WO2014071946A1 - Diagnostic pcr primers enabling exhaustive detection of non-human eukaryotic ssu rdna in human clinical samples - Google Patents

Abstract

The 18S PCR method is based on the detection of species-specific small subunit (SSU) rDNA, by amplification of all non-human SSU rDNA in human clinical samples. This is obtained by preferential PCR amplification of non-human DNA extracted from human clinical samples, using three or four different primer sets, high human DNA content samples (steril) and low human DNA content samples (fecal), respectively. The resulting PCR amplicons are then barcoded and sequenced using either sanger or NGS sequencing; single molecule sequencing to circumvent the complications of Sanger sequencing (basically most prevalent amplicon sequencing). This will enable the detection of all amplified non-human eukaryotes in the respective clinical sample. The technique is furthermore compatible with the existing 16S method for detection of all bacteria in clinical samples, in order to provide the most complete detection system of organisms in clinical samples to date.

Description

Diagnostic PCR primers enabling exhaustive detection of non-human eukaryotic SSU rDNA in human clinical samples

Field of invention

The invention discloses a method of detecting of non-human eukaryotic small subunit (SSU) rDNA in human clinical samples using 18S PCR.

General background (State of the Art)

The diagnosis of human parasitic infections have traditionally been based on i) direct identification by microscopy, antigen detection, or, more rarely, culture, or ii) indirect detection of antibodies. More recently, various genus- and/or species-specific diagnostic PCR analyses have been described and implemented for routine diagnosis of parasitic infections (2-8, 10, 12-34).

In order for individual PCR-based analyses to completely replace microscopy in clinical microbiology, multiple PCR analyses must be performed in order to target all parasites and fungi detectable by microscopy. A summary of limitations of microscopy- and PCR-based methods and how genetic diversity of disease-causing parasites influences our ability to detect them has recently been published (11).

A future goal will be to combine the sensitivity of PCR with the breadth of microscopy-based methods. This could be achieved by using broad specificity primers. Theoretically, any parasite or fungus present in clinical samples would be detectable, and infections with any mixed species would be identifiable. Given the potential of eg. deep sequencing, the sensitivity and the diagnostic efficiency of deep sequencing-based assays would rely on our ability to i) develop assay protocols, such as reduction of co-amplification of human DNA or active inhibition of PCR amplification of non-target DNA (e.g. human DNA) to reduce the amount of sequencing necessary, and ii) create software-based methods (filters and pipelines) to sort the information generated from the process, in order to end up with relevant information on any potential pathogen present in the sample. Due to this potential breadth and high sensitivity, health care

professionals would in the future not need to choose from vast panels of different microbiological analyses; the standard would be to submit one or two clinical samples and the challenge will be to interpret test results rather than decide which analyses should be performed. The human normal flora is not limited to bacteria; micro-eukaryotic organisms such as yeasts and unicellular parasites are frequent findings in particular skin, mucosal and stool samples from a large proportion of the human population. New technology has made it possible to obtain an overall picture of the bacteria and their interactions with each other (microbiomes) and with the host, and studies of intestinal microbiomes have for instance to some extent made it possible to stratify individuals into "enterotypes". New research shows that by studying enterotypes it can be predicted whether a person is at risk of developing disease, for instance diabetes or obesity. The same could be true for micro-eukaryotes, but in this context the eukaryotic part of the human body flora has been somewhat overlooked, ppartly due to methodological limitations.

Hence, detection of the eukaryotic organisms that may be present in the biological samples from humans has not yet been standardized in the same manner, and still relies largely on a combination of culture, microscopy, antigen or antibody detection. PCR-based methods have in recent years gained a foothold in the most modern microbiology laboratories, but diagnostic findings are still limited by the fact that the genetic diversity of most eukaryotic genera is scarcely investigated, and that the clinical significance of many yeasts and parasites is uncertain; these situations taken together results in a suboptimal diagnostic work-up in the routine setting, even when PCR methods are used.

There is a need for a multiple analysis of a clinical sample for detection of parasitic pathogens on the genus and species level. This will bypass, the needs for performing multiple single analyses and the risk for "choosing" the wrong tests regarding the clinical indication.

Summary of the invention

We have developed a method for the detection of non-human eukaryotic DNA in patient material such as body fluids, feces and tissue biopsies. The method enables comprehensive diagnostics of fungi and parasites in patient specimens, including stool samples, and is additionally sable to validate current state- of-the-art diagnostics in the field. It is also usable for phylogenetic interrogation, to test for associations between eukaryotes and disease phenotypes (eg. functional, inflammatory and infectious intestinal diseases). Finally, the invention is usable for a lab-on-a-chip or a similar robust, automatable, easy-to-use and cost-effective diagnostic tool to ensure future standardized, comprehensive diagnosis of parasites and fungi in human clinical samples.

