120 Figure 3.6: Box-Whisper diagram showing the effect of ethnicity on DACT1 tDMR methylation status in four ethnic groups. 122 Figure 3.8: Box-Whisper plot showing the effect of ethnicity on L81528 tDMR methylation status in four ethnic groups.
CHAPTER ONE
INTRODUCTION AND
LITERATURE REVIEW
Epigenetics
Currently, a widely accepted definition is "the study of processes that produce a heritable phenotype that does not strictly depend on DNA sequence" (Lieb et al., 2006). Because of its potential regulatory role in gene transcription, not unlike methyl-cytosine, hydroxymethyl-cytosine has been called the “fifth base” (Munzel et al., 2011; Tammen et al., 2013).
Localisation of DNA Methylation in the Human Genome
Tissue-Specific Differential DNA Methylation
Environmental Influences and Differential DNA Methylation .1 Age
- Nutrition and Diets
- Life Experiences
Possibly as a result of an 'epigenetic diet', several bioactive food components in the diet have been observed to alter gene expression via changes in DNA methylation (Hardy and Tollefsbol, 2011; Park et al., 2011). Diets rich in fruits and vegetables have been shown to have anticancer properties (Alegria-Torres et al., 2011; Borek, 2004).
Mapping of Genome-wide DNA Methylation
- Sodium Bisulfite Treatment of DNA Templates
- MALDI-TOF MS
- Pyrosequencing
- Methylation-Sensitive Restriction Enzyme PCR
- Capillary Electrophoresis
- Restriction Landmark Genomic Scanning
- Affinity Purification of Methylated DNA
MSRE combined with PCR can be followed up with methylation analysis using standard capillary electrophoresis platforms (An et al., 2013;. This is achieved by designing primers that flank the recognition site of the enzyme to be used (An et al., 2013; Melnikov et al ., 2005).
Role of Epigenetic Modifications in Gene Expression and Development
- Differential DNA Methylation and Gene Expression
- Imprinting
- Regulation of ncRNAs
- X Chromosome Inactivation
- Control of Alternate Promoters
A medical study of Human Papillomavirus (HPV) by Bierkens et al. 2013) found a significant association between DNA methylation and miRNA. DNA methylation blocks transcription initiation at promoters, but elongation of genes remains unaffected (Bird, 1995; Kulis et al., 2013).
Tissue-Specific Differentially Methylated Regions (tDMRs) and Control of Gene Expression
Additional tDMRs to distinguish semen from other tissues were identified by Igarashi et al. 2008), who also detected an age-related linear correlation of DNA methylation in the testes tissues. Non-CpG methylation correlated with positive expression in H1 cells in the study by Lister et al. 2015) found a negative correlation, so the function of non-CpG methylation is not known.
Tissue-Specific Differentially Methylated Regions (tDMRs) and its Application in Forensic Science
- Verification of DNA Samples
- Identification of Forensically Relevant Body Fluids/Tissues by Analysis of tDMRs
- Ancestry Informative Markers of Fluid/Tissue Donor
Vaginal fluid and menstrual blood, analogous to the two previously described reports by Lee et al. 2013) showed low methylation levels at PFN3. Nevertheless, the investigation by Moen et al. 2013) highlights the concept of methylation playing a major role;.
The Current Status of Forensic Fluid and Tissue Identification
- Conventional Methods Employed to Identify Forensically Relevant Body Fluids
- Saliva
- Semen
- Vaginal Fluid
- The Use of Biological Markers
- Protein-based Biomarkers
- RNA-based Biomarkers
- Microbial Biomarkers
- Aims
- Objectives
Semen is one of the most common bodily fluids found during criminal investigations, and in cases of sexual assault, its conclusive identification is usually required to substantiate the alleged crime (Romero-Montoya et al., 2011). Consequently, a negative PSA result may be due to degradation of the protein, and not necessarily indicate the absence of semen (Virkler and Lednev, 2009; Wasserstrom et al., 2013). Several markers for vaginal fluids, such as membrane-associated mucins, have been rendered unreliable due to expression in salivary glands (Liu et al., 2002; Nussbaumer et al., 2006).
RNU62 is unreliable as it is synthesized by polymerases different from those that synthesize precursor miRNAs (Baker, 2010; Ma et al., 2013b). Variations in the human microbiome have been attributed to diseases such as obesity, periodontitis, vaginosis and inflammatory bowel disease (Jorth et al., 2014). Antibiotics such as erythromycin induce changes in vaginal microbial communities (Choi et al., 2003; Witkin et al., 2007).
