METHOD FOR BODY FLUID IDENTIFICATION
20200270684 ยท 2020-08-27
Assignee
Inventors
Cpc classification
C12Q1/6881
CHEMISTRY; METALLURGY
International classification
Abstract
Crime scene investigators need to identify biological tissue or fluid types. Such analysis is typically done using conventional chemical, serological and enzymatic tests to identify the body fluid or tissue, however, these tests can be unreliable and often do not meet the specificity and sensitivity required for forensic analysis. The present invention provides a method for accurately identifying circulatory blood, saliva, spermatozoa, seminal fluid, menstrual fluid and vaginal material by detection of specific RNA sequences. In particular, the invention provides a method for determining the type of a biological sample, comprising the steps of detecting RNA from the sample associated with any one or more of HBD, SLC4A1, GYPA, FDCSP, HTN3, STATH, PRM1, TNP1, PRM2, KLK2, MSMB, TGM4, MMP10, STC1, MMP3, MMP11, CYP2B7P, Lactobacillus gasseri (L.gass) and Lactobacillus crispatus {L.crisp) and determining whether the sample is circulatory blood, saliva, spermatozoa, seminal fluid, menstrual fluid or vaginal material.
Claims
1. A method for determining the type of a biological sample, comprising the steps of detecting RNA from the sample associated with any one or more of HBD, SLC4A1, GYPA, FDCSP, HTN3, STATH, PRM1, TNP1, PRM2, KLK2, MSMB, TGM4, MMP10, STC1, MMP3, MMP11, CYP2B7P, Lactobacillus gasseri (L.gass) and Lactobacillus crispatus (L.crisp) and determining whether the sample is circulatory blood, saliva, spermatozoa, seminal fluid, menstrual fluid or vaginal material.
2. The method of claim 1, comprising detecting an RNA associated with one or more of SEQ ID Nos: 1 to 19.
3. The method of claim 1, wherein the step of detecting the RNA includes the use of one or more primers specific for any one or more of HBD, SLC4A1, GYPA, FDCSP, HTN3, STATH, PRM1, TNP1, PRM2, KLK2, MSMB, TGM4, MMP10, STC1, MMP3, MMP11, CYP2B7P, Lactobacillus gasseri (L.gass) and Lactobacillus crispatus (L.crisp).
4. The method of claim 3, wherein the one or more primers are selected from SEQ ID Nos: 20 to 57.
5. The method of claim 1, further comprising determining if the biological sample is circulatory blood, comprising the step of detecting RNA associated with HBD using primers of SEQ ID No: 20 and 21, and/or SLC4A1 using primers of SEQ ID No:22 and 23 and/or GYPA using primers of SEQ ID No: 24 and 25.
6. The method of claim 1, further comprising determining if the biological sample is saliva, comprising the step of detecting RNA associated with FDCSP using primers of SEQ ID No: 26 and 27, and/or HTN3 using primers of SEQ ID No: 28 and 29, and/or STATH using primers of SEQ ID No: 30 and 31.
7. The method of claim 1, further comprising determining if the biological sample is spermatozoa, comprising the step of detecting RNA associated with PRM1 using primers of SEQ ID No:32 and 33 and/or TNP1 using primers of SEQ ID No:34 and 35 and or PRM2 using primers of SEQ ID No: 36 and 37.
8. The method of claim 1, further comprising determining if the biological sample is seminal fluid, comprising the step of detecting RNA associated with KLK2 using primers of SEQ ID No:38 and 39, and/or MSMB using primers of SEQ ID No:40 and 41 and/or TGM4 using primers of SEQ ID No: 42 and 43.
9. The method of claim 1, further comprising determining if the biological sample is menstrual fluid, comprising the step of detecting RNA associated with MMP10 using primers of SEQ ID No:44 and 45, and/or STC1 using primers of SEQ ID No:46 and 47 and/or MMP3 using primers of SEQ ID No:48 and 49 and/or MMP11 using primers of SEQ ID No. 50 and 51.
10. The method of claim 1, further comprising determining if the biological sample is vaginal material, comprising the step of detecting RNA associated with CYP2B7P using primers of SEQ ID No:52 and 53 and/or L.gass using primers of SEQ ID No: 54 and 55 and/or L.crisp of SEQ ID No: 56 and 57.
11. The method of claim 1, further comprising testing for the presence of RNA of all of HBD, SLC4A1, GYPA, FDCSP, HTN3, STATH, PRM1, TNP1, PRM2, KLK2, MSMB, TGM4, MMP10, STC1, MMP3, MMP11, CYP2B7P, Lactobacillus gasseri (L.gass) and Lactobacillus crispatus (L.crisp) in the biological sample.
12. The method of claim 1, further comprising detecting the presence of RNA of any one or more of HTN3 and FDCSP; and/or SLC4A1, HBD, STC1 and MMP10 and/or TNP1, PRM1, KLK2, MSMB and CYP2B79.
13. The method of claim 3, wherein the primers are labelled.
14. The method of claim 13, wherein the primers are labelled with a fluorescence label, biotin, radioactive or non-radioactive label.
15. The method of claim 1, wherein the RNA is detected using an amplification method.
16. The method of claim 15, wherein the amplification method is selected from the group comprising polymerase chain reaction (PCR), reverse transcriptase PCR (RT-PCR), quantitative reverse transcriptase PCR (qRT-PCR), multiplex PCR, multiplex ligation-dependent probe amplification (MLPA) or quantitative PCR (Q-PCR).
17. A kit for use in the method of claim 1, the kit comprising at least one primer pair selected from SEQ ID Nos: 20 and 21, 22 and 23, 24 and 25, 26 and 27, 28 and 29, 30 and 31, 32 and 33, 34 and 35, 36 and 37, 38 and 39, 40 and 41, 42 and 43, 44 and 45, 46 and 47, 48 and 49, 50 and 51, 52 and 53, 54 and 55, and 56 and 57.
Description
BRIEF DESCRIPTION OF DRAWINGS
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.sup.1Expected results were disclosed after completion of mRNA analysis. BL=circulatory blood, SA=saliva, SP=spermatozoa, SF=seminal fluid, VM=vaginal material, NR=no result.
.sup.2CellTyper amplifications were performed as published [2]. PCR products were separated on a Genetic Analyzer 3130xl, with a peak amplitude threshold of 100 RFU.
[0208] The invention will now be exemplified by way of the following non-limiting examples.
EXAMPLE 1: IDENTIFICATION OF RNA STABLE REGIONS IN BODY SAMPLES
Materials and Methods
Identification of Body Fluid-Specific Candidate Genes
[0209] Candidate mRNAs for the identification of circulatory blood (HBD, SLC4A1) and menstrual fluid (MMP3, STC1) were selected from RNA-Seq data of degraded body fluids as published previously [22]. Semen marker candidates (TNP1, KLK2) were chosen from gene expression databases (TiGER, PaGenBase) [24,25] with respect to their physiological function in the body.
Primer Design
[0210] Primers for HBD, SLC4A1, MMP3 and STC1 were designed to target transcript stable regions (StaRs) as described previously [23] using the OligoAnalyzer 3.1 online tool (Integrated DNA Technologies, Inc., Coralville, Iowa, USA). Sequencing coverage maps were viewed using the Geneious v.5.6.7 software (Biomatters Ltd., Auckland, New Zealand) and regions of high coverage selected for primer design. Primers for TNP1 and KLK2 were designed using conventional primer design strategy. The specificity of all primers to their intended mRNA targets was verified using Primer-BLAST [26]. Primer sequences and expected amplicon sizes are listed in Table 2.
TABLE-US-00002 TABLE2 Primersequencesandexpectedampliconsizesofthenovelbodyfluid markers. Targetbody Accession Productsize fluid Marker number PrimerSequence(5-3) (bp) Circulatory Haemoglobin NM_000519.3 F:ACTGCTGTCAATGCCCTGTG 176 blood delta(HBD) R:ACCTTCTTGCCATGAGCCTT Solutecarrier NM_000342.3 F:AACTGGACACTCAGGACCAC 102 family4(anion R:GGATGTCTGGGTCTTCATATTCCT exchanger), member1 (Diegoblood group)(SLC4A1) Semen Transition NM_003284.3 F:GATGACGCCAATCGCAATTACC 102 containing protein1(during R:CCTTCTGCTGTTCTTGTTGCTG spermatozoa histoneto protamine replacement) (TNP1) Seminal Kallikrein-related NM_005551.4 F:CAGTCATGGATGGGCACACT 141 fluid peptidase2 R:ACCCTCTGGCCTGTGTCTTC (KLK2) Menstrual Matrix NM_002422.3 F:CCATGCCTATGCCCCTG 84 fluid metallopeptidase R:GTCCCTGTTGTATCCTTTGTCC 3(MMP3) Stanniocalcin1 NM_003155.2 F:TGCCCAATCACTTCTCCAACAG 103 (STC1) R:TTCTCCATCAGGCTGTCTCTG
Collection of Body Fluid Samples
[0211] Six samples each of 50 L circulatory blood, semen and seminal fluid (azoospermic), as well as saliva/buccal mucosa, menstrual and non-menstrual vaginal swabs were obtained from healthy, consenting volunteers, as approved by the University of Auckland Human Participants Ethics Committee (UAHPEC). Blood was drawn using a sterile AKKU-CHEK Safe-T-Pro Plus lancet (Roche Diagnostics USA, Indianapolis, Ind., USA). Blood, semen and seminal fluid aliquots were deposited onto sterile Cultiplast rayon swabs. Buccal, menstrual and vaginal samples were obtained by volunteers themselves using sterile swabs. All samples were allowed to dry overnight at ambient laboratory conditions and then extracted as described below.
RNA Extraction and Purification
[0212] Total RNA from body fluid samples was prepared as described previously [22,23] using the Promega DNA IQ and ReliaPrep RNA Cell Miniprep Systems (Promega Corporation, Madison, Wis., USA) following the manufacturer's instructions. Genomic DNA was removed by incorporating an on-column DNase I treatment during the RNA extraction process. RNA was eluted in 45 L nuclease-free water. The absence of genomic DNA was verified by real-time PCR using the Quantifiler Human DNA quantification kit (Life Technologies by Thermo Fisher Scientific, Inc., Waltham, Mass., USA) with 1 L purified RNA in a 12.5 L reaction. Samples which contained residual DNA were treated with TURBO DNase (Invitrogen by Thermo Fisher Scientific, Inc.) and re-quantified until no DNA was detectable.
cDNA Synthesis
[0213] Complementary DNA (cDNA) was prepared using the High Capacity cDNA Reverse Transcription Kit (Applied Biosystems by Thermo Fisher Scientific, Inc.) according to the manufacturer's instructions. Ten microlitres of DNA-free RNA were subjected to reverse transcription in a 20 L reaction. Synthesis was performed on a GeneAmp PCR System 9700 thermal cycler (Applied Biosystems by Thermo Fisher Scientific, Inc.) using the following program: 25 C. for 10 min, 37 C. for 120 min, followed by 85 C. for 5 min and hold at 4 C.
Polymerase Chain Reaction (PCR)
PCR Reactions
[0214] Body fluid cDNA samples were amplified using the QIAGEN Multiplex PCR Kit (Qiagen GmbH, Hilden, Germany) according to the manufacturer's instructions. Two microlitres of cDNA were amplified in 25 L PCR reactions containing 12.5 L of 2 PCR master mix. Primer concentrations for specificity testing were as follows: 0.05 M (HBD), 0.03 M (SLC4A1), 0.08 M (TNP1), 0.4 M (KLK2), 0.02 M (MMP3), 0.02 M (STC1). Primer concentrations for comparison were 0.2 M (circulatory blood), 0.05 M (semen), and 0.1 M (seminal and menstrual fluid), respectively. Finally, nuclease-free water was added to achieve a total volume of 25 L for each reaction.
PCR Cycling Conditions
[0215] PCR cycling conditions for amplification on the GeneAmp PCR System 9700 were as published previously [22,23,1]: initial denaturation at 95 C. for 15 min, followed by 35 cycles of 94 C. for 30 s, 58 C. for 3 min and 72 C. for 1 min, final elongation at 72 C. for 45 min and cooling down to 4 C.
Capillary Electrophoresis and Data Analysis
[0216] PCR products were separated on a Genetic Analyzer 3130xl (Applied Biosystems by Thermo Fisher Scientific, Inc.). One microliter of amplified PCR product was mixed with 9 L of a formamide/size standard stock solution, created by adding 15 L GeneScan 500 ROX to 1000 L HiDi formamide. Results were analysed with GeneMapper v.3.2.1 (Applied Biosystems by Thermo Fisher Scientific, Inc.) using a peak amplitude threshold of 50 RFU.
Results and Discussion
Selection of Body Fluid Marker Candidates
[0217] Whole transcriptome paired-end sequencing (2100 bp) of circulatory blood (2 donors) and menstrual fluid (1 donor) was performed in order to identify highly expressed biomarkers possibly exclusive to each body fluid type [22]. Processed and merged sequencing reads for each sample were aligned to the human reference sequence assembly hg19 (GRCh37) to allow for the determination of the maximum count values for each detected transcript [22]. Data were sorted by maximum count numbers and compared between sample types to exclude concomitantly expressed genes and identify highly abundant and possibly specific body fluid markers. Four mRNA candidates were identified from this data set: haemoglobin delta (HBD) and solute carrier family 4, member 1 (SLC4A1) for circulatory blood, as well as matrix metallopeptidase 3 (MMP3) and stanniocalcin 1 (STC1) for menstrual fluid.
[0218] Two further candidate genes were selected from two gene expression databases (TiGER, PaGenBase) [24,25] based on their putative physiological function in the human body: transition protein 1 (TNP1) for spermatozoa and kallikrein-related peptidase 2 (KLK2) for seminal fluid which may be free of spermatozoa.
RNA-Seq Data Analysis
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[0220] All menstrual fluid marker candidates were undetected in buccal mucosa (
Specificity Screening
[0221] The expression profiles of the six body fluid marker candidates were evaluated by singleplex endpoint RT-PCR. Six samples per body fluid (50 L circulatory blood and semen, whole buccal, menstrual and non-menstrual vaginal swabs) from various donors were amplified using 2 L of cDNA synthesised from total RNA. When cross-reactive peaks were observed (TNP1, MMP3 and STC1,
Haemoglobin Delta (HBD)
[0222] The haemoglobin delta or -globin gene is part of the human -globin gene cluster located on chromosome 11p15.5. Together with two alpha chains, two delta chains constitute the HbA.sub.2 tetramer (.sub.22), which comprises about 2-3% of the total haemoglobin in adult humans [27]. The coding region of HBD has strong sequence homology with HBB, both of which are expressed in bone marrow and reticulocytes [27,28]. Mutations in the HBD gene can result in clinically insignificant -thalassaemia, characterised by a reduced ability of the body to produce HbA.sub.2 [27].
[0223] HBD mRNA was exclusively present in circulatory blood and menstrual fluid (
[0224] Although HBD expression is known to reach only about 50% of that of HBB [27], our data show consistent and efficient detection of HBD mRNA and therefore demonstrate suitability of this marker for the identification of blood. The reduced expression of HBD is also advantageous given that the relatively strong and ubiquitous expression of HBB can lead to amplification from non-target body fluids [3,10]. While some of those observed signals may have been due to the presence of trace amounts of blood in a sample rather than true HBB expression, such findings clearly complicate the interpretation of results. Since HBD shows the same expression pattern as HBB, its reduced transcription rate is beneficial in this context as it increases marker specificity (
Solute Carrier Family 4 (Anion Exchanger), Member 1 (Diego Blood Group) (SLC4A1)
[0225] SLC4A1, also known as anion exchanger 1 (AE1) or band 3, is located on chromosome 17q21-22, and is the main integral protein in the erythrocyte membrane, connecting the lipid bilayer to the protein network through interactions with ankyrin-1 and proteins 4.1 and 4.2 [29]. SLC4A1 also interacts with glycophorin A (GYPA) and haemoglobin [30]. The C-terminal domain functions as an anion exchanger, increasing the overall capacity of blood to transport CO.sub.2 [29,30]. Numerous mutations in the SLC4A1 gene have been discovered, leading to conditions such as hereditary spherocytosis, southeast Asian ovalocytosis and hereditary acanthocytosis, all of which affect erythrocyte phenotype and result in minor to severe anaemia [29,30].
[0226]
Transition Protein 1 (During Histone to Protamine Replacement) (TNP1)
[0227] TNP1 has been mapped to chromosome 2q35-q36. Together with the larger TNP2, TNP1 replaces histones in the nuclei of elongating and condensing spermatids during spermiogenesis and is subsequently replaced by protamines [31]. TNP1 can destabilise nucleosomes and prevent DNA bending, and in turn promotes the repair of strand breaks by serving as an alignment factor [31]. Mutations in the promoter region of the TNP1 gene were found to reduce TNP1 expression and may contribute to male infertility [52].
[0228] Our results demonstrate strong expression of TNP1 in semen samples containing spermatozoa (
Kallikrein-Related Peptidase 2 (KLK2)
[0229] The gene encoding kallikrein-related peptidase 2 (KLK2), also referred to as human kallikrein 2, is located on chromosome 19q3.41. KLK2 is a serine protease synthesised by the prostate gland with high sequence identity to prostate-specific antigen (PSA/KLK3) [32]. It activates the zymogen forms of PSA and urokinase into their enzymatically active forms [32]. In addition, KLK2 possesses the ability to cleave semenogelins I and II, as well as fibronectin [33]. The enzymatic activity of KLK2 may be reversibly regulated by zinc ions, which are highest in the prostate and prostatic fluid [32].
[0230] As
Matrix Metallopeptidase 3 (MMP3)
[0231] Matrix metallopeptidases (MMPs) are a large family of zinc- or calcium-dependent endopeptidases which catabolise a wide range of substrates and thus regulate protein activity [35,36]. They engage in various roles during tissue degradation and remodelling processes, including menstruation [35,36]. Three members of this family, namely MMPs 7, 10 and 11, have been widely used as forensic menstrual fluid markers [1,3,5-7,36].
[0232] MMP3, also known as stromelysin-1 (mapped to 11q22.3) is another member of the MMP superfamily which is highly expressed during menstruation (
[0233] Our results identify MMP3 as a suitable menstrual fluid marker. This mRNA was strongly expressed on days 2 and 3 of the menstrual cycle. All six menstrual fluid samples produced peaks greater than 2000 RFU (
[0234] In previous research, MMPs 7, 10 and 11 were introduced as markers specific for the detection of menstruum. Since then, multiple studies reported their expression during uterine phases outside of menstruation [36,7,11]. MMPs have also been detected in circulatory blood [10,7,11], saliva, semen and skin [11]. One study even suggested MMP7 as a general vaginal secretion marker [18]. Here we also observed cross-reactions of MMP3 with saliva/buccal mucosa and vaginal material (
Stanniocalcin 1 (STC1)
[0235] Stanniocalcin 1 (STC1) was originally described as a homodimeric glycoprotein in the corpuscles of bony fishes, where it regulates calcium and phosphate homeostasis [37].
