenges that have arisen in attempting to establish the basis on which
the public cord blood bank would need to be constituted. These include (1) insufficient data
on the HLA frequencies in the South African population. This makes it difficult to determine
what alleles are frequent in the different population/linguistic groups; (2) studies that have
been performed are not representative of the entire South African population. The 1980’s
studies focused mainly on the Xhosa, White, and Mixed ancestry South African populations,
while the 2011 study included individuals from all the different linguistic groups, although
sample sizes remained relatively small; (3) the difficulty in comparing the results from the
nineteenth century studies with the more recent study due to the differences in the typing
methods that were used, since the resolution (two and four-digit level) differs; and (4) the
great degree of HLA diversity observed within and between the South African population
groups.
The information that has been gathered and studied makes it possible to conclude to some
extent that it is less suitable to constitute the first public umbilical cord blood bank in South
Africa based on HLA diversity, due to the vast HLA diversity within the South African
population. Intermixing between individuals belonging to distinct linguistic groups is
frequently observed and due to extreme patterns of diversity between the different
linguistic groups, it will not be possible to determine the ethnicity of an individual based on
the language that one speaks. Hence, it is not recommended to constitute the bank on the
basis of language.
It is, therefore, recommended to constitute the bank based on social race or major ethnic
groupings (Nguni, Sotho-Tswana, Tshivenda, and Xitsonga). The bank would have to be
representative of the entire South African population. It has been estimated that a
minimum number of 10 000 cord blood units would be needed to initiate the bank, of which
8 000 (80%) would have to be representative of Black South Africans, 900 (9%) of White
South Africans, 800 (9%) of Mixed ancestry South Africans, and 300 (3%) of Indian/Asian
South Africans.
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MATERIALS AND METHODOLOGY
This study was conducted in the Department of Immunology at the University of Pretoria
(UP). Ethical approval was granted in 2010 by the Faculty of Health Sciences Research Ethics
Committee (Protocol Number 131/2010) for the project entitled “Feasibility study for a
public cord blood stem cell bank in South Africa”, of which the currently proposed project
forms part. The proposal for this dissertation was submitted at the end of the year 2011,
followed by ethical approval, which was granted by the Ethics Committee of UP (Protocol
number 219/2011) (see Appendix A: University of Pretoria Ethics Approval Certificate).
Separate ethical approval was granted by UCT (Protocol number 523/2011) for the use of 20
samples obtained from UCT (see Appendix B: University of Cape Town Ethics Approval
Certificate). Twenty DNA samples were obtained from the Department of Immunology at
UCT, courtesy of Professor Clive Gray.
Each DNA sample had already been HLA typed by the Laboratory of Tissue Immunology (LTI)
at the time of commencement of the present study. The HLA typing was performed by low
or high resolution DNA typing techniques. Various typing techniques were used for HLA
typing of these samples (Table 8). Serological techniques provide information on the antigen
group (two-digit level resolution). DNA-based methods were initially performed as a
supplement to serology. However, they are now used routinely in many laboratories as the
‘gold standard’. Sequence specific oligonucleotide probe (SSOP) kits (Gen-Probe, California,
USA) were used for HLA class I and II low resolution typing. The second set of digits
corresponds to the specific protein present in an individual, which is determined by
DNA-based techniques and provides information on the DNA sequence. Sequence-specific
primers (SSP) from Invitrogen (Invitrogen, California, USA) and Olerup (Olerup, Oslo,
Norway) were used for class II high resolution (four-digit level resolution) HLA typing.
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Materials and Methodology
Advantages
- Quick and simple method
- Cross-matching
- Cost effective
- Requires viable
lymphocytes
Serology
Serology (antibody-based)
makes use of viable
lymphocytes to determine
the molecular markers
(antigens) present on the
surface of these cells. Blymphocytes are required
for HLA Class II typing.
Serology provides
information on the antigen
family.
The DNA-based typing
techniques make use of
sequence-specific
oligonucleotide probes
(SSOP) and primers (SSP) to
detect genetic variants. SSP
products are viewed by gel
electrophoresis.
- Gold Standard
Sequence-based typing
techniques make use of
specific primers to amplify
the polymorphic regions of
the gene, after which the
base sequence is
determined.
- Accurate
SBT
Summary
DNA-Based Typing
Table 8: Low to high resolution HLA typing techniques.
Disadvantages
- Identifies molecules on
the surface of the cells
- Difficult to obtain antisera
for rare antigens
- Not suitable for bone
marrow transplants
- Not advantageous for
large number of samples
due to gel electrophoresis.
- Accurate, robust, and time
efficient
- Dependant on previously
identified polymorphisms
- Low to high resolution
- Identify genetic
differences
- Mistyping can occur in the
case where an individual
possesses a novel allele
- High resolution typing
- Requires expensive
equipment
- High throughput
- Time consuming
- Identifies nucleotide
sequence of an allele
- Labour intensive
-
- Able to identify novel
alleles
4.1. Sample Selection and Quantification
This study made use of 20 genomic DNA samples isolated from peripheral blood
mononuclear cells (PBMCs). DNA extractions were performed using the QIAamp® Blood
Mini kit (Qiagen, Hilden, Germany). The procedure required at least 160 ng of DNA for each
sample with a concentration of 5 ng/μl (According to manufacturer’s guidelines).
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The 20 samples from UCT were quantified using the NanoDrop® ND-1000 version 3.3
spectrophotometer (Thermo Scientific, Massachusetts, USA) upon arrival at UP. The
NanoDrop calculates DNA concentrations by absorbance of Ultraviolet (UV) light at 260 nm.
The ratio of absorbance between DNA and protein (260/280) was used to assess the purity
of the DNA. An aliquot of 1 μl of each sample was used to assess the concentration.
AE buffer (the substance that DNA was suspended in) (Appendix C) was used as a blank. The
peak was at 260 nm and the 260/280 ratio for all the samples was between 1.8 and 1.9.
Quantification with the NanoDrop does not accurately assess DNA concentrations.
Therefore, an alternative method of quantification was used to verify that the
concentrations obtained from the NanoDrop were correct. This was performed using a
Quant-iT™ PicoGreen® double-stranded DNA assay kit (Invitrogen), a fluorescent nucleic
acid stain used to quantify double-stranded DNA in solution. The measurements were taken
in triplicate by using the NanoDrop® ND-3300 fluorospectrometer (Thermo Scientific).
A standard curve was created with Lambda DNA that accompanied the PicoGreen assay kit
(Figure 18). The standard curve consists of serial dilutions that create known DNA
concentrations (Table 9). The R2 value of the standard curve had to be at least 0.98. The DNA
standard curve and sample reading values were used to determine the concentrations of all
the sample amplicons. The NanoDrop provided sample concentrations in ng/ml, which were
converted to ng/μl using the following equation: Sample concentration (ng/μl) = sample
concentration (ng/ml)/1000*dilution factor*amount of PicoGreen (μl).
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Standard Curve
16000
y = 161.83x + 83.361
R² = 1
Mean RFU Value
14000
12000
10000
8000
6000
4000
2000
0
0
20
40
60
80
100
Concentrations (ng/ml)
Figure 18: Standard Curve.
Table 9: DNA concentrations of the 8-point standard curve.
Tube #
Well
DNA Amount
Tube 1
A12
200.00 ng/well
Tube 2
B12
100.00 ng/well
Tube 3
C12
50.00 ng/well
Tube 4
D12
25.00 ng/well
Tube 5
E12
12.50 ng/well
Tube 6
F12
6.25 ng/well
Tube 7
G12
3.125 ng/well
Tube 8
G12
0.00 ng/well
4.2. Integrity Check by Agarose Gel Electrophoresis
Agarose gel (1%) electrophoresis was used to determine whether samples were of genomic
nature and to ensure that no degradation had occurred (Figure 19).
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GEL
-
-
-
- - - -
-
Figure 19: Electrophoretic separation of DNA. The movement of charged particles through an electric
field (Image created by Juanita Mellet) (Data Source: Russel, 2006).
