High-resolution melting-curve (HRM) analysis for C. meleagridis identification in stool samples

Accepted Manuscript High-resolution melting-curve (HRM) analysis for C. meleagridis identification in stool samples Hanen Chelbi, Rym Essid, Refka Jelassi, Nesrine Bouzekri, I. Zidi, Hamza Ben Salah, Ilhem Mrad, Iness Ben Sgaier, Rym Abdelmalek, Sameh Aissa, Aida Bouratbine, Karim Aoun PII:

S0882-4010(17)31003-3

DOI:

10.1016/j.micpath.2017.12.070

Reference:

YMPAT 2706

To appear in:

Microbial Pathogenesis

Received Date: 5 September 2017 Revised Date:

22 December 2017

Accepted Date: 28 December 2017

Please cite this article as: Chelbi H, Essid R, Jelassi R, Bouzekri N, Zidi I, Ben Salah H, Mrad I, Ben Sgaier I, Abdelmalek R, Aissa S, Bouratbine A, Aoun K, High-resolution melting-curve (HRM) analysis for C. meleagridis identification in stool samples, Microbial Pathogenesis (2018), doi: 10.1016/ j.micpath.2017.12.070. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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YMPAT_2017_885_R1

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Title: High-Resolution Melting-curve (HRM) Analysis for C. meleagridis Identification in Stool Samples.

Chelbi Hanen1, Essid Rym1, Jelassi Refka 1,2, Bouzekri Nesrine1 ,Zidi I3, Ben Salah Hamza1, Mrad Ilhem4, Ben Sgaier Iness 1,Abdelmalek Rym5, Aissa Sameh5, Bouratbine Aida1, Aoun Karim1

2

of

Medical

Faculty of Sciences of Bizerte; University of Carthage,

3

Parasitology,

Biotechnology

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Affiliation:1LR 11-IPT-06 Laboratory Biomolecules, Pasteur Institute of Tunis,

and

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Department of Biology, Laboratoire Microorganismes et Biomolécules Actives, Sciences Faculty of Tunis, Tunis, Tunisia, 4 Gastroenterology department of regional hospital Ben Arous Tunisia; 5

Department of Infectious diseases, La Rabta hospital, Tunis, Tunisia.

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Correspondance E-mail : [email protected], [email protected]

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Abstract Background: Cryptosporidiosis represents a major public health problem. This infection, caused by a protozoan parasite of the genus Cryptosporidium, has been reported worldwide as a frequent cause of diarrhoea. In the immunocompetent host, the typical watery diarrhea

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can be self-limiting. However, it is severe and chronic, in the immunocompromised host and may cause death. Cryptosporidium spp. are coccidians, which complete their life cycle in both humans and animals. The two species C. hominis and C. parvum are the major cause of

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human infection. Compared to studies on C. hominis and C. parvum, only a few studies have developed methods to identify C. meleagridis. Aim: to develop a new real time PCR-coupled

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High resolution melting assay allowing the detection for C. meleagridis, in addition of the other dominant species (C. hominis and C. parvum). Methods: The polymorphic sequence on the dihydrofolate reductase gene (DHFR) of three species was sequenced to design primers pair and establish a sensitive real-time PCR coupled to a high-resolution melting-curve

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(HRM) analysis method, allowing the detection of Cryptosporidium sp. and discrimination between three prevalent species in Tunisia. We analyzed a collection of 42 archived human isolates of the three studied species. Results: Real-time PCR coupled to HRM assay allowed

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detection of Cryptosporidium, using the new designed primers, and basing on melting profile,

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we can distinguish C. meleagridis species in addition to C. parvum and C. hominis. Conclusion: we developed a qPCR-HRM assay that allows Cryptosporidium genotyping. This method is sensitive and able to distinguish three Cryptosporidium species. Keys words: Cryptosporidium sp.; C. meleagridis; DHFR gene; qPCR-HRM; genotyping

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ACCEPTED MANUSCRIPT 1. INTRODUCTION Cryptosporidium spp. are coccidian protozoan parasites, which complete their life cycle in both humans and animals. They are commonly considered as zoonotic parasites responsible for diarrheal diseases in humans worldwide. The diarrhea due to cryptosporidiosis may

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become profuse and chronic, depending on the health status of the host, and appears to be life threatening in people with various immune-system deficiencies. Many studies have confirmed the high mortality associated with Cryptosporidium infection in patients with human

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immunodeficiency virus infection [1-4]. In humans, C. parvum and C. hominis are responsible for most cases of cryptosporidiosis, but other species, such as C. meleagridis are occasionally

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involved [5-7]. In Tunisia, C. meleagridis revealed more prevalent (21.1%) [8]. This species mainly infects Birds, but it is considered as the third most commonly detected species in humans [9].

