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Microbial populations and their potential in forensic investigations

Quaak, F.C.A.

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41

Chapter 4

Microbial population analysis improves the evidential

value of faecal traces in forensic investigations

F.C.A. Quaak, M-L.M de Graaf, R. Weterings and I. Kuiper

International Journal of Legal Medicine, 131(2017): 45-51

Abstract

The forensic science community has a growing interest in microbial population analysis,

especially the microbial populations found inside and on the human body. Both their high

abundance, microbes outnumber human cells by a factor 10, and their diversity, different sites

of the human body harbour different microbial communities, make them an interesting tool

for forensics. Faecal material is a type of trace evidence which can be found in a variety of

criminal cases, but is often being ignored in forensic investigations. Deriving a human short

tandem repeat (STR) profile from a faecal sample can be challenging. However, the microbial

communities within faecal material can be of additional criminalistic value in linking a faecal

trace to the possible donor. We present a microarray technique in which the faecal microbial

community is used to differentiate between faecal samples and developed a decision model to

predict the possible common origin of questioned samples. The results show that this

technique may be a useful additional tool when no or only partial human STR profiles can be

generated.

Keywords Forensics, faeces, microbial community profiling, microarray

Microbial population analysis improves the evidential value of faecal traces

43

1. Introduction

Within the forensic community there is a growing interest in using microbial populations in

criminal investigations. Microorganisms are everywhere, very abundant and invisible. The

techniques to investigate microbial populations have improved tremendously over the past ten

years. Microarray analysis and next generation sequencing (NGS) for instance make it possible

to analyse the microbial community composition of a sample in a single assay. Because of this,

various microbial communities can now play an important role in forensics.

Relevant microbial populations for forensics are those found in water [1] and soil [2-4], for

linking a piece of evidence or trace to a crime scene. And maybe even the ones found inside

our houses can be valuable [5].

In the past decade numerous papers have been published on characterizing microbial

populations present inside and on the human body (skin, vagina, oral cavity and faeces) [e.g. 6-

9]. Within the forensic context microbial communities on the human skin (hands and feet)

have been analysed to link items to their users by NGS [10-12], showing the power of this

technique within the field of forensics. Another interesting application is post mortem interval

determination, since it is likely that the human associated microbial communities change during

decomposition of a corpse [13].

Probably the most extensively studied population within the medical context, is the one found

in the gut. A large part of this population is excreted in faeces, approximately 30-50% of dry

weight of faeces consists of microbes. The faecal microbial community has been described to

be stable under normal conditions and to differ between individuals [14-17], making this

community forensically interesting. Faecal material is a type of trace evidence which can be

found in a variety of cases, ranging from sexual assault cases to burglaries. It can be

encountered as visible or invisible smears on items which have been inserted anally or in

(attempted) burglaries or robberies as intentionally or unintentionally left deposits by a

criminal. Unfortunately, faeces is often being ignored in forensic investigations because it can

be challenging to generate a human STR profile from it [18-19]. The scope of this study is to

investigate the use of faecal microbial communities to establish a link of high criminalistic

value between victim/crime scene sample and suspect.

As Leake describes, the challenge in forensic investigations is that the final results have to be

presentable to a court and therefore, understandable to lay people [20]. NGS and its data

processing is very complex, due to amongst others chimera formation and the presence of

single reads [21]. NGS data can however be used to select relevant genera or species of

microorganisms for the forensic questions needed to be answered. Subsequently, for this

limited set of genera or species, probes can be designed which can be used in microarray

analysis [22]. Microarray analysis is reliable, relatively easy to use and compared to NGS less

labour intensive and (relatively) cost effective.

