Novel signal processing and classification methods for forensic species identification

  • Sorelle Jean Bowman

    Student thesis: Doctoral Thesis

    Abstract

    Non-human species identification from animal and bacterial origin, is an important
    aspect of forensic investigations and relates to criminal matters, food safety and
    security, wildlife forensics and biosecurity. Animal-derived biological sources take
    many forms, for example, illegal powdered animal parts used in medicines,
    adulterated meat products, blood of unknown unknown origin taken from a vehicle
    involved in a hit-and-run case, transferred pet hair on a suspects clothing. Many types
    of non-human material can represent valuable evidence in casework. Furthermore,
    bacterial species of focus in this study are biological agents that pose a concern to
    national security if deliberately disseminated in an act of bioterrorism, specifically
    Bacillus anthracis and Yersinia pestis which cause anthrax and plague, respectively.
    An ideal method for the detection and differentiation of both animal and bacterial
    species should demonstrate characteristics such as high sensitivity, specificity,
    rapidity, cost-effectiveness, simplicity and should ideally be able to be employed in a
    field setting.
    The aim of this thesis was to detect and identify species of forensic interest,
    differentiating them from each other and from potential hoax agents. This broad aim
    could be further divided as follows; (1) Examine a range of DNA extraction
    procedures with a view to identifying a fast and efficient method that removes PCR
    inhibitors, ideally suited for use in the field. (2) Examine the use of high resolution
    melting (HRM) analysis for its ability to differentiate between species of forensic
    interest, including animals and bacteria (biothreat agents). (3) Examine the use of
    targeted massively parallel sequencing (MPS) to identify biothreat agents and
    compare this with quantitative real time PCR (qPCR). (4) Employ novel signal
    processing and species classification methods for application to microfluidic capillary
    electrophoresis (MCE) of proteins, HRM and MPS. The aims span two genetic targets
    (proteins, DNA), four species detection methods (MCE, qPCR, HRM and MPS) and
    four classification methods (peak detection algorithms, Boolean logic gates,
    classification trees and MPS sequence alignment). They were combined in a number
    of different permutations to provide a suite of forensic species identification solutions Four commercial DNA extraction methods were applied to both Gram-negative
    (Bacillus species) and Gram-positive (E. coli) bacterial cultures and were evaluated
    for their application in matters of biosecurity. These were ChargeSwitch gDNA mini
    bacteria kit (Invitrogen), QIAamp DNA extraction kit (Qiagen) with and without
    bead-beating, and Isolate II Genomic DNA kit (Bioline). The Isolate II Genomic
    DNA kit was found to remove inhibitors cost effectively for the extraction of bacterial
    DNA from both culture and environmental samples.
    The universal 16S rRNA gene was targeted with HRM and used to generate
    derivative melt profiles for human and ten animal species typically encountered in
    forensic case work, as either consumed meats (Gallus gallus (chicken), Bos taurus
    (cow), Sus scrofa (pig) and Ovis aries (sheep)), domestic pets (Felis catus (cat) and
    Canis lupus familiaris (dog)) or Australian road kill (Vulpes vulpes (fox), Macropus
    (kangaroo), Vombatus ursinus (wombat) and Oryctolagus cuniculus (rabbit)). HRM
    derivative melt profiles were processed and analysed by random forest classification
    (such as classification trees, bagging and boosting) and peak detection algorithms
    with Boolean logic paths. Random forest classification, particularly bagging, was the
    most suitable for the purposes of animal species identification with a prediction
    accuracy of 90.8 % for the randomly partitioned test dataset and 70 % for the
    validation dataset across all species.
    Microfluidic capillary electrophoresis and HRM were evaluated for their use as a
    screening tool for bacterial species identification with a focus on Bacillus and
    Yersinia species, E. coli and powder-based hoax agents (e.g. Dipel and plain wheat
    flour). The signals generated from both platforms were characterised by peak
    detection algorithms and differentiated using Boolean logic paths. When applied to
    protein profiling by MCE, peak detection and classification by Boolean logic yielded
    predictive accuracy of 75 % with the test dataset, across all samples. Additionally,
    when the same algorithmic approach was applied to HRM derivative melt profiles, the
    seven Bacillus species could be differentiated into B. cereus group members and non-
    B. cereus group members MPS was used to develop a targeted sequencing approach to identify Bacillus species,
    in particular B. anthracis, in samples collected at the Canberra Airport. Two virulence
    plasmid markers (cya and capB) and a single chromosomal marker (16S rRNA gene)
    were targeted to establish background B. anthracis frequencies over a 12 month
    sampling period (from August 2011 to July 2012). The findings demonstrated
    effective reference alignment to define bacterial entry, dispersal and movement
    throughout the airport. Of the 20 samples sequenced, 15 were positive for the
    B. cereus group 16S rRNA gene, two samples were cya positive in the month of
    February 2012 and seven were capB positive in the sampling months of December
    2011 and June 2012. A total of four samples collected in the sampling month of
    February, 2012, were positive for all three markers, indicative of the potential
    presence of B. anthracis.
    In summary, this study has resulted in novel screening approaches, across multiple
    platforms, for the effective detection and analyses of both animal and bacterial species
    for forensic purposes. Moreover, the analysis has provided a foundation for future
    work involving targeted sequencing of the bacterial metagenomic background of a
    public transport hub and the movement of B. anthracis outside the anthrax belt.
    Date of Award2018
    Original languageEnglish
    SupervisorMichelle Gahan (Supervisor) & Dennis Mcnevin (Supervisor)

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