Detailed disclosure of the invention

The present invention discloses a method of detecting of non-human eukaryotic SSU DNA in human clinical samples. The SSU DNA is detected using 18S DNA amplification with primers specific for non-human eukaryotic SSU DNA such as fungi and parasites.

Preferred primers in the 18S DNA amplification are chosen from SEQ ID NO 1-12.

A method detecting of non-human eukaryotic SSU DNA where the 18S DNA amplification is followed by a post-amplification step for species or genus annotation. The post-amplification step can e.g. be performed by deep sequencing using microarrays or NGS technologies like the lllumina platform, Hiseq or Miseq.

The clinical samples are preferably from faeces, body fluid and biopsies.

Among the parasites detected are the following species: Acanthamoeba, Ancylostoma, Angiostrongylus, Anisakis, Ascaris, Babesia, Balamuthia, Balantidium, Baylisascaris, Blastocysts, Brugia, Capillaria,

Chilomastix, Clonorchis, Cryptosporidium, Cyclospora, Cystoisospora, Dicrocoelium, Dientamoeba,

Dioctophyme, Diphyllobothrium, Dracunculus, Echinococcus, Echinostoma, Entamoeba, Enterobius, Fasciola, Fasciolopsis, Giardia, Gnathostoma, Hymenolepis, Isospora, Leishmania, Microsporidia, Naegleria, Necator, Onchocerca, Opisthorchis, Paragonimus, Plasmodium, Pneumocystis, Pseudoterranova, Rhinosporidium, Sappinia, Schistosoma, Spirometra, Strongyloides, Taenia, Toxocara, Toxoplasma, Trichinella,

Trichobilharzia, Trichomonas, Trichuris, Trypanosoma and Wuchereria. A method of detecting outbreaks or infection due to intestinal eukaryotic pathogens, such as Giardia, Cryptosporidium, Cyclospora or microsporidia in faeces samples using above mentioned method. Definitions:

SSU rDNA: Small subunit (rRNA) 18S ribosomal RNA is a part of the ribosomal RNA. 18S rRNA is a component of the small eukaryotic ribosomal subunit (40S)

18S ONA amplification: Amplification of parts of the 18S gene by Polymerase Chain Reaction (PCR) V3-5 region: Variable region 3-5 of the 18S gene V9 region: Variable region 9 of the 18S gene.

Next-generation sequencing (NGS) technology allows for parallel massively parallel sequencing (deep sequencing). Examples of this technology is: The lllumina platforms, Genome Analyzer llx (GA), Hiseq and Miseq and the 454 (Pyrosequencing) by Roche.

By non-human eukaryote in human samples is meant microorganisms such as fungi and parasites.

OTU's: Operational taxonomic unit. An often used term for species distinction in microbiology, typically using rDNA and a percent similarity threshold for classifying microbes within the same, or different, OTUs.

The present investigation is based on the development of a method for the preferential amplification of non-human eukaryotic DNA, and our data hold great promise for its future application. A few studies have been performed focusing on eukaryotic diversity in environmental samples, using amplification of the 18S small subunit ribosomal (SSU) rRNA gene by universal primers targeting the V9 region (variable region 9)(1, 9) followed by 454-sequencing, proving the usefulness of this strategy. In the present investigation we have designed sets of primers targeting conserved regions of the 18S, spanning the V3-5 region (Fig. 2), without targeting human 18S, resulting in primer sets that are universal and target parasites and fungi with a very high coverage, without amplification of human DNA. Data suggest an ability to target a variety of fungi, plants and parasites; however, in most cases a high quality sequence spectrum was unobtainable by regular Sanger sequencing due to multiple sequences. Not surprisingly, this suggests a broad variety of amplicons illustrating a eukaryotic mixture. In order to distinguish between species, sequencing of single amplicons is necessary to determine the eukaryotic composition instead of only determining the most prevalent amplicon. Genomic DNAs will initially be submitted to either 3 PCR (sterile samples) or 4 PCR analyses (fecal samples), each targeting subsets of eukaryotic organisms (including helminths, protists (protozoa and Blastocysts) and fungi) using a general 18S approach, as illustrated in figure 1, 2 and 4.

The 18S rRNA gene was selected as target gene to enable amplification of the broadest possible spectrum of species by as few primer sets as possible, assuming that the nuclear SSU rRNA gene would be the most inter-species conserved gene. Hence, 18S rDNA sequences from the NCBI database were aligned for each species and a consensus sequence was generated. The consensus sequences for each species were used for phylogenetic analysis in order to group the organisms according to their 18S rDNA sequence. The consensus sequences from all species in each group were then aligned and primers were designed to amplify all species within each group. Additionally, a consensus sequence from human 18S rDNA was aligned with each group in order to enable the design primers that would amplify all eukaryotic 18S rDNA except human. Each primer set (#1-6; see example 1) was blasted against the entire eukaryotic database, using NCBi's Primer-Blast, with standard settings (excluding predicted Reference sequence transcripts and uncultured/ environmental samples).

