AcknowledgementsWe would like to thank Plymouth State University, the PSU Research Advisory Council, the PSU Student Research Advisory Council, and the New Hampshire Idea Network of Biological Research Excellence for funding support. We would like to thank the University of New Hampshire Hubbard Center for Genome Studies and Dartmouth College Molecular Biology Shared Resources Lab for sequencing. We would also like to thank Jon Bairam, Kevin Chesmore, Joel Dufour, Evyn Grimes, Ethan Johnson, Kathryn Kahrhoff, Lauren Oakes, Stacy Peterson, Ellen Rounds, Harlie Shaul, Lorna Smith, Kate-Lyn Skribiski, Zoe White, and Alycia Wiggins.

Conclusions

Departments of Biological Sciences at Plymouth State University, Plymouth NH

References

Detecting Genetic Variation in the Connective Tissue Growth Factor GeneAshley Kennedy, Kimberly Jesseman, Hailey Gentile, Amed Torres, Zachary Stevens, Heather E Doherty PhD

Future Directions

Connective Tissue Growth Factor

Detected Variants Predicted to Alter CTGF Protein Structure

Detecting Variants Using PolyPhred Cardiovascular disease is the leading cause of death in the United States. During a heart attack, cardiovascular tissue can become damaged, and healing of this tissue can lead to scarring of the heart, known as fibrosis (cdc.gov). Fibrosis can affect the elasticity of the heart, which can cause sudden cardiac death. Connective tissue growth factor (CTGF) is a gene that regulates tissue repair (Arnott et al., 2011) and is also consistently expressed in healthy tissues to maintain the integrity of the tissue. After tissue damage occurs, CTGF is expressed in fibroblast cells to initiate the healing process (Igarashi et al., 1993). Previous studies in animal models have shown that increased expression of CTGF leads to increased levels of fibrosis (Ohnishi et al., 2005). Our research focuses on identifying variants within CTGF that may alter the structure and function of the protein and may therefore impact fibrosis risk. In order to identify variations in CTGF, we collected cheek swabs from volunteers at Plymouth State. DNA from the cells was extracted, amplified and sent for sequencing. The returned sequences were then analyzed for variants and potential impacts on the CTGF protein were predicted using computer modeling programs. To date, we have detected the presence of 4 novel and 5 previously observed variants in CTGF sequences. Some variants are predicted to impact the structure and function of CTGF. Future research involves further sequencing to identify more CTGF variants, followed by an introduction of these variants into a cell culture model of wounding to determine their impacts on fibrosis. Identifying variants that impact fibrosis risk could lead to individualized fibrosis treatments for patients after a heart attack.

Sample Procurement and DNA ExtractionThis research was approved by the Plymouth State University IRB. Cheek cell samples from individuals at Plymouth State University were collected via cheek swabs. The samples were then de-identified and DNA was extracted. DNA Amplification and PurificationTo make many copies of the DNA, Polymerase Chain Reaction (PCR) was used to amplify the samples. Exons 1 and 2 were amplified together, and exons 3, 4, and 5, and the 3’ UTR of CTGF were amplified individually. Gel-electrophoresis was used to confirm that DNA was properly amplified. Amplicons were then purified using a QIAquick® PCR purification kit (Qiagen) and sent for sequencing at the Molecular Biology Core Facility at Dartmouth College or UNH Hubbard Center of Genome Studies. Detecting VariantsReturned sequences were analyzed for variants using PolyPhred software. In order to call variants, this program aligns sequences, filters fluorescent background, and assigns a quality score to each base in a DNA sequence. A variant will be called if the sequence is different from the published sequence (ensembl.org, build 84). Variant and Family History CorrelationTo determine if there was a correlation between specific variants and an individual’s genetic risk of developing cardiovascular disease, family history scores were calculated similar to Milne et al. (2008) for obesity, cardiovascular disease, diabetes, high blood pressure heart attack or stroke before age 65, and heart attack or stroke after age 65. Our model assigns a value of 1 for each incidence of the disease in the individual or any first-degree relatives and a value of 0.5 for each incidence of the disease in any second-degree relatives. Combined disease risk is the sum of all the risk factors. Variant scores were assigned as 0 for no copies of the variant, 1 for 1 copy, and 2 for 2 copies. A custom Python (Van Rossum, 2007) program using the statistical package from SciPy (Jones et al., 2001) was used to calculate Kendall’s Tau-b values and associated p-values. Values for Tau-b can range from -1 to 1, with values near 1 suggesting positive correlation, values near -1 suggesting negative correlation, and values close to 0 suggesting no correlation. A p-value of less than 0.05 was considered statistically significant. Protein Structure PredictionThe structural impact of detected variants was predicted using Phyre2, a 3-D protein-modeling program. Phyre2 compares inputted amino acid sequences to sequences with existing structures to predict the structure of the protein of interest. The program also utilizes de novo modeling for regions without known structure. Both the published structure of exon 2 and the structure of exon 2 with detected variants were modeled and compared. Published exon 3 and exon 3 with the one detected amino acid change were also modeled and compared.