The agarose gel was prepared as follows:
-
Seakem® LE Agarose powder (Lonza, Basel, Switzerland) was weighed in a heat
resistant Schott bottle.
-
Tris Borate EDTA (10x) electrophoresis buffer (Fermentas Life Sciences, Burlington,
Canada) was diluted with deionised water to create a 1x electrophoresis buffer.
-
The buffer was added to the agarose powder and heated to dissolve the powder.
-
Once the solution was clear, it was left to cool and 5 μl of Gel Red™ nucleic acid gel
stain (Biotium, San Francisco, USA) was added for visualisation under UV light.
-
The solution was poured into a gel plate and left to set for a minimum of 30 minutes.
A gel comb was inserted to create the wells.
Gel electrophoresis was performed with 2 μl of sample DNA and 3 μl of 6x Mass Ruler™
loading dye (Fermentas Life Sciences). The loading dye and DNA were mixed prior to loading
into the wells. The DNA migrated at a low velocity due to the size of genomic DNA. The gel
was visualised using a VersaDoc™ imaging system (BioRad, California, USA) together with
the Quantity One software program. A solid band was observed for each sample.
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4.3. GS GType HLA Typing Kit and Primers
A Roche designed GS GType HLA typing kit was used for typing of the samples. This is a SBT
assay, which made use of the 454 NGS instrument (GS Junior Instrument) (Roche Applied
Science, Penzberg, Germany). The typing test targets the most hypervariable and mostly
studied exons of the MHC region. The GS GType HLA Primer sets were made available as
two sets of kits, medium resolution (MR) and high resolution (HR). The two kits contained
different dried down primers for the amplification of various regions of different HLA genes.
The GS GType HLA MR Primer set (blue plate) contained eight different primer sets for the
MHC class I and II exons. The GS GType HLA HR Primer set (yellow plate) contained six
additional primer sets for the MHC class I and II exons. The HR set was used in combination
with the MR set to resolve some ambiguity. All primers for samples and negative controls
were labelled with multiplex identifiers (MIDs). This was the same across MR and HR plates
(Figure 20).
GS GType HLA MR Primer Set
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Neg.
1
2
3
4
5
6
7
8
9
10
Control
MID1
MID2
MID3
MID4
MID5
MID6
MID7
MID8
MID9
MID10
MID11
A
A-2
A-2
A-2
A-2
A-2
A-2
A-2
A-2
A-2
A-2
A-2
B
A-3
A-3
A-3
A-3
A-3
A-3
A-3
A-3
A-3
A-3
A-3
C
B-2
B-2
B-2
B-2
B-2
B-2
B-2
B-2
B-2
B-2
B-2
D
B-3
B-3
B-3
B-3
B-3
B-3
B-3
B-3
B-3
B-3
B-3
E
C-2
C-2
C-2
C-2
C-2
C-2
C-2
C-2
C-2
C-2
C-2
F
C-3
C-3
C-3
C-3
C-3
C-3
C-3
C-3
C-3
C-3
C-3
G
DQB1-2
DQB1-2
DQB1-2
DQB1-2
DQB1-2
DQB1-2
DQB1-2
DQB1-2
DQB1-2
DQB1-2
DQB1-2
H
DRBx-2
DRBx-2
DRBx-2
DRBx-2
DRBx-2
DRBx-2
DRBx-2
DRBx-2
DRBx-2
DRBx-2
DRBx-2
Blank
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GS GType HLA HR Primer Set
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Neg.
1
2
3
4
5
6
7
8
9
10
Control
MID1
MID2
MID3
MID4
MID5
MID6
MID7
MID8
MID9
MID10
MID11
A
A-4
A-4
A-4
A-4
A-4
A-4
A-4
A-4
A-4
A-4
A-4
B
B-4
B-4
B-4
B-4
B-4
B-4
B-4
B-4
B-4
B-4
B-4
C
C-4
C-4
C-4
C-4
C-4
C-4
C-4
C-4
C-4
C-4
C-4
D
DPB1-2
DPB1-2
DPB1-2
DPB1-2
DPB1-2
DPB1-2
DPB1-2
DPB1-2
DPB1-2
DPB1-2
DPB1-2
E
DQA1-2
DQA1-2
DQA1-2
DQA1-2
DQA1-2
DQA1-2
DQA1-2
DQA1-2
DQA1-2
DQA1-2
DQA1-2
F
DQB1-3
DQB1-3
DQB1-3
DQB1-3
DQB1-3
DQB1-3
DQB1-3
DQB1-3
DQB1-3
DQB1-3
DQB1-3
Blank
G
H
Figure 20: Medium and high resolution plate layout. The disposition of the exon specific primers and
samples on the 96-well plates for MR as well as HR primer sets. The letters indicate the MHC target
gene, while the digits indicate the exon of interest. Samples are organised by column and the MID’s
(grey area) are used to identify each sample and the negative control.
4.4. Polymerase Chain Reaction
Polymerase chain reaction is a fast and inexpensive method used to amplify small segments
of DNA. This method is used to amplify the DNA region in question and to ensure that a
sufficient amount of DNA is present for molecular and genetic analysis.
The kits for this study contained 14 primer sets (two primers for a specific region of the MHC
gene) with unique sequences complementary to a specific region of the MHC (Table 10).
There were eight primer sets for the MR plate and six for the HR plate. Each one of the 10
samples also had a unique MID tag sequence (Table 11) for automated software
identification after pooling and sequencing. Figure 21 indicates the position of the MID tags
relative to the sequence of interest and sequencing primers.
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Table 10: The primer sequences and amplicons’ fragment length (post-PCR).
Plate
MR
HR
Amplicon
Gene Specific Primer Sequence (5’-3’)
HLA-A2 5'
GAAACGGCCTCTGTGGGGAGAAGCAA
HLA-A2 3'
GGTGGATCTCGGACCCGGAGACTGT
HLA-A3 5'
GACTGGGCTGACCGCGGGGT
HLA-A3 3'
GAGGGTGATATTCTAGTGTTGGTCCCAA
HLA-B2 5'
AGAGCTCGGGAGGAGCGAGGGGACCGCAG
HLA-B2 3'
ACTCGAGGCCTCGCTCTGGTTGTAGTA
HLA-B3 5'
AGAGCTCGGGCCAGGGTCTCACA
HLA-B3 3'
ACTCGAGGGAGGCCATCCCCGGCGACCTAT
HLA-C2 5'
AGTCGACGAAGCGGCCTCTGCGGA
HLA-C2 3'
ACTCGAGGGGCCGGGGTCACTCA
HLA-C3 5'
ACGTCGACGGGCCAGGGTCTCACA
HLA-C3 3'
ACCTCGAGGTCAGCAGCCTGACCACA
DQB1-2 5'
AGGATCCCCGCAGAGGATTTCGTGTACCA
DQB1-2 3'
TCCTGCAGGACGCTCACCTCTCCGCTGCA
DRB1-x 5'
CCGGATCCTTCGTGTCCCCACAGCACG
DRB1-x 3'
CCGAATTCCGCTGCACTGTGAAGCTCTC
HLA-A4 5'
GGTTCTGTGCTCTCTTCCCCAT
HLA-A4 3'
GGGCTTGGAACCCTCAGTGA
HLA-B4 5'
CTGGTCACATGGGTGGTCC
HLA-B4 3'
AGATATGACCCCTCATCCC
HLA-C4 5'
CAAAGTGTCTGAATTTTCTGACTCTTCCC
HLA-C4 3'
TGAAGGGCTCCAGAAGGACTT,
TGAAGGGCTCCAGGACTT
DPB1-2 5'
GCTGCAGGAGAGTGGCGCCTCCGCTCAT
DPB1-2 3'
CGGATCCGGCCCAAAGCCCTCACTC
DQA1-2 5'
GTTTCTTCCATCATTTTGTGTATTAAGGT
DQA1-2 3'
CCATGAGAAGATCTGGGGACCTCT
DQB1-3 5'
TGGAGCCCACAGTGACCATCTCC
DQB1-3 3'
AGTGACATCAGGGATAAGAGATGGGAA
Amplicon
Length (bp)
514
479
446
425
481
653
370
366
746
448
446
411
490
451
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Table 11: The MID sequence tags for the GS GType HLA typing kit. This table
indicates the position of the tag on the plate, MID name, and sequence.