[0236] In humans, the STC1 gene is located on chromosome 8p21.2, and the protein may also regulate intracellular calcium and/or phosphate levels as an autocrine or paracrine factor and thus contribute to bone formation [37,38]. In contrast to its function in fish, STC1 activity in humans is thought to be local rather than systemic due to its absence from the circulation [38]. Nevertheless, STC1 appears to be a pleiotropic factor, and other proposed functions include involvement in ischemia, angiogenesis, muscle contractility, as well as immune and inflammatory responses [37,38]. These processes are all known to take place in the endometrium before, during and after menstruation.
[0237] Our data confirm that STC1 mRNA is undetectable in circulatory blood samples (
Comparison to Existing Markers
[0238] The sensitivity of the six novel body fluid candidates was compared to corresponding well-characterised markers published previously [1] using primer concentrations of 0.2 M (circulatory blood), 0.05 M (semen), and 0.1 M (seminal and menstrual fluid), for comparison, respectively and the same cDNA samples. HBD and SLC4A1 were compared to Glycophorin A (GYPA), TNP1 to protamine 2 (PRM2), KLK2 to transglutaminase 4 (TGM4), and MMP3 and STC1 to MMP11. As
Conclusion
[0239] This Example evaluated the expression of six new mRNAs for forensic body fluid identification by singleplex endpoint reverse transcription (RT-PCR) and partly using RNA-Seq and have evaluated their expression patterns. All marker candidates were highly abundant in their respective target body fluid type compared to other bodily sources. HBD and SLC4A1 can be used to confirm the presence of circulatory blood. TNP1 mRNA was present in semen which contains spermatozoa, while KLK2 mRNA was exclusive to seminal fluid regardless of spermatozoa presence. MMP3 and STC1 can be used to identify menstrual fluid samples.
[0240] All six candidate mRNAs showed increased signal intensity in the same samples compared to corresponding known markers using equal primer concentrations [1]. With the exception of KLK2, the increase in APH reached statistical significance up to an extreme p-value of 5.10.sup.21 for MMP3 compared to MMP11. Based on RNA-Seq and CE results, both MMP3 and STC1 mRNA appear to be more abundant in the endometrium during menstruation than MMP11 and can therefore facilitate the identification of a blood stain resulting from menses. In particular the detection of STC1 can be useful for discrimination between circulatory blood and menstrual fluid due to its absence from the circulatory system (
[0241] Single cross-reactions were observed for TNP1 with saliva and menstrual fluid, for MMP3 with saliva and vaginal material, and for STC1 with two non-menstrual vaginal samples (
Summary
[0242] The simultaneous assessment of multiple mRNAs per body fluid can help avoid false positives, since it is less likely that all typed markers would falsely indicate the presence of a certain body fluid [9]. The six novel mRNAs characterised here can increase the probative value of mRNA typing results by expanding the panel of useful forensic body fluid markers. Larger and improved multiplex systems could be developed, incorporating some or all of the above markers in addition to well-known transcripts.
Example 2: Multiplex Testing
Materials and Methods
Sample Collection
[0243] Human bodily samples were obtained from healthy volunteers with full informed consent. Samples for specificity testing included circulatory blood, liquid saliva, semen (containing spermatozoa), azoospermic seminal fluid, menstrual fluid, and vaginal material for RNA, as well as blood from a male individual for DNA. Donors were between 24 and 53 years of age and included males and females for circulatory blood and saliva. Blood was placed on sterile Cultiplast rayon swabs (LP Italiana SPA, Milano, Italy) in aliquots between 5-0.05 L. Saliva and semen were deposited on swabs in aliquots of 10-0.25 L, and 2-0.25 L, respectively. Semen donors included two azoospermic individuals. MF and VM were obtained by volunteers themselves using swabs provided for them. Volunteers donating semen, menstrual fluid, or vaginal material were asked to abstain from sexual intercourse for one week prior to sample collection.
[0244] Mixtures of body fluids were prepared by adding increasing volumes of blood or semen (1 L, 5 L, and 10 L) to 1/3 of a MF swab. Likewise, 1 L, 5 L, or 10 L saliva was added to 1/3 of a VM swab, as well as to 2 L semen placed on a swab. Finally, 2 L semen and 10 L saliva were added to a VM swab. All samples were prepared in duplicate, except for mixtures of MF and semen.
[0245] For the sensitivity study, decreasing volumes of circulatory blood (2.5-0.05 L), saliva (5-0.25 L), semen (1-0.05 L), and seminal fluid (1-0.05 L) were extracted, whereas decreasing RNA concentrations were reverse transcribed for MF and VM. All samples were prepared in duplicate and reverse transcribed using 10 L and 1 L RNA.
[0246] For the species specificity testing, circulatory blood and saliva were collected opportunistically from 24 species, including primates, monkeys, birds, cat, chicken, dog, guinea pig, otter, rabbit, sheep, and wallaby. Samples were kindly supplied by pet owners, veterinarians, and Auckland Zoo staff. A total of 41 samples (20 circulatory blood and 21 saliva/buccal mucosa) were obtained. DNA fractions collected during extraction were retained from all species.
DNA/RNA Co-Extraction and RNA Purification
[0247] DNA/RNA co-extractions were carried out as described previously [53] using the Promega DNA IQ System (Promega Corporation, Madison, Wis., USA), following the manufacturer's instructions. DNA was eluted in 50 L elution buffer.
[0248] Crude RNA lysates were further processed using the ReliaPrep RNA Cell Miniprep System (Promega) as published [53]. RNA was eluted in 45 L nuclease-free water. Purified RNA samples were immediately DNase treated using the TURBO DNAfree Kit (Ambion). The manufacturer's instructions were followed, adding 4.5 L 10 TURBO DNase Buffer and 2 L TURBO DNase to each sample.
Quantification of RNA and DNA Samples
[0249] RNA samples of human origin were quantified using the Quantifiler Human DNA Quantification Kit (Applied Biosystems) as described in [53]. If residual genomic DNA was detected in an RNA sample, the extract was again DNase treated and re-quantified. This was repeated (no more than three times) until no human genomic DNA was detectable in both quantification duplicates of the same sample.
[0250] The DNA concentration of the human body fluid sample was determined via use of the Quantifiler System as described above. Animal DNA was quantified using the Qubit 2.0 Fluorometer and Qubit dsDNA High Sensitivity Assay Kit (Molecular Probes by Life Technologies, Inc.). Reactions were performed according to the manufacturer's instructions using 2 L of each sample.
Reverse Transcription of RNA Samples
[0251] DNA-free RNA samples (10 L or 1 L) were reverse transcribed using the High Capacity cDNA Reverse Transcription Kit (Applied Biosystems) according to the manufacturer's instructions. Each reaction comprised a total volume of 20 L.
Primer and Multiplex Design
[0252] Primers for HBD, SLC4A1, FDCSP, HTN3, MMP10, STC1, and CYP2B7P were designed to target transcript stable regions (StaRs) [23] using the OligoAnalyzer 3.1 online tool (Integrated DNA Technologies, Inc., Coralville, Iowa, USA). Sequencing coverage maps were viewed in Geneious v.5.6.7 (Biomatters Ltd., Auckland, New Zealand) and regions of high read coverage were selected for primer design. Primers for TNP1, KLK2, and MSMB were designed using conventional primer design strategy, whereas primers for PRM1 were adopted from the literature [94]. The specificity of all primers to their intended mRNA target was verified using Primer-BLAST (National Center for Biotechnology Information, U.S. National Library of Medicine, Bethesda, Md., USA).
[0253] Primers were compiled into three multiplex assays: [0254] 1) a duplex combining FDCSP and HTN3 (multiplex D), [0255] 2) a quadruplex including HBD, SLC4A1, MMP10, and STC1 (multiplex Q), and [0256] 3) a pentaplex combining PRM1, TNP1, KLK2, MSMB, and CYP2B7P (multiplex P).
[0257] Optimized primer concentrations were as follows: [0258] 1) 0.05 M FDCSP and 0.012 M HTN3, [0259] 2) 0.05 M HBD, 0.04 M SLC4A1, 0.04 M MMP10, and 0.02 M STC1, and [0260] 3) 0.03 M PRM1, 0.04 M TNP1, 0.14 M KLK2, 0.03 M MSMB, and 0.02 M CYP2B7P.
Primer sequences and expected amplicon sizes are listed in
Multiplex Endpoint PCR
[0261] PCR was performed on a GeneAmp PCR System 9700 in 25 L reactions using 12.5 L Qiagen Multiplex PCR buffer, 2.5 L primer mix, and 2 L or 10 L cDNA. Where 2 L cDNA was used, the total reaction volume of 25 L was achieved by the addition of 8 L nuclease-free water. DNA samples were amplified using an input of approximately 1.5 ng, performing dilutions where necessary. DNA from blood was preferred over saliva due to the potential of co-extracting plant material in animal saliva samples.
[0262] Amplification negative controls (ANEG) comprised nuclease-free water in place of cDNA. Amplification positive controls (APOS) were prepared from pooled cDNA from four known samples per body fluid (buccal samples for multiplex D, menstrual fluid samples for multiplex Q, and semen and vaginal material samples for multiplex P) from various individuals. Each sample was tested for the presence of all target mRNAs prior to pooling. The resulting APOS samples were diluted in TE buffer to display peak heights of around 10,000 relative fluorescent units (RFU) without over-amplification.
[0263] The protocol for RT-PCR [1] was optimized by adjusting the annealing temperature and duration, as well as the final elongation time. To allow for the use of a universal amplification protocol, PCR conditions were selected as those which maximised target signals simultaneously in all three multiplex assays. Final optimized PCR conditions were: [0264] initial denaturation at 95 C. for 15 min, followed by [0265] 35 cycles of 94 C. for 30 s, 60 C. for 3 min and 72 C. for 1 min, [0266] final elongation at 72 C. for 10 min, and [0267] cooling down to 4 C.
Capillary Electrophoresis and Data Analysis
[0268] PCR products were separated on a 3500xL Genetic Analyzer (Applied Biosystems). Briefly, 9.6 L Hi-Di was mixed with 0.4 L GeneScan 600 LIZ dye Size Standard v2.0 (Applied Biosystems) per sample, to which 2 L of PCR product was added. One amplification positive control and one negative control were injected per every 22 samples analysed. Samples were injected at a voltage of 1.2 kV for 24 s. Results were analysed using GeneMapper ID-X v.1.5 (Applied Biosystems) and an analytical threshold of 50 RFU.
Results
Species Specificity
[0269] As shown in Table 3, all primate blood samples (except squirrel monkey) produced signals for the two circulatory blood markers. Most signals were observed for HBD, particularly in primate and rabbit blood. This was expected, since primate mRNA is very similar to human mRNA (e.g., 98% sequence identity between human and northern white-cheeked gibbon HBD [54]). Furthermore, haemoglobins are widely expressed in many bird and mammal species, although some only possess a pseudogene [55]. STC1 was only observed in the grey-headed flying fox sample. A signal the size of MMP10 plus 2 bp was detected in cat blood. Amplification products of the same size as CYP2B7P were detected in the siamang gibbon and cotton-top tamarin samples. This could be the result of CYP2B7P expression in primates, whereas humans only possess a pseudogene. The cotton-top tamarin sample also displayed an off-scale MSMB peak.
[0270] The majority of animal saliva samples did not indicate the presence of target amplification products. Only the bonnet macaque sample produced FDCSP, SLC4A1, MSMB, and CYP2B7P signals. FDCSP was also detected in the squirrel monkey and dog samples. The cotton-top tamarin sample displayed MSMB and CYP2B7P peaks, which were also observed in blood. These were unlikely to originate from residual DNA, since the amplification of DNA did not give rise to comparable signals. Therefore, MSMB or low levels of CYP2B7P mRNA may be present in circulatory blood or saliva of some primate species.
TABLE-US-00003 TABLE 3 Specificity of the three multiplex assays for circulatory blood and saliva collected from 24 species. FDCSP HTN3 HBD SLC4A1 MMP10 STC1 PRM1 TNP1 KLK2 MSMB CYP2B7P Species (blood samples) Bonnet macaque .. .. 3204 92145 .. .. .. .. .. .. .. Cotton-top tamarin .. .. 11979 19404 .. .. .. .. .. 96135 2382 Pygmy marmoset .. .. 97323 9726 .. .. .. .. .. .. .. Siamang gibbon .. .. 97296 92955 .. .. .. .. .. .. 1791 Spider monkey .. .. 11436 .sup.924.sup.1 .. .. .. .. .. .. .. Squirrel monkey .. .. 29073 .. .. .. .. .. .. .. .. Capybara .. .. .. .. .. .. .. .. .. .. .. Cat .. .. 1134 .. .sup.723.sup.2 .. .. .. .. .. .. Dog .. .. .. .. .. .. .. .. .. .. .. Grey-headed flying fox .. .. 135 .. .. 10395 .. .. .. .. .. Lovebird .. .. .. .. .. .. .. .. .. .. .. Meerkat .sup.144.sup.1 .. .. .. .. .. .. .. .. .. .. Otter .. .. 5217 .. .. .. .. .. .. .. .. Porcupine .. .. .. .. .. .. .. .. .. .. .. Rabbit .. .. 96063 .. .. .. .. .. .. .. .. Red panda .. .. 924 .. .. .. .. .. .. .. .. Tasmanian devil .. .. .. .. .. .. .. .. .. .. .. Tiger .. .. .. .. .. .. .. .. .. .. .. Wallaby .. .. 171 .. .. .. .. .. .. .. .. Wood duck .. 6972 255 .sup.822.sup.1 .. .. .. .. .. .. .. ENEG.sup.3 .. .. .. .. .. .. .. .. .. .. .. APOS 24518 16888 4017 13919 12540 7815 8691 747 17583 27125 12753 ANEG .. .. .. .. .. .. .. .. .. .. .. Species (saliva samples) Bonnet macaque 91815 .. .. 8814 .. .. .. .. .. 11795 1365 Cotton-top tamarin .. .. .. .. .. .. .. .. .. 34483 976 Golden lion tamarin .. .. .. .. .. .. .. .. .. .. .. Pygmy marmoset .. .. .. .. .. .. .. .. .. .. .. Spider monkey .. .. .. .. .. .. .. .. .. .. .. Squirrel monkey 180 .. .. .. .. .. .. .. .. .. .. Capybara .. .. .. .. .. .. .. .. .. .. .. Cat .. .. .. .. .. .. .. .. .. .. .. Chicken .. .. .. .. .. .. .. .. .. .. .. Dog 8604 .. .. .. .. .. .. .. .. .. .. Grey-headed flying fox .. .. .. .. .. .. .. .. .. .. .. Guinea pig .. .. .. .. .. .. .. .. .. .. .. Lovebird .. .. .. .. .. .. .. .. .. .. .. Otter .. .. .. .. .. .. .. .. .. .. .. Rabbit.sup.4 .. .. .. .. .. .. .. .. .. .. .. Red panda .. .. .. .. .. .. .. .. .. .. .. Sheep .. .. .. .. .. .. .. .. .. .. .. Tasmanian devil .. .. .. .. .. .. .. .. .. .. .. Tiger .. .. .. .. .. .. .. .. .. .. .. Wallaby .. .. .. .. .. .. .. .. .. .. .. Wood duck .. .. .. .. .. .. .. .. .. .. .. ENEG.sup.3 .. .. .. .. .. .. .. .. .. .. .. APOS 24518 16888 8926 7023 10442 3283 3676 2131 12182 12411 7392 ANEG .. .. .. .. .. .. .. .. .. .. .. .sup.1Observed product sized 1-2 bp smaller than expected .sup.2Observed product sized 1-2 bp larger than expected. .sup.3Extraction negative control. .sup.4Absence of signal was expected, since the DNA concentration from the same sample was below the detection threshold.
[0271] The remaining signals may have originated from amplification of trace amounts of mRNA due to overloading PCR reactions, since sample volumes were difficult to estimate. Additional amplification products outside expected marker positions were observed in most samples. These possibly resulted from unspecific primer binding and may be avoided by further increasing the annealing temperature [56].
[0272] Animal DNA samples mostly displayed raised baselines and numerous unspecific amplification products of peak heights below 1,000 RFU. Although some peaks were of the same size as expected marker products, this likely occurred by coincidence. The appearance of several unexpected signals in combination with a noisy baseline was a good indicator for the presence of DNA. Signals exceeding 4,000 RFU were observed for TNP1 from bonnet macaque, pygmy marmoset, siamang gibbon, and spider monkey. This may be due to the fact that the TNP1 primers amplified DNA. In addition, MSMB was observed in the golden lion tamarin sample.
Body Fluid Specificity
[0273]
[0274] The human DNA sample produced a peak of 60 RFU for MMP10 (
[0275] To evaluate the potential for false positives due to excessive sample input, ten samples per body fluid from five donors (10 L saliva, 5 L blood, 2 L semen, and whole MF and VM swabs) were amplified. Target marker signals were typically over-amplified, i.e. in the 70,000-90,000 RFU range (Table 4). Exceptions were HTN3 in saliva from donor A, menstrual fluid samples from donor R, and CYP2B7P in menstrual fluid samples, which were considerably lower. This corroborates previous findings of high variation in transcript abundance among individuals and samples [4,10].