A microbial population microarray containing bacterial and fungal probes has been developed

in this study. Probes for 16S rRNA, GroEL gene and 18S rRNA markers of relevant genera

and species were designed from 454 sequencing data as described by Benschop et al. [22]. 16S

Chapter 4

44

rRNA and 18S rRNA are universal markers for the identification of bacteria and fungi

respectively. The GroEL gene was used to differentiate between Enterobacteria [23]. The

microarray was tested for reproducibility with a variety of faecal samples and a (statistical) data

analysis method has been developed to calculate similarity between microarray profiles. We

constructed a decision model which can be used for determining the evidential value of the

calculated similarity between microbial communities of questioned samples.

2. Materials and methods

2.1. Sample collection

Stool samples were collected from 35 healthy volunteers of different ages (ranging from

children to adults). Because no human DNA analysis is performed and all samples were made

anonymous with a unique code, informed consent was not necessary. Samples were kept in the

refrigerator after collection by volunteers and frozen upon arrival at the laboratory.

2.2. DNA extraction

DNA extraction was carried out using the QIAamp Stool Mini kit (Qiagen) with the pathogen

detection protocol, preceded by mechanical lysis of the cells. The pathogen detection protocol

uses a more stringent lysis method, making it possible to lyse Gram-positive bacteria and other

cells with thick cell walls. 175-250 mg faeces was sampled from the frozen specimens and

transferred to 2 ml FastPrep Lysing matrix E tubes (MP Biomedicals). 1 ml buffer ASL was

added and the samples were homogenized using the FastPrep system (MP Biomedicals) at

speed 5.5 m/s for 30 sec. Samples were centrifuged for 1 min at 13,000 rpm and supernatant

was transferred to a clean 2 ml tube. Buffer ASL was added to a final volume of 1.4 ml and

extraction was carried out according to manufacturer’s pathogen detection protocol. The

samples were incubated at 95°C (lysis step Qiagen protocol) and DNA was eluted in 100 µl

25% or 100% AE buffer. Yield was measured using ND1000 spectrophotometer (Thermo

Scientific). The amount of human DNA within the extracts was measured using an Alu assay

with a lower detection limit of 0.5 pg/µl [24-25].

For six of the 35 faecal samples a duplicate extraction was performed to test the reproducibility

of the method. In total 41 extracts from faecal samples and 5 negative extraction controls were

used for PCR and microarray analysis.

2.3. Development of microarray

A total of 616 probes targeting 16S rRNA, GroEL and 18S rRNA genes were designed from

previous microarray experiments [22]. The probes were selected to target (sets of) species,

specific genera, groups of genera or families. The majority of probes target microorganisms

known to be present in faeces, in addition probes targeting microorganisms known to be

present in e.g. saliva, skin or vaginal samples were selected. The microarray was designed for

forensic purposes and only the part of the microbial population covered by the selected probes

is being analysed. In total 13 phyla of bacteria and 7 eukaryotic genera were covered. The

Microbial population analysis improves the evidential value of faecal traces

45

targets of the probes match to the data in RDP v10 or GenBank. Still taxon identification may

not be accurate, because the probes have not been tested on known species or a mock

community sample. Probe sequences were submitted in Agilent’s custom design microarray

tool. Probes were randomly synthesized on slides containing 8 arrays with 15k probes per array

(8x15k array slides), each probe is present in 18-fold on an array. The 8x15k slides were

synthesized in batches of 5 slides by Agilent using SurePrint technology. In total 3 batches

have been produced.

2.4. Microarray analysis

Asymmetric amplification was carried out in a multiplex reaction using universal bacterial

marker 16S_V1-5, Enterobacteria marker GroEL and fungal marker 18S (primers see Table 1).

25 µl PCR mixtures contained 1x Multiplex Master Mix (Qiagen), primers in concentrations as

described in Table 1, approximately 5 ng genomic DNA and distilled water. The following

PCR program was used on GeneAmp 9700 PCR system with golden block (Applied

Biosystems): 15 min 95°C, 30 cycles 30 sec 94°C, 90 sec 52°C, 80 sec 72°C and a final

extension of 10 min 72°C. In total 74 reactions were carried out (35 faecal samples, 6 duplicate

extractions, 33 multiple PCR reactions (for 8 extracts)) for the faecal samples. Three non-

template PCRs and five extraction negative controls were also analysed on the microarray to

determine background noise. PCR products were visualized on 1,8% ethidium bromide stained

agarose gels.