CHAPTER TWO
IDENTIFICATION OF TISSUE SPECIFIC DIFFERENTIAL DNA METHYLATION IN
HUMAN BODY FLUIDS
Data Acquisition
Gene expression data from normal surrogate tissues of four human body fluids, namely saliva (surrogate tissues: salivary glands and tongue), blood, semen (surrogate tissues: prostate) and vaginal fluid (surrogate tissues: uterus and cervix) were mined. Tissue-specific gene expression and regulation (TiGER) expression database (http://bioinfo.wilmer.jhu.edu/tiger/). The GXA consists of both microarray and RNASeq data and provides information on gene expression patterns under different biological conditions.
Gene expression data are reanalyzed to detect genes showing interesting baseline and differential expression (Petryszak et al., 2014). Serial Analysis of Gene Expression (SAGE) data from the NCBI Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/). From the above databases, gene expression data were integrated, and genes specifically upregulated or overexpressed in surrogate tissues of interest, but not expressed or marginally expressed in other tissues, were selected for further investigation.
Methylation Data Acquisition
The HuGE Index is a comprehensive mRNA expression database containing expression data of thousands of genes in normal human tissues and organs. Expression data were obtained using high-density oligonucleotide array technology ( Haverty et al., 2002 ). GEO is an international public repository that archives and freely distributes microarrays, next-generation sequencing, and other forms of high-throughput functional genomics data submitted by researchers (Barrett et al., 2013).
As a result, the entire dataset was transformed into the latest human genome assembly coordinate system (GRCh38), which enabled the integration of ENCODE and NGSMethDB data.
Data Integration and Exclusion of Differentially Expressed Genes Present in More than One Surrogate Tissue
Elimination of Genes with No Evidence of Protein Synthesis
Genes and Genome Data
Mapping CpG Island and Methylation Information
Final Selection of Candidate Tissue-Specific Differentially Methylated Regions
CpG sequences that mapped to a single chromosomal location were selected, while those to different locations on human chromosomes were discarded.
Primer Design for PCR
Sample Collection
DNA Extraction and Quantification
Before performing PCR; Approximately 100 ng of DNA from saliva, blood, semen, and vaginal fluid were separately restricted with HhaI in a 10 L reaction containing 1 L of CutSmart buffer (New England Biolabs, Ipswitch, MA, USA) and HhaI (New England Biolabs, Ipswitch, MA). , USA) at different concentrations of 10 U, 2 U and 0.2 U. To ensure successful restriction, the control reaction also included restriction of 100 ng of unmethylated artificial template DNA using the same concentrations of HhaI as for body fluids.
MSRE-PCR
Tissue-Specific Gene Expression Data
Exclusion of Differentially Expressed Genes Present in More than One Surrogate Tissue
Elimination of Genes with Unconfirmed Protein Products
Methylation Data
After finding 952 genes in which CGIs were located, the mean, median and min-max methylation levels for each of the CGIs were calculated (based on the pooled ENCODE and NGSMethDB dataset). CGIs of 50 genes were selected that showed mean, median, and minimum peak methylation levels above 75% (heavily methylated and low to normal expression in non-target tissues), but overexpression in saliva, blood, semen, and vaginal fluid. (methylation levels are listed in Table 2, Appendix A). As several CGIs within certain genes showed high methylation, all of these CGIs were included.
Final Selection of CGIs for Primer Design
After initial analysis of the methylation status of target CGIs in saliva, blood, semen, and vaginal fluid by MSRE-PCR, only four candidate genes (HPCAL1, ZNF282, PTPRS, and PMEPA1) showed differential methylation profiles and were thus selected for analysis. further. .
Optimisation of the Hha I Restriction Reaction
Body Fluid Identification using MSRE-PCR .1 HPCAL1 Gene-based Primer Set
- PTPRS Gene-based Primer Set
- ZNF282 Gene-based Primer Set
- PMEPA1 Gene-based Primer Set
Research studies have provided compelling evidence for a link between DNA methylation and gene expression (Kulis et al., 2013; Schilling and Rehli, 2008). Current methods that use miRNA markers, such as those tested by Zubakov et al. 2010) are also unable to distinguish between vaginal fluid and saliva. It is of great importance in oncogenic transformation; diseases associated with PTPRS include ureteroceles and pineal gland cancer (Belinky et al., 2015).