Building up a clear picture of the genetic diversity of clinical sample-material including human intestinal eukaryotic microbiome pinpoints rDNA targets from relevant OTUs for developing specific probes on a chip (Lab-on-a-chip for relevant eukaryotes) appropriate for diagnostic and phylogenetic interrogation. While initially this will enable 18S signature tags only, this can be expanded by designing chips that will enable targeted analysis of resistance and virulence markers. Depending on the amount of resolution among OTUs represented on the chip, this has the potential to radically improve, facilitate and standardize diagnostic work-up, and - importantly - accelerate investigation of outbreaks due to intestinal eukaryotic pathogens, such as Giardia, Cryptosporidium, Cyclospora or microsporidia.

An amplification step (ie. PCR) using the described primer sets can be combined with one of several post- amplifications tools for annotation of the detected eukaryotes. Including deep sequencing by using next generation sequencing (NGS) technologies like the lllumina platform, Hiseq and Miseq among others, in combinations with appropriate annotations software. The use of microarrays would be another example of a post amplification tool for species or genus annotation.

Figure legends Figure 1: Phylogenetic representation of parasites based on genus specific consensus sequence. Circles indicate the six different groups used for primer design.

Figure 2: Graphic representation of nucleotide conservation degree across the entire SSU rRNA gene for genus-specific consensus sequence alignment within each group. Dots indicate primer annealing position and lines indicate amplification area.

Figure 3: Illustrates an example of primer design before performing Miseq NGS. DNA-fragment of interest (DOl) is needed to be amplified with primers with modifications, which includes Adaptors, Barcode, buffer, Forward Sequencing Primer (FSP) site, Keys, and forward and reverse primer. 2 rounds of cPCR is needed for attachment. First round amplifies the DOl and in the second round the modifications are attached. The adaptors functions as anchors for DOl, which docks with other adaptors already fixated on the surface of the flow cell. The barcode works as a tag during sequencing to identify contents of a sample. Keys and buffer have no real function other than increasing the melting temperature. FSP is the binding site for the forward sequencing primer and the site for reverse sequencing primer to bind is the buffer key and reverse primer combined, known as Multiplex Indexing Sequencing Primer/Reverse Sequencing Primer, abbreviated MISP/RSP.

Figure 4: 1% Agarose gel showing PCR amplification of parasitic DNA.

Figure 5: Lanes 1-6 represent PCR products from feces and lanes 7-12 represent PCR products from different samples from normally sterile body compartments/tissues. Lane 8 is amplified by PanPa_G2-4, lanes 1-5, 7,9 and 1 1-12 by PanPa_G3-l , lane 6 by PanPa_G6-l and lane 10 by PanPa_G6-2. 1)

Galactomyces geotrichum, 2) Saccharomyces cerevisiae, 3) Enterobius vermicularis, 4) Tomato, 5) Pear, 6) Blastocystis hominis, 7) Schistosoma mansoni from urine, 8) Echinococcus granulosus from lung, 9)

Cryptococcus sp. from corpus vitreum, 10) Balantidium coli from lung, 1 1) Strongyloides stercoralis from pleura exudate and 12) Fusarium solani from ocular fluid.

Example 1 : Primer design

Definition of target species for amplification

The 18S platform is intended to amplify total non-human eukaryotic small subunit (SSU; 18S) rDNA in genomic DNA extracted from human samples and the 18S PanPa primers should have a strong tolerance to human DNA in samples from "sterile" areas i.e. spinal fluid, blood and biopsies. Moreover, DNA from parasites and fungi present in the intestine should be preferentially amplified compared to DNA from higher mammals (animals), plants and aquatic life (fish) that may be present due to dietary intake. The 18S rDNA gene was selected as target gene to ensure amplification of the broadest possible spectrum of species with as few primers as possible, assuming that the 18S rDNA sequence would be the most interspecies conserved gene throughout all eukaryotic genera.