Table 1 Primer sequences, labels and final concentrations used in PCR amplification

Primer name Primer sequence Final concentration per reaction Label

16S_V1-5-f 5’-AGA GTT TGA TCH TGG YTC AG-3’ 12.5 pmol Pho

5’-TGG CTC AGG ATG AAC GCT G-3’ 1.25 pmol Pho

16S_V1-5-r 5’-TCA CGR CAC GAG CTG ACG AC-3’ 25 pmol Cy3

Entero-f 5’-GGT AGA AGA AGG CGT GGT TGC-3’ 6.25 pmol Pho

Entero-r 5’-ATG CAT TCG GTG GTG ATC ATC AG-3’ 12.5 pmol Cy3

18S-f 5’-TGC AGT TAA AAA GCT CGT AG-3’ 6.25 pmol Pho

18S-r 5’-TAA GAA CGG CCA TGC ACC AC-3’ 12.5 pmol Cy3

PCR products were purified using Performa DTR Gel cartridge columns (Edge Bio) according

to the manufacturers’ protocol. Exonuclease treatment was performed on the purified PCR

products by incubation with Lambda Exonuclease (New England Biolabs) for 40 min at 37°C.

Single-stranded PCR products were purified using Performa DTR Gel cartridge columns and

hybridized to the 8x15k microarray slides using Oligo aCGH/ChIP-Chip Hybridization Kit

(Agilent Technologies) according to ‘Agilent Oligonucleotide Array-Based CGH for Genomic

DNA Analysis Enzymatic Labeling for Blood, Cells or Tissues Protocol Version 6.0’ using

sterile water, Hi-RPM buffer and Blocking Agent (Agilent Technologies). Hybridization was

carried out in a hybridization oven (Agilent Technologies) for 24 hours at 37°C and 20 RPM.

Washing of the slides was performed using OligoCHG Wash Buffers 1 and 2 (Agilent

Technologies) according to the ‘Enzymatic Labeling for Blood, Cells or Tissue Protocol’.

Chapter 4

46

The microarrays were scanned with Axon GenePix 4400A (Molecular Devices) at 532nm with

5mm pixel size, 1 line to average and 0.1% saturation for the entire slide. GenePix Pro 7

software (Molecular Devices) and the microarray GAL-file (Agilent Technologies) were used

to automatically search features and extract data from the images. Features (probes) were

excluded manually if impurities, like small dust particles or dried droplets, were present.

2.5. Data analysis

The signal to noise ratio (SNR), calculated by GenePix Pro 7 software (Molecular devices), was

used for each feature on the array. Only the features that exceeded two times the detection

limit of the array scanner (SNR=3) were taken into account. Features with a median SNR

above 6 were regarded as positive, median SNR below 6 was set to 0. For each probe the

median SNR of 18 features on the microarray was used for further analysis. In this way the

effect of outliers was minimised.

Normalization of the profiles was performed by dividing the median SNR per probe by the

total median SNR for that profile (=sum of all median SNRs for one microarray). Bray-Curtis

(BC) distances between 74 normalized profiles derived from 35 faeces samples (902

comparisons) were calculated using Poptools (www.poptools.org) in MS Excel to determine

the similarity between profiles [3]. Calculated distances were used to create a decision model

using Statistica v10 (Statsoft Inc.) for the comparison of profiles of questioned samples.

3. Results and discussion

3.1. DNA extraction

Total DNA was extracted from 175-250 mg of the faecal material provided by 35 volunteers

(age range between 1 and 60 years). The DNA concentration of the extracts varied between 14

and 636 ng/µl. For 12 individuals no human DNA was measured using the Alu assay. For the

23 other individuals minimal amounts of human DNA were measured, on average 3 pg/µl.