Column on plate
MID name
MID sequence
1
MID1
ACGAGTGCGT
2
MID3
AGACGCACTC
3
MID4
AGCACTGTAG
4
MID6
ATATCGCGAG
5
MID7
CGTGTCTCTA
6
MID8
CTCGCGTGTC
7
MID9
TAGTATCAGC
8
MID10
TCTCTATGCG
9
MID13
CATAGTAGTG
10
MID16
TCACGTACTA
11 (negative control)
MID11
TGATACGTCT
Forward Adaptor (A)
Library Key
MID 1
Forward Primer
Reverse Adaptor (B)
Library Key
MID 2
Reverse Primer
Figure 21: The position of the MID tags and primers, relative to the DNA sequence of interest (Image
created by Juanita Mellet, adapted from Figure 1 in Bentley et al., 2009).
The PCR step included the preparation of a master mix consisting of the reagents listed in
Table 12. Each PCR step included the processing of two plates, one MR and one HR plate
and the volumes in the table were calculated accordingly.
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Table 12: The reagents for making up the PCR master mix.
PCR Master Mix for HR Setup
Stock
Concentration
1 well (μl)
2 PCR plates
(μl)
Molecular Biology Grade Water (Sigma
Aldrich, Missouri, USA)
-
14.80
2516
Glycerol (Merck, New Jersey, USA)
80%, w/v
3.125
531.25
PCR Buffer II (Applied Biosystems,
California, USA)
10x
2.50
425
MgCl2 (Applied Biosystems)
25 mM
1.80
306
PCR Nucleotide Mix (Roche Applied
Science)
10 mM
0.375
63.75
AmpliTaq Gold DNA
(Applied Biosystems)
5 U/μl
0.40
68
Polymerase
PCR was carried out on the GeneAmp® 9700 PCR System (Applied Biosystems) using the PCR
conditions, as indicated in Table 13.
Table 13: PCR protocol
Temperature (˚C)
Duration
95.0
10 min
95.0
15 sec
62.0
30 sec
72.0
5 min
4.0
x35 cycles
∞
4.5. Amplicon Purification
The purification step for this study was performed using AMPure XP Beads® (Beckman
Coulter, California, USA). This highly efficient method is primarily used for purifying
amplicons exceeding 100 bp. This process is based on a magnetic bead-based solid phase
reversible immobilisation (SPRI) technology. The magnetic microparticles target size-specific
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nucleic acids and immobilise them on these particles by making use of specific buffer
conditions. The advantage of this technology is that it has the ability to recover
approximately 80% of the input DNA after purification and the yield of high quality data.
The Ampure Beads were added to the amplified DNA amplicons. These beads are magnetic,
and therefore, attached to the DNA amplicons after PCR. By making use of the Ambion®
96-well magnetic-ring stand (Applied Biosystems), DNA was separated from the
contaminants present in the sample liquid (Figure 22). Samples were washed subsequently
with 70% Ethanol (Appendix C) in order to rid the samples of contaminants. Such
contaminant could interfere with the downstream processes and reduce the number of
usable reads obtained from sequencing. TE buffer (Appendix C) was used to elute the DNA
from the magnetic beads.
1
2
3
Magnetic Beads
Contaminants
Magnetic ring
Magnetic ring
Magnetic ring
Figure 22: Amplicon purification after PCR. (1) Ampure beads were added to the sample and attached
to the nucleic acids for immobilisation, while contaminants remain in solution. (2) A magnetic ring
stand is used to produce a magnetic field that pulls the beads out of solution. This separates
contaminants and subsequent washing of nucleic acids produces high quality DNA. (3) The addition of
TE Buffer to the sample, eluted the nucleic acids from the magnetic beads (Image created by Juanita
Mellet, adapted from an image on the Beckman Coulter website: www.beckmancoulter.com).
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4.6. Amplicon Quantitation
Quantitation of the amplicons was also performed using the Quant-iT™ PicoGreen® doublestranded DNA assay kit (Invitrogen). This was performed in the same manner as previously
described in section 4.2 of this chapter. The measurements were taken in duplicate due to
the small volumes available. In the case where sample concentrations were less than
5 ng/μl, samples were further analysed on the Agilent 2100 Bioanalyzer (Agilent
Technologies, California, USA).
4.7. Amplicon Normalisation and Sample Pooling
The Emulsion PCR (emPCR) amplification step required that all amplicons be present at
equimolar ratios. The HLA Amplicon Dilution Calculator (available on www.454.com/my454)
was used for computation of dilutions required for normalisation (Figure 23). The calculator
was pre-populated with the names (blue column) and sizes (grey column) of amplicons. The
spreadsheet automatically calculated the amount of 1x TE buffer and DNA (dark green
column) necessary for each sample. The normalisation of the samples produced a
concentration of 1 x 109 molecules/μl. The calculator also indicated the amount of each
sample that was needed to create the equimolar pool for emPCR (light green column).
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Figure 23: A representation of the GS Gtype HLA Assay Amplicon Dilution Calculator for MR and HR
runs (GS Gtype HLA Assay Manual, March 2011).
There are two 454 sequencing systems that can be used for the sequencing of these
samples, the GS FLX and the GS Junior systems. This study made use of the GS Junior
system, which is able to run five samples of 14 amplicons (MR and HR) each on one full run.
The experiments performed included the processing of two full plates (MR and HR) at a
time, and therefore, each emPCR step consisted of 10 samples, which made up two
complete runs on the GS Junior sequencer.
Amplicon and sample pooling were performed for 10 samples (two plates) of 14 amplicons
each. The amount of 1x TE buffer and DNA were added based on the volumes from the
dilution calculator. Pooling of 10 samples for the GS Junior system consisted of two pools
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(A and B) of five samples each, while the negative control were added to pool B. The
samples had to be at a final concentration of 2 x 106 molecules/μl for emPCR.
4.8. Emulsion PCR and Sequencing
The emPCR and sequencing steps were performed by Inqaba Biotec™ (Pretoria). Emulsion
PCR and bead recovery were carried out as described in the emPCR amplification method
manual (Roche Applied Sciences, emPCR Amplification Method Manual, March 2012), while
the GS Junior sequencing run was carried out as described in the GS Junior sequencing
method manual (Roche Applied Sciences, Sequencing Method Manual, GS Junior Series,
March 2012). Emulsion PCR is the process of isolating DNA to beads by small adaptors
attached to the end of the DNA fragments. These adaptors provide primer sequences for
amplification and sequencing of a specific region. One adaptor contains a 5’-biotin tag that
immobilises the DNA to the streptavidin-coated beads. The immobilised DNA on the beads
was emulsified with the amplification reagents to create water-in-oil microreactors
(Figure 24a). DNA attaches to capture-beads by adaptors. Each bead carries a unique
single-stranded DNA fragment (Figure 24b). Each bead was captured in a microreactor
where amplification occurred. After amplification, beads with attached amplicons were
loaded into a PicoTiterPlate (PTP) (Figure 24c and e). These plates were designed to load a
single bead into each well of the plate, which allows the sequencing of one bead at a time
and produces data accordingly. The well is made compact by adding tiny beads and
sequencing enzymes to the plate (Figure 24d and f).
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(a)
(b)
(c)
(d)
(e)
(f)
Figure 24: DNA is mixed with capture-beads. (a) Beads and PCR reagents get emulsified in water-inoil amplification microreactors. (b) DNA attaches to capture-beads by adaptors. Each bead carries a
unique single-stranded DNA fragment. (c) Beads were loaded into the picotiterplate and (d) the wells
were filled with beads and sequencing enzymes. (e) A Scanning Electron micrograph of the
picotiterplate. (f) A micrograph of the tiny beads that were added to the wells (Reproduced with
permission from Roche).
