13 Soil Forensics

7/30/2019 13 Soil Forensics

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Soil Forensics

Soil components Abio/c parent material: stable over /me,

affected slightly by climate, weather (slowprocesses)

Can look at elemental analyses, organic ma?er, rareelements glass rubber, etc.

Biological frac/on: ora and fauna Metagenomic DNA

How can these components be used forforensics

SOIL IS THE ULTIMATE MIXTURE SAMPLE!!!

Abio/c analyses Forensic geologists: X-ray diffrac/on (mineral

content), Infrared spectroscopy (organicfrac/on); ICP-MS (elemental composi/on)

Fatal car crash, suspects ee down river bank;apprehended hours later; deny being in thearea mud on shoe

Sample taken from shoe print at river bank,Munsell color chart, microscopic morphologicalcomparisons = put suspect at crime scene eventhough he was denying ever being near the river


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Forensic comparison ofsoils,

Fitzpatrick, et al. 2009

Next steps

Sulfur par9cles and content similar (Munsell) Xray Diffrac9on (XRD) pa erns similar Diffuse Reectance Infrared Fourier Transform

spectroscopy (DRIFT) collects and analyzes sca ered IR energy

Compared to alibi loca/on soil

The shoe and shoe print had higher similarityacross all measures than they did with thealibi soil

Suspect found guilty of hit and run inSupreme Court of South Australia


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Caught and convicted

In a murder case in California, vic/ms body was dropped at a oilwell apron where gravel which was transported from 300 milesouth was used. Soil material found in the suspects car wascompared with those around the oil well apron. The ques/onedsample from the car contained rock fragments which were thesame with the imported gravel (1, 4).

13th INTERPOL Forensic Science Symposium, Lyon,France, October 16-19 2001

FORENSIC EXAMINATION OF SOIL EVIDENCE

Blue thread gave key informa/on in a rape case in UpperMichigan. Three ower pots had been /pped over and spilledon the oor in the struggle. Po ng soil on the suspects shoewas compared with one of those ower pot spillings. Smallclipping of blue thread existed both in that ower pot sampleand on the shoe of the suspect (1).

Alterna/ve approach

Prole the bio/c component of the soil Human ID = discrete en/ty Soil = small dened domain w in a larger

con/nuum Spa/o-temporal dynamics need to be considered

What is needed to use soil as evidence andor intelligence data


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COMMUNITY STRUCTURE

Driven by soil type andenvironmental factors(Girvan, 2003 )

Microfauna present areindica/ve of the soil

Sampled sites willdisclose variability

h p://cropsoil.psu.edu/extension/livingmulch/images/ _soil_9lth.jpg

FORENSIC SOIL ANALYSIS

Soil characteriza/on Physical traits Chemical elemental

components

Early 2000s conceptarose: Horswell et. al

DNA prolingof soil bacteria

Terminal Restric/on Fragment Polymorphisms-Experimentally Derived Lengths

G CG C G CG C

G CG C G CG C G CG CG CG C

G CG C G CG C


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PREVIOUS STUDIES

Meyers et. al (2008) T-RFLP method 16S rRNA eubacterial gene

Assump/ons

Soil diversity exists Sample Homogeneity Temporal variability

Heath et. al (2006) T-RFLP method 16S rRNA eubacterial gene

Conclusions Ecosystem-discrimina/on

Indicator TRFs Within ecosystem

clustering Temporal variability

Moreno et. al(2005)

Sampled 3 soil types

Wet Dry season

Bacterial DNAproling

LH-PCR & CE

Mul/variate analyses 16S rRNA

HYPOTHESIS

H0: Soil bio/ccommuni/es do

not vary among soiltype


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Moreno and others

Prior studies Queried only eubacteria

Increase number of taxa assayed

Maintain discrimina/on & increaseresolu/on

Lilliana I. Mor eno, 1 , 2 M.A., M.Fs.; DeEtta K. Mills, 1 , 2 Ph.D.; James Entry, 3 Ph.D.; Robert T. Sautter, 2 , w

M.S.; and Kalai Mathee, 1 , 2 M.S., Ph.D.

Microbial Metagenome Profiling Using AmpliconLength Heterogeneity-Polymerase ChainReaction Proves More Effective Than ElementalAnalysis in Discriminating Soil Specimens

ABSTRACT: The combination of soils ubiquity and its intrinsic abiotic and biotic information can contribute greatly to the forensic eld.Although there are physical and chemical characterization methods of soil comparison for forensic purposes, these require a level of expertise notalways encountered in crime laboratories. We hypothesized that soil microbial community proling could be used to discriminate between soiltypes by providing biological ngerprints that confer uniqueness. Three of the six Miami-Dade soil types were randomly selected and sampled. Wecompared the microbial metagenome proles generated using amplicon length heterogeneity-polymerase chain reaction analysis of the 16S rRNAgenes with inductively coupled plasma optical emission spectroscopy analysis of 13 elements (Al, B, Ca, Cu, Fe, K, Mg, Mn, Na, P, S, Si, and Zn)that are commonly encountered in soils. BrayCurtis similarity index and analysis of similarity were performed on all data to establish differenceswithin sites, among sites, and across two seasons. These data matrices were used to group samples that shared similar community patterns usingnonmetric multidimensional scaling analysis. We concluded that while chemical characterization could provide some differentiation betweensoils, microbial metagenome proling was better able to discriminate between the soil types and had a high degree of reproducibility, thereforeproving to be a potential tool for forensic soil comparisons.