There was no correlation between the amount of faecal material used and the DNA yield. For

the DNA extraction we used the pathogen detection extraction protocol to preferentially lyse

the microbial cells since human STR profiling was not performed for these samples. Only

minimal amounts of human DNA was detected within the extracts.

3.2. Microarray analysis

One of the DNA extracts was used as a positive control on each microarray slide. In total 74

microarrays (14 slides from 3 different production batches) from faecal samples and 8 negative

extraction/PCR controls were analysed. The median signal to noise ratio (SNR, calculated by

the GenePix software) for each of de 616 probes was calculated for all samples separately, and

after normalization the profiles were compared to each other.

In the extraction and PCR negative controls on average 54±30 probes were detected: the

general bacterial probe and mostly probes with targets at higher taxonomic levels (families or

orders). The general bacterial probe was detected in all faecal samples as well. Ten other

Microbial population analysis improves the evidential value of faecal traces

47

probes were detected in all negative controls and all faecal samples, e.g. probes targeting the

phylum Euryarchaeota, the family Lachnospiraceae or the genus Corynebacterium. The other

probes detected in negative control samples were not detected in all negative control samples

and/or in all faecal samples. Sample preparation was not carried out under sterile conditions,

resulting in the expected detection of small numbers of probes in negative controls. In the

faecal samples on average 480±83 of the 616 probes were detected. In total 96 probes

(including the general bacterial probe) were detected in all faecal samples, suggesting that there

may be families or genera which are present in all faecal samples. For instance, probes targeting

the genera Faecalibacterium, Prevotella, Bacteroides or the family Lachnospiraceae were detected for

all samples [6]. But these probes may also be detected in samples from other body sites, as

described previously [22]. Two of the thirteen probes targeting eukaryotic species were

detected in all individuals and four probes were detected in less than 18 individuals. Because

the number on eukaryotic probes on the microarray is limited, conclusions on proportions of

bacterial versus eukaryotic taxa within and between individuals cannot be made based on these

results. The number of eukaryotic probes on the microarray should be increased to make these

comparisons possible. Note that taxon identification may not be accurate, because the probes

have not been tested on known species or a mock community sample. Therefore results on

taxon level should be taken with precaution.

When analysing the microarrays from the first ten individuals, 456 of the 616 probes were

detected for five or more of the individuals and 141 probes were detected for all ten

individuals. Some of the probes were only detected in one (n=43) or two (n=31) of the

individuals. In total 160 probes were detected in less than five of the individuals. This shows

that, besides some overlap, there are clear differences in faecal microbial communities between

individuals. Note that with the microarray only a selected part of the microbial community is

characterised.

To test whether this technique is useful for forensic investigations, the technical and within

sample variation should be smaller than the between individual variation. The technical

variation was tested with five DNA extracts by comparing two microarray profiles from the

same DNA extract, generated with microarrays on the same slide. The similarity between the

microarray profiles of each DNA extract was calculated using the Bray-Curtis (BC) distance.

This distance is commonly used in ecology and proved to be suitable for comparing bacterial

profiles. A BC distance close to 0 indicates high similarity between profiles [3, 26-28]. BC

distances of 0.03-0.06 were calculated as within slide similarities for these five samples. Within

sample variation was determined by duplicate DNA extraction of six faecal samples. BC

distances calculated between the profiles of the duplicate DNA extracts ranged from 0.04-0.12.

When comparing faecal samples from different individuals, the BC distance between the

microarray profiles should thus ideally be larger than 0.12. For the first ten individuals BC

distances of 0.16-0.49 have been calculated as between individual variation. Therefor this faecal

microbial population assay has the potential to differentiate between individuals and more

samples were analysed.