Acquisition of sequence data

In order to avoid designing primers based on the total 727,916 18S DNA sequences in NCBI's database (Search [2012-10-22]: "18S OR Small subunit NOT bacteria"), a collection of 56 acknowledged parasite small subunit (SSU) parasites defined by the Center for Disease Control and Prevention (CDC:

http://www.cdc.gov/parasites/az/index.html). was used as target species for PCR amplification. The 56 different parasites (Acanthamoeba, Ancylostoma, Angiostrongylus, Anisakis, Ascaris, Babesia, Balamuthia, Balantidium, Baylisascaris, Blastocystis, Brugia, Capillaria, Chilomastix, Clonorchis, Cryptosporidium, Cyclospora, Cystoisospora, Dicrocoelium, Dientamoeba, Dioctophyme, Diphyllobothrium, Dracunculus, Echinococcus, Echinostoma, Entamoeba, Enterobius, Fasciola, Fasciolopsis, Giardia, Gnathostoma,

Hymenolepis, Isospora, Leishmania, Microsporidia, Naegleria, Necator, Onchocerca, Opisthorchis,

Paragonimus, Plasmodium, Pneumocystis, Pseudoterranava, Rhiaosporidium, Sappinia, Schistosoma, Spirometra, Strongyloides, Taenia, Toxocara, Toxoplasma, Trichinella, Trichobilharzia, Trichomonas, Trichuris, Trypanosoma and Wuchereria) represented a total of 8,690 18S rDNA sequences, when discarding sequences shorter than 500 nt (7,031 Protozoa/Stramenopiles and 1,659 Helminths). The sequences were downloaded and imported into CLC Main Workbench for further analysis.

Grouping species according to phylogeny

18S rDNA sequences were aligned using ClustalW [PMCID: PMC308517] for each species a consensus sequence was generated. Species consensus sequences for each genus were aligned and a genus-specific consensus sequence was determined. Genus-specific consensus sequences were used for phylogenetic analysis in order to group the parasites according to their 18S rDNA sequence. The analysis determined that the 56 different parasites could be categorized into six different groups (Gl, G2, G3, G4, G5 and G6), see figure I.

Design primers

Species-specific consensus sequences from all species in each group were aligned, including a human 18S consensus sequence, to enable determination of primer sites incompatible with human 18S rDNA e.g. they bind to eukaryote DNA other than human DNA. Different potential primer locations were visually determined according to the nucleotide conservation degree plot and primer sequences were manually determined and tested using NCBI's web-tool (Primer-BLAST), on different pure DNA samples (DNA extractions from isolated parasite material) and on different clinical samples (patient samples from blood, lung cysts, spinal fluids, urine and feces). This resulted in the development of six different primer pairs, see figure 2:

#1: PanPa_G2F3 (AACTG6AGG6CAAGTCTG6TGC; SEQ ID NO 1) and PanPa_G2R3

(ACCACGGATCGTCAGTTGGCATCG; SEQ ID NO 2);

#2: PanPa_G2F4 (CGATGCCAACTGACGATCCG; SEQ ID NO 3) and PanPa_G2R4

(CAGTCACGACGGTGATTAACCAGT; SEQ ID NO 4);

#3: PanPa_G3Fl (GCCAGCAGCCGCGGTAATTC; SEQ ID NO 5) and PanPa_G3Rl

(ACATTCTTGGCAAATGCTTTCGCAG; SEQ ID NO 6);

#4: PanPa_G4F2 (AAGGAAGGCAGCAGGCGCG; SEQ ID NO 7) and PanPa_G4R2

(GGTGGTGCCCTTCCGTCAATG; SEQ ID NO 8);

#5: PanPa_G6Fl (TGGAGGGCAAGTCTGGTGCC; SEQ ID NO 9) and PanPa_G6Rl

( ACG GTATCTG ATCGTCTTCG ATCCC; SEQ ID NO 10);

#6: PanPa_G6F2 (AGGGGATCGAAGACGATCAGATACCG; SEQ ID NO 11) and PanPa_G6R2

(ACAGACCTGTTATTGCCTCAAACTTCCC; SEQ ID NO 12)

Due to complications with high concentrations of human DNA in samples from sterile areas (non-fecal), two different groups of primer pairs were designed to be applied to "high-human-DNA-content (sterile)" samples (PanPa_G2-4, PanPa_G3-l and PanPa_G6-2) and fecal samples (PanPa_G2-3, PanPa_G3-l,

PanPa_G4-2 and PanPa_G6-l), respectively. The "fecal" primers retain the extremely broad specificity, but with a risk of human 18S rDNA amplification, in the event of very low concentration of non-human eukaryotic DNA. The "sterile" primers lose broadness by lowering non-specific PCR amplification of human 18S rDNA, in samples containing primarily human DNA.

Example 2: In silico analysis

Definition of criteria for PCR amplification

/n silico analysis of each primer pair was performed using NCBI's Primer BLAST web tool. Each primer pair was analyzed for potential amplification to all bacterial, eukaryotic, parasitic, viral and human DNA sequences available at NCBI's nr database (previously known as Non-Redundant: All GenBank + RefSeq Nucleotides + EMBL + DDBJ + PDB sequences; Excluding HTGS0,1,2, EST, GSS, STS, PAT, WGS). All Primer- BLASTs were performed by excluding predicted Refseq transcripts and uncultured / environmental samples. During initial testing of the primers on a series of DNA extracted from isolated parasites, it was clear that the primers were able to amplify DNA from parasite species to which the primers had more than two mismatches, probably due to the low-stringency PCR setup. For this reason, in silico analysis of eukaryote and parasite PCR amplification was performed using a cut-off primer stringency setup at two mismatches within the last five nucleotides in the 3' end and a total of five nucleotide mismatches along the entire primer (standard settings).

