Chronological age estimation of biological samples from a crime scene is currently considered an important part of forensic phenotyping, as it provides critical investigative leads when no matched DNA profile could be obtained. During the past years, epigenetic methylation pattern analysis has been shown to be useful in forensic age estimation. However, since previous studies mainly focus on DNA N6-methyldeoxyadenosine modifications, estimating the age of a specific donor in complex mixtures is theoretically infeasible. As the most abundant internal RNA modification, N6-methyladenosine (m6A) is recently also found to be associated with physiological aging. In this proof-of-principle study, we have systematically evaluated the RNA methylation pattern in peripheral blood samples from different age groups (Group 1: 20-30 years old; Group 2: 40-50 years old; Group 3: 60-70 years old) using the Nanopore direct RNA sequencing technology, in order to investigate the potential application of RNA methylation pattern in forensic age estimation. In addition, we have explored the age-associated differences of RNA methylation pattern in previously reported blood-specific genes. In summary, significant methylation beta value differences were observed in 17,416 different m6A sites and 22,095 different 5-methylcytosine (m5C) sites, respectively. Moreover, significant methylation beta value differences between multiple age groups were observed in 9 m6A sites and 10 m5C sites located on 9 different blood-specific genes (ALAS2, ANK1, ARHGAP26, CASP1, GYPA, HMBS, JAML, SPTB and CD93). Our findings demonstrated that RNA methylation markers have the potential in estimating the age of a specific donor that contribute a certain type of body fluid in complex forensic mixtures.

Authors

• Shouyu Wang (Fudan university, China)
• Ruiyang Tao (Shanghai Key Laboratory of Forensic Medicine, China)
• Xinyu Lei (Fudan university, China)
• Qi Wang (Fudan university, China)
• Anqi Chen (Fudan university, China)
• Suhua Zhang (Fudan university, China)
• Chengtao Li (Fudan university, China)