A particular sequencing primer was annealed to the DNA strand as an adaptor for the DNA
strand to be sequenced, whereafter DNA polymerase synthesized the complementary
strand, illustrated in Figure 25. Each deoxynucleotide triphosphate (dNTP) that is being
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added at a time is detected and removed after the reaction by the enzyme Apyrase. The
enzymes for this reaction are subsequently degraded by heat-inactivation. The
incorporation of a single dNTP causes pyrophosphate (PPi) to be released. In the presence of
adenosine 5’ phosphosulphate, adenosine triphosphate (ATP) sulphurylase converts PPi to
ATP. The conversion of luciferin to oxyluciferin requires ATP. This conversion generates
visible light to the amount of ATP being produced (Ahmadian et al., 2006). The light is
detected and represented in a pyrogram, where the heights of the peaks indicate the
amount of nucleotides at a particular position.
Figure 25: A schematic representation of the process of 454 sequencing (Reproduced with permission
from Roche).
4.9. Software Analysis
The raw sequencing data were analysed by Roche (USA) on Conexio Genomics ATF 454
software (version 3.2.0) (Conexio Genomics, Fremantle, Australia) and by Juanita Mellet on
JSI SeqHLA 454 software (version 3.11.0) (JSI Medical Systems, Kippenheim, Germany). The
software was designed for sequence reads according to the primer sequences. Sample
sequences were compared to the sequences currently in the IMGT/HLA database (Robinson
et al., 2013). The sequence reads were sorted to individual samples according to the MID
tags and to a specific locus by primer sequences. A genotype was assigned to each sample
based on the sequences present in the database. In some cases a mismatch was present at
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one or more bases, which indicates the presence of a relatively rare sequence that have not
yet been documented in the database.
4.10. Statistical Analysis
The statistical analysis for this study was performed in consultation with Professor Piet
Becker (Biostatistician) from the Biostatistics unit at the Medical Research Council (MRC),
Pretoria. This was an exploratory study that assessed the agreement between the results
from different HLA typing methods in order to determine whether NGS HLA typing is able to
determine the HLA types that have been determined by conventional typing methods.
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CONVENTIONAL TECHNIQUES VERSUS NEXT GENERATION SEQUENCING
FOR HLA TYPING
5.1. Introduction
The success of the first twin transplantation in 1954 (Merrill et al., 1956) and the various
transplantations that followed thereafter, highlighted the importance of HLA matching
between donors and recipients. Only a tiny percentage of the entire population possesses a
twin or an HLA identical sibling. Registries and public cord blood banks across the globe
provide unrelated HLA donors for patients in need of transplantation. All units that enter a
registry or public bank need to undergo HLA typing for the various loci of interest. There are
currently many techniques available for HLA typing, although not all are accurate in
determining the exact HLA genotypes of individuals. It is, therefore, essential to determine
which technique will best suit the HLA typing needs for the establishment of a public cord
blood bank in South Africa.
In the past 60 years, HLA typing has moved from serological to cellular and currently the
most recent molecular typing techniques. During the 1960’s, serological techniques used to
be the ‘gold standard’ for tissue typing, since this was the only method of determining
individuals’ HLA surface markers at the time. Serological techniques detect antigenic
differences on the surfaces of the cells by antibodies (antisera). Even though this method is
still being used in several laboratories, there are numerous limitations, that include crossreactivity and the inability to accurately assign antigens. Cross-reactivity is the process in
which the same antigen is recognised by multiple antibodies, that could lead to false
positive results. In the mid 1990’s, DNA-based techniques became more popular and were
used in addition to serological techniques due to the inability of serological techniques to
accurately and reliably assign antigens (Bozón et al., 1997). Many laboratories have
discontinued the use of serological techniques and are simply making use of DNA and SBT
techniques. Low to high resolution DNA-based typing methods that are frequently used in
various laboratories are PCR-SSOP and PCR-SSP. The low to high resolution techniques have
the ability to accurately genotype individuals but due to the ever increasing number of new
alleles, it becomes challenging to accurately determine genotypes, since the assignment of
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genotypes relies on previously identified alleles. Low resolution typing techniques have
proven sufficient for solid organ transplants. In contrast, the transplantation of stem cells
requires accurate high resolution genotyping of the HLA alleles for matching between
donors and recipients. Inaccurate typing could lead to inadequate HLA matching between
donors and recipients, that could ultimately increase the chances of rejection of
transplanted tissue and the possibility of developing GVHD.
DNA and SBT techniques have contributed significantly to the current knowledge available
on the HLA genes and their immense allelic diversity. DNA-based techniques are able to
accurately assign HLA genotypes. In some cases complementary experiments are required
to resolve ambiguous results. Sequence-based typing methods provide the highest possible
resolution.
Since the completion of the human genome project (HGP) in 2003, and the discovery of the
diversity that exists between individuals, various high-throughput NGS technologies have
emerged. The initial goals of these technologies were to (1) increase the number of bases
per run; (2) increase reads per run; (3) decrease the cost per base; (4) achieve higher
accuracy; (5) achieve higher coverage; and (6) increased speed. The 454 platform was the
first NGS system to be launched in 2005. Since then several other platforms have emerged.
These platforms were initially only used for genomic sequencing but also has the potential
for research and diagnostic purposes in various fields. Even though these newly developed
techniques have already shown potential in identifying novel alleles, the more conventional
techniques are still preferred for routine procedures. The high degree of diversity present in
the genes of the HLA region has resulted in the development of a kit that would assign HLA
genotypes in a single run. The first potential NGS diagnostics kit for HLA typing was launched
in 2011 by Roche. This kit enables multiplex typing of the various polymorphic loci of the
HLA region. The ambiguous nature of these genes makes it challenging to assign accurate
genotypes. Until recently, only exons 2 and 3 were routinely sequenced in determining the
HLA genotypes of individuals. This newly developed HLA typing technique attempted to
reduce the ambiguous typing results by sequencing additional exons (Table 14). A study by
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Lank and co-workers has identified that the sequencing of exons 2, 3, and 4 enables
differentiation between 85% of all documented HLA alleles (Lank et al., 2012).
Table 14: GS Gtype HLA primer sets for medium and high resolution typing.
High Resolution (MR + HR)
Medium Resolution
GS Gtype MR Primer Set
GS Gtype HR Primer Set
HLA-A
exons 2, 3
HLA-A
exon 4
HLA-B
exons 2, 3
HLA-B
exon 4
HLA-C
exons 2, 3
HLA-C
exon 4
DQB1
exon 2
DQB1
exon 3
DRB1 1,3,4,5
exon 2
DPB1
exon 2
DQA1
exon 2
(Available at: www.454.com website)
It is well known that the HLA genes are some of the most diverse genes in the human body.
The purpose of this study was to determine (1) whether NGS HLA typing is efficient in
determining the HLA alleles in South African individuals, (2) the degree of complexity that
this technique reveals, and (3) the value of NGS HLA typing in establishing a cord blood
bank.
5.2. Results and Discussion
This study performed HLA genotyping by 454 NGS on 20 samples that had previously been
HLA typed by conventional methods at the LTI.
5.2.1. Software Analysis
The raw data generated by sequencing was analysed by two different software programs,
Conexio 454 ATF and JSI medical (SeqHLA 454). The Conexio analysis was performed by
another laboratory in the USA. The JSI medical software for 454 HLA sequencing displayed
the combined, sorted, and aligned results, which will be described in more detail below
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(Figure 26). Sequences generated from 454 sequencing were compared with sequences
already present in the HLA/IMGT database. The HLA database version 3.11.0 was installed
prior to commencement of the analysis. The HLA database version consists of all the current
allele sequences present in the database. Genotypes were assigned when full sequence
alignment occurred with a sequence present in the database. The consensus sequence is
displayed at the top of the view panel and displays the sequence of all the possible alleles
for a specific gene. The combined sequence is the sequence combined for haplotype 1 and 2
for the data. The sequencing data is visualised as a pseudo-electropherogram, representing
the coverage for each base and the minimum coverage as a red dotted line. The forward
and reverse reads for exon 2 is also indicated and the coverage for each sequence at the
left, with a (*) indicating a perfect match. The perfect match table (left of the sequences)
shows perfectly matched alleles for selected exons. Heterozygous positions are highlighted
in royal blue and indicate the differences between haplotype 1 and 2 for selected exons. In
cases where an allele cannot be accurately assigned, a list of possible alleles will be listed in
the results table (Figure 26a). The fragment sequences are the sequences highlighted with
light blue colour. Italicised letters indicate reverse strands. The matching table at the
bottom of the view panel indicates the number of matches to certain sequences, sorted by
probability. The most probable allele is listed at the top, while the least probable allele is
listed at the bottom. Alleles with mismatches (highlighted in red) are also displayed in the
match table and will indicate in which exon the mismatch occurs. In the case where there
are no mismatches for a given sequence, the alleles will be listed in the results window.