KEYWORDS: forensic science, soil forensics, microbial forensics, microbial proling, amplicon length heterogeneity (ALH), soil metagenome,inductively coupled plasma optical emission spectroscopy, elemental analysis

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J Forensic Sci, November 2006, Vol. 51, No. 6doi:10.1111/j.1556-4029.2006.00264.x

Available online at: www.blackwell-synergy.com

FI UULU-1

ULU-2

SOIL SAMPLING SITES (2005)

Moreno et. al


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LH-PCRAll16S

rRNAgenes

V1-V2

355 R

Length (bp) R e

l a t i v e

I n t e n s i

t y

All PCR !products !

27F

D.Mills

LENGTH HETEROGENEITY(LH-PCR)

ADVANTAGES Natural vs ar/cially

generated fragments

No post-PCR manipula/on

Rapid, robust, reproduciblemethod

Could be used in mostcrime laboratories

DISADVANTAGES Do not know what

microorganisms arepresent

Clone libraries andsequencing needed fordeni/ve iden/ca/onto species level

So does TRFLP and others


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FOOD WEB APPROACH

Soil

Plants

Bacteria

Fungi

Nematodes

Communitystructure

Mul/ple trophiclevels

Unique prole s


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SOIL SAMPLE COLLECTION

Soil Type

(PBP or ULU)

SP1

(A,B,C)

SP2

(D,E,F)

SP3

(G,H,I)

DNA Extrac/on

LENGTH HETEROGENEITY(LH-PCR)

Four universal primers sets

Tested each primer set individually

Op/mized two duplex PCR reac/ons Bacterial Fungal

Nematode Plant

PRIMER SELECTIONPRIMER NAME TAXA

(UNIVERSALMARKER)

TARGET REGION

27f EUBACTERIA 16S rRNA gene

355r EUBACTERIA 16S rRNA gene

NEM_ITS1f NEMATODE Ribosomal ITS1

NEM_ITS1r NEMATODE Ribosomal ITS1

trn Lf PLANT chloroplast gene

trn Lr PLANT chloroplast gene

FUN_ITS1 FUNGI Ribosomal ITS 1

FUN_ITS2r FUNGI Ribosomal ITS 2


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LH-PCR PARAMETERS Single vs. Duplex

Bacterial Fungal 28 cycles

Nematode Plant Step up 35 cycles

DNA SEPARATION WITHABI PRISM 310

GENETIC ANALYZER

The DNA is separated ina single capillary throughelectrophoresis

Electropherograms weregenerated and analyzedwith GeneMapper TM

research so ware, version3.7

Bacteria V1_V2 Single Reac/on

SINGLE VS. DUPLEX PROFILE

Bacteria V1_V2 Duplex Reac/on


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ANALYSES METHODS Mul/variate Data Transforma/on

-- Square root transformed Bray Cur/s Similarity

Bio/c data does not have normal distribu/on; noskew if amplicon is missing

ANOSIM tests the null hypothesisGlobal R=0, no differences exist

Global R=1, no similarity exists

ANOSIM RESULTS

Individual taxon Global R

Fungal ITS1 0.208

Nematode ITS1 0.370

Bacteria V1_V2 0.424

Plant trn L 0.554

ANOSIM RESULTSCombina/on of markers Global R

Bacteria Plant 0.251

Bacteria Fungal 0.424

B ac ter ia N em at od e 0.6 77

Nematode Plant 0.769

Nematode Fungal 0.350

Combina/on of markers Global R

Fungal Plant 0.369

Bacteria Nematode Plant 0.619

Bacteria Fungal Nematode 0.431

Bacteria Fungal Plant 0.438

Bacteria Nematode Plant Fungal0.663


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NONMETRIC-MULTIDIMENSIONALSCALING (MDS)

Bacteria V1_V2 similarity

MDS RESULTS

Fungal ITS similarity

Plant trn L similarity

Nematode ITSsimilarity


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FOUR TAXA COMBINED

SIMPER METHOD

Similarity Percentages

iden/es variables (amplicons)driving dissimilari/es betweensubplots and or sites

SIMPER RESULTSAMPLICON

(BP)AVERAGE

AMPLICONABUNDANCE

(ULU)

AVERAGEAMPLICON

ABUNDANCE(PBP)

AMPLICONPERCENT

DISSIMILARITY(cumula/ve%)