In order to determine the degree of unintended amplification of bacterial, viral and human DNA, primer stringency towards annealing and amplification was set very low, in order to illustrate a worst-case scenario. Two nucleotide mismatches within the last five nucleotides at the 3' end and a total of eight nucleotide mismatches along the entire primer were allowed.

Analysis of available eukaryotic sequence data

Each primer set was blasted against the entire NCBI eukaryotic nr database, which resulted in PCR amplification of 3,254 different species by the primers PanPa_G2-3, 614 by PanPa_G2-4, 13,714 by

PanPa_G3-l, 13,824 by PanPa_G4-2, 11,460 by PanPa_G6-l and 5,825 by PanPa_G6-2, a total of 42,252 eukaryotic species by the four fecal primer sets (23,726 unique species) and a total of 20,153 eukaryotic species by the three sterile primer sets (16,888 unique species) (tabte 1). For comparison, an in silico analysis of the well-known universal eukaryotic primers EukA and Euk B [PMID: 3049748] was performed using the same setup, resulting in PCR amplification of 2,621 different species.

Table 1: In silico analysis of all PanPa primers PCR amplification of different species, including universal eukaryotic primers EukA/EukB.

Primer # Different species Description

; PanPa_G2-3 3254 Fecal primers !

PanPa_G2-4 ^" ~ 614^{* "} Sterile primers

' PanPalG3-i 1371 ~ ^{' "}. ^" Fecal and sterile primers

PanPa_G4-2 13824 ^" Fecal primers ::::: ]

PanPaJ36-l 11460 ^"~ ^{" " "} Fecal primers

PanPa_G6-2 ^" 5825 ^{" "} Sterile primers

Sum total 42252 Total fecal

Sum unique 23726 Duplicates removed

Sum total 20153 Total sterile

Sum unique 16888 Duplicates removed

. EukA/EukB 2621 Total Analysis of available parasitic sequence data

An additional analysis was performed on the primers' ability to amplify parasitic DNA. A total of 11 separate parasite genera are amplified by PanPa_G2-3, 11 by PanPa_G2-4, 34 by PanPa_G3-l, 49 by PanPa_G4-2, 17 by PanPa_G6-l and 15 by PanPa_G6-2; a total of 53 parasite genera by the four fecal primer sets and a total of 43 parasites by the three sterile primer sets. At species level, 53 different parasites are amplified by PanPa_G2-3, 52 by PanPa_G2-4, 203 by PanPa_G3-l, 304 by PanPa_G4-2, 224 by PanPa_G6-l and 140 by PanPa_G6-2; a total of 783 species by the four fecal primer sets (380 unique) and a total of 395 species by the three sterile primer sets (273 unique), see table 2.

All primer binding sites were imported into Excel including amplicon size, primer binding location and primer nucleotide mismatches. Amplicon sizes range between 148-1,822 nucleotides for PanPa_G2-3

(mean 560, SD 97), 563-1,310 for PanPa_G2-4 (mean 628, SD 64), 200-1,709 for PanPa_G3-l (mean 404, SD 51), 190-5,003 for PanPa_G4-2 (mean 785, SD 146), 200-3,928 for PanPa_G6-l (mean 474, SD 69) and 284- 2,316 for PanPa_G6-2 (mean 473, SD 83) , see table 2. Table 2: In silico analysis of PanPa primers on parasites.

_ - Amplicon size Amplicon size Amplicon Genus Species V

range mean nucleotide SD

PanPa_G2-3 11 53 148-1,822 560 97

^' PanPa J32-4 11 ^{" "} Z 1 ⁵² I I 563-1,310 ^~_I 628 ^" I 64 II

; PanPa_G3-i 34 " ^{" " "}203 ^{" "} 200-1,709 ^"" 404 51

PanPa_G4-2 49 _ I ³⁰⁴ I 190-5,003 785 _ 146

^' PanPa_G6-l 17 ~ 224 ^{" "} 200-3,928 474 69

^' PanPa_G6-2 15 140 ^" 284-2,316 ^" 473 ~ 83

Analysis of unintentional PCR amplification

Each primer set was then analyzed for unintentional PCR amplification of bacterial, viral and human DNA, resulting in a very low number of possible amplifications at a (very) high level of allowed mismatches, see table 3.

Table 3: In silico analysis of unintentional PCR amplification of PanPa primers on Bacterial, Viral and Human DNA.