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(a)
(b)
Figure 26: JSI SeqHLA 454 software results for HLA-B. The screenshots (cropped) display the typing
results at the B locus for samples 1 and 8. (a) The result window (red rectangular boxes) for sample 1
indicates a heterozygote with a genotype ambiguity string. (b) The result window for sample 8
indicates a heterozygote with unambiguous typing results, genotype assignment
B*15:10:01/B*57:02:01.
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5.2.2. Allelic and Genotypic Agreement
The results from this study are displayed in Tables 15 (samples 1-10) and 16 (samples 11-20)
below. The first 10 samples (samples 1-10) have previously been typed by high resolution
SBT at class I and SSP at class II loci, while the second 10 samples (samples 11-20) were
typed by low resolution Luminex at class I and high resolution SSP at class II loci. These
tables display the results from the previously identified genotypes by conventional
techniques (column 1), and the results obtained from this study by NGS MR (column 2) and
HR (column 3). The conventional techniques display results at a two- to four-digit level of
resolution, depending on the technique that was used. The 454 sequencing technique
displays results at a four- to eight-digit level of resolution, although only the four-digit level
is indicated here for comparison purposes. Alleles were said to be in agreement when the
most probable allele assigned was identical to the conventional typing result at the
four-digit level. Disagreement between the different techniques is indicated in blue.
Side-by-side comparison of the results obtained from conventional and 454 NGS typing for
samples 1-10 showed 96% and 99% agreement with conventional typing techniques for MR
and HR, respectively. The results for samples 11-20 showed 95% and 98% agreement with
conventional typing techniques for MR and HR, respectively. It was possible to assign
accurate genotypes to 95.5% of the loci of interest for the total number of 20 samples by
MR, compared to 98.5% for HR. In some cases, manual editing of the sequences was
required due to the technical limitations of 454 NGS.
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HLA-DQB1
HLA-DRB1
HLA-C
HLA-B
HLA-A
Table 15: HLA typing results for samples 1-10 by conventional and NGS typing techniques.
ID
Ethnicity
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
Mixed ancestry
Tanzanian
SA Black
Mixed ancestry
SA Black
Mixed ancestry
Kenyan
SA Black
SA Black
Mixed ancestry
Mixed ancestry
Tanzanian
SA Black
Mixed ancestry
SA Black
Mixed ancestry
Kenyan
SA Black
SA Black
Mixed ancestry
Mixed ancestry
Tanzanian
SA Black
Mixed ancestry
SA Black
Mixed ancestry
Kenyan
SA Black
SA Black
Mixed ancestry
Mixed ancestry
Tanzanian
SA Black
Mixed ancestry
SA Black
Mixed ancestry
Kenyan
SA Black
SA Black
Mixed ancestry
Mixed ancestry
Tanzanian
SA Black
Mixed ancestry
SA Black
Mixed ancestry
Kenyan
SA Black
SA Black
Mixed ancestry
Conventional Techniques
(SBT and SSP)
02:01
03:01
30:02
68:02
68:02
74:01
02:01
66:01
02:01
29:02
03:01
11:01
02:01
02:02
68:02
68:02
68:02
74:01
30:01
43:01
07:02
08:01
08:01
44:03
07:02
15:03
13:02
35:02
45:01
45:07
07:02
07:06
45:01
51:01
15:10
57:02
15:03
15:10
15:10
42:01
07:01
07:02
07:01
14:03
02:10
07:02
04:01
06:02
06:02
16:01
07:02
07:02
16:01
16:01
03:04
18:01
02:10
08:04
04:01
17:01
01:01
03:01
03:01
13:02
11:01
13:02
07:01
11:04
11:02
13:01
15:01
15:01
03:01
15:03
03:01
13:02
11:01
13:02
03:02
04:01
02:01
05:01
02:01
06:04
03:19
06:09
02:02
03:01
03:01
06:03
05:02
06:02
02:01
06:02
02:01
06:09
06:02
06:09
03:02
04:02
454 NGS (MR)
02:01
30:02
68:02
02:01
02:01
03:01
02:01
68:02
68:02
30:01
07:02
08:01
07:02
13:02
45:01
07:02
45:01
15:10
15:03
15:10
07:01
07:01
02:10
04:01
06:02
07:02
16:01
03:04
02:10
04:01
01:01
03:01
11:01
07:01
11:02
15:01
03:01
03:01
11:01
03:02
02:01
02:01
03:01*
02:02
03:01
05:02
02:01
02:01
06:02
03:02
03:01
68:02
74:01
66:01
29:02
11:01
02:02
68:02
74:01
43:01
08:01
44:03
15:03
35:02
45:01*
07:05*
51:01
57:02
15:10
42:01
07:02
14:03
07:02
06:02
16:01
07:02
16:01
18:01
08:04
17:01
03:01
13:02
13:02
11:04
13:01
15:01
15:03
13:02
13:02
04:01
05:01
06:04
06:09
03:01
06:03
06:02
06:02
06:09
06:09
04:02
454 NGS (HR)
02:01
30:02
68:02
02:01
02:01
03:01
02:01
68:02
68:02
30:01
07:02
08:01
07:02
13:02
45:01
07:02
45:01
15:10
15:03
15:10
07:01
07:01
02:10
04:01
06:02
07:02
16:01
03:04
02:10
04:01
01:01
03:01
11:01
07:01
11:02
15:01
03:01
03:01
11:01
03:02
02:01
02:01
03:19
02:02
03:01
05:02
02:01
02:01
06:02
03:02
03:01
68:02
74:01
66:01
29:02
11:01
02:02
68:02
74:01
43:01
08:01
44:03
15:03
35:02
45:07
07:05*
51:01
57:02
15:10
42:01
07:02
14:03
07:02
06:02
16:01
07:02
16:01
18:01
08:04
17:01
03:01
13:02
13:02
11:04
13:01
15:01
15:03
13:02
13:02
04:01
05:01
06:04
06:09
03:01
06:03
06:02
06:02
06:09
06:09
04:02
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HLA-DQB1
HLA-DRB1
HLA-C
HLA-B
HLA-A
Table 16: HLA typing results for samples 11-20 by conventional and NGS typing techniques.