Diagnosis of cryptosporidiosis is based on conventional microscopic detection of oocysts in

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stools, but this technique does not allow species identification and is not adapted for epidemiological investigations. Several methods have been developed for the identification and characterization of Cryptosporidium at the molecular level including conventional and

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semi-nested PCR [10], single-strand conformation polymorphism (SSCP) [11], mutation

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scanning [12-13] and PCR-restriction fragment length polymorphism (RFLP) [8]. Although these approaches are very useful and effective, the electrophoretic analysis can be quite time consuming to perform. Moreover, DNA amplification and detection are expensive and prone to contamination with the endpoint reading on agarose gels yielding no quantitative information. Multiplex real-time PCR using fluorescent detection probes through the possibility of combining assays for the detection of different targets into one reaction [14]. However, this technique is relatively expensive.

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ACCEPTED MANUSCRIPT Due to the increased demand for rapid, high-throughput diagnosis and genetic analysis of pathogens, there has been a considerable focus on the evaluation and development of advanced detection methods that obviate the need for electrophoretic analysis, reduce the risk of contamination and substantially decrease labour time and reagent costs. High-resolution

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melting (HRM) analysis is a relatively new post-PCR analysis that allows direct characterization of PCR amplicons in a closed system. Probe-free HRM real-time PCR does not require the multiplex method. There has no manual post-PCR processing. Then, it has a

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low reaction cost relative to other methods for rapid screening and detection of closely related species in a laboratory.

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The aim of this study is to develop a new real time PCR-coupled High resolution melting assay (method) allowing the detection for C. meleagridis, the third most common species involved in human cryptosporidiosis, in addition of the other dominant species (C. hominis and C. parvum).

2.1.

Control groups

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2. MATERIALS AND METHODS

PCR assay was evaluated on 42 stool samples corresponding to: 22 stool specimens revealed positives for Cryptosporidium (as following: 11 C.

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parvum, 2 C. hominis, 2 C. meleagridis and 7 species not identified) by nested PCRRFLP targeting the 18S rRNA gene as previously described [8].

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20 faecal samples of individuals with no history of parasitic infections or with DNA of other

parasites

(Plasmodium,

Toxoplasma,

Blastocystis,

Entamoeba

and

Microsporidia). This set of samples will serve to evaluate the specificity of our assay. All these isolates were investigated for the presence of Cryptosporidium sp. using a real time PCR coupled to high-resolution melting-curve (HRM) analysis method.

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DNA extraction

DNA was extracted from all collected samples, using QIAamp DNA Stool Mini-Kit (Qiagen Inc., Hilden, Germany), according to the manufacturer’s recommendations. Detection of Cryptosporidium species

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2.3.

Detection of Cryptosporidium species was performed using a two-step 18S rRNA nested PCR followed by a RFLP analysis as described by Essid et al. [8] and qPCR-HRM methods [15]. Primer design for C. meleagridis detection by qPCR-HRM

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2.4.

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2.4.1. DHFR gene sequencing

We amplified a band of 575 bp belonging to the dihydrofolate reductase (DHFR) gene by conventional PCR using the primers: CINF: GTGGGGATTTAACTTGATTT and CINR: GGTATTTCTGGGAAATAAGT [15]. DHFR gene of six positive samples, identified

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previously as C. parvum (2 samples), C. hominis (2 samples) and C. meleagridis (2 samples) by Nested PCR-RFLP method targeting the 18S rRNA gene [8], was sequenced by Sanger method

and

compared

with

reference

sequences.

The

BLASTN

program

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(https://blast.ncbi.nlm.nih.gov) was used to achieve the alignment of the resulting sequences with those published in the NCBI database.