[0276] Low-level cross-reactions were observed for all markers and body fluids, except for MMP10, STC1, PRM1, and MSMB in circulatory blood, HBD, SLC4A1, PRM1, and KLK2 in saliva, and HTN3 in menstrual fluid. This confirms previous reports of low transcript abundance in non-target body fluids for all currently known mRNAs [3,39,10,14]. Most signals were below 500 RFU and would likely be absent if a suitable analytical threshold were applied and target marker peaks were in the ideal range of 4,000-12,000 RFU on a 3500xL instrument. However, cross-reactions exceeding 10,000 RFU were observed for FDCSP in two MF samples from two donors, for MMP10 in two saliva, one semen, and three VM samples, as well as for MSMB in one VM sample. This demonstrates relatively higher FDCSP, MMP10, and MSMB transcript abundance in non-target body fluids and consequently lower specificity compared to the remaining mRNAs. Nevertheless, no cross-reactions were observed at ideal sample input (
TABLE-US-00004 TABLE 4 Body fluid specificity of the three multiplex assays using excessive RNA and cDNA input. FDCSP HTN3 HBD SLC4A1 MMP10 STC1 PRM1 TNP1 KLK2 MSMB CYP2B7P Saliva Donor N - sample 1 93714 97272 .. .. .. 282 .. .sup.144.sup.2 .. .. .. Donor N - sample 2 92152 95698 .. .. .. 267 .. .. .. 2889 .. Donor T - sample 1 89502 95826 .. .. 6687 162 .. 189 .. 1512 .. Donor T - sample 2 90609 97206 .. .. 7206 105 .. .. .. 6792 411 Donor M - sample 1 93675 97530 .. .. 22950 129 .. .sup.162.sup.1 .. 1896 .. Donor M - sample 2 90129 93996 .. .. 6168 159 .. .sup.198.sup.1 .. 1356 516 Donor P - sample 1 90780 95970 .. .. 16875 .. .. .. .. .. .. Donor P - sample 2 88005 95583 .. .. 7191 .. .. .. .. .. .. Donor A - sample 1 90423 70950 .. .. .. 309 .. 141 .. .. .. Donor A - sample 2 89871 72678 .. .. 3078 213 .. .sup.147.sup.2 .. .. .. APOS 7621 25905 1523 5725 5170 2258 3850 1574 15293 9162 4459 ANEG .. .. .. .. .. .. .. .. .. .. .. Circulatory blood Donor N - sample 1 .. .. 97215 89445 .. .. .. 798 .. .. 474 Donor N - sample 2 73 61 97023 89022 .. .. .. .. .. .. 651 Donor T - sample 1 .. .. 97443 90954 .. .. .. .sup.96.sup.2 .. .. 678 Donor T - sample 2 .. .. 97548 92568 .. .. .. .sup.162.sup.1 .. .. .. Donor M - sample 1 .. .. 97356 94188 .. .. .. .sup.201.sup.1 .. .. .. Donor M - sample 2 .. .. 97560 91539 .. .. .. .sup.273.sup.2 .. .. .. Donor P - sample 1 54 .. 97590 91941 .. .. .. .sup.207.sup.1 .. .. 561 Donor P - sample 2 123 60 95763 90180 .. .. .. .sup.162.sup.1 51 .. .. Donor A - sample 1 132 .. 97464 90681 .. .. .. .sup.120.sup.2 .. .. .. Donor A - sample 2 .. .. 97746 91569 .. .. .. .. .. .. .. APOS 7621 25905 3245 8669 6780 1451 3850 1574 15293 9162 4459 ANEG .. .. .. .. .. .. .. .. .. .. .. Semen Donor F - sample 1 147 87 .. .. 10245 108 97239 96120 94941 97650 .. Donor F - sample 2 144 69 .. .. 486 1905 95214 95703 92271 97542 .. Donor O - sample 1 .. .. 2181 .. 4191 .. 93078 95721 90954 97437 1341 Donor O - sample 2 .. .. .. .. .. 2175 94923 95535 90402 97380 .. Donor T - sample 1 .. .. .. .. .. .sup.132.sup.1 92289 96165 90306 97608 .. Donor T - sample 2 .. .. .. .. .. .. 97542 96648 95403 97752 .. Donor S - sample 1 .. .. .. .sup.231.sup.1 .. .sup.132.sup.1 .. .. 93138 97542 .. Donor S - sample 2 .. .. .. .. .. .sup.135.sup.1 .. .. 90924 97254 .. Donor U - sample 1 .. .. .. .. .. 132 .. .. 89532 97431 315 Donor U - sample 2 138 51 .. .. 69 2217 .. .. 89925 97062 1101 APOS 7621 25905 1523 5725 5170 2258 9116 2547 26109 18068 12395 ANEG .. .. .. .. .. .. .. .. .. .. .. Menstrual fluid Donor A - sample 1 2942 .. 74133 70018 71260 75906 .. .. .. .. 2856 Donor A - sample 2 .. .. 73777 68184 69349 75952 91 246 200 188 7209 Donor M - sample 1 3169 .. 80809 73771 74882 82648 .. .. 150 .. 5929 Donor M - sample 2 13634 .. 81136 75101 76717 83062 .. 4502 .. .. 18981 Donor C - sample 1 13709 .. 73629 67180 68632 75493 .. 4172 .. .. 30405 Donor C - sample 2 8568 .. 76050 70476 71121 77740 .. .. 130 .. 27420 Donor P - sample 1 1986 .. 82946 79066 79609 84603 .. .. 156 .. 72072 Donor P - sample 2 .. .. 95502 92733 93350 97088 .. .. 118 .. 21720 Donor R - sample 1 75 .. 59778 56261 61697 38894 101 311 246 201 18882 Donor R - sample 2 61 .. 47644 34200 75738 28891 .. .. .. 2992 20818 APOS 7621 25905 3245 8669 6780 1451 9116 2547 26109 18068 12395 ANEG .. .. .. .. .. .. .. .. .. .. .. Vaginal material Donor A - sample 1 .. .. .. .. 4103 .. .. .. .. .. 73572 Donor A - sample 2 .. .. 112 235 66 .. .. .. 66 .. 61708 Donor M - sample 1 .. .. .. .. 30624 1032 96 137 188 10189 76121 Donor M - sample 2 .. .. .. .. 17068 2059 .. 88 77 4127 68506 Donor P - sample 1 .. .. .. .. 7065 .. .. .. 80 .. 73504 Donor P - sample 2 .. .. .. .. 5800 436 .. .. 107 .. 74947 Donor Q - sample 1 .. .. .. .. 1661 .. .. .. 1967 2699 90156 Donor Q - sample 2 52 .. .. .. 56 .. 84 159 129 1815 87435 Donor R - sample 1 76 .. .. .. 20848 267 .. .. 310 .. 80585 Donor R - sample 2 3455 74 110 .. 7284 1079 .. .. .. 7942 84383 ENEG .. .. .. .. .. .. .. .. .. .. .. APOS 7621 25905 3245 8669 6780 1451 9116 2547 26109 18068 12395 ANEG .. .. .. .. .. .. .. .. .. .. .. .sup.1Observed product sized 1-2 bp smaller than expected. .sup.2Observed product sized 1-2 bp larger than expected.
[0277] It is therefore essential to limit sample input amounts and avoid over-amplification, although this may result in overlooking minor components of body fluid mixtures. HTN3, HBD, SLC4A1, and PRM1 appeared to be the most specific markers. Examples of electropherograms for the three multiplex assays are shown in
Sensitivity
[0278] The lower limit of detection (LOD) for the three multiplexes was approximately 0.5 L saliva (multiplex D), 0.05 L circulatory blood (multiplex Q), 0.05 L semen containing spermatozoa (multiplex P), and 0.25 L azoospermic seminal fluid (multiplex P) using 10 L RNA for cDNA synthesis. For MF (multiplex Q) and VM (multiplex P), the LOD was approximately 1/50.sup.th of the RNA obtained from a whole swab, using 1 L RNA for cDNA synthesis. These results were similar to other forensic multiplex systems [3,1,39,5,59].
Precision
[0279] The precision of the three multiplexes was evaluated by triplicate amplification of the same cDNA samples. Standard deviations () and coefficients of variation (CV), expressed as divided by the mean, were calculated from resulting peak heights.
[0280] The saliva markers displayed dispersion around the mean of 67% and 39% for FDCSP, and 77% and 103% for HTN3. This demonstrates a higher level of variability around the mean for HTN3, and moderate to low precision for both markers. Variability ranged between 8% and 49% for HBD, and between 18% and 36% for SLC4A1. Both markers therefore showed higher precision than the saliva markers. Less dispersion appeared to occur in MF samples. MMP10, STC1, and CYP2B7P showed variability between 21-24%, 14-16%, and 18-19%, respectively. These values demonstrate moderate to good levels of precision among replicates and samples, particularly for STC1. Variability ranged between 14-93% for PRM1, 7-53% for TNP1, 14-141% for KLK2, and 16-51% for MSMB. The high dispersion of KLK2 in one semen sample (141%) was due to failure of amplification in two replicates. KLK2 was also undetected in one replicate of a second semen sample, whereas all other mRNAs were consistently detected. Although high variability of peak heights is expected for mRNA analysis [60], further research including a greater number of replicates may determine CV values more precisely.
The Effect of Multiplexing
[0281] To investigate the effect that multiplexing has on target detection, 12 samples, i.e. two per body fluid, were amplified for a total of three replicates in both multiplex and uniplex reactions. All samples had previously shown ideal peak heights in multiplex amplifications. As
[0282] Whereas no clear tendency towards increased signals in uni- or multiplex was observed for PRM1, TNP1 appeared to perform slightly better in multiplex. This mRNA was consistently detected in multiplex, while two uniplex replicates failed to amplify. KLK2 and MSMB respectively were also undetected in four and two of 12 replicates using uniplex reactions, whereas only three and zero replicates failed in multiplex. The effect of multiplexing for CYP2B7P was negligible, although standard deviations were slightly higher in multiplex.
[0283] In 60% of 30 marker observations averaged from triplicate amplifications, the target markers exhibited less peak height variance in multiplex than in uniplex (data not shown). TNP1, KLK2, and MSMB exclusively showed higher precision in multiplex. Thus, while multiplexing exerted a negative effect on absolute peak height and therefore target detection, the markers had a tendency towards increased precision and consistent amplification in multiplex. The loss in peak height due to multiplexing was counteracted by the adjustment of primer concentrations, which balanced signals among markers within the same multiplex.
Resolution of Body Fluid Mixtures
[0284] All body fluid mixtures were correctly identified, except for one sample of 1 L saliva mixed with 2 L semen (
[0285] CYP2B7P was not observed in any mixture containing menstrual fluid. This was likely because this mRNA was present below the detection threshold. TNP1 was also undetected in two samples containing semen, likely due to amplification failure. Two unexpected signals (MMP10, 58 RFU and KLK2, 50 RFU) resulted from elevated baseline. Importantly, greater body fluid volumes did not necessarily produce higher peaks. Although HBD signals increased with larger blood volumes in the first set of mixtures with MF, the second set of mixtures did not show this correlation. This probably resulted from differences in template abundance among samples.
Detection of Seminal mRNAs in Post-Coital Vaginal Samples
[0286] To evaluate the time frame during which seminal mRNAs could be detected on vaginal swabs collected post intercourse, 24 samples with a time since intercourse (TSI; known from self-declared information through a daily questionnaire. The donor supplied vaginal swabs on 24 consecutive days in a controlled experiment) between one and six days were amplified using multiplex P. The results are shown in
[0287] All four seminal markers were consistently detected for up to three days post intercourse. The lowest signal from a TSI 3 d sample was 1,469 RFU for PRM1 (sample D19). Swabs collected four days post coitus also exhibited all four seminal markers, except sample D10, which did not show a KLK2 signal, possibly resulting from amplification failure. The two samples collected after five days (D11 and D26) each displayed MSMB and one additional marker. Whereas one sample with a TSI of six days (D12) was undetected, the second sample (D27) showed a PRM1 peak (903 RFU). Hence, the identification of seminal mRNAs in post-coital samples using the pentaplex is possible for up to six days. These results demonstrate a considerable enhancement of marker detection in post-coital samples compared to previous studies [10], which reported that the detection of seminal mRNAs was limited to samples with a TSI1 d.
Stability Studies
[0288] The forensic literature reported successful mRNA amplification from body fluids up to 56 years after deposition [61]. In this research, the ability to detect and identify aged body fluids, aged RNA, and aged cDNA samples was investigated. Five single-source samples for each of these three categories were selected with regard to storage time and subjected to amplification using all three multiplex assays, performing cDNA dilutions where necessary. In addition, an aged cDNA sample obtained from a nosebleed was analysed. The results are shown in
[0289] All aged circulatory blood samples (17-25 months old) were correctly identified, with no cross-reactions observed. Aged RNA samples (29-35 months old) correctly exhibited all target markers, except for CYP2B7P, which was absent from the menstrual fluid sample. Aged cDNA samples (15-30 months old) were also successfully amplified, with no cross-reactions present. In the aged MF cDNA sample, the menstrual fluid marker STC1 was undetected, however a strong CYP2B7P signal provided additional confidence in the vaginal origin of the sample.
[0290] The nosebleed sample correctly exhibited signals for HBD and SLC4A1, whereas FDCSP, HTN3, PRM1, TNP1, and KLK2 were undetected. However, MMP10, STC1, CYP2B7P, and in particular MSMBwere observed. This may be problematic, since these results falsely indicate the presence of a mixture of MF and semen. One previous study also reported the amplification of CYP2B7P from nasal mucosa [39]. An analytical threshold (AT) of 200 RFU would prevent false positive identification of STC1 and CYP2B7P, but still allow for MMP10 and MSMB to be identified. Caution is therefore warranted in the interpretation of mRNA profiling results in the possible presence of nasal mucosa. Consequently, a MMP10 signal without detecting STC1 or CYP2B7P was considered not confirmatory for MF (unless the MMP10 peak height exceeds those of the circulatory blood markers), whereas MSMB must be accompanied by a second semen marker to confirm the presence of semen.
Case-Type Samples
[0291] Case-type samples were processed in a blind study, in which sample sources were withheld from the researcher. A total of twelve samples (six swabs (samples 1-6) and six tape lifts (samples 7-12)) were analysed. All samples were initially amplified using 10 L RNA and 10 L cDNA. Subsequent cDNA dilutions were performed where necessary. Based on the results obtained in the previous sections, dilutions were required if peak heights exceeded 20,000 RFU. An analytical threshold of 400 RFU was applied for peak allocation. To compare results to a previously used method, all samples or highest dilutions thereof were also amplified using CellTyper [1]. The results are displayed in
[0292] Three samples (3, 8, and 11) exhibited no marker peaks using either multiplex system. Sample 3 was a saliva sample from a chicken, and therefore correctly lacking mRNA results. Sample 8 was obtained from the inside of the crotch of a pair of men's undergarments from an azoospermic male. Hence, the presence of seminal fluid was probable. Sample 11 was a tape lift from a coffee cup and therefore expected to contain saliva. The collected material may have been insufficient to produce a result for these two samples.
[0293] Samples 1 (vaginal swab), 2 (skin swab of saliva and blueberry juice), 7 (inside of the crotch of a pair of men's undergarments), and 12 (bloodstain) were undetermined using CellTyper. The new multiplex confirmed the presence of vaginal material for sample 1. This demonstrates that Lactobacilli can be unreliable VM markers in some individuals. The detection of CYP2B7P, however, enabled determination of the source of this sample. A TNP1 signal (611 RFU) was obtained for sample 2. This result was not informative, since the signal could have originated from residual genomic DNA, although the RT control was devoid of target signals. For sample 7, the new multiplex confirmed the presence of seminal fluid. TNP1 added strong support for the presence of semen, but should be interpreted with some caution due to the risk of amplification from DNA. MMP10 was not informative, since no corresponding mRNAs were detected. Finally, HBD and SLC4A1 were observed in sample 12 (tape lift of a bloodstain). This correctly confirmed the presence of circulatory blood. These results demonstrate improved body fluid detection using the new multiplex compared to CellTyper in three of the four samples.
[0294] Sample 4 was identified as VM using the new multiplex. Although this was a correct result, the assay failed to detect saliva as the second component (
[0295] Both multiplexes correctly confirmed the presence of saliva in sample 6. This sample further contained traces of blood, which neither assay detected. The possible presence of saliva was also expected for sample 9 (tape lift from the neck and upper front of a T-shirt). The new multiplex detected FDCSP, MMP10, and MSMB. These signals were insufficient to infer the presence of a body fluid. CellTyper detected corresponding marker types (STATH and MMP11), which also did not confirm a body fluid. It appears that mRNA background levels may be present on some everyday objects, which could be addressed by further research.
The improved multiplex confirmed the presence of circulatory blood in sample 10. MMP10 was also observed, but was not informative due to the absence of additional mRNAs. This sample was collected from the inside of the crotch of a pair of men's undergarments, with traces of blood applied. CellTyper detected TGM4, which indicated the presence of seminal fluid, but failed to detect blood. Overall, the new multiplex seemed to be more sensitive for circulatory blood and seminal mRNAs, whereas CellTyper was more sensitive for saliva. Further adjustment of primer concentrations may increase the sensitivity of the new multiplex for saliva.
Conclusions
[0296] Overall, the results demonstrate successful application of the three endpoint RT-PCR multiplex assays to the identification of low abundance and aged body fluid samples, as well as to the resolution of mixtures and case-type samples. The optimized system showed similar specificity and sensitivity to other forensic multiplex assays [3,1,59], with improved results for case-type samples compared to CellTyper [1].
[0297] The species specificity study demonstrated that some primer sequences were not human-specific. HBD was frequently amplified from non-human blood samples, particularly from primates, cat, and rabbit. Large, red stains should therefore be analysed with caution. Cotton-top tamarin, bonnet macaque, and siamang gibbon samples also readily produced false positives for CYP2B7P and MSMB. Saliva samples gave fewer false positives, although dog saliva produced a FDCSP signal. The occurrence of multiple extra peaks in an electropherogram was a strong indicator of the presence of genomic DNA. The analyst should therefore carefully review the framework of the case and consider whether samples may be giving false positive results. The absence of a DNA profile can additionally indicate the presence of a non-human body fluid. If the presence of animal body fluids is suspected, additional species testing should be carried out.
[0298] Across all human body fluids, higher volumes of body fluid, RNA, and cDNA generally produced stronger signals. There was no indication of inhibitory effects at increased template amounts, although high-template samples may show increased baseline noise and non-specific peaks that could fall into marker windows. False positives readily occurred in overloaded PCR reactions. These may be caused by low-level gene expression in non-target body fluids or artefact formation resulting from non-specific primer annealing. It was therefore essential to adjust cDNA input amounts to establish marker specificity. Replicate amplifications may be useful to identify cross-reactions. RT controls can provide additional information on whether DNA may have contributed to a signal. An analytical threshold of 400 RFU is recommended to additionally help prevent false positive marker identification.
[0299] Throughout this study, high inter-individual and inter-sample variation was observed, although the body fluids detected were consistent among replicates. This was expected due to the multitude of factors that affect gene expression [4] and the inability, at present, to measure the human-specific RNA concentration in a sample [62]. The impact of this variation was further exacerbated by low precision among replicates. Multiplexing increased overall precision, but had a detrimental effect on absolute peak height for most markers. Additionally, stochastic effects were prominent in low-template samples. Drop-out was observed for various markers at low RNA concentrations, whereas the same markers re-appeared at even lower RNA concentrations.
[0300] Mixtures of vaginal material and semen in samples collected post intercourse were successfully identified for up to six days. It is important to note that mixtures with uneven proportions may not be fully resolved. Whereas the major component was successfully detected in all mixtures analysed, the minor component(s) may be undetected because of low abundance, resulting in signals below the detection threshold. However, this is a general limitation of the technique. In view of the above results, the developed multiplex system provides a reliable and sensitive method for body fluid and cell type assessment of forensic samples.