Primer Bacteria Virus Homo sapiens

PanPa_G2-3 ~~ ^' ~ ~~ Ϊ ~~ ^{" ~} Γ^" 13 hits

PanPa_G2-4 0 1 0 hits

PanPa_G3-l 6³ 2 1 hits

PanPa_G4-2 21^b 7^e 12 hits

PanPa_G6-l 5^C 3 0 hits

PanPa G6-2 4^d 0 0 hits ³ Azotobacter vinelandii, Bacterium L0352, Bacterium L0358, Bacterium L0393, Uncultured bacterium, Ureaplasma urealyticum.

Azoarcus aromaticum, Azospirillum brasilense, Azotobacter vinelandii, Bradyrhizobium japonicum, Burkholderia gladioli, Burkholderia g/umoe, Cronobocter sakazakii, Delftia sp., Desulfarculus baarsii, gamma proteobacterium HdNl, Granulicella mallensis, Haliangium ochraceum, Herbaspirillum seropedicae, Methylibium petroleiphilum, Methylobacterium extorquens, Methylobacterium nodulans, Novosphingobium aromaticivorans, Pseudomonas syringae, Ralstonia eutropha, Ralstonia solanacearum, Stenotrophomonas maltophilia.

Allochromatium vinosum, Bacterium L0393, Mycobacterium gilvum, Uncultured bacterium, Ureaplasma urealyticum.

d Lactobacillus acidophilus, Lactobacillus amylovorus, Uncultured bacterium, Uncultured rumen bacterium.

Adenovirus type, Bacillus licheniformis, Bovine herpesvirus 5, Human adenovirus C, Human adenovirus type 5, Mastadenovirus, Saccharopolyspora erythraea, Shigella flexneri bacteriophage.

Example 3 : Studies of primer abilities on parasite DNA and extracted DNA from patient samples DNA samples

In order to determine each primer set's ability to amplify target DNA, a series of control PCR's were performed using DNA extracted from isolated parasite materials as templates. DNAs from Ascaris, Babesia, Leishmania, Plasmodium, Strongyloides, Taenia and Toxoplasma were tested and each PCR amplicon was sequenced to verify correct amplification. Furthermore, PCR test were also performed using a bacterial test panel containing purified DNA from 30 different bacteria {Aeromonas caviae, Bacillus cereus, Bacillus fragilis, Bacillus subtilis, Campylobacter coli, Campylobacter jejuni, Campylobacter upsaliensis, Citro freundil, Clostridium difficile, Clostridium Perfringens, Clostridium Sordellii, Enterobacter Cloacae,

Escherichia coli, Hafnia alvei, Klebsiella sp, Listeria monocytogenes serogr 1, Plesiomonas shigelloides, Proteus mirabilis, Pseudomonas sp, Salmonella enteraditis, Salmonella paratyphi B, Serratia marcescens, Shigella dysenteri, Shigella flexneri, Staphylococcus aureus, Streptococcus pyrogenes, Vibrio cholerae ogawe, Vibrio parahaem, Yersinia enterocolitica 02 and Yersinia enterocolitica 03); Data not shown.. In few cases the primers was found to amplify unintentional products in the absence of eukaryotic target DNA.

Human "sterile" samples

In order to determine the primer sets' ability to amplify target DNA from DNA extracted from clinical samples, several "sterile" patient samples representing tissue biopsies (lung, blood, urine and spinal fluid) were analyzed. PCR on DNA extracted from lung biopsy or pleura exudate gave positive results for

Echinococcus, Balantidium and Strongyloides stercoralis and Schistosoma in a urine sample. Importantly, these infections could have been difficult to diagnose in the absence of this PCR setup. (Fig. 5) Human "fecal" samples

In order to determine the primers sets' ability to amplify target DNA from DNA extracted from fecal samples, several samples were analyzed. Generally, PCR amplifications from fecal samples result in a multitude of amplicons. This makes Sanger sequencing impractical due to difficulties in separating amplicons from different species before sequencing. In most cases, the sequencing chromatography only showed a mixture of all four nucleotides along the entire amplicon, making a species determination impossible. However, in a few cases it was possible to mechanically isolate (cut out) single PCR products from a 1% agarose gel and determine species origin from these PCR products by sequencing. Using this procedure lodamoeba, Dientamoeba fragilis and Enterobius vermicularis, and several fungal species have been identified from human fecal samples. (Fig. 5)

Example 4: Proof of concept by use of NGS

Study design

In an effort to circumvent the problems with species determination of multiple PCR amplicons in fecal samples, a t study where performed by implementing next generation sequencing (lllumina platform, Miseq) to determine amplification of eukaryotic SSU rDNA. Sequences were annotated by the BlON meta (BION) software (35). In this experiment, genomic DNA from fecal samples from 12 patients with diarrhea and 12 healthy controls was analyzed. Of the 12 healthy patients, six samples tested positive for