Chapter 4

48

The microarrays were produced in three batches of five slides. The positive control was

analysed on all slides and ten other samples have been analysed on more than one slide. Even

though the slides from different production batches should perform the same, Table 2

illustrates that the range of BC distances calculated between microarray profiles from the same

DNA extract was smaller within one production batch of slides than between production

batches. All BC distances within a production batch were 0.06 or smaller for this sample, and

between production batches distances of 0.15 to 0.30 have been calculated. This trend was

seen for all comparisons of profiles from one individual between production batches (data not

shown). The BC distances calculated between the profiles of the positive control, which has

been analysed on all slides, varied from 0.06-0.20 within a production batch and between 0.09-

0.37 between production batches (Supplementary Table 1).

The above shows that comparison of profiles from the same DNA extract generated on slides

from different production batches can result in large BC distances. Small BC distances are

expected between duplicate analyses, but this is only seen for within slide or within batch

duplicates (Table 2 and Supplementary Table 1).

Table 2 BC distances calculated between microarray profiles from one sample. Slide 004 and 005 are from batch 1 (the sample was analysed in duplicate on slide 005 for within slide comparison), slide 007 from batch 2 and slide 011 and 012 from batch 3. BC distances calculated between profiles analysed on slides within a batch are in bold.

To further investigate the capability of the microarray to differentiate between individuals, BC

distances were calculated between the profiles per production batch of slides. Figure 1 shows

that for the 35 individuals a BC<0.11 was only calculated between profiles from the same

individual and a BC>0.21 was only calculated for profiles from different individuals. Profiles

from the same individual are either profiles from duplicate extractions from the same faecal

sample or profiles from duplicate or multiple analyses from the same DNA extract. For the

profiles from the same individual almost 70% of the BC distances calculated were 0.11 or

smaller. When profiles from different individuals were compared more than 75% of

comparisons yielded BC distances larger than 0.21. There is a small range where the BC

distances calculated between profiles from the same individual overlap with distances

calculated between profiles from different individuals. From a microbiological point of view

this is not surprising, as there are certain ‘core’ microorganisms which will be detected in a

large number of individuals [6-9,14-17]. Supplementary Figure 1 shows that this overlapping

area is larger when the profiles from all production batches are analysed together. This again

shows that comparisons between samples should be performed with profiles generated on the

same slide or slides from the same batch.

Batch # Slide # 004 005 005 007 011 012

004 0

005 0,05 0

005 0,04 0,04 0

2 007 0,15 0,16 0,15 0

011 0,28 0,29 0,27 0,22 0

012 0,28 0,30 0,28 0,22 0,06 0

1

3

Microbial population analysis improves the evidential value of faecal traces

49

Figure 1 Histogram with distribution of 902 Bray-Curtis distances (x-axis) calculated between 74 microarray profiles from 35 faecal samples, samples analysed per production batch of slides. The number of observations of Bray-Curtis distances is indicated on the y-axes. Solid lines represent distances (n=54) calculated between profiles originating from samples from the same individual (duplicate extraction and/or duplicate PCR). Dotted lines represent distances (n=848) calculated between profiles originating from faecal samples from different individuals.

Additional calculations have been performed to test whether detection of ‘rare’ probes affected

the BC distances calculated. ‘Rare’ probes have been defined as probes detected in less than

half of the number of individuals or probes with total SNR<1000 summed over all samples.

For 194 probes the total SNR over all samples was less than 1000, and 139 probes were

detected in less than 18 individuals. 134 of these probes were classified as ‘rare’ for both of the

calculations. For both calculations the histogram of BC distances was not affected by deleting

the ‘rare’ probes from the calculation (data not shown). To test the influence of high

abundance probes, 135 randomly selected probes which were detected in 23-35 individuals

were deleted from the dataset. This did not affect the histogram either (data not shown). The

above shows that the BC distance is a robust measure to compare community profiles.

