An optimization-based study of the impact of different parameters on DNA degradation

Abstract

Human remains exposed to various conditions throughout time are frequently utilized for DNA analysis of tissue or bone for identification reasons. The deterioration and loss of DNA in particular environmental settings have previously created a dilemma for investigators. The post-mortem interval (PMI) or time since death is sometimes the most sought-after piece of information in a medical death investigation. Based on the discovery that DNA degradation has a disproportionate effect on the analysis of bigger genetic loci in particular studies, it was postulated that DNA degradation as a result of autolysis or putrefaction might be useful as a possible rate-of-change indicator of PMI. As a sample, goat liver tissue was used. It was incubated in three testing factors, namely pH, salt content, and sugar concentration, and was compared to a control sample that had not been incubated in any of these parameters. The samples were incubated for 24, 48, and 72 hours. An appropriate Lysis Buffer was used to isolate the DNA. Following the isolation of the DNA, quantitative analysis was performed in a UV-Visible Spectrophotometer (for both the control and incubated samples), followed by Agarose Gel Electrophoresis. It was discovered that the data provided by these studies, taken as a whole, show that we can give information relevant for calculating the post-mortem interval, mostly within the first 72 hours following death. Later, it was decided to use Response Surface Methodology (RSM) to optimize the parameters we had picked, with the BBD design model being the favoured choice. This was done with the help of software named, Design-Expert v11. A Fit Summary that showed a linear model fit for the data. ANOVA was used to generate an equation that may be used to quantify the amount of DNA present in a tissue sample that has been exposed to a specific value of pH, salt concentration, and sugar concentration. Finally, a 3-D response surface curve was produced, two factors at a time, to highlight the variance in DNA loss when those two parameters are taken into account.