REFERENCES
[0301] [1] R. I. Fleming, S. Harbison, The development of a mRNA multiplex RT-PCR assay for the definitive identification of body fluids, Forensic Sci Int Genet. 4 (2010) 244-256. [0302] [2] J. Juusola, J. Ballantyne, Messenger RNA profiling: A prototype method to supplant conventional methods for body fluid identification, Forensic Sci Int. 135 (2003) 85-96. [0303] [3] A. Lindenbergh, M. de Pagter, G. Ramdayal, M. Visser, D. Zubakov, M. Kayser, T. Sijen, A multiplex (m)RNA-profiling system for the forensic identification of body fluids and contact traces, Forensic Sci Int Genet. 6 (2012) 565-577. [0304] [4] T. Sijen, Molecular approaches for forensic cell type identification: On mRNA, miRNA, DNA methylation and microbial markers, Forensic Sci Int Genet. 18 (2015) 21-32. [0305] [5] J. Juusola, J. Ballantyne, Multiplex mRNA profiling for the identification of body fluids, Forensic Sci Int. 152 (2005) 1-12. [0306] [6] J. Juusola, J. Ballantyne, mRNA profiling for body fluid identification by multiplex quantitative RT-PCR, J Forensic Sci. 52 (2007) 1252-1262. [0307] [7] C. Haas, B. Klesser, C. Maake, W. Bar, A. Kratzer, mRNA profiling for body fluid identification by reverse transcription endpoint PCR and realtime PCR, Forensic Sci Int Genet. 3 (2009) 80-88. [0308] [8] C. Haas, E. Hanson, W. Br, R. Banemann, A. Bento, A. Berti, E. Borges, C. Bouakaze, A. Carracedo, M. Carvalho, mRNA profiling for the identification of bloodresults of a collaborative EDNAP exercise, Forensic Sci Int Genet. 5 (2011) 21-26. [0309] [9] C. Haas, E. Hanson, A. Kratzer, W. Bar, J. Ballantyne, Selection of highly specific and sensitive mRNA biomarkers for the identification of blood, Forensic Sci Int Genet. 5 (2011) 449-458. [0310] [10] A. D. Roeder, C. Haas, mRNA profiling using a minimum of five mRNA markers per body fluid and a novel scoring method for body fluid identification, Int J Legal Med. 127 (2013) 707-721. [0311] [11] M. van den Berge, A. Carracedo, I. Gomes, E. A. Graham, C. Haas, B. Hjort, P. Hoff-Olsen, O. Maronas, B. Mevag, N. Morling, H. Niederstatter, W. Parson, P. M. Schneider, D. S. Court, A. Vidaki, T. Sijen, A collaborative European exercise on mRNA-based body fluid/skin typing and interpretation of DNA and RNA results, Forensic Sci Int Genet. 10 (2014) 40-48. [0312] [12] M. L. Richard, K. A. Harper, R. L. Craig, A. J. Onorato, J. M. Robertson, J. Donfack, Evaluation of mRNA marker specificity for the identification of five human body fluids by capillary electrophoresis, Forensic Sci Int Genet. 6 (2012) 452-460. [0313] [13] C. Haas, E. Hanson, M. J. Anjos, R. Banemann, A. Berti, E. Borges, A. Carracedo, M. Carvalho, C. Courts, G. De Cock, M. Dotsch, S. Flynn, I. Gomes, C. Hollard, B. Hjort, P. Hoff-Olsen, K. Hribikova, A. Lindenbergh, B. Ludes, O. Maronas, N. McCallum, D. Moore, N. Morling, H. Niederstatter, F. Noel, W. Parson, C. Popielarz, C. Rapone, A. D. Roeder, Y. Ruiz, E. Sauer, P. M. Schneider, T. Sijen, D. S. Court, B. Sviezena, M. Turanska, A. Vidaki, L. Zatkalikova, J. Ballantyne, RNA/DNA co-analysis from human saliva and semen stainsresults of a third collaborative EDNAP exercise, Forensic Sci Int Genet. 7 (2013) 230-239. [0314] [14] E. K. Hanson, J. Ballantyne, Highly specific mRNA biomarkers for the identification of vaginal secretions in sexual assault investigations, Sci Justice. 53 (2013) 14-22. [0315] [15] C. Cossu, U. Germann, A. Kratzer, W. Br, C. Haas, How specific are the vaginal secretion mRNA-markers HBD1 and MUC4? Forensic Sci Int Gen Supplement Series. 2 (2009) 536-537. [0316] [16] C. Nussbaumer, E. Gharehbaghi-Schnell, I. Korschineck, Messenger RNA profiling: A novel method for body fluid identification by real-time PCR, Forensic Sci Int. 157 (2006) 181-186. [0317] [17] M. Bauer, D. Patzelt, Evaluation of mRNA markers for the identification of menstrual blood, J Forensic Sci. 47 (2002) 1278-1282. [0318] [18] S. M. Park, S. Y. Park, J. H. Kim, T. W. Kang, J. L. Park, K. M. Woo, J. S. Kim, H. C. Lee, S. Y. Kim, S. H. Lee, Genome-wide mRNA profiling and multiplex quantitative RT-PCR for forensic body fluid identification, Forensic Sci Int Genet. 7 (2013) 143-150. [0319] [19] D. Zubakov, E. Hanekamp, M. Kokshoorn, W. van Ijcken, M. Kayser, Stable RNA markers for identification of blood and saliva stains revealed from whole genome expression analysis of time-wise degraded samples, Int J Legal Med. 122 (2008) 135-142. [0320] [20] R. Fang, C. F. Manohar, C. Shulse, M. Brevnov, A. Wong, O. V. Petrauskene, P. Brzoska, M. R. Furtado, Real-time PCR assays for the detection of tissue and body fluid specific mRNAs, Int Congr Ser. 1288 (2006) 685-687. [0321] [21] Z. Wang, M. Gerstein, M. Snyder, RNA-Seq: A revolutionary tool for transcriptomics, Nat Rev Genet. 10 (2009) 57-63. [0322] [22] M. H. Lin, D. F. Jones, R. Fleming, Transcriptomic analysis of degraded forensic body fluids, Forensic Sci Int Genet. 17 (2015) 35-42. [0323] [23] M. H. Lin, P. P. Albani, R. Fleming, Degraded RNA transcript stable regions (StaRs) as targets for enhanced forensic RNA body fluid identification, Forensic Sci Int Genet. 20 (2016) 61-70. [0324] [24] X. Liu, X. Yu, D. J. Zack, H. Zhu, J. Qian, TiGER: A database for tissue-specific gene expression and regulation, BMC Bioinformatics. 9 (2008) 271. [0325] [25] J. Pan, S. Hu, D. Shi, M. Cai, Y. Li, Q. Zou, Z. Ji, PaGenBase: A pattern gene database for the global and dynamic understanding of gene function, PloS one. 8 (2013) e80747. [0326] [26] J. Ye, G. Coulouris, I. Zaretskaya, I. Cutcutache, S. Rozen, T. L. Madden, Primer-BLAST: A tool to design target-specific primers for polymerase chain reaction, BMC Bioinformatics. 13 (2012) 134. [0327] [27] M. H. Steinberg, G. P. Rodgers, HbA2: Biology, clinical relevance and a possible target for ameliorating sickle cell disease, Br J Haematol. 170 (2015) 781-787. [0328] [28] J. Ross, A. Pizarro, Human beta and delta globin messenger RNAs turn over at different rates, J Mol Biol. 167 (1983) 607-617. [0329] [29] A. lolascon, S. Perrotta, G. W. Stewart, Red blood cell membrane defects, Rev Clin Exp Hematol. 7 (2003) 22-56. [0330] [30] R. C. Williamson, A. M. Toye, Glycophorin A: Band 3 aid, Blood Cells Mol Dis. 41 (2008) 35-43. [0331] [31] M. L. Meistrich, B. Mohapatra, C. R. Shirley, M. Zhao, Roles of transition nuclear proteins in spermiogenesis, Chromosoma. 111 (2003) 483-488. [0332] [32] J. Lvgren, K. Airas, H. Lilja, Enzymatic action of human glandular kallikrein 2 (hK2). Substrate specificity and regulation by Zn2+ and extracellular protease inhibitors, Eur J Biochem. 262 (1999) 781-789. [0333] [33] J. A. Clements, N. M. Willemsen, S. A. Myers, Y. Dong, The tissue kallikrein family of serine proteases: Functional roles in human disease and potential as clinical biomarkers, Crit Rev Clin Lab Sci. 41 (2004) 265-312. [0334] [34] J. Lvgren, C. Valtonen-Andre, K. Marsal, H. Lilja, A. Lundwall, Measurement of prostate-specific antigen and human glandular kallikrein 2 in different body fluids, J Androl. 20 (1999) 348-355. [0335] [35] S. E. Gill, W. C. Parks, Metalloproteinases and their inhibitors: Regulators of wound healing, Int J Biochem Cell Biol. 40 (2008) 1334-1347. [0336] [36] M. Bauer, D. Patzelt, Identification of menstrual blood by real time RT-PCR: Technical improvements and the practical value of negative test results, Forensic Sci Int. 174 (2008) 55-59. [0337] [37] Y. Yoshiko, J. E. Aubin, Stanniocalcin 1 as a pleiotropic factor in mammals, Peptides. 25 (2004) 1663-1669. [0338] [38] B. H. Yeung, A. Y. Law, C. K. Wong, Evolution and roles of stanniocalcin, Mol Cell Endocrinol. 349 (2012) 272-280. [0339] [39] M. van den Berge, B. Bhoelai, J. Harteveld, A. Matai, T. Sijen, Advancing forensic RNA typing: On non-target secretions, a nasal mucosa marker, a differential co-extraction protocol and the sensitivity of DNA and RNA profiling, Forensic Sci Int Genet. 20 (2016) 119-129. [0340] [40] Sachs A B. Messenger RNA degradation in eukaryotes. Cell. 1993; 74:413-21. [0341] [41] Houseley J, Tollervey D. The many pathways of RNA degradation. Cell. 2009; 136:763-76. [0342] [42] Frazo C, McVey C E, Amblar M, Barbas A, Vonrhein C, Arraiano C M, et al. Unraveling the dynamics of RNA degradation by ribonuclease II and its RNA-bound complex. Nature. 2006; 443:110-4. [0343] [43] van Hoof A, Parker R. Messenger RNA degradation: beginning at the end. Current Biology. 2002; 12:R285-R7. [0344] [44] Christodoulou D C, Gorham J M, Herman D S, Seidman J. Construction of normalized RNA-seq libraries for Next-Generation Sequencing using the crab duplex-specific nuclease. Current Protocols in Molecular Biology. 2011:4.12. 1-4. 1. [0345] [45] Fleige S, Waif V, Huch S, Prgomet C, Sehm J, Pfaffl M W. Comparison of relative mRNA quantification models and the impact of RNA integrity in quantitative real-time RT-PCR. Biotechnology Letters. 2006; 28:1601-13. [0346] [46] Rowley J W, Oler A J, Tolley N D, Hunter B N, Low E N, Nix D A, et al. Genome-wide RNA-seq analysis of human and mouse platelet transcriptomes. Blood. 2011; 118:e101-e11. [0347] [47] Schroeder A, Mueller O, Stocker S, Salowsky R, Leiber M, Gassmann M, et al. The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Molecular Biology. 2006; 7:3. [0348] [48] Auer H, Lyianarachchi S, Newsom D, Klisovic M I. Chipping away at the chip bias: RNA degradation in microarray analysis. Nature Genetics. 2003; 35:292-3. [0349] [49] Fleige S, Pfaffl M W. RNA integrity and the effect on the real-time qRT-PCR performance. Molecular Aspects of Medicine. 2006; 27:126-39. [0350] [50] Romero I G, Pai A A, Tung J, Gilad Y. RNA-seq: Impact of RNA degradation on transcript quantification. BMC Biology. 2014; 12:42. [0351] [51] Antonov J, Goldstein D R, Oberli A, Baltzer A, Pirotta M, Fleischmann A, et al. Reliable gene expression measurements from degraded RNA by quantitative real-time PCR depend on short amplicons and a proper normalization. Laboratory Investigation. 2005; 85:1040-50. [0352] [52] Miyagawa Y, Nishimura H, Tsujimura A, Matsuoka Y, Matsumiya K, Okuyama A, Nishimune Y, Tanaka H. Single-nucelotide polymorphisms and mutation analyses of the TNP1 and TNP2 genes of fertile and infertile human male populations. Journal of Andrology. 2005; 26:779-786. [0353] [53] P. P. Albani, R. Fleming, Novel messenger RNAs for body fluid identification, Science & Justice. (2018) 58:145-152. [0354] [54] D. A. Benson, M. Cavanaugh, K. Clark, I. Karsch-Mizrachi, D. J. Lipman, J. Ostell, et al., GenBank, Nucleic Acids Res. 41 (2013) D36-D42. [0355] [55] R. C. Hardison, Evolution of hemoglobin and its genes, Cold Spring Harb Perspect Med. 2 (2012) a011627. [0356] [56] O. Henegariu, N. A. Heerema, S. R. Dlouhy, G. H. Vance, P. H. Vogt, Multiplex PCR: Critical parameters and step-by-step protocol, BioTechniques. 23 (1997) 504-511. [0357] [57] G. E. Lemack, M. Goldstein, Presence of sperm in the pre-vasectomy reversal semen analysis: Incidence and implications, J Urol. 155 (1996) 167-169. [0358] [58] I. S. Fraser, G. McCarron, R. Markham, T. Resta, Blood and total fluid content of menstrual discharge, Obstet Gynecol. 65 (1985) 194-198. [0359] [59] C. Haas, B. Klesser, A. Kratzer, W. Br, mRNA profiling for body fluid identification, Forensic Sci Int Genet Supplement Series. 1 (2008) 37-38. [0360] [60] J. Harteveld, A. Lindenbergh, T. Sijen, RNA cell typing and DNA profiling of mixed samples: Can cell types and donors be associated? Sci Justice. 53 (2013) 261-269. [0361] [61] H. Nakanishi, M. Hara, S. Takahashi, A. Takada, K. Saito, Evaluation of forensic examination of extremely aged seminal stains, Leg Med. 16 (2014) 303-307. [0362] [62] A. Lindenbergh, P. Maaskant, T. Sijen, Implementation of RNA profiling in forensic casework, Forensic Sci Int Genet. 7 (2013) 159-166. [0363] [63] Zhao, Shanrong, Baohong, Zhang, Ying Zhang, William Gordon, Sarah Du, Theresa Paradis, Michael Vincent, and David von Schack. Bioinformatics for RNA-Seq Data Analysis. BIOINFORMATICS-UPDATED FEATURES AND APPLICATIONS (2016): 125. [0364] [64] Chomczynski, Piotr, and Nicoletta Sacchi. The single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction: twenty-something years on. Nature protocols 1(2) (2006): 581-585. [0365] [65] Berensmeier, Sonja. Magnetic particles for the separation and purification of nucleic acids. Applied microbiology and biotechnology 73(3) (2006): 495-504. [0366] [66] Matson, R. S. (2008). Microarray Methods and Protocols. Boca Raton, Fla.: CRC. pp. 7-29. ISBN 1420046659. [0367] [67] Kumar, A. (2006). Genetic Engineering. New York: Nova Science Publishers. pp. 101-102. ISBN 159454753X). [0368] [68] Rio, D. C., Ares, M., Hannon, G. J., & Nilsen, T. W. Purification of RNA using TRIzol (TRI reagent). Cold Spring Harbor Protocols, (2010), pdb-prot5439. [0369] [69] Mardis, E. R. (2008). The impact of next-generation sequencing technology on genetics. Trends in genetics, 24(3), 133-141. [0370] [70] Metzker, M. L. (2010). Sequencing technologiesthe next generation. Nature Reviews Genetics, 11(1), 31-46. [0371] [71] Reis-Filho, J. S. (2009). Next-generation sequencing. Breast Cancer Res, 11(Suppl 3), S12. [0372] [72] Schuster, S. C. (2008). Next-generation sequencing transforms today's biology. Nature methods, 5(1), 16-18. [0373] [73] Mutz, K. O., Heilkenbrinker, A., Lonne, M., Walter, J. G., & Stahl, F. (2013). Transcriptome analysis using next-generation sequencing. Current opinion in biotechnology, 24(1), 22-30. [0374] [74] Fuller, C. W., Middendorf, L. R., Benner, S. A., Church, G. M., Harris, T., Huang, X., Jovanovich, S. B., Nelson, J. R., Schloss, J. A., Schwartz, D. C, & Vezenov, D. V. (2009). The challenges of sequencing by synthesis. Nature biotechnology, 27(11), 1013-1023. [0375] [75] Patel, R. K., & Jain, M. (2012). NGS Q C Toolkit: a toolkit for quality control of next generation sequencing data. PloS one, 7(2), e30619. [0376] [76] Trapnell, C., Pachter, L., & Salzberg, S. L. (2009). TopHat: discovering splice junctions with RNA-Seq. Bioinformatics, 25(9), 1105-1111. [0377] [77] Mullis, K. B., & Gibbs, F. F. R. (1994). Richard A. Morgan and W. French Anderson. The Polymerase chain reaction, 357. [0378] [78] Davies, M. J., Shah, A., & Bruce, I. J. (2000). Synthesis of fluorescently labelled oligonucleotides and nucleic acids. Chemical Society Reviews, 29(2), 97-107. [0379] [79] Proudnikov, D., & Mirzabekov, A. (1996). Chemical methods of DNA and RNA fluorescent labeling. Nucleic acids research, 24(22), 4535-4542. [0380] [80] Kutyavin, I. V., Afonina, I. A., Mills, A., Gorn, V. V., Lukhtanov, E. A., Belousov, E. S., Singer, M. J., Walburger, D. K., Lokhov, S. G., Gall, A. A., Dempcy, R., Reed, M. W., Meyer, R. B. & Hedgpeth, J. (2000). 3-minor groove binder-DNA probes increase sequence specificity at PCR extension temperatures. Nucleic Acids Research, 28(2), 655-661. [0381] [81] Pon, R. T. (1991). A long chain biotin phosphoramidite reagent for the automated synthesis of 5-biotinylated oligonucleotides. Tetrahedron letters, 32(14), 1715-1718. [0382] [82] Agrawal, S., Christodoulou, C., & Gait, M. J. (1986). Efficient methods for attaching non-radioactive labels to the 5 ends of synthetic oligodeoxyribonucleotides. Nucleic acids research, 14(15), 6227-6245. [0383] [83] Sambrook et al., Eds, 1987, Molecular Cloning, A Laboratory Manual, 2nd Ed. Cold Spring Harbor Press. [0384] [84] Tyagi, S., & Kramer, F. R. (1996). Molecular beacons: probes that fluoresce upon hybridization. Nature biotechnology, (14), 303-8. [0385] [85] R Carters, R., Ferguson, J., Gaut, R., Ravetto, P., Thelwell, N., & Whitcombe, D. (2008). Design and use of scorpions fluorescent signaling molecules. In Molecular beacons: Signalling nucleic acid probes, methods, and protocols (pp. 99-115). Humana Press. [0386] [86] Eisel, D.; Grnewald-Janho, S.; Krushen, B., ed. (2002). DIG Application Manual for Nonradioactive in situ Hybridization (3rd ed.). Penzberg: Roche Diagnostics. [0387] [87] Simmons, D. M., Arriza, J. L., & Swanson, L. W. (1989). A complete protocol for in situ hybridization of messenger RNAs in brain and other tissues with radio-labeled single-stranded RNA probes. Journal of Histotechnology, 12(3), 169-181. [0388] [88] Bowden, A., Fleming, R., & Harbison, S. (2011). A method for DNA and RNA co-extraction for use on forensic samples using the Promega DNA IQ system. Forensic Science International: Genetics, 5(1), 64-68). [0389] [89] Tatiana A. Tatusova, Thomas L. Madden (1999), Blast 2 sequencesa new tool for comparing protein and nucleotide sequences, FEMS Microbiol Lett. 174:247-250. [0390] [90] Needleman, S. B. and Wunsch, C. D. (1970) J. Mol. Biol. 48, 443-453. [0391] [91] Rice,P. Longden,l. and Bleasby, A. EMBOSS: The European Molecular Biology Open Software Suite, Trends in Genetics June 2000, vol 16, No 6. pp. 276-277. [0392] [92] Huang, X. (1994) On Global Sequence Alignment. Computer Applications in the Biosciences 10, 227-235. [0393] [93] Thompson et al., 1994, Nucleic Acids Research 24, 4876-4882. [0394] [94] Bauer, D. Patzelt, Protamine mRNA as molecular marker for spermatozoa in semen stains, Int J Legal Med. 117 (2003) 175-179.