The histogram in Figure 1 can be used as a decision model in casework examinations. When a

(crime scene) sample is compared to a reference sample (on the same slide or slides from the

same batch), and a BC distance <0.11 is calculated between the microarray profiles, there is a

high probability that both samples originated from the same individual. When a distance is

calculated within the overlapping area, the frequency of that distance within the common

source group can be divided by the frequency of that distance within the different source

Chapter 4

50

group. This suits the Bayesian approach for calculating the likelihood of a common source for

these samples.

In addition to calculating a BC distance between two microarray profiles, the number and

types of probes which have been detected should be taken into account when interpreting the

evidence. The probe targets are known and presence or absence of specific targets could be of

criminalistic relevance. Calculating the BC distance between two microarray profiles is only

part of determining the likelihood ratio using the Bayesian approach.

3.3. Faecal microbial population analysis in forensics

Our experiments show that microbial population profiling of faecal samples for forensic

purposes is possible with microarray analysis. The model for comparison is based on a

relatively small number of individuals. This number is based on previous studies on soil

microbial communities, where we showed that increasing the sample size from 22 to 50 soil

samples had slight influence on the model [3]. Even increasing from 50 to 65 soil samples had

no further influence (unpublished results). Although soil microbial communities and faecal

microbial communities are different, adding more faecal samples to our database may change

the model slightly, but no major changes are expected. This will be evaluated in future

experiments.

We only used relatively large sample sizes in these experiments, where in casework the amount

of faeces recovered can be minimal. When less material is available it is possible that less

abundant microbes within a community may be missed when profiling the population. We

have already successfully used our microarray on minimal traces in casework for the

determination of the origin of a sample (unpublished results). But additional experiments using

various sample sizes need to be performed to study the effects of sample size on community

composition and set criteria for comparing faecal microbial communities in casework.

The application of microbial profiling in combination with human STR profiling can be

valuable. But individualization of human faeces based on human STR profiling can be

challenging because of the high abundance of microbial DNA [18-19]. Vanderberg and

Johnson show that it is possible to generate (partial) human STR profiles from faeces or faecal

smears, but some samples yielded no (partial) profile. In those cases, microbial population

profiling may be used as a complementary technique [20].

In addition, microbial population profiling can be used for determining the origin of a

questioned sample. In sexual assault cases it can be of high importance to know whether a

stain could be faeces or otherwise [22]. Until now the origin of a sample can be determined by

presumptive tests, mRNA/miRNA profiling or DNA methylation [29-31], but there are no

(presumptive) tests for faecal origin determination. The composition of the microbial

population varies at different locations of the human body [6] and this variation may be used

to determine from which body site the cells in a sample originate [22].

Microbial population analysis improves the evidential value of faecal traces

51

4. Conclusions

Our experiments show that we have developed a reproducible and reliable microarray method

to generate microbial population profiles from faecal samples and a method to objectively

compare these profiles. When no or only a partial human STR profile is generated from a

faecal sample, the microbial population profile can be used to increase the evidential value of

that trace.

We show that reliable comparisons can only be made between profiles generated on slides

produced in the same batch. When the technique is applied in casework, preferably

comparisons between microarray profiles from one slide should be made.

The histogram with distributions of BC distances between microarray profiles from the same

individual and microarray profiles from different individuals can be used as a decision model

when comparing questioned faecal samples based on microbial population profiles. This type

of decision model fits the Bayesian approach, which is recommended by the European

Network of Forensic Science Institutes (ENFSI) guidelines for reporting evaluative forensic

evidence [32]. Next to the calculated BC distance the taxonomic information provided by the

microarray probes should also be used when evaluating the evidence.

5. Future experiments

In all experiments presented in this paper fresh faecal samples have been used of more or less

similar samples sizes. When applied in casework it is possible that only minimal trace evidence

is available. In future experiments the influences of sample size and storage conditions on the

microarray profiles will be further evaluated. Additionally, profiles from more individuals will

be added to the database and comparisons between reference samples and stains will also be

performed to further validate the technique.