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2.4.2. Primer design

From the consensus sequence, a design of primer pair was performed using Primer 3 tool (http://frodo.wi.mit.edu/primer3/). The designed primer pair was used for the detection and Cryptosporidium species identification by qPCR-HRM. We created amplicons with nonoverlapping melting temperatures and smaller than 300 bp, to have higher sensibility in the HRM analysis. 2.4.3. Primers Specificity test

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ACCEPTED MANUSCRIPT The specificity of the designed primers was tested using the BLASTN program. We compared our primer sequences to those published in the EMBL database of the DHFR gene of other parasites such as (Plasmodium, Toxoplasma, Blastocystis, Entamoeba and Microsporidia). Real-time PCR coupled with High-resolution melting assay (qPCR-HRM)

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2.5.

All isolates were investigated for the presence of Cryptosporidium sp., by a real-time PCR coupled to high-resolution melting-curve (qPCR-HRM) analysis method, using the designed primers. We adjusted primers concentration and temperature correction controls to minimize

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interferences. The PCR reaction was performed in a total volume of 10 µl containing 1 µl

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DNA, 30 nM of each designed primers (HanF 5’-TCCTAGCCGAAGTAGAACA-3’ and HanR 5’-CGGTGTTGTTAACTTTGCA-3’), 5 µl ssoPCR Master Mix (SsoFast™ EvaGreen® Supermix; Bio-Rad) and sterile DNase/RNase-free water (Sigma, St. Louis, USA) using a CFX 96 real-time PCR machine (Bio-Rad). The cycling conditions were as follows: initial denaturation at 95°C for 15 min, followed by 40 cycles of denaturation at 95°C

2.6.

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for 5 sec, annealing at 60°C for 30 sec, and final extension step at 72°C for 1 sec. Species differentiation and genotyping by melting curve analysis

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After the last PCR cycle, conventional melting step was performed from 45°C to 80°C with a slow ramp (0.2°C/sec) with continuous detection through the ramp. Data were generated

and

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using the CFX96 tm real-time PCR detection system (Bio-Rad Laboratories, Inc). For qPCR HRM

reactions

optimization,

the

following

parameters

were

varied:

melting temperature (Tm), DNA concentration, annealing temperature, single primer concentration and number of cycles. 2.7.

Comparison between real-time PCR-HRM assays and conventional PCR-RFLP

The performance, sensitivity and specificity of the new real-time PCR-HRM assay for the detection and species identification of Cryptosporidium sp. were determined by assaying the

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ACCEPTED MANUSCRIPT 42 samples collected. Then, the obtained results were compared to microscopy Modified Ziehl-Neelsen staining (MZN), nested PCR-RFLP as reported previously [8] and PCR-HRM assay protocol used by Soliman and Othman, 2009 [15].

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3. Statistical analysis: Means (±SD) of the melting temperature in five independent assays were calculated.

4. RESULTS DHFR sequencing:

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4.1.

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Sequencing of 575 bp band from DHFR gene was obtained for reference control DNA. In order to confirm the species identity of Cryptosporidium, alignment of our sequences with those published in the NCBI database shows 100% identity to the species C. parvum, C. hominis and 92% for C. meleagridis.

Specificity of predesigned primer

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4.2.

The specificity of new primer pair was tested. Indeed, the alignment of primers by the BLASTN program showed 100% of similarity with sequence of DHFR gene of three species

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of Cryptosporidium sp. The alignment of primers with sequences of DHFR gene in the

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published databases NCBI and EMBL shows no identity with sequences of other protozoa in close taxonomic relationship with Cryptosporidium (Plasmodium, Toxoplasma, Blastocystis, Entamoeba and Microsporidia) 4.3.

Sample categorization based on HRM curve profile

In order to examine the reproducibility and consistency of each melting profile, amplicons representing the reference control DNA from each Cryptosporidium species were tested in triplicate and repeated on several different days by keeping the same chemistry environment

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ACCEPTED MANUSCRIPT with similar reagents and DNA concentrations. Our results demonstrated that the reproducibility of the assay was very high with consistent melting patterns between runs for each species analyzed on different days.