Dientamoeba fragilis and of these six samples, three were positive also for Blastocystis. Of the 12 diarrhea patients, six samples tested positive for Blastocystis and of these six samples, four tested positive for Dientamoeba fragilis and two for Entamoeba dispar (table 4). The remaining samples from six healthy and six diarrhea patients were all negative for all analyzed parasites: Giardia intestinalis/lamblia/duodenalis, Entamoeba histolytica, Entamoeba dispar, Cryptosporidium parvum, Cryptosporidium hominis and Blastocystis sp. In addition, an artificial construct (AC) representing a mixture of 1/3 fungal DNA, 1/3 bacteria DNA and 1/3 parasite DNA was included, resulting in a total of 25 samples. The AC sample contains four different fungi (Candida albicans, Candida krusei, Candida glabrata and Saccharomyces cerevisiae), 30 different bacteria (Aeromonas caviae, Bacillus cereus, Bacillus fragilis, Bacillus subtilis, Campylobacter coli, Campylobacter jejuni, Campylobacter upsaliensis, Citro freundil, Clostridium difficile, Clostridium

Perfringens, Clostridium Sordellii, Enterobacter Cloacae, Escherichia coli, Hafnia alvei, Klebsiella sp, Listeria monocytogenes serogr 1, Plesiomonas shigelloides, Proteus mirabilis, Pseudomonas sp, Salmonella enteraditis, Salmonella paratyphi B, Serratia marcescens, Shigella dysenteri, Shigella flexneri,

Staphylococcus aureus, Streptococcus pyrogenes, Vibrio cholerae ogawe, Vibrio parahaem, Yersinia enterocolitica 02 and Yersinia enterocolitica 03) and eight different parasites {Ascaris suum, Taenia saginata, Trichuris trichura, Neospora, Trichinella, Strongyloides stercoralis, Enterobius vermicularis and Toxoplasma gondii) DNA.

Table 4: Overview of patient samples.

Initially each sample was PCR-amplified using four primer sets (fecal primers). A subsequent PCR was performed using an adapter 1-barcode-forward-primer and reverse-primer-adaptor 2 setup (Fig. 3). With four different primer sets for each sample, a total of 50 primer sets were designed (four different primer sets per sample containing identical barcode). See figure 3. As shown I table 5 and 6 approximately half of the produced sequences where annotated to one taxonomic level. Table 5 Results from MiSeq sequencing.

G2-primer set G3 -primer set G4-primer set G6-primer set

Sequences

Output from MiSeq 881,342 1.068,643 404,594 1.082,893

No hit 175,076 13,745 159,550 80,381

Low Q score 24,496 86,740 1,271 110,373

Short sequence 593,376 101,536 235, 188 185,946

Annotated by BION 88,394 866,622 8,585 706, 193

Output total from 3.437,472 sequences

MiSeq

Annotated total by 1.669,794 sequences

BION

Table 6 An overview of sequences represented in each taxonomic level

Level Domain Phylum Class Order Family Genus Species

No. of sequences 1,386,554 1,386,554 1,363,132 1,362,927 1,331,898 1,037,235 665,968

% annotated 100.0 % 100.0 % 98.3 % 98.3 % 96.1 % 74.8 % 48.0 %

Table 7 Overview of total phyla, classes, orders, familia, genera,

and species represented.

Level Phyla Classes Orders Familia Genera Species

No. 21 38 63 88 155 188

As shown in the above tables around 188 eukaryotic species were found in the 24 fecal samples emphasizing the strength of the current invention, in terms of detecting a very broad range of eukaryotic species.

Table 8 Comparison of PCR results for protozoan infection to results obtained by MiSeq.

Healthy with protozoan infection (cohort 1)