ASSOCIATEDTAXA

137 0.07 0.49 2.91 P

129 0.00 0.39 5.56 F

139 0.39 0.00 8.21 N

339 0.00 0.34 10.68 B

153 0.33 0.00 13.07 P

114 0.00 0.33 15.32 N

124 0.26 0.00 17.16 N

150 0.05 0.27 18.90 P N

341 0.27 0.00 20.63 B

120 0.17 0.26 22.34 N

177 0.25 0.00 24.05 N F P

340 0.21 0.08 25.75 B

Overall80% Dissimilarity


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BACTERIAL VS METAGENOMIC DNAPROFILING OF SOIL COMMUNITIES

Bacteria do discriminate

Metagenomic DNA discriminates as well All 4 taxa contribute to differences Uniqueness

APPLICATIONS Forensic Laboratories

Trace evidence Bioterrorism

Intelligence (geographic origin) Microbial Ecology Field

Bioremedia/on Seasonal Temporal Varia/on

ARTICLE IN PRESS

An ecoinformatics tool for microbial community studies:Supervised classification of Amplicon LengthHeterogeneity (ALH) profiles of 16S rRNA

Chengyong Yang a , DeEtta Mills b, Kalai Mathee b, Yong Wang a , Krish Jayachandran c ,Masoumeh Sikaroodi d , Patrick Gillevet d , Jim Entry e, Giri Narasimhan a ,*

a Bioinformatics Research Group (BioRG), School of Computer Science, Florida International University, Miami, Florida, 33199, USA b Department of Biological Sciences, Florida International University, Miami, Florida, USA

c Department of Environmental Sciences, Florida International University, Miami, Florida, USAd Microbial and Environmental Biocomplexity, Department of Environmental Sciences and Policy, George Mason University,

Manassas, Virginia, USAeUSDA Agricultural Research Service, Northwest Irrigation and Soils Research Laboratory, Kimberly, Idaho, USA

Received 18 January 2005; received in revised form 22 April 2005; accepted 24 June 2005

Abstract

Support vector machines (SVM) and K-nearest neighbors (KNN) are two computational machine learning tools that perform supervised classification. This paper presents a novel application of such supervised analytical tools for microbialcommunity profiling and to distinguish patterning among ecosystems. Amplicon length heterogeneity (ALH) profiles fromseveral hypervariable regions of 16S rRNA gene of eubacterial communities from Idaho agricultural soil samples and fr omChesapeake Bay marsh sediments were separately analyzed. The profiles fr om all available hypervariable regions wereconcatenated to obtain a combined profile , which was then provided to the SVM and KNN classifiers. Each profile waslabeled with information about the location or time of its s ampling. We hypothesized that after a learning phase us ingfeature vectors from labeled ALH profiles, both these classifiers would have the capacity to predict the labels of previouslyunseen samples. The resulting classifiers wer e able to predict the labels of the Idaho soil samples with high accuracy. T heclassifiers were less accurate for the classification of the Chesapea ke Bay sediments suggesting greater similarity within theBays microbial community patterns in the sampled sites. The profiles obtained from the V1+V2 region were moreinformative than that obtained from any other single region. However, combining them with profiles from the V1 region(with or without the profiles from the V3 r egion) resulted in the most accurate classification of the samples. The addition

0167-7012/$ - see front matter D 2005 Elsevier B.V. All rights reserved.doi:10.1016/j.mimet.2005.06.012

* Corresponding author. Tel.: +1 305 348 3748; fax: +1 305 348 3549. E-mail address: [email protected] (G. Narasimhan).

Journal of Microbiological Methods xx (2005) xxxxxxwww.elsevier.com/locate/jmicmeth

MIMET-02319; No of Pages 14

DTD 5


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Support Vector Machines

Training set vs unknowns usingLH-PCR (all) concatenated data

Single 16S domains


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Tested three domain combina/ons

Chesapeake Bay sediment samplesV1 +V2 domain only

Conclusion Get to know your computer

science colleagues!!

Large databases need to beestablished with prole dataand then unknowns can beclassied as to where andwhen they were collected;no longer a crap shoot


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What is needed to use soil as

forensic evidence

HomeSFI 2010

Post-event 2007Posters & presentationsProgrammeCriminal topicsEnvironmentaltopicsKeynote SpeakersMedia CoverageReferring sitesOrganisingcommitteeSponsors

Soil Forensics International

SFI 2010

3rd International Workshop on Criminaland Environmental Soil Forensics

The dirty evidence: soil and geoforensiccontributions to intelligence gathering andenvironmental and public safety

The next international meeting on soil forensicanalysis and investigation will be held in Long Beach,California, USA, from 31st October 2010 to 4thNovember 2010.

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T h e 2n d S oi l F or en s ic s I nt er na ti on a l Co nf er en c e h tt p: // ww w. s oi lf or en s ic s in te rn a ti on a l. or g/ s 20 1 0. ph p

Same thing as all other forensicdisciplines

Expert witnesses exper/se in the eld Balance of fundamental soil science with

forensics interpreta/on Acceptability of methods

But: no soil standards, no SWG-SOIL, notraining, no common SOPs, no standard sta/s/cs,no prociency tes/ng

Legal considera/ons Daubert, Frye standards met

Class par9cipa9on!


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Ques9ons?

Soil mapping is possible only because men canexamine a prole at one point and successfullypredict its occurrence at another point where

surface indica9ons are similar.--- Author unknown

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13 Soil Forensics