ID
Ethnicity
11
12
13
14
15
16
17
18
19
20
11
12
13
14
15
16
17
18
19
20
11
12
13
14
15
16
17
18
19
20
11
12
13
14
15
16
17
18
19
20
11
12
13
14
15
16
17
18
19
20
SA Black
SA Black
SA Black
SA Black
Mixed ancestry
SA Black
SA Black
Mixed ancestry
SA Black
SA Black
SA Black
SA Black
SA Black
SA Black
Mixed ancestry
SA Black
SA Black
Mixed ancestry
SA Black
SA Black
SA Black
SA Black
SA Black
SA Black
Mixed ancestry
SA Black
SA Black
Mixed ancestry
SA Black
SA Black
SA Black
SA Black
SA Black
SA Black
Mixed ancestry
SA Black
SA Black
Mixed ancestry
SA Black
SA Black
SA Black
SA Black
SA Black
SA Black
Mixed ancestry
SA Black
SA Black
Mixed ancestry
SA Black
SA Black
Conventional Techniques
(Luminex and SSP)
29:XX
36:01
23:XX
43:XX
23:XX
66:XX
26:XX
80:XX
02:XX
68:XX
23:XX
34:XX
24:XX
68:XX
02:XX
29:XX
03:XX
34:XX
30:01
30:01
44:XX
53:XX
15:03
15:03
45:XX
58:XX
15:01
18:XX
51:XX
53:XX
07:XX
44:XX
08:XX
58:XX
40:XX
15:03
44:XX
44:XX
15:03
58:XX
04:XX
07:XX
04:XX
18:XX
06:XX
16:XX
02:XX
04:XX
04:XX
14:XX
04:XX
07:XX
06:XX
07:XX
03:XX
04:XX
02:XX
04:XX
02:XX
06:XX
11:01
11:01
03:01
15:01
12:01
13:01
04:05
07:01
01:02
14:04
03:01
13:01
12:01
13:01
07:01
13:01
13:01
15:02
04:04
08:04
06:02
06:02
03:01
06:02
03:01
06:02
02:02
03:02
05:01
05:03
03:01
06:03
05:01
06:03
02:02
06:03
06:02
06:03
03:19
08:04
454 NGS (MR)
29:02
23:01
23:01
26:01
02:03
23:01
24:02
02:01
03:01
30:01
44:03
15:01
45:01
15:01
51:01
07:05
08:01
40:01
44:03
15:03
04:01
04:01
06:02
02:02
04:01
04:01
06:02
03:04
02:10
02:10
11:01
03:01
12:01
04:05
01:02
03:01
12:01
07:01
13:01
04:04
06:02
03:01
03:01
02:02
05:01
03:01
05:01
02:01*
06:02
03:01*
36:01
43:01
66:01
80:01
68:02
34:02
68:01
29:01
34:02
30:01
53:01
15:03
58:02
18:01
53:01
44:03
58:02
15:03
44:03
58:01
07:01
18:01
16:01
04:01
14:02
07:02
07:02
04:01
04:01
06:02
11:01
15:01
13:01
07:01
14:04
13:01
13:01
13:01
15:01
08:04
06:02
06:02
06:02
03:02
05:03
06:03
06:03
06:03
06:03
08:04
454 NGS (HR)
29:02
23:01
23:01
26:01
02:03
23:01
24:02
02:01
03:01
30:01
44:03
15:01
45:01
15:01
51:01
07:05
08:01
40:01
44:03
15:03
04:01
04:01
06:02
02:02
04:01
04:01
06:02
03:04
02:10
02:10
11:01
03:01
12:01
04:05
01:02
03:01
12:01
07:01
13:01
04:04
06:02
03:01
03:01
02:02
05:01
03:01
05:01
02:02
06:02
03:19
36:01
43:01
66:01
80:01
68:02
34:02
68:01
29:01
34:02
30:01
53:01
15:03
58:02
18:01
53:01
44:03
58:02
15:03
44:03
58:01
07:01
18:01
16:01
04:01
14:02
07:02
07:02
04:01
04:01
06:02
11:01
15:01
13:01
07:01
14:04
13:01
13:01
13:01
15:01
08:04
06:02
06:02
06:02
03:02
05:03
06:03
06:03
06:03
06:03
08:04
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The genotypic disagreement between the conventional techniques and 454 NGS MR typing
was mainly due to ambiguous typing results, indicated by (*). Many alleles are identical
across exons 2 and 3 of the HLA genes, since countless polymorphisms are located outside
the sequenced region (Robinson et al., 2013). Therefore, it is challenging to accurately
assign genotypes by only sequencing these two exons. Several ambiguous typing results
were obtained from the results produced by only the MR kit. Sequencing of additional exons
with the HR kit resolved several ambiguities. Disagreement between results obtained from
conventional and 454 NGS HR sequencing was observed at three loci. The disagreement was
observed for samples 6 (HLA-B in an individual of Mixed ancestry), 12 (HLA-B in a Black
South African), and 19 (HLA-DRB1 in a Black South African). The disagreement observed for
sample 6 is due to the sequences of B*07:05 and B*07:06 being identical across the
sequenced regions (Robinson et al., 2013). The nucleotide change that distinguishes
B*07:05 from B*07:06 is present in exon 5 of the HLA-B gene, which is outside of the region
sequenced. Even though disagreement was observed for sample 6, when compared to the
previous typing results, the correct genotype was still present in the ambiguity string for this
study. A study by Paximadis and co-workers failed to identify HLA-B*07:05 in the Black
South African populations, while B*07:06 was found to be relatively frequent in the Black
South African population (Paximadis et al., 2011). The allele frequency database, on the
contrary, indicates a higher frequency of B*07:05 in the Zulu population compared to
B*07:06 (Gonzalez-Galarza et al., 2011).
Class I alleles for samples 11-20 were typed by Luminex, which is a low resolution
genotyping technique, that types up to a two-digit level of resolution. The high number of
B*15 alleles that exist requires additional kits to assign accurate genotypes. Therefore, all
the B*15 alleles are typed to a four-digit level compared to the two-digit level for the other
class I alleles. The disagreement observed for sample 12 could have occurred due to the
limitations of the Luminex technology to accurately determine heterozygous alleles.
HLA-B*15:01 is observed frequently in all populations, whereas B*15:03 is present at a
relatively high frequency in Black South African individuals, while observed at a very low
frequency in South African Caucasians (Paximadis et al., 2011; Gonzalez-Galarza et al.,
2011). The technique that was used, could therefore, account for the incorrectly assigned
homozygous genotype and the disagreement observed. The 454 NGS DRB1 locus
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assignment for sample 19 also showed disagreement with the conventional typing results.
DRB1*15:02 differs from DRB1*15:01 by a non-synonymous change in the DNA sequence
that causes Valine (Val) to be substituted with Glycine (Gly) at position 86 of the
peptide-binding groove (Marsh and Bodmer, 1993). Several studies have indicated the high
frequency of the DRB1*15:01 allele in Caucasian individuals; this allele has also been
observed in Black African individuals at a lower frequency (Paximadis et al., 2011; Fernando
et al., 2012). The DRB1*15:02 allele has previously been observed in Chinese individuals
(Fernando et al., 2012). According to recent papers that investigated the diversity in the
South African population, the DRB1*15:02 allele was not detected in Black South African
individuals (Paximadis et al., 2011; Gonzalez-Galarza et al., 2011). Therefore, this could
indicate the presence of either a rare allele present in this particular African individual or
the mistyping of the DRB1 locus by PCR-SSP.
It is well known that the allele frequencies of variants differ between individuals with
different geographical ancestries. African individuals are known to have the most ancient
genomes. A recently published study has indicated that Africans carry three times more
low-frequency alleles compared with Europeans and Asians (The 1000 Genomes Project
Consortium, 2012). The limited studies that have targeted the African population, and the
extreme diversity of these individuals, affect the certainty with which a genotype is
assigned. In many instances genotypes are assigned based on the frequency of the genotype
in a given population. This could affect the assignment of rare alleles, especially in the
African population, in which many have not yet been identified.
5.2.3. Unambiguous and Ambiguous Genotype Assignment
Several loci were assigned a unique genotype, while others produced a string of ambiguities.
Although sequencing additional exons has resolved some ambiguity, it is not completely
resolved, since ambiguous typing results were still produced for several loci. Table 17
illustrates the different loci and the unambiguous and ambiguous genotypes assigned at a
four-digit level of resolution for the different MR and HR typing kits.
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Table 17: Unambiguous and ambiguous typing results by 454 sequencing.