TABLE-US-00005 Hemoglobindelta(HBD) SEQIDNO:1 AGGGCAAGTTAAGGGAATAGTGGAATGAAGGTTCATTTTTCATTCTCACAAACTAATGAA ACCCTGCTTATCTTAAACCAACCTGCTCACTGGAGCAGGGAGGACAGGACCAGCATAAAA GGCAGGGCAGAGTCGACTGTTGCTTACACTTTCTTCTGACATAACAGTGTTCACTAGCAA CCTCAAACAGACACCATGGTGCATCTGACTCCTGAGGAGAAGACTGCTGTCAATGCCCTG TGGGGCAAAGTGAACGTGGATGCAGTTGGTGGTGAGGCCCTGGGCAGATTACTGGTGGTC TACCCTTGGACCCAGAGGTTCTTTGAGTCCTTTGGGGATCTGTCCTCTCCTGATGCTGTT ATGGGCAACCCTAAGGTGAAGGCTCATGGCAAGAAGGTGCTAGGTGCCTTTAGTGATGGC CTGGCTCACCTGGACAACCTCAAGGGCACTTTTTCTCAGCTGAGTGAGCTGCACTGTGAC AAGCTGCACGTGGATCCTGAGAACTTCAGGCTCTTGGGCAATGTGCTGGTGTGTGTGCTG GCCCGCAACTTTGGCAAGGAATTCACCCCACAAATGCAGGCTGCCTATCAGAAGGTGGTG GCTGGTGTGGCTAATGCCCTGGCTCACAAGTACCATTGAGATCCTGGACTGTTTCCTGAT AACCATAAGAAGACCCTATTTCCCTAGATTCTATTTTCTGAACTTGGGAACACAATGCCT ACTTCAAGGGTATGGCTTCTGCCTAATAAAGAATGTTCAGCTCAACTTCCTGAT Solutecarrierfamily4(anionexchanger),member1(Diegobloodgroup) (SLC4A1) SEQIDNO:2 GAACGAGTGGGAACGTAGCTGGTCGCAGAGGGCACCAGCGGCTGCAGGACTTCACCAAGG GACCCTGAGGCTCGTGAGCAGGGACCCGCGGTGCGGGTTATGCTGGGGGCTCAGATCACC GTAGACAACTGGACACTCAGGACCACGCCATGGAGGAGCTGCAGGATGATTATGAAGACA TGATGGAGGAGAATCTGGAGCAGGAGGAATATGAAGACCCAGACATCCCCGAGTCCCAGA TGGAGGAGCCGGCAGCTCACGACACCGAGGCAACAGCCACAGACTACCACACCACATCAC ACCCGGGTACCCACAAGGTCTATGTGGAGCTGCAGGAGCTGGTGATGGACGAAAAGAACC AGGAGCTGAGATGGATGGAGGCGGCGCGCTGGGTGCAACTGGAGGAGAACCTGGGGGAGA ATGGGGCCTGGGGCCGCCCGCACCTCTCTCACCTCACCTTCTGGAGCCTCCTAGAGCTGC GTAGAGTCTTCACCAAGGGTACTGTCCTCCTAGACCTGCAAGAGACCTCCCTGGCTGGAG TGGCCAACCAACTGCTAGACAGGTTTATCTTTGAAGACCAGATCCGGCCTCAGGACCGAG AGGAGCTGCTCCGGGCCCTGCTGCTTAAACACAGCCACGCTGGAGAGCTGGAGGCCCTGG GGGGTGTGAAGCCTGCAGTCCTGACACGCTCTGGGGATCCTTCACAGCCTCTGCTCCCCC AACACTCCTCACTGGAGACACAGCTCTTCTGTGAGCAGGGAGATGGGGGCACAGAAGGGC ACTCACCATCTGGAATTCTGGAAAAGATTCCCCCGGATTCAGAGGCCACGTTGGTGCTAG TGGGCCGCGCCGACTTCCTGGAGCAGCCGGTGCTGGGCTTCGTGAGGCTGCAGGAGGCAG CGGAGCTGGAGGCGGTGGAGCTGCCGGTGCCTATACGCTTCCTCTTTGTGTTGCTGGGAC CTGAGGCCCCCCACATCGATTACACCCAGCTTGGCCGGGCTGCTGCCACCCTCATGTCAG AGAGGGTGTTCCGCATAGATGCCTACATGGCTCAGAGCCGAGGGGAGCTGCTGCACTCCC TAGAGGGCTTCCTGGACTGCAGCCTAGTGCTGCCTCCCACCGATGCCCCCTCCGAGCAGG CACTGCTCAGTCTGGTGCCTGTGCAGAGGGAGCTACTTCGAAGGCGCTATCAGTCCAGCC CTGCCAAGCCAGACTCCAGCTTCTACAAGGGCCTAGACTTAAATGGGGGCCCAGATGACC CTCTGCAGCAGACAGGCCAGCTCTTCGGGGGCCTGGTGCGTGATATCCGGCGCCGCTACC CCTATTACCTGAGTGACATCACAGATGCATTCAGCCCCCAGGTCCTGGCTGCCGTCATCT TCATCTACTTTGCTGCACTGTCACCCGCCATCACCTTCGGCGGCCTCCTGGGAGAAAAGA CCCGGAACCAGATGGGAGTGTCGGAGCTGCTGATCTCCACTGCAGTGCAGGGCATTCTCT TCGCCCTGCTGGGGGCTCAGCCCCTGCTTGTGGTCGGCTTCTCAGGACCCCTGCTGGTGT TTGAGGAAGCCTTCTTCTCGTTCTGCGAGACCAACGGTCTAGAGTACATCGTGGGCCGCG TGTGGATCGGCTTCTGGCTCATCCTGCTGGTGGTGTTGGTGGTGGCCTTCGAGGGTAGCT TCCTGGTCCGCTTCATCTCCCGCTATACCCAGGAGATCTTCTCCTTCCTCATTTCCCTCA TCTTCATCTATGAGACTTTCTCCAAGCTGATCAAGATCTTCCAGGACCACCCACTACAGA AGACTTATAACTACAACGTGTTGATGGTGCCCAAACCTCAGGGCCCCCTGCCCAACACAG CCCTCCTCTCCCTTGTGCTCATGGCCGGTACCTTCTTCTTTGCCATGATGCTGCGCAAGT TCAAGAACAGCTCCTATTTCCCTGGCAAGCTGCGTCGGGTCATCGGGGACTTCGGGGTCC CCATCTCCATCCTGATCATGGTCCTGGTGGATTTCTTCATTCAGGATACCTACACCCAGA AACTCTCGGTGCCTGATGGCTTCAAGGTGTCCAACTCCTCAGCCCGGGGCTGGGTCATCC ACCCACTGGGCTTGCGTTCCGAGTTTCCCATCTGGATGATGTTTGCCTCCGCCCTGCCTG CTCTGCTGGTCTTCATCCTCATATTCCTGGAGTCTCAGATCACCACGCTGATTGTCAGCA AACCTGAGCGCAAGATGGTCAAGGGCTCCGGCTTCCACCTGGACCTGCTGCTGGTAGTAG GCATGGGTGGGGTGGCCGCCCTCTTTGGGATGCCCTGGCTCAGTGCCACCACCGTGCGTT CCGTCACCCATGCCAACGCCCTCACTGTCATGGGCAAAGCCAGCACCCCAGGGGCTGCAG CCCAGATCCAGGAGGTCAAAGAGCAGCGGATCAGTGGACTCCTGGTCGCTGTGCTTGTGG GCCTGTCCATCCTCATGGAGCCCATCCTGTCCCGCATCCCCCTGGCTGTACTGTTTGGCA TCTTCCTCTACATGGGGGTCACGTCGCTCAGCGGCATCCAGCTCTTTGACCGCATCTTGC TTCTGTTCAAGCCACCCAAGTATCACCCAGATGTGCCCTACGTCAAGCGGGTGAAGACCT GGCGCATGCACTTATTCACGGGCATCCAGATCATCTGCCTGGCAGTGCTGTGGGTGGTGA AGTCCACGCCGGCCTCCCTGGCCCTGCCCTTCGTCCTCATCCTCACTGTGCCGCTGCGGC GCGTCCTGCTGCCGCTCATCTTCAGGAACGTGGAGCTTCAGTGTCTGGATGCTGATGATG CCAAGGCAACCTTTGATGAGGAGGAAGGTCGGGATGAATACGACGAAGTGGCCATGCCTG TGTGAGGGGCGGGCCCAGGCCCTAGACCCTCCCCCACCATTCCACATCCCCACCTTCCAA GGAAAAGCAGAAGTTCATGGGCACCTCATGGACTCCAGGATCCTCCTGGAGCAGCAGCTG AGGCCCCAGGGCTGTGGGTGGGGAAGGAAGGCGTGTCCAGGAGACCTTCCACAAAGGGTA GCCTGGCTTTTCTGGCTGGGGATGGCCGATGGGGCCCACATTAGGGGGTTTGTTGCACAG TCCCTCCTGTTGCCACACTTTCACTGGGGATCCCGTGCTGGAAGACTTAGATCTGAGCCC TCCCTCTTCCCAGCACAGGCAGGGGTAGAAGCAAAGGCAGGAGGTGGGTGAGCGGGTGGG GTGCTTGCTGTGTGACCTTGGGCAAGTCCCTTGACCTTTCCAGCCTATATTTCCTCTTCT GTAAAATGGGTATATTGATGATAATACCCACATTACAGGATGGTTACTGAGGACCAAAGA TACATGTAAAATAGGGCTTTGTAAACTCCACAGGGACTGTTCTATAGCAGTCATCATTTG TCTTTGAACGTACCCAAGGTCACATAGCTGGGATTTGAACTGAGCCGTGCAGCTGGGATT TGAACCAGGCCTTCTGATTTCAAGGTCCGAGCTCTGTCCTCTGTCAGTCATGCGTCCACT TTCCCTTCCCCTGTGACTCCTCCCTTCCCCACTCTGCTCCCAGCCCCTACCTTGAGACCC TCTTCTCTGGGCCCAGAGAGAGGCGTCCTGGTGAGGACAAGGTACAGGCAAGGATGATCC AGGGATTGGGCCTGGGACTCAGGCCTCCTAAGTGTTTGGTTCCTCCCTCCAAACACTCAT TAGTTCACTCATTCATTCATTCCACAAACATTTACTGAGGGCCCCGGAATCAGTGGACTC CGAGGGGACTGAGACAAGCCCTGCCCTGGGGTGGGGGTGGGGGGCAAGGTACAGTTGATT CTACATTTGGATAGGGAGTGGGGGAGGGTGGGAAGGTAGGGGCGGGAGAGTGAGGGGGTT TGTAATTTATTAATTGCGTATTTTCTAAGAGTTTTCAACATAGTTTGGCTTCACACACAA CTTCAGGCCCCTCATTTGAGAGCCATTATCCTCAACTCCATCTAAACTGAATCTTGGGGA GAACCCAGATCTGACCAATTGGGGTAGGAGACAGCAGGCTCTCCAAGAACATGGGCAAAT TTATTTTTTTATAAAACAAAAAGATAAAAAGAGTTGAAAGACGTGAAAGTGGTGAGAGAT GGAGGAAACAGAATCAGGAAGTGGTAGAAAAGAGAGGAGGTGGCTGGGCGCAGTGGCTCA CGTTTGTAATCCCAGCACTTTGGGAGGCCAAGTTGGGCGGATCATTTGAGGTCAGGAGTT TGAGACCAGCCTGGCCAACATGGTGAAACCCCGTTACTACTAAAAATACAAAAATTAGCT GGGTGTCTCGTGGCAGGCACCTGTAATCCCAGCTACTTAGAAGGCTGAGGCAAGAGAATC ACCTGAACCCAGGAGGTGGAGGTTGCAGTGAGCCAAGATTGCACCACTGCACTCCAGCCT GGGCAACAGAGCGAGACCCTGTCTCAAAAAAAAAAAAAAAAAAAAAAAAAAAACGGAAGG AAACATCAGCCTTGGGGGCCACAGACTCAACATGTGTGTGTGGTGGGGTTCCAGCCCAAC ATAGAGTAACATTATTTGTACCTCCCAGGCTAGCTCAGTCCATGGGAGGCTCTCCTGTCC CTGAAAGCTGACACCCACCTTTCACCACTTCGCCCATGCTACAGTTCAGTTTCCTCGTCT GTAAAATGGGGATGATAATGGTACCTACCTTGCAGTGTTGTTATAAGGATTAAAGGAGAC AGTGCAAGAAAAGGCCTTGGTTGGTGAAGAGCCCAACCTCGGAGGGGAGCTGCTGGGATC CTCCTTATCTTGACTGGGATGTCCCTGTCTCCCCCTCCCCTTGCTCCTTGAACATGGCCA AGGAAAGTGAAAAACAAAAATTATTCACTCTGCTAGCACCCTTCCCCTTGATGCCTGGGA ATAGGTTTTGCCAATAAACGTATCTGTGTTGGA GlycophorinA(MNSbloodgroup)(GYPA) SEQIDNO:3 AAAATGCCTCCCCTGCCTATCAGCTGATGATGGCCGCAGGAAGGTGGGCCTGGAAGATAA CAGCTAGCAGGCTAAGGTCAGACACTGACACTTGCAGTTGTCTTTGGTAGTTTTTTTGCA CTAACTTCAGGAACCAGCTCATGATCTCAGGATGTATGGAAAAATAATCTTTGTATTACT ATTGTCAGAAATTGTGAGCATATCAGCATTAAGTACCACTGAGGTGGCAATGCACACTTC AACTTCTTCTTCAGTCACAAAGAGTTACATCTCATCACAGACAAATGATACGCACAAACG GGACACATATGCAGCCACTCCTAGAGCTCATGAAGTTTCAGAAATTTCTGTTAGAACTGT TTACCCTCCAGAAGAGGAAACCGAGATAACACTCATTATTTTTGGGGTGATGGCTGGTGT TATTGGAACGATCCTCTTAATTTCTTACGGTATTCGCCGACTGATAAAGAAAAGCCCATC TGATGTAAAACCTCTCCCCTCACCTGACACAGACGTGCCTTTAAGTTCTGTTGAAATAGA AAATCCAGAGACAAGTGATCAATGAGAATCTGTTCACCAAACCAAATGTGGAAAGAACAC AAAGAAGACATAAGACTTCAGTCAAGTGAAAAATTAACATGTGGACTGGACACTCCAATA AATTATATACCTGCCTAAGTTGTACAATTTCAGAATGCAATTTTCATTATAATGAGTTCC AGTGACTCAATGATGGGGAAAAAAATCTCTGCTCATTAATATTTCAAGATAAAGAACAAA TGTTTCCTTGAATGCTTGCTTTTGTGTGTTAGCATAATTTTTAGAATTGTTTGAGAATTC TGATCCAAAACTTTAGTTGAATTCATCTACGTTTGTTTAATATTAACTTAACCTATTCTA TTGTATTATAATGATGATTCTGTCAAATGAAAGGCTTGAAATACCTAGATGAAGTTTAGA TTTTCTTCCTATTGTAAACTTTTGAGTCTGGTTTCATTGTTTTAAATAAATTAAGGGGAC ACTAAAGTCCTATCATTCATTTCCTTCATTGCTGAACAGGCAAGATATAATATTACATGA ATGATTACTATATTTTGTTCACACTAATAAAGCTTATGCTCAGAAATGCCATACACACAC ACACACACACACACAAACACACACATTTATCATTTAATGCATAAATCAACACAAAAGGTT TTCCCATTAATATGAAATATTACATATATATAAGTGCCATATTTAAAATAATTTGTCTAA CAGTAGAACTGTGTCGGAGCACTCACTGAAGCTTGCATTCCACTGAAAGAGTTATTTGTG TAAGTAGAGTATCCGGAGAAGGAAAAGAACTTACGACCTTTCTTTATAACAGAAACTCAA CTCTAAATTCAACAAGATGTGCAAACCGGACATGCAGGTGAATATTTTAATAGGTTACTA TAAGGTTCTCAATTAAATTCTTTAATCTGTCCAGTCCCAGTTTCTCTTATTAATAAAACT TTGGAAATTGCTTTAAACCATTTAAAGGAAATTTCTAGATATAGAAACTAAGGACTGTGA CTATACAGCTGTCACTCATTTGTAGTAAAACTTAAAAAGCAAAAACAAAAAACAAAAAAG ACCTTCCTGTGATACTTTATTTCCGAACTAATAAAAATCTATATGACTTTTTATTATTGT GTGATAACCAAGTAAATGTTTTCTATTTTGCATATTTTCAGGCATGGTAACAGAAATTTA CCTTTTAATAAATTAAAAAATCTAAATTTTAACCTACTTGTATGTTCGGAGAGTGTTTTT GTACTATATTGACTACTTAAAATAGAGAATGAGACTAAGAAGGGAACATTTCTGTTGATA CATGTTTTTTAAAAGTAATTTTAAGAGCATTATTAGGTTAATTAATCCAATTAATGACCC AAATGCCAAGGTAATTTTAAATTTACATTTTTAATAAAAGCAACATGTTGAAACAAGAGA GGGTGAGATTAACCTTTTTGCTAAAGTAATTTACAAGTCAAAGACAGGAAGAGATCAGAG TGAATGTGCCTTCTTAACCAGAGCTACAGAATTTAGTGAATAATTAAAGTACAAACTGCT TTGACCTCCTTGAACTTTTCCAAGCAATTTCTCTGTACTTCTATATATGAATGTCTTAGC CAATTTTCTGCTACTATAACAGAATACGACAGACTGGGTAATTTAAAAAGAAAAGAAATT TATTTTCTTCCTAGTTCTGGAGGCTGGGAAGGCGAAGGGCATGGCACTGACATCTGCCTT GTAACTGATGAGAACCTTCTTACTGCATGATAACAAAGCAGCAAGGCAAGCAAAAGCGTA AGATGAAGAGAGAGGAAATGAAGCCAAACACATCCTTTCATCAGAAGCCCATTCCCTCTA TAAGGCGTTATTACATTTATGAGAATGGAGTCCTCATGACCTAATCGTGACCTTAAAGGC CCCTCCCAACACTGTTACAATGGCAATTAAATTTCAACAAAGGTTCCAGAGGTGACATTC GAATCAGCAATGAAATTTTCATAGTTAAATTTGGTATTCGTGGGGGAAGAAATGACCATT TCCCTTGTATTTTTATAATTAAATCAGCAAAATATTGTAATAAAGAAATCTTTCCTGTGA AGATACCATGACCCCAAAAAAAAAAA Folliculardendriticcellsecretedprotein(FDCSP) SEQIDNO:4 CTCCATTCCATTATACCTTTGAGTATATAAAACAGCTACAATATTCCAGGGCCAGTCACT TGCCATTTCTCATAACAGCGTCAGAGAGAAAGAACTGACTGAAACGTTTGAGATGAAGAA AGTTCTCCTCCTGATCACAGCCATCTTGGCAGTGGCTGTTGGTTTCCCAGTCTCTCAAGA CCAGGAACGAGAAAAAAGAAGTATCAGTGACAGCGATGAATTAGCTTCAGGGTTTTTTGT GTTCCCTTACCCATATCCATTTCGCCCACTTCCACCAATTCCATTTCCAAGATTTCCATG GTTTAGACGTAATTTTCCTATTCCAATACCTGAATCTGCCCCTACAACTCCCCTTCCTAG CGAAAAGTAAACAAGAAGGAAAAGTCACGATAAACCTGGTCACCTGAAATTGAAATTGAG CCACTTCCTTGAAGAATCAAAATTCCTGTTAATAAAAGAAAAACAAATGTAATTGAAATA GCACACAGCATTCTCTAGTCAATATCTTTAGTGATCTTCTTTAATAAACTTGAAAGCAAA