Recent studies using tDMR to identify body fluids have shown different levels of methylation in saliva, blood, semen, and vaginal fluids (Igarashi et al., 2008; Madi et al., 2012; Schilling and Rehli, 2008; Smith et al., 2008). , 2014). Assessment of DNA methylation status using MSRE-PCR is less time-consuming and labor-intensive than bisulfite conversion (Melnikov et al., 2005; Rein et al., 1997). In addition, it is highly advantageous due to its compatibility with current STR typing (An et al., 2013).
CHAPTER THREE
METHYLATION PROFILING OF SALIVA FROM THE DIVERSE SOUTH AFRICAN
POPULATION USING tDMR-BASED MARKERS
Sample collection
Donors gave signed informed consent after the aims of the study were described (Appendix B). The study was conducted in accordance with the methods established by the Biomedical Research Ethics Committee of the University of Kwa-Zulu Natal (Westville Campus).
DNA Extraction and Quantification
30-50 n=5 Above 50 n=1 Black participants may belong to any one, or be a mixture of the four major ethnic groups living in South Africa, including the Nguni, Sotho, Shangaan-Tsonga and Venda groups. Coloreds of South Africa are said to be the result of admixture mating between individuals from reproductively isolated ancestral populations, thus having mixed ancestry.
Selection of tDMR Markers and Primer Design for PCR
The artificial DNA templates for the amplification control for PCR success were obtained by PCR amplification of the 481 bp portion of the pCR®2.1 TOPO® vector (Invitrogen, Carlsbad, CA, USA) (An et al., 2013; Choi et al., 2014). Multiplex PCR was developed to amplify Hhal recognition sites (GCGC) of the tDMRs of USP49, L81528, DACT1 and PFN3 genes. Approx. 100 ng of DNA from each participant was digested with Hhal in a 10 L reaction containing 1 L CutSmart Buffer (New England Biolabs, Ipswitch, MA, USA) and 0.2 U of the Hhal restriction enzyme (New England Biolabs, Ipswitch, MA). USA).
To ensure complete digestion, the unmethylated artificial DNA template (481 bp part of the pCR®2.1 TOPO® vector) was restricted. Samples are placed in an autosampler tray that moves up and down to place the sample on the capillary and electrode for the injection process (Butler, 2012; Moretti et al., 2001). For men, the peak height of Amelogenin was calculated by the sum of two peak heights (An et al., 2013).
Individual DNA Methylation Profiling using MSRE-PCR
Calculation and Comparison of Methylation Levels of Selected tDMRs between Four Ethnic Groups
- USP49 tDMR-based Marker
ANOVA was performed to determine whether there was a significant difference in the methylation status of the four tDMRs in the saliva of four ethnic groups. The statistical analysis of the methylation status of the USP49 tDMR showed that the marker showed a slight variation among the four ethnic groups (p = 0.05) (Table 3.7). The statistical analysis of the methylation status of the DACT1 tDMR showed that the marker showed significant variation among the four ethnic groups (p = 0.02) (Table 3.8).
The statistical analysis of the methylation status of the L81528 tDMR showed that the marker showed significant variation between the four ethnic groups (p = 0.03) (Table 3.9). The statistical analysis of the methylation status of the PFN3 tDMR showed that the marker did show slight variation between the four ethnic groups (p = 0.05) (Table 3.10). Methylation profiling of saliva indicated a clear variation in the methylation status of the four tDMRs; especially between the Colored and Black ethnic groups.
CHAPTER FOUR
GENERAL DISCUSSION AND
CONCLUSION
Markers of differential DNA methylation are persistent, resulting in cellular differentiation with specific expression of these specific genes (Eckhardt et al., 2006; Jones and Takai, 2001). Another aim of the study was to unravel whether the methylation status of previously documented tDMRs is tissue-specific (An et al., 2013; Choi et al., 2014; Lee et al., 2012). Recent studies have targeted variation in DNA methylation to differentiate between populations (Fraser et al., 2012; Heyn et al., 2013; Moen et al., 2013).
In South African research, two recent medical studies found differential DNA methylation in blacks (Matatiele et al., 2015) and people of color (Masemola et al., 2015). The study by Choi et al. showed that the sperm-specific tDMR L81528 is hypomethylated in saliva. In addition, bisulfite-treated DNA must be analyzed within a certain time period due to the instability of single strands (An et al., 2012).
APPENDIX A
