A bacterial signature-based method for the identification of seven forensically relevant human body fluids

• NCJ Number: 307474
• Journal: Forensic Science International: Genetics Volume: 65 Dated: 2023
• Author(s): Denise Wohlfahrt; Antonio Limjuco Tan-Torres; Raquel Green; Kathleen Brim; Najai Bradley; Angela Brand; Eric Abshier; Francy Nogales; Kailey Babcock; J.Paul Brooks; Sarah Seashols-Williams; Baneshwar Singh
• Date Published: 2023
• Grant Number(s): 2016-DN-BX-0151
• Agencies: NIJ-Sponsored
• Publication Type: Research (Applied/Empirical)
• Award: Forensic Body Fluid Identification Using Microbiome Signature Attribution

Annotation

The study found that a new bacterial signature-based method for the identification of human body fluids is fast, efficient, sensitive, accurate, and easy to add into a forensic high throughput sequencing (HTS) panel.

Abstract

A newly developed bacterial signature-based method for the identification of human body fluids that accurately identified human body fluid samples is fast, efficient, sensitive, accurate, and can be easily added into a forensic high throughput sequencing (HTS) panel. This method identifies seven body fluids at once, with an overall accuracy of 89%. Recently, several new molecular methods (mRNA, miRNA, DNA methylation, etc.) have been proposed for the detection and identification of body fluids. In this study, a novel non-human DNA (microbiome) based method was developed for the identification of the majority of forensically relevant human biological samples. Eight hundred and twelve (n = 812) biological samples (semen, vaginal fluid, menstrual blood, saliva, feces, urine, and blood) were collected and preserved using methods commonly used in forensic laboratories for evidence collection. Variable region four (V4) of 16 S ribosomal DNA (16 S rDNA) was amplified using a dual-indexing strategy and then sequenced on the MiSeq FGx sequencing platform using the MiSeq Reagent Kit v2 (500 cycles) and following the manufacturer’s protocol. Machine learning prediction models were used to assess the classification accuracy of the newly developed method. As there was no significant difference in bacterial communities between vaginal fluid, menstrual blood, and female urine, these were combined as female intimate samples. Except in urine, the bacterial structures associated with male and female body fluid samples were not significantly different from one another. Bacteria from saliva, feces, blood, male urine, and semen differed significantly, while bacteria from female intimate samples did not differ significantly from each other. Venous blood and menstrual blood could be accurately differentiated. Bacterial signatures in urine can be used to differentiate male and female samples. (Published Abstract Provided)