Sample 1 2 3 4 5 6

Method PCR^A MiSeq⁸ PCR MiSeq PCR MiSeq PCR MiSeq PCR MiSeq PCR MiSeq

Bastocystis V 33157 V 12085 218 61 V 24229 - 30859

D. fragilis V 0 V 0 V 0 V 0 V 0 V 0

E. dispar - 0 - 0 0 0 - 0 0

IBS with protozoan infection (cohort 3) Sample 13 14 15 16 17 18

Method PCR MiSeq PCR MiSeq PCR MiSeq PCR MiSeq PCR MiSeq PCR MiSeq

Bastocystis V 5122 V 6649 V 10872 V 35816 V 331 V 880

D. fragilis V 0 - 0 V 0 0 V 0 V 0

E. dispar - 0 V 0 - 0 V 0 - 0 0

^A V denotes this sample tested positive for the respective protozoa in PCR

B Number of sequence found by MiSeq

Table 5 Overview of how much of the artificial sequences in the

POOL were able to be found by MiSeq

Artificial sequences in Pool Found by MiSeq Level

Fungi

Candida krusei Yes Species/family

Saccharomyces cerevisiae Yes Species

Candida glabrata Yes Species/family

Candida albicans Yes Species/family

Parasite

Ascaris suum Yes Genus

Taenia saginata Yes Genus/Species

Trichuris trichiura Yes Species

Neospora caninum Yes Genus

Trichinella sp. Yes Order

Strongyloides stercoralis No -

Enterobius vermicularis No -

Toxoplasma gondii Yes Species/Genus Table 8 and 9 shows the ability to detect know microorganisms described in table 4 and the AC construct. For the fecal samples Dientamoeba fragilis and Entamoeba dispar where not annotated, likewise where Strongyloides stercoralis and Enterobius vermicularis not annotated in the AC construct. Since these organisms previously have been detected after regular sanger sequencing, using these primer sets (Fig. 5) the absence of annotation is believed to rely on BION software incapability's, For the fecal samples it was noted that the detection of Blastocystis was superior to conventional PCR detection.

The data presented here clearly illustrate the extreme broad use of these primer sets and their ability to detect eukaryotic organisms originated from the human body. This is also illustrated in table 10 and llshowing an hereto now new correlation between protozoan parasites and intestinal fungi. Table 6 Diversity among micro-eukaryotic microbiota between healthy individuals and patients with IBS

Healthy individuals Patients with IBS

Genus Mean Median Std dev Mean Median Std dev

Apium (celery) 1,931 62 4,155 630 6 1,594

Galactomyces (fungi) 11,799 149 23,800 34 23 33

Saccharomyces 1,378 1,051 1,311 3,517 3,544 2,451

(fungi)

Toxoplasma 2,581 86 3,371 717 879 501

(protozoa)

Triticum (wheat) 879 21 1,480 174 39 236

Table 7 Comparison of micro-eukaryotic microbiota

Healthy individuals with Healthy individuals with

protozoan infection no protozoan infection

Genus Mean Median Std dev Mean Median Std dev

Apium (celery) 3,257 276 5,242 341 6 612

Aspergillus (fungi) 20 10 26 3,577 1,094 4,419

Edyuillia (fungi) 8 8 7 376 157 469

Eladia (fungi) 7 5 5 2,713 1,098 3,517

Emericella (fungi) 3 3 2 2,670 875 3,166

Eupenicillium (fungi) 202 4 344 902 892 778

Eurotium (fungi) 9 9 8 442 183 553

Galactomyces (fungi) 11,791 204 20,952 11,807 21 26,343

Hemicarpenteles (fungi) 8 8 7 379 159 472

Malassezio (fungi) 26 7 32 854 608 976

Neosartorya (fungi) 7 6 5 2,851 1,156 3,693

Penicillium (fungi) 168 10 324 3,490 1,539 4,668 References

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Claims

Claims

1. A method of detecting of non-human eukaryotic SSL) rDNA in human clinical samples using 18S DNA amplification.

2. A method according to claim 1 where the primers in the 18S DNA amplification are chosen from SEQ ID NO 1-12.

3. A method according to claim 1-2 where the 18S DNA amplification is followed by a post-amplification step for species or genus annotation.

4. A method according to claim 3, where the post-amplification step is deep sequencing using microarrays or NGS technologies like the lllumina platform, Hiseq or Miseq.

5. A method according to claim 1-4 where the sample is from faeces, spinal fluid, blood or biopsies

6. A method according to claim 5, where parasites detected are among the following species:

Acanthamoeba, Ancylostoma, Angiostrongylus, Anisakis, Ascaris, Babesia, Balamuthia, Balantidium, Baylisascaris, Blastocysts, Brugia, Capillaria, Chilomastix, Clonorchis, Cryptosporidium, Cyclospora,

Cystoisospora, Dicrocoelium, Dientamoeba, Dioctophyme, Diphyllobothrium, Dracunculus, Echinococcus, Echinostoma, Entamoeba, Enterobius, Fasciola, Fasciolopsis, Giardia, Gnathostoma, Hymenolepis, Isospora, Leishmania, Microsporidia, Naegleria, Necator, Onchocerca, Opisthorchis, Paragonimus, Plasmodium, Pneumocystis, Pseudoterranova, Rhinosporidium, Sappinia, Schistosoma, Spirometra, Strongyloides, Taenia, Toxocara, Toxoplasma, Trichinella, Trichobilharzia, Trichomonas, Trichuris, Trypanosoma and Wuchereria.

7. A method according to claim 5, where outbreaks due to intestinal eukaryotic pathogens, such as Giardia, Cryptosporidium, Cyclospora or microsporidia are detected in faeces samples.