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The melting characteristics of DHFR amplicons from all species were assessed by plotting three different curves (Figure 1, 2a, 2b). In the present study, aligned melt curve (Figure 1), the normalized fluorescence curves (Figure 2a) and difference plot melt curve (Figure 2b)

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produced only different plots that were easily distinguishable for each species. Genotyping of Cryptosporidium sp. basing on melting curve analysis revealed: one peak at Tm 76°C±0.10

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specific to C. parvum, one peak at Tm 77.5°C±0.05 specific to C. meleagridis and one peak at Tm 78°C±0.04 specific to C. hominis (Figure 1). In this work, we succeed to identify C. meleagridis, in addition to C. parvum and C. hominis, with good discrimination showing the presence of three normalized curve with different form and colors (Figure 2).

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Screen of our cohort shows: All stool specimens positive by nested PCR [8] were also positive by qPCR-HRM analysis allowed identification in all cases of C. hominis, C. parvum and C. meleagridis. We also succeed to identify species in seven samples positive by PCR but

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not species identified by enzymatic digestion (PCR-RFLP method). Six of new identified samples were genotyped as C. parvum and one was identified as C. hominis. Three

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amplifications were noted in negative samples by PCR-RFLP test and positive by microscopy. They were identified as: 2 C. hominis and one C. meleagridis (table 2). No amplifications were noted in samples with others parasites (figure 3).

For ten new identified isolates, amplification and specie genotyping were confirmed using the protocol of Soliman and Othman [15] and sequencing Syber Green amplified product using new designed primer. Compared to Soliman and Othman’s protocol, our HRM assay is able to detect in addition to C. parvum and C. hominis the C. meleagridis specie (Table 3). 7

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Comparison between real-time PCR-HRM assay and conventional nested PCR-

HRM Twenty-five of 42 faecal samples were positive for Cryptosporidium sp. by our PCR-HRM. However, specific Cryptosporidium amplification was only detected in 21 (84.0% of 25)

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samples via conventional nested PCR-RFLP, in 22 (88.0% of 25) samples via Soliman and Othman’s protocol [15]. The same stool samples (N =42) were also analyzed for species identification by real-time PCR-HRM. Species identified in all positive samples were in

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accordance to our previously published work [8] (table 2).

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In the present study, the sensitivity and the specificity of the conventional nested PCR-RFLP and real-time PCR-HRM assays for detection of Cryptosporidium were also evaluated. As for the sensitivity (the ability of the assay to identify true positive Cryptosporidium infections), our real-time PCR-HRM assay had higher sensitivity (100%) compared to conventional nested PCR-RFLP (74.1%) and Soliman and Othman’s HRM assay (84.1%) (Table 2). With

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regards to the specificity (the ability of the assay to identify true negative Cryptosporidium infections), our real-time PCR- HRM assay and conventional nested PCR-RFLP both assays

(Table 2).

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gave higher specificity (100%) as compared to Soliman and Othman’s HRM assay (98.1%)

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In our previous work, 10 samples were not identified at species level. However, these samples were amplified and identified as 6 C. parvum, 3 C. hominis and 1 C. meleagridis based on their melting profile obtained by the HRM assay (Table 3).

5. Discussion Development of easy-to-use, cost-effective, and reliable new diagnostic methods accessible to primary health care providers for screening patients is needed for the rapid detection and species identification of Cryptosporidium. Rapid screening for cryptosporidiosis at primary 8

ACCEPTED MANUSCRIPT health limit transmission and would reduce the unnecessary use of medicament provided to Cryptosporidium patients misdiagnosed as having cryptosporidiosis illnesses. Identification of species was obtained in all samples and correlated with nested PCR results.

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Melting curve analysis was also evaluated for diagnosis and differentiation of several other parasites. A FRET real-time PCR assay for rapid detection and differentiation of Plasmodium species in returning travelers and migrants was evaluated [16]. Most PCR-based genotyping assays available for Cryptosporidium sp. requires the additional step of restriction enzyme

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digestion [17]. The HRM-PCR using the genotype allele changes within the DHFR gene, but

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requires no restriction enzymes to differentiate all genotypes. To improve the detection sensitivity, PCR assays using multicopy rRNA genes have been developed [18]. However, associated problems with heterogeneity of the rDNA transcription units have also been reported [19]. The use of the single gene copy DHFR sequence would not be affected by such heterogeneity [11]. It is important to develop species discriminatory techniques in conjunction

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with multilocus genotyping and subtyping to characterize individual isolates fully, to aid epidemiological studies, outbreak tracing and in validation of phylogenetic studies.