GATTTTGGTTTCTTAATTTCCACAAAAAAAAAA Histatin3(HTN3) SEQIDNO:5 GGGAGATTTCAACGTGTTTAAATACATCAGCCATCTAGGAAAGGACATCTCTTGAGACTT CACTTCAGCTTCACTGACTTCTGGATTCTCCTCTTGAGTAAAAGGACTCAGCCAACTATG AAGTTTTTTGTTTTTGCTTTAATCTTGGCTCTCATGCTTTCCATGACTGGAGCTGATTCA CATGCAAAGAGACATCATGGGTATAAAAGAAAATTCCATGAAAAGCATCATTCACATCGA GGCTATAGATCAAATTATCTGTATGACAATTGATATCTTCAGTAATCACGGGGCATGATT ATGGAGGTTTGACTGGCAAATTCGCTTTGGACTCGTGTATTCTCATTTGTCATACCGCAT CACACTACCACTGCTTTTTGAAGAATTATCATAAGGCAATGCAGAATAAAAGAAATACCA TGATTTAGTGAATTCTGTGTTTCAGGATACTTCCCTTCCTAATTATCATTTGATTAGATA CTTGCAATTTAAATGTTAAGCTGTTTTCACTGCTGTTTCTGAGTAATAGAAATTCATTCC TCTCCAAAAGCAATAAAATTCAAGCACATTATTATGTGAAAAAAAAAAAAAAAAAAAAAAA (polynucleotide,statherin(STATH) SEQIDNO:6 GAGTGTTTAAATACATTGGCCCTCTAGGGTAGCACATCATCTCTTGAAGCTTCACTTCAA CTTCACTACTTCTGTAGTCTCATCTTGAGTAAAAGAGAACCCAGCCAACTATGAAGTTCC TTGTCTTTGCCTTCATCTTGGCTCTCATGGTTTCCATGATTGGAGCTGATTCATCTGAAG AGTATGGGTATGGCCCTTATCAGCCAGTTCCAGAACAACCACTATACCCACAACCATACC AACCACAATACCAACAATATACCTTTTAATATCATCAGTAACTGCAGGACATGATTATTG AGGCTTGATTGGCAAATACGACTTCTACATCCATATTCTCATCTTTCATACCATATCACA CTACTACCACTTTTTGAAGAATCATCAAAGAGCAATGCAAATGAAAAACACTATAATTTA CTGTATACTCTTTGTTTCAGGATACTTGCCTTTTCAATTGTCACTTGATGATATAATTGC AATTTAAACTGTTAAGCTGTGTTCAGTACTGTTTCTGAATAATAGAAATCACTTCTCTAA AAGCAATAAATTTCAAGCACATTTTTACATAAAAAAAA Protamine1(PRM1) SEQIDNO:7 GACTCACAGCCCACAGAGTTCCACCTGCTCACAGGTTGGCTGGCTCAGCCAAGGTGGTGC CCTGCTCTGAGCATTCAGGCCAAGCCCATCCTGCACCATGGCCAGGTACAGATGCTGTCG CAGCCAGAGCCGGAGCAGATATTACCGCCAGAGACAAAGAAGTCGCAGACGAAGGAGGCG GAGCTGCCAGACACGGAGGAGAGCCATGAGGTGCTGCCGCCCCAGGTACAGACCGCGATG TAGAAGACACTAATTGCACAAAATAGCACATCCACCAAACTCCTGCCTGAGAATGTTACC AGACTTCAAGATCCTCTTGCCACATCTTGAAAATGCCACCATCCAATAAAAATCAGGAGC CTGCTAAGGAACAATGCCGCCTGTCAATAAATGTTGAAAAGTCATCCCAAAAAAAAAAAA AAAAAA Transitionprotein1(TNP1) SEQIDNO:8 GCCCCTCATTTTGGCAGAACTTACCATGTCGACCAGCCGCAAATTAAAGAGTCATGGCAT GAGGAGGAGCAAGAGCCGATCTCCTCACAAGGGAGTCAAGAGAGGTGGCAGCAAAAGAAA ATACCGTAAGGGCAACCTGAAAAGTAGGAAACGGGGCGATGACGCCAATCGCAATTACCG CTCCCACTTGTGAGCCCCCAGCGGGCTCTGCCCTGGTGCGCTTCACACAGCACCAAGCAG CAACAAGAACAGCAGAAGGGGAACTGCCAAGGAGACCTGATGTTAGATCAAAGCCAGAGA GGAGCCTATGGAATGTGGATCAAATGCCAGTTGTGACGAAATGAGGAATGTATATGTTGG CTGTTTTTCCCCAACATCTCAATAAAACTTTGAAAGCAGAAAAAAAAAAAAAAAA Protamine2(PRM2) SEQIDNO:9 AGACCAGACCAACAGTAACACCAAGGGCAGGTGGGCAGGCCTCCGCCCTCCTCCCCTACT CCAGGGCCCACTGCAGCCTCAGCCCAGGAGCCACCAGATCTCCCAACACCATGGTCCGAT ACCGCGTGAGGAGCCTGAGCGAACGCTCGCACGAGGTGTACAGGCAGCAGTTGCATGGGC AAGAGCAAGGACACCACGGCCAAGAGGAGCAAGGGCTGAGCCCGGAGCACGTCGAGGTCT ACGAGAGGACCCATGGCCAGTCTCACTATAGGCGCAGACACTGCTCTCGAAGGAGGCTGC ACCGGATCCACAGGCGGCAGCATCGCTCCTGCAGAAGGCGCAAAAGACGCTCCTGCAGGC ACCGGAGGAGGCATCGCAGAGAGTCCCTAGGTGACCCCCTCAACCAGAACTTTCTTTCCC AAAAGGCTGCAGAACCAGGAAGAGAACATGCAGAAGGCACTAAGCTTCCTGGGCCCCTCA CCCCCAGCTGGAAATTAAGAAAAAGTCGCCCGAAACACCAAGTGAGGCCATAGCAATTCC CCTACATCAAATGCTCAAGCCCCCAGCTGGAAGTTAAGAGAAAGTCACCTGCCCAAGAAA CACCGAGTGAGGCCATAGCAACTCCCCTACATCAAATGCTCAAGCCCTGAGTTGCCGCCG AGAAGCCCACAAGATCTGAGTGAAATGAGCAAAAGTCACCTGCCCAATAAAGCTTGACAA GACACTC Kallikreinrelatedpeptidase2(KLK2) SEQIDNO:10 AGCCCCAAACTCACCACCTGGCCGTGGACACCTGTGTCAGCATGTGGGACCTGGTTCTCT CCATCGCCTTGTCTGTGGGGTGCACTGGTGCCGTGCCCCTCATCCAGTCTCGGATTGTGG GAGGCTGGGAGTGTGAGAAGCATTCCCAACCCTGGCAGGTGGCTGTGTACAGTCATGGAT GGGCACACTGTGGGGGTGTCCTGGTGCACCCCCAGTGGGTGCTCACAGCTGCCCATTGCC TAAAGAAGAATAGCCAGGTCTGGCTGGGTCGGCACAACCTGTTTGAGCCTGAAGACACAG GCCAGAGGGTCCCTGTCAGCCACAGCTTCCCACACCCGCTCTACAATATGAGCCTTCTGA AGCATCAAAGCCTTAGACCAGATGAAGACTCCAGCCATGACCTCATGCTGCTCCGCCTGT CAGAGCCTGCCAAGATCACAGATGTTGTGAAGGTCCTGGGCCTGCCCACCCAGGAGCCAG CACTGGGGACCACCTGCTACGCCTCAGGCTGGGGCAGCATCGAACCAGAGGAGTTCTTGC GCCCCAGGAGTCTTCAGTGTGTGAGCCTCCATCTCCTGTCCAATGACATGTGTGCTAGAG CTTACTCTGAGAAGGTGACAGAGTTCATGTTGTGTGCTGGGCTCTGGACAGGTGGTAAAG ACACTTGTGGGGTGAGTCATCCCTACTCCCAACATCTGGAGGGGAAAGGGTGATTCTGGG GGTCCACTTGTCTGTAATGGTGTGCTTCAAGGTATCACATCATGGGGCCCTGAGCCATGT GCCCTGCCTGAAAAGCCTGCTGTGTACACCAAGGTGGTGCATTACCGGAAGTGGATCAAG GACACCATCGCAGCCAACCCCTGAGTGCCCCTGTCCCACCCCTACCTCTAGTAAATTTAA GTCCACCTCACGTTCTGGCATCACTTGGCCTTTCTGGATGCTGGACACCTGAAGCTTGGA ACTCACCTGGCCGAAGCTCGAGCCTCCTGAGTCCTACTGACCTGTGCTTTCTGGTGTGGA GTCCAGGGCTGCTAGGAAAAGGAATGGGCAGACACAGGTGTATGCCAATGTTTCTGAAAT GGGTATAATTTCGTCCTCTCCTTCGGAACACTGGCTGTCTCTGAAGACTTCTCGCTCAGT TTCAGTGAGGACACACACAAAGACGTGGGTGACCATGTTGTTTGTGGGGTGCAGAGATGG GAGGGGTGGGGCCCACCCTGGAAGAGTGGACAGTGACACAAGGTGGACACTCTCTACAGA TCACTGAGGATAAGCTGGAGCCACAATGCATGAGGCACACACACAGCAAGGATGACGCTG TAAACATAGCCCACGCTGTCCTGGGGGCACTGGGAAGCCTAGATAAGGCCGTGAGCAGAA AGAAGGGGAGGATCCTCCTATGTTGTTGAAGGAGGGACTAGGGGGAGAAACTGAAAGCTG ATTAATTACAGGAGGTTTGTTCAGGTCCCCCAAACCACCGTCAGATTTGATGATTTCCTA GCAGGACTTACAGAAATAAAGAGCTATCATGCTGTGGTTTATTATGGTTTGTTACATTGA TAGGATACATACTGAAATCAGCAAACAAAACAGATGTATAGATTAGAGTGTGGAGAAAAC AGAGGAAAACTTGCAGTTACGAAGACTGGCAACTTGGCTTTACTAAGTTTTCAGACTGGC AGGAAGTCAAACCTATTAGGCTGAGGACCTTGTGGAGTGTAGCTGATCCAGCTGATAGAG GAACTAGCCAGGTGGGGGCCTTTCCCTTTGGATGGGGGGCATATCTGACAGTTATTCTCT CCAAGTGGAGACTTACGGACAGCATATAATTCTCCCTGCAAGGATGTATGATAATATGTA CAAAGTAATTCCAACTGAGGAAGCTCACCTGATCCTTAGTGTCCAGGGTTTTTACTGGGG GTCTGTAGGACGAGTATGGAGTACTTGAATAATTGACCTGAAGTCCTCAGACCTGAGGTT CCCTAGAGTTCAAACAGATACAGCATGGTCCAGAGTCCCAGATGTACAAAAACAGGGATT CATCACAAATCCCATCTTTAGCATGAAGGGTCTGGCATGGCCCAAGGCCCCAAGTATATC AAGGCACTTGGGCAGAACATGCCAAGGAATCAAATGTCATCTCCCAGGAGTTATTCAAGG GTGAGCCCTTTACTTGGGATGTACAGGCTTTGAGCAGTGCAGGGCTGCTGAGTCAACCTT TTATTGTACAGGGGATGAGGGAAAGGGAGAGGATGAGGAAGCCCCCCTGGGGATTTGGTT TGGTCTTGTGATCAGGTGGTCTATGGGGCTATCCCTACAAAGAAGAATCCAGAAATAGGG GCACATTGAGGAATGATACTGAGCCCAAAGAGCATTCAATCATTGTTTTATTTGCCTTCT TTTCACACCATTGGTGAGGGAGGGATTACCACCCTGGGGTTATGAAGATGGTTGAACACC CCACACATAGCACCGGAGATATGAGATCAACAGTTTCTTAGCCATAGAGATTCACAGCCC AGAGCAGGAGGACGCTGCACACCATGCAGGATGACATGGGGGATGCGCTCGGGATTGGTG TGAAGAAGCAAGGACTGTTAGAGGCAGGCTTTATAGTAACAAGACGGTGGGGCAAACTCT GATTTCCGTGGGGGAATGTCATGGTCTTGCTTTACTAAGTTTTGAGACTGGCAGGTAGTG AAACTCATTAGGCTGAGAACCTTGTGGAATGCAGCTGACCCAGCTGATAGAGGAAGTAGC CAGGTGGGAGCCTTTCCCAGTGGGTGTGGGACATATCTGGCAAGATTTTGTGGCACTCCT GGTTACAGATACTGGGGCAGCAAATAAAACTGAATCTTGTTTTCAGACCTTAAAAAAAAA AAAAAAAAAAAA Microseminoproteinbeta(MSMB) SEQIDNO:11 GTACCTGTCTATAAGGAGTCCTGCTTATCACAATGAATGTTCTCCTGGGCAGCGTTGTGA TCTTTGCCACCTTCGTGACTTTATGCAATGCATCATGCTATTTCATACCTAATGAGGGAG TTCCAGGAGATTCAACCAGGAAATGCATGGATCTCAAAGGAAACAAACACCCAATAAACT CGGAGTGGCAGACTGACAACTGTGAGACATGCACTTGCTACGAAACAGAAATTTCATGTT GCACCCTTGTTTCTACACCTGTGGGTTATGACAAAGACAACTGCCAAAGAATCTTCAAGA AGGAGGACTGCAAGTATATCGTGGTGGAGAAGAAGGACCCAAAAAAGACCTGTTCTGTCA GTGAATGGATAATCTAATGTGCTTCTAGTAGGCACAGGGCTCCCAGGCCAGGCCTCATTC TCCTCTGGCCTCTAATAGTCAATGATTGTGTAGCCATGCCTATCAGTAAAAAGATTTTTG AGCAAACACTTGAAAAAAAAAAA Transglutaminase4(TGM4) SEQIDNO:12 GGACCGACTGTGTGGAAGCACCAGGCATCAGAGATAGAGTCTTCCCTGGCATTGCAGGAG AGAATCTGAAGGGATGATGGATGCATCAAAAGAGCTGCAAGTTCTCCACATTGACTTCTT GAATCAGGACAACGCCGTTTCTCACCACACATGGGAGTTCCAAACGAGCAGTCCTGTGTT CCGGCGAGGACAGGTGTTTCACCTGCGGCTGGTGCTGAACCAGCCCCTACAATCCTACCA CCAACTGAAACTGGAATTCAGCACAGGGCCGAATCCTAGCATCGCCAAACACACCCTGGT GGTGCTCGACCCGAGGACGCCCTCAGACCACTACAACTGGCAGGCAACCCTTCAAAATGA GTCTGGCAAAGAGGTCACAGTGGCTGTCACCAGTTCCCCCAATGCCATCCTGGGCAAGTA CCAACTAAACGTGAAAACTGGAAACCACATCCTTAAGTCTGAAGAAAACATCCTATACCT TCTCTTCAACCCATGGTGTAAAGAGGACATGGTTTTCATGCCTGATGAGGACGAGCGCAA AGAGTACATCCTCAATGACACGGGCTGCCATTACGTGGGGGCTGCCAGAAGTATCAAATG CAAACCCTGGAACTTTGGTCAGTTTGAGAAAAATGTCCTGGACTGCTGCATTTCCCTGCT GACTGAGAGCTCCCTCAAGCCCACAGATAGGAGGGACCCCGTGCTGGTGTGCAGGGCCAT GTGTGCTATGATGAGCTTTGAGAAAGGCCAGGGCGTGCTCATTGGGAATTGGACTGGGGA CTACGAAGGTGGCACAGCCCCATACAAGTGGACAGGCAGTGCCCCGATCCTGCAGCAGTA CTACAACACGAAGCAGGCTGTGTGCTTTGGCCAGTGCTGGGTGTTTGCTGGGATCCTGAC TACAGTGCTGAGAGCGTTGGGCATCCCAGCACGCAGTGTGACAGGCTTCGATTCAGCTCA CGACACAGAAAGGAACCTCACGGTGGACACCTATGTGAATGAGAATGGCGAGAAAATCAC CAGTATGACCCACGACTCTGTCTGGAATTTCCATGTGTGGACGGATGCCTGGATGAAGCG ACCGGATCTGCCCAAGGGCTACGACGGCTGGCAGGCTGTGGACGCAACGCCGCAGGAGCG AAGCCAGGGTGTCTTCTGCTGTGGGCCATCACCACTGACCGCCATCCGCAAAGGTGACAT CTTTATTGTCTATGACACCAGATTCGTCTTCTCAGAAGTGAATGGTGACAGGCTCATCTG GTTGGTGAAGATGGTGAATGGGCAGGAGGAGTTACACGTAATTTCAATGGAGACCACAAG CATCGGGAAAAACATCAGCACCAAGGCAGTGGGCCAAGACAGGCGGAGAGATATCACCTA TGAGTACAAGTATCCAGAAGGCTCCTCTGAGGAGAGGCAGGTCATGGATCATGCCTTCCT CCTTCTCAGTTCTGAGAGGGAGCACAGACGACCTGTAAAAGAGAACTTTCTTCACATGTC GGTACAATCAGATGATGTGCTGCTGGGAAACTCTGTTAATTTCACCGTGATTCTTAAAAG GAAGACCGCTGCCCTACAGAATGTCAACATCTTGGGCTCCTTTGAACTACAGTTGTACAC TGGCAAGAAGATGGCAAAACTGTGTGACCTCAATAAGACCTCGCAGATCCAAGGTCAAGT ATCAGAAGTGACTCTGACCTTGGACTCCAAGACCTACATCAACAGCCTGGCTATATTAGA TGATGAGCCAGTTATCAGAGGTTTCATCATTGCGGAAATTGTGGAGTCTAAGGAAATCAT GGCCTCTGAAGTATTCACGTCTTTCCAGTACCCTGAGTTCTCTATAGAGTTGCCTAACAC AGGCAGAATTGGCCAGCTACTTGTCTGCAATTGTATCTTCAAGAATACCCTGGCCATCCC TTTGACTGACGTCAAGTTCTCTTTGGAAAGCCTGGGCATCTCCTCACTACAGACCTCTGA CCATGGGACGGTGCAGCCTGGTGAGACCATCCAATCCCAAATAAAATGCACCCCAATAAA AACTGGACCCAAGAAATTTATCGTCAAGTTAAGTTCCAAACAAGTGAAAGAGATTAATGC TCAGAAGATTGTTCTCATCACCAAGTAGCCTTGTCTGATGCTGTGGAGCCTTAGTTGAGA TTTCAGCATTTCCTACCTTGTGCTTAGCTTTCAGATTATGGATGATTAAATTTGATGACT TATATGAGGGCAGATTCAAGAGCCAGCAGGTCAAAAAGGCCAACACAACCATAAGCAGCC AGACCCACAAGGCCAGGTCCTGTGCTATCACAGGGTCACCTCTTTTACAGTTAGAAACAC CAGCCGAGGCCACAGAATCCCATCCCTTTCCTGAGTCATGGCCTCAAAAATCAGGGCCAC CATTGTCTCAATTCAAATCCATAGATTTCGAAGCCACAGAGTCTCTCCCTGGAGCAGCAG ACTATGGGCAGCCCAGTGCTGCCACCTGCTGACGACCCTTGAGAAGCTGCCATATCTTCA GGCCATGGGTTCACCAGCCCTGAAGGCACCTGTCAACTGGAGTGCTCTCTCAGCACTGGG ATGGGCCTGATAGAAGTGCATTCTCCTCCTATTGCCTCCATTCTCCTCTCTCTATCCCTG AAATCCAGGAAGTCCCTCTCCTGGTGCTCCAAGCAGTTTGAAGCCCAATCTGCAAGGACA