Methods

Introduction

CTGF Variants Detected in the PSU Sample

No Correlation Between Variants and Family History Scores

Risk Factor Exon 2 Tau-b Exon 2 p-value Exon 3 Tau-b Exon 3 p-valueObesity -0.057 0.643 -0.095 0.293

Cardiovascular Disease -0.005 0.969 -0.087 0.334Diabetes 0.044 0.718 -0.062 0.493

High Blood Pressure -0.059 0.632 -0.123 0.174Heart Attack/Stroke <65 -0.136 0.265 -0.072 0.425Heart Attack/Stroke >65 0.010 0.935 -0.086 0.340

Combined Risk -0.073 0.550 -0.145 0.107

Table 2: Kendall’s Tau-b values calculated for each risk factor and combined risk for all factors. Risk factors were tested for association to two nonsynoymous SNPs in exon 2 and the G to A change in exon 3. Values for b range from -1 to 1 with values <0 suggesting a negative correlation, values > 1 suggesting positive correlation, and values close to 0 suggesting no correlation. p<0.05 was considered statistically significant. n=33 individuals for exon 2 and n=58 individuals for exon 3. Results for Table 2: No correlation was observed between individuals with the exon 2 variants and family history score, suggesting this subset of variants does not appear to impact cardiovascular disease risk factors. No statistically significant correlation was observed between the G/A variant in exon 3 and family history score. Combined risk showed a Tau-b value of -0.145 and a p-value of 0.107. While these results are not statistically significant, they do suggest a possible negative correlation between having an A base at this location in exon 3 and total cardiovascular disease risk. A larger sample size is will increase statistical power and determine if this SNP results in protection against cardiovascular disease risk.

 Exon Ensembl Location

Published Frequency

SNP Frequency

Nucleotide Change

A.A. Change

Sample Size (n)

2 6:131950889 N/A 0.056 G/A C56Y   

89  

2 6:131950840 >0.99 0.989 A/C Syn2 6:131950819 >0.99 0.989 A/C Syn2 6:131950812 >0.99 0.989 C/G H83D2 6:131950812 N/A 0.056 T/C V94A3 6:131950313 <0.01 0.006 G/A V174M 1775 6:131949291 N/A 0.020 C/T Syn 2055 6:131949279 <0.01 0.005 C/T Syn

3' UTR 6:131949153 N/A 0.020 C/T UTR 205

Figure 1: The connective tissue growth factor gene is composed of 5 exons that code for individual protein domains. Each arrow indicates amplified portions of exons 1&2, 3,4, and 5.

A B

Table 1: Summary of variants identified in CTGF from our PSU sample, which is comprised mostly of individuals of European descent. Information for variants about gene location, Ensembl location, frequency of occurrence, type of mutation, and sample size are listed.

Result for Table 1: Through sequencing, 9 variants were detected. Overall 4 of the detected variants caused a nonsynonymous mutation, meaning they had the potential to alter the structure and function of the protein. 4 of the 9 detected variants were newly discovered and 5 were previously identified. 2 novel, nonsynonymous variants were found in exon 2, occurring at a common frequency of greater than 5%. The remaining 2 newly identified mutations were uncommon variants detected in exon 5 and the 3’ untranslated region, occurring at a frequency of less than 5%. These findings indicate that there are likely CTGF variants that are still undetected, and these variants may alter disease risk. An increase in sampling and sequencing will uncover more variants, leading to a better understanding of the genetic factors surrounding fibrosis.

DCBA

Figure 2: The above chromatograms represent homozygous (A) and heterozygous (B) variants generated by fluorescent Sanger sequencing and visualized using Polyphred. Homozygous refers to an individual with the same copy of a particular base inherited from both parents. Heterozygous refers to an individual with two different bases at the same location inherited separately from each parent. Both homozygous and heterozygous variants can be detected with Polyphred through alignment to published sequence (ensembl.org, Build 84). The comparison identifies the location and base pair changes present in CTGF.

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• 9 CTGF variants have been detected in a population primarily of European descent

• 4 detected variants are predicted to potentially alter protein structure of CTGF and may impact fibrosis risk

• Nonsynonymous variants in exon 2 do not appear to be associated with cardiovascular disease risk factors

• The variant in exon 3 may be protective against total cardiovascular disease risk

• Further sequencing to identify additional variants in a more diverse population

• Perform correlation analysis between synonymous variants and cardiovascular disease risk

• Introducing CTGF variants into a cell culture model of wounding to determine impacts on fibrosis

Figure 3: Predicted CTGF 3-D protein structures for specific exons: (A) Predicted structure for published exon 2. (B) Predicted structure for exon 2 with a cysteine to a tyrosine change at amino acid 57 (C57Y), a histidine to aspartic acid change at amino acid 83 (H83D), and a valine to an alanine change at amino acid 94 (V94A). (C) Predicted structure for published exon 3. (D) Predicted structure for exon 3 with the valine to methionine change at amino acid 174 (V174M). Figure 3 Results: In exon 2 (comparing A and B), three amino acid changes results in minor alterations to the predicted structure. The C57Y amino acid change has the potential to disrupt disulfide bridges which could impact protein folding. The H83D amino acid change results in a conversion of a basic amino acid to an acidic one, which could affect charge interactions. The V94A amino acid change alone is unlikely to have an impact on the structure of exon 2 due to the similarity of the amino acids. However, the amino acid changes at 83 and 94 together appear to alter the charge interactions and form a new beta-pleated sheet. In exon 3 (comparing C and D), the V174M results in minor changes to the structure as both amino acids are hydrophobic.