Locus
Samples with
unambiguous genotypes
(Four-digit level of resolution)
Ambiguous alleles
(Four-digit level of resolution)
MR
HR
MR
HR
HLA-A
0
4
34
17
HLA-B
4
7
28
19
HLA-C
3
2
26
27
HLA-DPB1
5
5
24
24
HLA-DQA1
1
1
30
30
HLA-DQB1
0
3
35
25
HLA-DRB1
3
3
29
29
DRB3/4/5/6/7
3
3
25
25
The table compares the unambiguous and ambiguous typing results between the MR and
HR kits. The additional primer sets incorporated through the HR kit resolved a great degree
of ambiguity for most of the loci, except for HLA-C, where the ambiguity increased by
sequencing additional exons. The sequencing of additional exons did not entirely resolve the
issue of ambiguity, with HLA-C, -DQA1, and -DRB1 having the most ambiguous allelic
assignments. The ambiguous typing results obtained from this study are inconsistent with
the results from a paper published in 2011 by Holcomb and co-workers. The unambiguous
assignment of genotypes is lower, while the ambiguity observed is greater compared with
the results of Holcomb and co-workers (Holcomb et al., 2011). The degree of ambiguity
observed could be as a result of little knowledge on the polymorphisms present in Black
African individuals at these loci. Ambiguous typing results occur as a result of phase
ambiguity or partial sequences generated from sequencing. Sequence-based typing
techniques only sequence certain exons of interest (exons 2, 3, and 4), which generate
ambiguous typing results. This results from genetic polymorphisms situated outside the
routinely sequenced regions. A study by Wang and co-workers has been able to resolve
some ambiguity by sequencing several additional class I and II exons (Wang et al., 2012).
Another way to resolve ambiguity according to Erlich and co-workers is to sequence the
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entire gene of interest (Erlich et al., 2011). Although sequencing the entire gene would be
lucrative, it is not yet cost effective. The reduction in sequencing costs might enable
sequencing of the essential HLA genes as a possible solution to resolve ambiguity.
Differences were also observed between the results from the two different software
programs that were used to analyse the data (Table 18). This is in all likelihood as a result of
the different HLA database versions that were installed prior to analysis. The HLA database
is regularly updated to include newly discovered alleles in order to assist in assigning
accurate genotypes. The Conexio 454 ATF software used the HLA database version 3.2.0
(updated 15/10/2010) for the data analysis, whereas JSI SeqHLA 454 software used version
3.11.0 (updated 01/17/2013). Since 2010, more than 2 000 novel alleles have been
discovered and submitted to the database (Figure 11, Chapter 2), thereby increasing the
number of ambiguities due to sequencing only certain exons. Therefore, the software
version that was installed for the Conexio software is outdated and not a true
representation of the alleles currently present in the HLA database.
Table 18: Unambiguous and ambiguous typing results produced by different software programs.
Locus
Samples with unambiguous
genotype (four-digit level of
resolution)
Ambiguous alleles (four-digit
level of resolution)
Conexio
JSI
Conexio
JSI
HLA-A
6
4
12
17
HLA-B
8
7
20
19
HLA-C
6
2
21
27
HLA-DPB1
6
5
20
24
HLA-DQA1
6
1
17
30
HLA-DQB1
7
3
21
25
HLA-DRB1
11
3
9
29
DRB3/4/5/6/7
14
3
5
25
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5.2.4. Low to High Resolution HLA Typing
The resolution of HLA typing has fluctuated over the years with the emergence of various
typing techniques. Conventional techniques such as serology and DNA based techniques are
not reliable in distinguishing the ever increasing number of alleles being discovered.
Sequence-specific primers and SSOP are commonly used in clinical laboratories for low and
high resolution HLA typing, respectively. Sequence-specific oligonucleotide typing is able to
determine alleles based on known variants, however, rare variants cannot be identified by
this technique. This makes it challenging to assign HLA genotypes accurately. The class II
alleles for all 20 samples were genotyped by PCR-SSP (described in Chapter 4) at the LTI. The
SSP method has high specificity and is able to genotype at a high resolution. This method of
HLA typing has a high success rate and has proven to be accurate. Techniques such as SSOP
and SSP require updated probes and primers in order to detect the allelic diversity present
at these loci. Alternative methods are used to complement these techniques to resolve
ambiguous typing results. The high number of unknown variants present in African
individuals make it challenging to assign genotypes accurately. It is, therefore, essential that
a reliable high resolution typing technique is used for accurate determination of the HLA
alleles and genotypes. True high resolution typing provides information up to the four-digit
level of resolution. The 454 NGS technology (MR and HR kits) has enabled a higher
resolution of HLA genotyping up to the eight-digit level. The highest level of resolution
might be of significance for research purposes, which involve the discovery of possible
vaccines for HIV. However, the four-digit level of resolution is sufficient for HLA genotyping.
It is important to note that accurate four-digit typing is not necessarily obtainable by only
sequencing exons 2 and 3. Therefore, additional exons might need to be sequenced to
resolve ambiguous typing results at the allelic level. It is also possible to adjust the software
to display the results to the four-digit level. The equipment used and the resolution of
typing for these genes will depend on the laboratory needs. It is necessary for each
institution to consider the value of higher resolution typing and whether it is in fact relevant
and cost-effective.
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5.2.5. Technical Limitations
The library preparation for this technique is time consuming, labour intensive, and
expensive (Erlich et al., 2011). The cost of sequencing is also relatively high compared to
conventional techniques. However, a study by Holcomb and co-workers has indicated that
sequencing 20 samples by Sanger sequencing would be more time consuming and the costs
for reagents would be more or less equivalent to reagents for NGS (Holcomb et al., 2011).
An individual would require a certain level of expertise to perform the various procedures
involved due to its complexity. DNA quality and quantity has been shown to play a
significant role in obtaining accurate results and high quality DNA is not always obtainable. A
quality assessment was performed for all amplicons before proceeding to emPCR, and
although HLA-A and -C exons were of the lowest quantities prior to emPCR, genotypes were
still accurately assigned for these loci. This provides a clear indication of the robust nature of
the 454 NGS system. Manual editing was required for several samples, which necessitated
knowledge on HLA analysis.
Bi-directional coverage is the process of sequencing both the forward and reverse strands of
the DNA. For several samples, this technique was unable to accurately sequence the reverse
strand for certain exons of interest and also had difficulty in accurately calling the bases at
the ends of sequences. The 454 NGS technique relies on emitted light intensities to
differentiate between the different nucleotides, which resulted in inaccurate base-calling at
times. Another known limitation is the inability to detect the length of homopolymeric
regions. The emitted light intensities make it difficult to determine the number of bases
present for a specific nucleotide. Numerous short reads were also generated from the
sequencing run, which were filtered out prior to the analysis by specific software tools. This
study made use of two different kits that enabled the sequencing of additional exons. Some
exonic regions failed to be sequenced for several samples. This was particularly observed for
HLA-A, exon 4. Although the presence of exon 4 sequences would have assisted in resolving
some ambiguous results, its absence did not affect the accuracy at which the genotypes
were called for HLA-A. In some cases sequences of exon 4 were likewise not present in the
HLA database. This is due to the fact that exon 4 has only recently been included in HLA
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typing. In the case where a reference sequence of exon 4 was absent, genotypes were
assigned based on the sequences across exons 2 and 3.
The technical limitations mentioned have been addressed and compensated for in the GS
FLX Titanium system (Roche). A new set of HLA primers is also being developed, which will
produce longer amplicons and additional exonic and intronic sequences of these genes. The
current trials encountered by the current 454 NGS technique will facilitate in improving
third- and fourth-generation sequencing in the future.
5.3. Concluding Remarks
HLA genotyping is performed on a regular basis for various applications. Hematopoietic
stem cell transplantation is the most frequent application and has been effective in treating
many fatal diseases worldwide. Donors and recipients require allele level matching at five
HLA loci (10 alleles) for bone marrow transplantation, while UCB only requires matching at
three HLA loci (six alleles). Mismatching between donors and recipients could lead to severe
adverse reactions. Therefore, accurate genotyping of the HLA genes is crucial for the
outcome of transplantation. The ever increasing number of HLA alleles, being discovered
daily, creates a challenge to accurately genotype individuals for transplantation purposes.
The strong LD, known to exist between genes within the MHC region, suggests that other
relevant genes may also play a critical role. Haplotype matching has shown to improve the
outcome of transplantation, and could therefore, also be due to the function of other
important genes.
Next generation sequencing technologies have developed in the last decade and are
continuously improving. The NGS (Roche/454 pyrosequencing) technique is quicker and
more efficient than conventional techniques in determining the HLA genotypes of
individuals. The results from this study indicate the importance of sequencing additional
exons in order to resolve ambiguity for transplantation. Therefore, it is possible to conclude
that the 454 NGS HR HLA typing kit has the potential as an alternative method to provide
accurate genotyping in routine clinical and diagnostic laboratories. This technique also has
the potential to be used as an HLA typing tool, in a public cord blood bank in South Africa, to
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ultimately improve HLA matching between donors and recipients. However, the limitation
of cost will possibly prevent this technique from being implemented in clinical laboratories
in South Africa.