TTTCTCAAGGGCCATGTGGTTTTGCAGACAACCCTGTCCTCAGGCCTGAACTCACCATAG AGACCCATGTCAGCAAACGGTGACCAGCAAATCCTCTTCCCTTATTCTAAAGCTGCCCCT TGGGAGACTCCAGGGAGAAGGCATTGCTTCCTCCCTGGTGTGAACTCTTTCTTTGGTATT CCATCCACTATCCTGGCAACTCAAGGCTGCTTCTGTTAACTGAAGCCTGCTCCTTCTTGT TCTGCCCTCCAGAGATTTGCTCAAATGATCAATAAGCTTTAAATTAAACTCTACTTCAAA AAAAAAAAAAAAAAAAAAAAAAAAAAA Matrixmetallopeptidase10(stromelysin2)(MMP10) SEQIDNO:13 AGAAGCCCAGTAGACAAAGAAGGTAAGGGCAGTGAGAATGATGCATCTTGCATTCCTTGT GCTGTTGTGTCTGCCAGTCTGCTCTGCCTATCCTCTGAGTGGGGCAGCAAAAGAGGAGGA CTCCAACAAGGATCTTGCCCAGCAATACCTAGAAAAGTACTACAACCTCGAAAAGGATGT GAAACAGTTTAGAAGAAAGGACAGTAATCTCATTGTTAAAAAAATCCAAGGAATGCAGAA GTTCCTTGGGTTGGAGGTGACAGGGAAGCTAGACACTGACACTCTGGAGGTGATGCGCAA GCCCAGGTGTGGAGTTCCTGACGTTGGTCACTTCAGCTCCTTTCCTGGCATGCCGAAGTG GAGGAAAACCCACCTTACATACAGGATTGTGAATTATACACCAGATTTGCCAAGAGATGC TGTTGATTCTGCCATTGAGAAAGCTCTGAAAGTCTGGGAAGAGGTGACTCCACTCACATT CTCCAGGCTGTATGAAGGAGAGGCTGATATAATGATCTCTTTTGCAGTTAAAGAACATGG AGACTTTTACTCTTTTGATGGCCCAGGACACAGTTTGGCTCATGCCTACCCACCTGGACC TGGGCTTTATGGAGATATTCACTTTGATGATGATGAAAAATGGACAGAAGATGCATCAGG CACCAATTTATTCCTCGTTGCTGCTCATGAACTTGGCCACTCCCTGGGGCTCTTTCACTC AGCCAACACTGAAGCTTTGATGTACCCACTCTACAACTCATTCACAGAGCTCGCCCAGTT CCGCCTTTCGCAAGATGATGTGAATGGCATTCAGTCTCTCTACGGACCTCCCCCTGCCTC TACTGAGGAACCCCTGGTGCCCACAAAATCTGTTCCTTCGGGATCTGAGATGCCAGCCAA GTGTGATCCTGCTTTGTCCTTCGATGCCATCAGCACTCTGAGGGGAGAATATCTGTTCTT TAAAGACAGATATTTTTGGCGAAGATCCCACTGGAACCCTGAACCTGAATTTCATTTGAT TTCTGCATTTTGGCCCTCTCTTCCATCATATTTGGATGCTGCATATGAAGTTAACAGCAG GGACACCGTTTTTATTTTTAAAGGAAATGAGTTCTGGGCCATCAGAGGAAATGAGGTACA AGCAGGTTATCCAAGAGGCATCCATACCCTGGGTTTTCCTCCAACCATAAGGAAAATTGA TGCAGCTGTTTCTGACAAGGAAAAGAAGAAAACATACTTCTTTGCAGCGGACAAATACTG GAGATTTGATGAAAATAGCCAGTCCATGGAGCAAGGCTTCCCTAGACTAATAGCTGATGA CTTTCCAGGAGTTGAGCCTAAGGTTGATGCTGTATTACAGGCATTTGGATTTTTCTACTT CTTCAGTGGATCATCACAGTTTGAGTTTGACCCCAATGCCAGGATGGTGACACACATATT AAAGAGTAACAGCTGGTTACATTGCTAGGCGAGATAGGGGGAAGACAGATATGGGTGTTT TTAATAAATCTAATAATTATTCATCTAATGTATTATGAGCCAAAATGGTTAATTTTTCCT GCATGTTCTGTGACTGAAGAAGATGAGCCTTGCAGATATCTGCATGTGTCATGAAGAATG TTTCTGGAATTCTTCACTTGCTTTTGAATTGCACTGAACAGAATTAAGAAATACTCATGT GCAATAGGTGAGAGAATGTATTTTCATAGATGTGTTATTACTTCCTCAATAAAAAGTTTT ATTTTGGGCCTGTTCCTTAAAAAAAAAAAAAAAAAAA Stanniocalcin1(STC1) SEQIDNO:14 CAGTTTGCAAAAGCCAGAGGTGCAAGAAGCAGCGACTGCAGCAGCAGCAGCAGCAGCGGC GGTGGCAGCAGCAGCAGCAGCGGCGGCAGCAGCAGCAGCAGCGGAGGCACCGGTGGCAGC AGCAGCATCACCAGCAACAACAACAAAAAAAAATCCTCATCAAATCCTCACCTAAGCTTT CAGTGTATCCAGATCCACATCTTCACTCAAGCCAGGAGAGGGAAAGAGGAAAGGGGGGCA GGAAAAAAAAAAAACCCAACAACTTAGCGGAAACTTCTCAGAGAATGCTCCAAAACTCAG CAGTGCTTCTGGTGCTGGTGATCAGTGCTTCTGCAACCCATGAGGCGGAGCAGAATGACT CTGTGAGCCCCAGGAAATCCCGAGTGGCGGCTCAAAACTCAGCTGAAGTGGTTCGTTGCC TCAACAGTGCTCTACAGGTCGGCTGCGGGGCTTTTGCATGCCTGGAAAACTCCACCTGTG ACACAGATGGGATGTATGACATCTGTAAATCCTTCTTGTACAGCGCTGCTAAATTTGACA CTCAGGGAAAAGCATTCGTCAAAGAGAGCTTAAAATGCATCGCCAACGGGGTCACCTCCA AGGTCTTCCTCGCCATTCGGAGGTGCTCCACTTTCCAAAGGATGATTGCTGAGGTGCAGG AAGAGTGCTACAGCAAGCTGAATGTGTGCAGCATCGCCAAGCGGAACCCTGAAGCCATCA CTGAGGTCGTCCAGCTGCCCAATCACTTCTCCAACAGATACTATAACAGACTTGTCCGAA GCCTGCTGGAATGTGATGAAGACACAGTCAGCACAATCAGAGACAGCCTGATGGAGAAAA TTGGGCCTAACATGGCCAGCCTCTTCCACATCCTGCAGACAGACCACTGTGCCCAAACAC ACCCACGAGCTGACTTCAACAGGAGACGCACCAATGAGCCGCAGAAGCTGAAAGTCCTCC TCAGGAACCTCCGAGGTGAGGAGGACTCTCCCTCCCACATCAAACGCACATCCCATGAGA GTGCATAACCAGGGAGAGGTTATTCACAACCTCACCAAACTAGTATCATTTTAGGGGTGT TGACACACCAGTTTTGAGTGTACTGTGCCTGGTTTGATTTTTTTAAAGTAGTTCCTATTT TCTATCCCCCTTAAAGAAAATTGCATGAAACTAGGCTTCTGTAATCAATATCCCAACATT CTGCAATGGCAGCATTCCCACCAACAAAATCCATGTGACCATTCTGCCTCTCCTCAGGAG AAAGTACCCTCTTTTACCAACTTCCTCTGCCATGTTTTTCCCCTGCTCCCCTGAGACCAC CCCCAAACACAAAACATTCATGTAACTCTCCAGCCATTGTAATTTGAAGATGTGGATCCC TTTAGAACGGTTGCCCCAGTAGAGTTAGCTGATAAGGAAACTTTATTTAAATGCATGTCT TAAATGCTCATAAAGATGTTAAATGGAATTCGTGTTATGAATCTGTGCTGGCCATGGACG AATATGAATGTCACATTTGAATTCTTGATCTCTAATGAGCTAGTGTCTTATGGTCTTGAT CCTCCAATGTCTAATTTTCTTTCCGACACATTTACCAAATTGCTTGAGCCTGGCTGTCCA ACCAGACTTTGAGCCTGCATCTTCTTGCATCTAATGAAAAACAAAAAGCTAACATCTTTA CGTACTGTAACTGCTCAGAGCTTTAAAAGTATCTTTAACAATTGTCTTAAAACCAGAGAA TCTTAAGGTCTAACTGTGGAATATAAATAGCTGAAAACTAATGTACTGTACATAAATTCC AGAGGACTCTGCTTAAACAAAGCAGTATATAATAACTTTATTGCATATAGATTTAGTTTT GTAACTTAGCTTTATTTTTCTTTTCCTGGGAATGGAATAACTATCTCACTTCCAGATATC CACATAAATGCTCCTTGTGGCCTTTTTTATAACTAAGGGGGTAGAAGTAGTTTTAATTCA ACATCAAAACTTAAGATGGGCCTGTATGAGACAGGAAAAACCAACAGGTTTATCTGAAGG ACCCCAGGTAAGATGTTAATCTCCCAGCCCACCTCAACCCAGAGGCTACTCTTGACTTAG ACCTATACTGAAAGATCTCTGTCACATCCAACTGGAAATTCCAGGAACCAAAAAGAGCAT CCCTATGGGCTTGGACCACTTACAGTGTGATAAGGCCTACTATACATTAGGAAGTGGCAG TTCTTTACTCGTCCCCTTTCATCGGTGCCTGGTACTCTGGCAAATGATGATGGGGTGGGA GACTTTCCATTAAATCAATCAGGAATGAGTCAATCAGCCTTTAGGTCTTTAGTCCGGGGG ACTTGGGGCTGAGAGAGTATAAATAACCCTGGGCTGTCCAGCCTTAATAGACTTCTCTTA CATTTTCGTCCTGTAGCACGCTGCCTGCCAAAGTAGTCCTGGCAGCTGGACCATCTCTGT AGGATCGTAAAAAAATAGAAAAAAAGAAAAAAAAAAGAAAGAAAGAGGGAAAAAGAGCTG GTGGTTTGATCATTTCTGCCATGATGTTTACAAGATGGCGACCACCAAAGTCAAACGACT AACCTATCTATGAACAACAGTAGTTTCTCAGGGTCACTGTCCTTGAACCCAACAGTCCCT TATGAGCGTCACTGCCCACCAAAGGTCAATGTCAAGAGAGGAAGAGAGGGAGGAGGGGTA GGACTGCAGGGGCCACTCCAAACTCGCTTAGGTAGAAACTATTGGTGCTTGACTCTCACT AGGCTAAACTCAAGATTTGACCAAATCGAGTGATAGGGATCCTGGTGGGAGGAGAGAGGG CACATCTCCAGAAAAATGAAAAGCAATACAACTTTACCATAAAGCCTTTAAAACCAGTAA CGTGCTGCTCAAGGACCAAGAGCAATTGCAGCAGACCCAGCAGCAGCAGCAGCAGCACAA ACATTGCTGCCTTTGTCCCCACACAGCCTCTAAGCGTGCTGACATCAGATTGTTAAGGGC ATTTTTATACTCAGAACTGTCCCATCCCCAGGTCCCCAAACTTATGGACACTGCCTTAGC CTCTTGGAAATCAGGTAGACCATATTCTAAGTTAGACTCTTCCCCTCCCTCCCACACTTC CCACCCCCAGGCAAGGCTGACTTCTCTGAATCAGAAAAGCTATTAAAGTTTGTGTGTTGT GTCCATTTTGCAAACCCAACTAAGCCAGGACCCCAATGCGACAAGTAGTTCATGAGTATT CCTAGCAAATTTCTCTCTTTCTTCAGTTCAGTAGATTTCCTTTTTTCTTTTCTTTTTTTT TTTTTTTTTTTTTGGCTGTGACCTCTTCAAACCGTGGTACCCCCCCTTTTCTCCCCACGA TGATATCTATATATGTATCTACAATACATATATCTACACATACAGAAAGAAGCAGTTCTC ACAATGTTGCTAGTTTTTTGCTTCTCTTTCCCCCACCCTACTCCCTCCAATTCCCCCTTA AACTTCCAAAGCTTCGTCTTGTGTTTGCTGCAGAGTGATTCGGGGGCTGACCTAGACCAG TTTGCATGATTCTTCTCTTGTGATTTGGTTGCACTTTAGACATTTTTGTGCCATTATATT TGCATTATGTATTTATAATTTAAATGATATTTAGGTTTTTGGCTGAGTACTGGAATAAAC AGTGAGCATATCTGGTATATGTCATTATTTATTGTTAAATTACATTTTTAAGCTCCATGT GCATATAAAGGTTATGAAACATATCATGGTAATGACAGATGCAAGTTATTTTATTTGCTT ATTTTTATAATTAAAGATGCCATAGCATAATATGAAGCCTTTGGTGAATTCCTTCTAAGA TAAAAATAATAATAAAGTGTTACGTTTTATTGGTTTCAAAAAAAAAAAAAAAAAAAA Matrixmetallopeptidase3(MMP3) SEQIDNO:15 AAAGCAAGGATGAGTCAAGCTGCGGGTGATCCAAACAAACACTGTCACTCTTTAAAAGCT GCGCTCCCGAGGTTGGACCTACAAGGAGGCAGGCAAGACAGCAAGGCATAGAGACAACAT AGAGCTAAGTAAAGCCAGTGGAAATGAAGAGTCTTCCAATCCTACTGTTGCTGTGCGTGG CAGTTTGCTCAGCCTATCCATTGGATGGAGCTGCAAGGGGTGAGGACACCAGCATGAACC TTGTTCAGAAATATCTAGAAAACTACTACGACCTCAAAAAAGATGTGAAACAGTTTGTTA GGAGAAAGGACAGTGGTCCTGTTGTTAAAAAAATCCGAGAAATGCAGAAGTTCCTTGGAT TGGAGGTGACGGGGAAGCTGGACTCCGACACTCTGGAGGTGATGCGCAAGCCCAGGTGTG GAGTTCCTGATGTTGGTCACTTCAGAACCTTTCCTGGCATCCCGAAGTGGAGGAAAACCC ACCTTACATACAGGATTGTGAATTATACACCAGATTTGCCAAAAGATGCTGTTGATTCTG CTGTTGAGAAAGCTCTGAAAGTCTGGGAAGAGGTGACTCCACTCACATTCTCCAGGCTGT ATGAAGGAGAGGCTGATATAATGATCTCTTTTGCAGTTAGAGAACATGGAGACTTTTACC CTTTTGATGGACCTGGAAATGTTTTGGCCCATGCCTATGCCCCTGGGCCAGGGATTAATG GAGATGCCCACTTTGATGATGATGAACAATGGACAAAGGATACAACAGGGACCAATTTAT TTCTCGTTGCTGCTCATGAAATTGGCCACTCCCTGGGTCTCTTTCACTCAGCCAACACTG AAGCTTTGATGTACCCACTCTATCACTCACTCACAGACCTGACTCGGTTCCGCCTGTCTC AAGATGATATAAATGGCATTCAGTCCCTCTATGGACCTCCCCCTGACTCCCCTGAGACCC CCCTGGTACCCACGGAACCTGTCCCTCCAGAACCTGGGACGCCAGCCAACTGTGATCCTG CTTTGTCCTTTGATGCTGTCAGCACTCTGAGGGGAGAAATCCTGATCTTTAAAGACAGGC ACTTTTGGCGCAAATCCCTCAGGAAGCTTGAACCTGAATTGCATTTGATCTCTTCATTTT GGCCATCTCTTCCTTCAGGCGTGGATGCCGCATATGAAGTTACTAGCAAGGACCTCGTTT TCATTTTTAAAGGAAATCAATTCTGGGCTATCAGAGGAAATGAGGTACGAGCTGGATACC CAAGAGGCATCCACACCCTAGGTTTCCCTCCAACCGTGAGGAAAATCGATGCAGCCATTT CTGATAAGGAAAAGAACAAAACATATTTCTTTGTAGAGGACAAATACTGGAGATTTGATG AGAAGAGAAATTCCATGGAGCCAGGCTTTCCCAAGCAAATAGCTGAAGACTTTCCAGGGA TTGACTCAAAGATTGATGCTGTTTTTGAAGAATTTGGGTTCTTTTATTTCTTTACTGGAT CTTCACAGTTGGAGTTTGACCCAAATGCAAAGAAAGTGACACACACTTTGAAGAGTAACA GCTGGCTTAATTGTTGAAAGAGATATGTAGAAGGCACAATATGGGCACTTTAAATGAAGC TAATAATTCTTCACCTAAGTCTCTGTGAATTGAAATGTTCGTTTTCTCCTGCCTGTGCTG TGACTCGAGTCACACTCAAGGGAACTTGAGCGTGAATCTGTATCTTGCCGGTCATTTTTA TGTTATTACAGGGCATTCAAATGGGCTGCTGCTTAGCTTGCACCTTGTCACATAGAGTGA TCTTTCCCAAGAGAAGGGGAAGCACTCGTGTGCAACAGACAAGTGACTGTATCTGTGTAG ACTATTTGCTTATTTAATAAAGACGATTTGTCAGTTATTTTATCTT (polynucleotide,matrixmetallopeptidase11(MMP11) SEQIDNO:16 AAGCCCAGCAGCCCCGGGGCGGATGGCTCCGGCCGCCTGGCTCCGCAGCGCGGCCGCGCG CGCCCTCCTGCCCCCGATGCTGCTGCTGCTGCTCCAGCCGCCGCCGCTGCTGGCCCGGGC TCTGCCGCCGGACGCCCACCACCTCCATGCCGAGAGGAGGGGGCCACAGCCCTGGCATGC AGCCCTGCCCAGTAGCCCGGCACCTGCCCCTGCCACGCAGGAAGCCCCCCGGCCTGCCAG CAGCCTCAGGCCTCCCCGCTGTGGCGTGCCCGACCCATCTGATGGGCTGAGTGCCCGCAA CCGACAGAAGAGGTTCGTGCTTTCTGGCGGGCGCTGGGAGAAGACGGACCTCACCTACAG GATCCTTCGGTTCCCATGGCAGTTGGTGCAGGAGCAGGTGCGGCAGACGATGGCAGAGGC CCTAAAGGTATGGAGCGATGTGACGCCACTCACCTTTACTGAGGTGCACGAGGGCCGTGC TGACATCATGATCGACTTCGCCAGGTACTGGCATGGGGACGACCTGCCGTTTGATGGGCC TGGGGGCATCCTGGCCCATGCCTTCTTCCCCAAGACTCACCGAGAAGGGGATGTCCACTT CGACTATGATGAGACCTGGACTATCGGGGATGACCAGGGCACAGACCTGCTGCAGGTGGC AGCCCATGAATTTGGCCACGTGCTGGGGCTGCAGCACACAACAGCAGCCAAGGCCCTGAT GTCCGCCTTCTACACCTTTCGCTACCCACTGAGTCTCAGCCCAGATGACTGCAGGGGCGT TCAACACCTATATGGCCAGCCCTGGCCCACTGTCACCTCCAGGACCCCAGCCCTGGGCCC CCAGGCTGGGATAGACACCAATGAGATTGCACCGCTGGAGCCAGACGCCCCGCCAGATGC CTGTGAGGCCTCCTTTGACGCGGTCTCCACCATCCGAGGCGAGCTCTTTTTCTTCAAAGC GGGCTTTGTGTGGCGCCTCCGTGGGGGCCAGCTGCAGCCCGGCTACCCAGCATTGGCCTC TCGCCACTGGCAGGGACTGCCCAGCCCTGTGGACGCTGCCTTCGAGGATGCCCAGGGCCA