Our microarray design will be updated and probes targeting microorganisms present at other

forensically interesting body sites (e.g. vagina, skin, mouth) will be added to make dual use of

the microarray possible. Next to linking a (faecal) trace to a possible donor, the updated design

could also be used for determination of the origin of the cells in a sample and/or excluding

certain body sites as possible sources of a questioned sample. The evidential value of a trace

can be increased by combining microbial population profiling with for instance mRNA

profiling, because these techniques are targeting independent parameters within a sample.

Chapter 4

52

Acknowledgements

We are very grateful to all volunteers who provided stool samples for this study. And we

would like to thank M.P.J. Piët (Netherlands Forensic Institute) for critically reading this

manuscript.

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Chapter 4

54

Supplementary material

Supplementary Table 1 BC distances calculated between microarray profiles from the positive control sample. Slides 002-005 are from batch 1, slides 006-010 from batch 2 (the sample was analysed in duplicate on slide 006 for within slide comparison) and slides 011-015 from batch 3. BC distances calculated between profiles analysed on slides within a batch are in bold.

0,0

3

0,0

4

0,0

5

0,0

6

0,0

7

0,0

8

0,0

9

0,1

0

0,1

1

0,1

2

0,1

3

0,1

4

0,1

5

0,1

6

0,1

7

0,1

8

0,1

9

0,2

0

0,2

1

0,2

2

0,2

3

0,2

4

0,2

5

0,2

6

0,2

7

0,2

8

0,2

9

0,3

0

0,3

1

0,3

2

0,3

3

0,3

4

0,3

5

0,3

6

0,3

7

0,3

8

0,3

9

0,4

0

0,4

1

0,4

2

0,4

3

0,4

4

0,4

5

0,4

6

0,4

7

0,4

8

0,4

9

0,5

0

0,5

1

0,5

2

0,5

3

0,5

4

0,5

5

0,5

6

0,5

7

0,5

8

0,5

9

0

2

4

6

8

10

12

No

of

ob

s:

Sa

me

0

20

40

60

80

100

120

140

160

No

of

ob

s:

Diffe

ren

t

Supplementary Figure 1 Histogram with distribution of 2,701 Bray-Curtis distances (x-axis) calculated between 74 microarray profiles from 35 faecal samples, samples analysed for all three production batches together. The number of observations of Bray-Curtis distances is indicated on the y-axes. Solid lines represent distances (n=153) calculated between profiles originating from the same individual (duplicate extraction and/or duplicate PCR). Dotted lines represent distances (n=2,548) calculated between profiles originating from faecal samples from different individuals.

Batch Slide # 002 003 004 005 006 006 007 008 009 010 011 012 013 014 015

002 0

003 0,18 0

004 0,14 0,07 0

005 0,16 0,12 0,07 0

006 0,21 0,17 0,16 0,17 0

006 0,22 0,18 0,17 0,19 0,06 0

007 0,19 0,17 0,16 0,17 0,06 0,08 0

008 0,18 0,16 0,15 0,18 0,09 0,09 0,07 0

009 0,20 0,10 0,12 0,16 0,12 0,13 0,13 0,10 0

010 0,26 0,14 0,18 0,22 0,19 0,20 0,20 0,19 0,12 0

011 0,37 0,24 0,29 0,34 0,27 0,27 0,27 0,26 0,21 0,14 0

012 0,32 0,23 0,25 0,30 0,22 0,21 0,22 0,20 0,18 0,12 0,08 0

013 0,35 0,24 0,28 0,33 0,26 0,26 0,27 0,25 0,20 0,13 0,08 0,09 0

014 0,33 0,21 0,25 0,31 0,26 0,26 0,26 0,24 0,19 0,11 0,07 0,08 0,07 0

015 0,29 0,17 0,21 0,25 0,22 0,22 0,22 0,22 0,15 0,09 0,14 0,13 0,16 0,11 0

1

2

3