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CHAPTER 6
CONCLUDING REMARKS
The discovery of the first HLA molecule in 1952 led to further investigation of the function of
these molecules and the pivotal role they play in immune responses. The HLA genes are the
most diverse genes in humans. The true diversity of the HLA genes has only become
apparent in the last decade with the emergence of DNA- and sequence-based typing
methods. HLA typing has moved from cellular- to molecular-based techniques over the
years and several typing applications are still performed routinely, with typing for
transplantation being the most frequent. The extreme diversity in the Black South African
population poses a challenge in obtaining matching donors for individuals in need of a
transplant. The SABMR does not reflect the demographics of South Africa, since the majority
of donors are Caucasian. Cord blood stem cell transplantation is an effective treatment
alternative for many fatal diseases and requires less strict HLA matching criteria when
compared to bone marrow. For that reason there is a continuous need for a public umbilical
cord blood bank in order to cater for the diverse South African population. Through accurate
HLA genotyping, it will be possible to increase the degree on HLA matching between
individuals and most likely decrease the chances of developing GVHD and graft rejection.
An objective of the present study was to consider the racial/ethnic groups, linguistic groups,
and the HLA diversity of the South African population and thereby determine how a public
umbilical cord blood bank would need to be constituted (Chapter 3). South Africa is a
multiracial, as well as a multicultural country, presently populated by more than 50 million
individuals. It is comprised of White, Black, Indian/Asian, and Mixed ancestry individuals
that together make up this diverse population. The Black population is the most diverse
population group in South Africa and consists of four broad groupings, namely: Nguni
(Includes Zulu, Xhosa, Ndebele, and Swazi), Sotho-Tswana (Southern, Northern, and
Western Sotho population), Tsonga, and Venda. Individuals belonging to the Nguni group
have shown increased linguistic and genetic similarities. Another cluster is formed by the
Sotho-speaking individuals and it has also been indicated that the Venda group show close
similarity to the Tsonga group. The White South African population is mainly of European
descent and the majority speak Afrikaans and English. The Mixed ancestry South African
population is comprised of individuals of mixed ancestral lineage and mainly speaks
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Concluding Remarks
Afrikaans. The Indian and Asian South Africans are largely English-speaking. The language
that one speaks is often an indication of one’s ethnicity, which could reflect cultural
groupings as well as cross-cultural marriages. A great degree of intermixing occurs between
Black South African individuals, which therefore, indicate that language is not a true
representation of an individual’s ethnicity. Certain population groups tend to reside in
certain provinces, while others are evenly distributed across all of the nine provinces.
Gauteng is the smallest of the nine provinces and also the most populous. Individuals from
all the different linguistic groups currently reside in Gauteng, making it the most diverse
province in South Africa.
Black African individuals are known to possess the most ancient and diverse genomes
compared with other population groups across the globe. The vast diversity of these genes
and population specific genetic variants decreases the probability of obtaining an HLA
matching donor. It is, therefore, not recommended to constitute the cord blood bank on the
basis of HLA diversity. Intermixing between individuals belonging to distinct linguistic groups
is frequently observed. The extreme patterns of diversity that exist between the different
linguistic groups render it difficult to determine the ethnicity of an individual based on the
language spoken. Therefore, it is likewise not recommended to constitute the bank on the
basis of language.
Through the knowledge obtained from the literature, it is recommended to constitute the
bank based on social race or major ethnic groupings (Nguni, Sotho-Tswana, Tshivenda, and
Xitsonga), since genetic similarities have been shown to exist between individuals belonging
to the same major ethnic groups. Gauteng would be the most suitable province for the
establishment of the first public umbilical cord blood bank in South Africa. The bank would
have to be representative of the entire South African population. It has been estimated that
a minimum number of 10 000 cord blood units would be needed to initiate the bank, of
which 8 000 (80%) would have to be representative of Black South Africans, 900 (9%) of
White South Africans, 900 (9%) of Mixed ancestry South Africans, and 200 (2%) of
Indian/Asian South Africans.
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A further objective included the validation of a 454 NGS HLA typing kit on South African
samples obtained from the SABMR. This part of the study was performed to determine
whether the NGS HLA typing kit would be a feasible option for HLA genotyping in a public
cord blood bank or whether the information would be too detailed for the needs of a public
bank (Chapter 5).
The HLA genotyping results for this part of the study indicated accurate HLA genotyping for
the majority of the South African individuals and also for two individuals from other African
countries. The disagreement observed with conventional typing results could have occurred
due to the limitations of conventional techniques in detecting the diversity present in South
African individuals. Sequencing of additional exons was able to resolve some ambiguities.
Various challenges would still need to be addressed before NGS can be used as a suitable
HLA typing method. This technique displays potential as an alternative HLA typing method
for cord blood banks and clinical laboratories. However, the high cost of sequencing and
labour intensity will most likely prevent this technique from being implemented in South
Africa.
In conclusion, the main findings of the present study include:
- The diversity of the HLA genes in the South African population is extremely vast
- There is a great degree of intermixing between the different linguistic groups of Black
South Africans
- A public umbilical cord blood bank in South Africa should be constituted on the basis of
either race or major ethnic groups (Nguni, Sotho-Tswana, Tshivenda, and Xitsonga)
- The cord blood bank would need to be representative of the South African population
- The 454 NGS MR typing kit is not sufficient in accurately distinguishing between the
different HLA alleles
- The 454 NGS HR HLA typing kit was able to accurately detect the majority of the HLA
genotypes for selected African and South African individuals
- The sequencing of additional exons resolves some ambiguities
- The 454 NGS technique for HLA typing has the potential to be used as a diagnostic tool in
a public bank as well as clinical laboratories in South Africa
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Limitations of the present study:
- There is little information available on the HLA diversity within the South African
population
- Previously conducted studies are not representative of the South African population
- There is limited high resolution HLA data available on the South African population
- HLA ambiguity remains a challenge due to sequencing of only the variable regions
Future studies implemented should focus on the following:
- Investigate the HLA diversity in South Africans by using DNA- or sequence-based methods
on a large cohort
- Consider the possibility that various other genes could be found to be significant in
matching between donors and recipients
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APPENDICES
APPENDIX A
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Appendices
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APPENDIX B
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APPENDIX C
Solutions:
AE Buffer
10 mM Tris-Cl
0.5 mM EDTA
pH 9.0
1% Agarose gel - Research Tray
0.6 g agarose
60 ml 1x TBE
3 μl Gel Red
Dissolve the Agarose and the TBE in the microwave until a clear solution is formed. Add Gel
Red to the clear solution and mix. Pour cool liquid into the gel tray with combs to form the
wells. Allow to cool for 30-45 min.
2% Agarose gel - Diagnostic Tray
4 g agarose
200 ml 1x TBE
20 μl Gel Red
Dissolve the Agarose and the TBE in the microwave until a clear solution is formed. Add Gel
Red to the clear solution and mix. Pour cool liquid into the gel tray with combs to form the
wells. Allow to cool for 30-45 min.
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Appendices
70% Ethanol
70 ml Ethanol (100%)
30 ml Molecular Grade Water
80% Glycerol (w/v)
Pre-weigh a 50 ml tube. Add 30 ml of 100% glycerol. Determine the weight of the glycerol by
subtracting the weight of the tube from the total weight. Calculate the total volume of
solution (in ml) that will make 80% w/v by multiplying the weight of glycerol by 1.25.
Determine the amount of molecular biology grade water to add to the glycerol by
subtracting the weight of glycerol (in grams) from the total volume of solution needed (in
ml). Add the amount of molecular biology grade water calculated to the glycerol and mix
thoroughly.
1x Tris EDTA (TE) Buffer
10mM Tris-HCl (pH 7.6-8.0)
1 mM EDTA
Make up to 100 ml by adding ddH2O and store at room temperature
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