CATTTGGTTCTTCCAAGGTGCTCAGTACTGGGTGTACGACGGTGAAAAGCCAGTCCTGGG CCCCGCACCCCTCACCGAGCTGGGCCTGGTGAGGTTCCCGGTCCATGCTGCCTTGGTCTG GGGTCCCGAGAAGAACAAGATCTACTTCTTCCGAGGCAGGGACTACTGGCGTTTCCACCC CAGCACCCGGCGTGTAGACAGTCCCGTGCCCCGCAGGGCCACTGACTGGAGAGGGGTGCC CTCTGAGATCGACGCTGCCTTCCAGGATGCTGATGGCTATGCCTACTTCCTGCGCGGCCG CCTCTACTGGAAGTTTGACCCTGTGAAGGTGAAGGCTCTGGAAGGCTTCCCCCGTCTCGT GGGTCCTGACTTCTTTGGCTGTGCCGAGCCTGCCAACACTTTCCTCTGACCATGGCTTGG ATGCCCTCAGGGGTGCTGACCCCTGCCAGGCCACGAATATCAGGCTAGAGACCCATGGCC ATCTTTGTGGCTGTGGGCACCAGGCATGGGACTGAGCCCATGTCTCCTCAGGGGGATGGG GTGGGGTACAACCACCATGACAACTGCCGGGAGGGCCACGCAGGTCGTGGTCACCTGCCA GCGACTGTCTCAGACTGGGCAGGGAGGCTTTGGCATGACTTAAGAGGAAGGGCAGTCTTG GGCCCGCTATGCAGGTCCTGGCAAACCTGGCTGCCCTGTCTCCATCCCTGTCCCTCAGGG TAGCACCATGGCAGGACTGGGGGAACTGGAGTGTCCTTGCTGTATCCCTGTTGTGAGGTT CCTTCCAGGGGCTGGCACTGAAGCAAGGGTGCTGGGGCCCCATGGCCTTCAGCCCTGGCT GAGCAACTGGGCTGTAGGGCAGGGCCACTTCCTGAGGTCAGGTCTTGGTAGGTGCCTGCA TCTGTCTGCCTTCTGGCTGACAATCCTGGAAATCTGTTCTCCAGAATCCAGGCCAAAAAG TTCACAGTCAAATGGGGAGGGGTATTCTTCATGCAGGAGACCCCAGGCCCTGGAGGCTGC AACATACCTCAATCCTGTCCCAGGCCGGATCCTCCTGAAGCCCTTTTCGCAGCACTGCTA TCCTCCAAAGCCATTGTAAATGTGTGTACAGTGTGTATAAACCTTCTTCTTCTTTTTTTT TTTTTAAACTGAGGATTGTCATTAAACACAGTTGTTTTCTAAAAAAAAAAAAAAAA CytochromeP450family2subfamilyBmember7pseudogene (CYP2B7P1) SEQIDNO:17 CTGGAACCATGGAGCTCAGCGTCCTCCTCTTCCTTGCACTCCTCACAGGCCTCTTGCTAC TCCTGGTTCAGCGTCACCCTAACTCCCATGGCACCCTCCCACCAGGGCCCCGCCCTCTGC CCCTTTTGGGGAACCTTCTGCAGATGGACAGAAGAGGCCTACTCAAATCCTTTCTGAGGT TCCGAGAGAAATATGGGGACGTCTTCACGGTACACCTGGGACCGAGGCCCGTGGTCATGC TGTGTGGAGTAGAGGCCATACGGGAGGCCCTGGTGGACAACGCTGAGGCCTTCTCTGGCC GGGGAAAAATCGTCATCATGGACCCAGTCTACCAGGGATATGGCATGCTCTTTGCCAATG GAAACCGCTGGAAGGTGCTTCGGCGATTCTCTGTGACCACCATGAGGGACTTCGGGATGG GAAAGCGGAGTGTGGAGGAGCGGATTCAGGACGAGGCTCAGTGTCTGATAGAGGAACTTC GGAAATCCAAGGGAGCCCTCGTGGACCCCACCTTCCTCTTCCATTCCATTACCGCCAACA TCATCTGCTCCATCATCTTTGGAAAACGCTTCCACTACCAAGATCAAGAGTTCCTGAAGA CGCTGAACTTGTTCTGCCAGAGTTTCTTACTCATCAGCTCTATATCCAGCCAGCTGTTTG AGCTCTTCTCTGGCTTCTTGAAATACTTTCCTGGGGCACACAGGCAAGTTTACAAAAACC TACAGGAAATCAATGCTTACATTGGCCACAGTGTGGAGAAGCACCGTGAAACCCTGGACC CCAGCGCCCCCAGGGACCTCATCGACACCTACCTGCTCCACATGGAAAAAGAGAAATCCA ACCCACACAGTGAATTCAGCCACCAGAACCTCATCATCAACACGCTCTCGCTCTTCTTTG CTGGCACTGAGACCACCAGCACCACTCTCCGCTACGGCTTCCTGCTCATGCTCAAATACC CTCATGTCGCAGAGAGAGTCTACAAGGAGATTGAACAGGTGGTTGGCCCACATCGCCCTC CAGCGCTTGATGACCGAGCCAAAATGCCATACACAGAGGCAGTCATCCGTGAGATTCAGA GATTTGCTGACCTTCTCCCCATGGGTGTGCCCCACATTGTCACCCAACACACCAGCTTCT GAGGGTACACCATCCCCAAGGACACGGAAGTATTTCTCATCCTGAGCACTGCTCTCCGTG ACCCACACTACTTTGAAAAACCAGACGCCTTCAATCCTGACCACTTTCTGGATGCCAATG GGGCACTGAAAAAGAATGAAGCTTTTATCCCCTTCTCCTTAGGGAAGCGGATTTGTCTTG GTGAAGGCATTGCCCGTGCGGAATTGTTCCTCTTCTTCACCACCATCCTCCAGAACTTCT CCGTGGCCAGCCCCGTGGCTCCTGAAGACATCGATCTGACACCCCAGGAGTGTGGTGTGG GCAAAATACCCCCAACATACCAGATCTGCTTCCTGCCCCGCTGAAGGGGCTGAGGGAAGG GGGTCAAAGGATTCCAGGGTCATTCAGTGTCCCCACCTCTGTAGATAATGGCTCTGACTC CCTGCAACTTCCTGCCTCTGAGAGACCTGCTGCAAGCCAGCTTCCTTCCCTTCCATGGCA CCAGTTGTCTGAGGTCGCAGTGCAAATGAGTGGAGGAGTGAGATTATTGAAAATTATAAT ATACAAAATTATATATATATATTTTGAGACAGAGTCTCACTCAGTTGCCCAGGCTGGAGT GCAGTGGCGTGATCTCGGCTCACTGCAACCTCCACCCCCGGGGTTCAAGAAATTCTCCTG CCTCAGCCTCCCTAGTAGCTGGGATTACAGGTGTGTGCTACCATGCCTGGCTAATTTTTG TATTTTTAGTAGAGATGGGGTTTCACCGTGTTGGCCAGGCTGATCTCAAACTCCTGAACT CAAGTGATTCACCCACCTTAGCCTCCCAAAGTGCTGGGATTACAGGTGTGAGTCACCATG CCCGGCCATGTATATATATAATTTTAAAAATTAAGATGAAATTCACATAAAATAAAATTA GCCATTTTAAAGTGTACAATTTAGTGGTGTGTGGTTCATTCACAAAGCTGTACAACCACC ACCATCTAGTTCCAAACATTTTCTTTTTTTCTGAGACGGAGTCTCACTCTGTCACCCAGG TTCGAGTTCAGTGGTCTTGAACTCCTGATGTCAGGTGATTCTCCTAGTTCCAAATGTTTT CATTATCTCCCCCCAACAAAACCCATACCTATCAAGCTGTCACTCCCCATACCCCATTCT CTTTTTCATCTCAGCCCCTGTCAATCTGGTTTTTGTCCTTATGGACTTACCAATTCTGAA TATTTCCTATAAACAGAATCACACAATATTTGATTTTTTTTTTAAAACTAAGCCTTGCTC TGTCTCCCAGGCTGGAGTGCTGTGGCGTGATTTTGGTTCACTGCAACCTCCGCCTTCCAA GTTCAAGAGATTCTCCTGCCTCAGCTTCCAAGTAGCTGGGATTACAGGCATGTGGTACCA CGCCTGGCTAATTTTCTTGTATTTTTAGTAGGGACATGTTGGCCAGGCTGGTTGTGAGCT CCTGGCCTCAGGTGATCCACACGCCTCAGTGTCCCAGAGTGCTGATATTACAGGCGTAAT ATGTGATCTTTTGTGTCTGGTTCCTTTCACGTTGAACGCTATTTTTGAGGTTCGTGCCTG TTGTAGACCACAGTCACACACTGCTGTAGTCTTCCCCCATCCTCATTCCCAGCTGCCTCC TCCTACTGTTTCCCTCTATCAAAAAGCCTCCTTGGCGCAGGTTCCCTGAGCTGTGGGATT CTGCACTGGTGCTTTGGATTCCCTGATATGTTCCTTCAAATCCACTGAGAATTAAATAAA CATCGCTAAAGCATGACCTCCCCACGTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA Lactobacillusgasseri SEQIDNO:18 CAATGGACGCAAGTCTGATGGAGCAACGCCGCGTGAGTGAAGAAGGGTTTCGACTCGTAA AGCTCTGTTGGTAGTGAAGAAAGATAGAGGTAGTAACTGGCCTTTATTTGACGGTAATTA CTTAGAAAGTCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGT TGTCCGGATTTATTGGGCGTAAAGCGAGTGCAGGCGGTTCAATAAGTCTGATGTGAAAGC CTTCGGCTCAACCGGAGAATTGCATCAGAAACTGTTGAACTTGAGTGCAGAAGAGGAGAG TGGAACTCCATGTGTAGCGGTGGAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGG CGGCTCTCTGGTCTGCAACTGACGCTGAGGCTCGAAAGCATGGGTAGCGAACAGGATTAG ATACCCTGGTAGTCCATGCCGTAAACGATGAGTGCTAAGTGTTGGGAGGTTTCCGCCTCT CAGTGCTGCAGCTAACGCATTAAGCACTCCGCCTGGGGAGTACGACCGCAAGGTTGAAAC TCAAAGGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGAAGCAAC GCGAAGAACCTTACCAGGTCTTGACATCCAGTGCAAGCCTAAGAGATTAGGAGTTCCCTT CGGGGACGCTGAGACAGGTGGTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGT TAAGTCCCGCAACGAGCGCAACCCTTGTCATTAGTTGCCATCATTAAGTTGGGCACTCTA ATGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCATCATGCCCCT TATGACCTGGGCTACACACGTGCTACAATGGACGGTACAACGAGAAGCGAACCTTCGAAG GCAAGCGGATCTCTGAAAGCCGTTCTCAGTTCGGACTGTAGGCTGCAACTCGCCTACACG AAGCTGGAATCGCTAGTAATCGCGGATCAGCACGCCGCGGTGAATACGTTCCCGGG Lactobacilluscrispatus SEQIDNO:19 CGGCGTGCCTAATACATGCAAGTCGAGCGAGCGGAACTAACAGATTTACTTCGGTAATGA CGTTAGGAAAGCGAGCGGCGGATGGGTGAGTAACACGTGGGGAACCTGCCCCATAGTCTG GGATACCACTTGGAAACAGGTGCTAATACCGGATAAGAAAGCAGATCGCATGATCAGCTT TTNAAAGGCGGCGTAAGCTGTCGCTATGGGATGGCCCCGCGGTGCATTAGCTAGTTGGTA AGGTAAAGGCTTACCAAGGCGATGATGCATAGCCGAGTTGAGAGACTGATCGGCCACATT GGGACTGAGACACGGCCCAAACTCCTACGGGAGGCAGCAGTAGGGAATCTTCCACAATGG ACGCAAGTCTGATGGAGCAACGCCGCGTGAGTGAAGAAGGTTTTCGGATCGTAAAGCTCT GTTGTTGGTGAAGAAGGATAGAGGTAGTAACTGGCCTTTATTTGACGGTAATCAACCAGA AAGTCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCG GATTTATTGGGCGTAAAGCGAGCGCAGGCGGAAGAATAAGTCTGATGTGAAAGCCCTCGG CTTAACCGAGGAACTGCATCGGAAACTGTTTTTCTTGAGTGCAGAAGAGGAGAGTGGAAC TCCATGTGTAGCGGTGGAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTC TCTGGTCTGCAACTGACGCTGAGGCTCGAAAGCATGGGTAGCGAACAGGATTAGATACCC TGGTAGTCCATGCCGTAAACGATGAGTGCTAAGTGTTGGGAGGTTTCCGCCTCTCAGTGC TGCAGCTAACGCATTAAGCACTCCGCCTGGGGAGTACGACCGCAAGGTTGAAACTCAAAG GAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGAAGCAACGCGAAG AACCTTACCAGGTCTTGACATCTAGTGCCATTTGTAGAGATACAAAGTTCCCTTCGGGGA CGCTAAGACAGGTGGTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTC CCGCAACGAGCGCAACCCTTGTTATTAGTTGCCAGCATTAAGTTGGGCACTCTAATGAGA CTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCATCATGCCCCTTATGAC CTGGGCTACACACGTGCTACAATGGGCAGTACAACGAGAAGCGAGCCTGCGAAGGCAAGC GAATCTCTGAAAGCTGTTCTCAGTTCGGACTGCAGTCTGCAACTCGACTGCACGAAGCTG Hemoglobindelta(HBD) SEQIDNO:20 ACTGCTGTCAATGCCCTGTG Hemoglobindelta(HBD) SEQIDNO:21 ACCTTCTTGCCATGAGCCTT Solutecarrierfamily4(anionexchanger),member1(Diegobloodgroup) (SLC4A1) SEQIDNO:22 AACTGGACACTCAGGACCAC Solutecarrierfamily4(anionexchanger),member1(Diegobloodgroup) (SLC4A1) SEQIDNO:23 GGATGTCTGGGTCTTCATATTCCT GlycophorinA(MNSbloodgroup)(GYPA) SEQIDNO:24 CAGACAAATGATACGCACAAACG GlycophorinA(MNSbloodgroup)(GYPA) SEQIDNO:25 CCAATAACACCAGCCATCACC Folliculardendriticcellsecretedprotein(FDCSP) SEQIDNO:26 CTCTCAAGACCAGGAACGAGAA Folliculardendriticcellsecretedprotein(FDCSP) SEQIDNO:27 GGGCAGATTCAGGTATTGGAATAG Histatin3(HTN3) SEQIDNO:28 AAGCATCATTCACATCGAGGCTAT Histatin3(HTN3) SEQIDNO:29 ATGCGGTATGACAAATGAGAATACAC Statherin SEQIDNO:30 CTTGAGTAAAAGAGAACCCAGCCA Statherin SEQIDNO:31 TTCTGGAACTGGCTGATAAGGG Protamine1(PRM1) SEQIDNO:32 GCCAGGTACAGATGCTGTCGCAG Protamine1(PRM1) SEQIDNO:33 GTGTCTTCTACATCTCGGTCTG Transitionprotein1(TNP1) SEQIDNO:34 GATGACGCCAATCGCAATTACC Transitionprotein1(TNP1) SEQIDNO:35 CCTTCTGCTGTTCTTGTTGCTG Protamine2(PRM2) SEQIDNO:36 CGTGAGGAGCCTGAGCGA Protamine2(PRM2) SEQIDNO:37 CGATGCTGCCGCCTGT Kallikreinrelatedpeptidase2(KLK2) SEQIDNO:38 TTCTCTCCATCGCCTTGTCTG Kallikreinrelatedpeptidase2(KLK2) SEQIDNO:39 AGTGTGCCCATCCATGACTG Microseminoproteinbeta(MSMB) SEQIDNO:40 CTTTGCCACCTTCGTGACTTTATG Microseminoproteinbeta(MSMB) SEQIDNO:41 ACAGTTGTCAGTCTGCCACT Transglutaminase4(TGM4) SEQIDNO:42 TGAGAAAGGCCAGGGCG Transglutaminase4(TGM4) SEQIDNO:43 AATCGAAGCCTGTCACACTGC Matrixmetallopeptidase10(stromelysin2)(MMP10) SEQIDNO:44 CCCACTCTACAACTCATTCACAGAG Matrixmetallopeptidase10(stromelysin2)(MMP10) SEQIDNO:45 GGTTCCTCAGTAGAGGCAGG Stanniocalcin1(STC1) SEQIDNO:46 CTGCCCAATCACTTCTCCAACA Stanniocalcin1(STC1) SEQIDNO:47 TTTCTCCATCAGGCTGTCTCT Matrixmetallopeptidase3(MMP3) SEQIDNO:48 CCATGCCTATGCCCCTG Matrixmetallopeptidase3(MMP3) SEQIDNO:49 GTCCCTGTTGTATCCTTTGTCC (Matrixmetallopeptidase11(MMP11) SEQIDNO:50 CAAGACTCACCGAGAAGGGG (Matrixmetallopeptidase11(MMP11) SEQIDNO:51 GCCTTGGCTGCTGTTGTGT CytochromeP450family2subfamilyBmember7pseudogene (CYP2B7P1) CCGTGAGATTCAGAGATTTGCTGAC CytochromeP450family2subfamilyBmember7pseudogene (CYP2B7P1) SEQIDNO:53 TGAGAAATACTTCCGTGTCCTTGG Lactobacillusgasseri SEQIDNO:54 CAGAGCAAGCGGAAGCACA Lactobacillusgasseri/Lactobacilluscrispatus SEQIDNO:55 TTGCTTACTTACTGCTCCCCG Lactobacilluscrispatus SEQIDNO:56 GAGAAAGCCAAGCGGAAGC Lactobacillusgasseri/Lactobacilluscrispatus SEQIDNO:57 TTGCTTACTTACTGCTCCCCG