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We developed a real-time PCR assays that can readily detect Cryptosporidium parasite and discriminate between three species responsible for the most cases of human cryptosporidiosis.

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In fact, by performing HRM analysis on isolate amplicons, we were now able to rapidly distinguish between three species not two: C. parvum, C. hominis and C. meleagridis. The objective of this study was also to perform the sensitivity and specificity of the real-time PCR-HRM using new designed primers flunking target sequence generated shorter than 500 bp (300 bp). The genotyping using developed assays (qPCR-HRM) of seven isolates non identified at the species level by PCR-RFLP suggests a good sensitivity of this assay. Moreover, absence of amplifications in negative samples and in samples with others parasites suggests a good specificity of this technique [10]. Loss of sensitivity occurs if the test assay 9

ACCEPTED MANUSCRIPT does not produce a positive result when the gold standard assay is determined to be positive. Our developed method gives 100% Cryptosporidium sensitivity higher than PCR-RFLP (74.1%) and the protocol of Soliman and Othman (84.1%). Loss of specificity occurs due to a false-positive result (positive by the PCR-RFLP assay but not by our qPCR-HRM assays). In

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this study, none false positive was observed.

These results are in correlation with others studies [20] and conclude that the use of this assay in a diagnostic laboratory would provide a sensitive and specific diagnosis of the main

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parasitic diarrheal infections and could improve patient management and infection control. However, this technique present a disadvantage is that the dye binds to all double-stranded

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DNA in a sample, which includes primer dimers and nonspecific products. This limitation can be overcome by acquiring fluorescence data at a temperature that denatures the non-specific products and leaves the specific products intact.

In conclusion, the present real-time PCR-coupled with melting-curve analysis approach is

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suited for the rapid screening of large numbers of Cryptosporidium oocyst DNA samples. This approach, although qualitative, has some advantages over some electrophoretic

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techniques [21-22] particularly in relation to analysis time, sample through-put, and data storage and analysis capacities.

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Conflict of interest:

The authors have declared no conflicting interests

Funding :

This study received financial support from Ministry of Higher Education and Scientific Research, Tunisia (LR11-IPT-06 ), and from collaborative intern program (PCI-06 2013) of Pasteur Institute of Tunis.

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References [1] Goldstein ST, Juranek DD, Ravenholt O, Hightower AW, Martin DG, Mesnikb JL,

Griffiths SD, Bryant AJ, Reich RR, Herwaldt BL (1996) Cryptosporidiosis an outbreak associated with drinking water despite state-of-the-art water treatment. Ann Intern Med

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124:459–468.

[2] Mor SM, DeMaria A Jr, Griffiths JK, Naumova EN (2009) Cryptosporidiosis in the elderly population of the United States. Clin Infect Dis 48:698–705.

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[3] Nahrevanian H, Assmar M (2008) Cryptosporidiosis in immunocompromised patients in the Islamic Republic of Iran. J Microbiol Immunol Infect 41:74–77.

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[4] Sodqi M, Marih L, Lahsen AO, Bensghir R, Chakib A, Himmich H, El Filali KM (2012) Causes of death among 91 HIV-infected adults in the era of potent antiretroviral therapy. Presse Med 41:e386–e390.

[5] Pedraza-Diaz S, Amar C, and McLauchlin J (2000). The identification and

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characterisation of an unusual genotype of Cryptosporidium from human faeces as Cryptosporidium meleagridis. FEMS Microbiol. Lett 189(2):189-194 [6] Essid R, Chelbi H, Siala E, Bensghair I, Menotti J, Bouratbine A (2017). Polymorphism

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298–303.

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study of Cryptosporidium hominis gp60 subtypes circulating in Tunisia. Microb Pathog 110,

[7] Chalmers RM, Elwin K, Thomas AL, Guy EC, Mason B (2009) Long-term Cryptosporidium typing reveals the aetiology and species-specific epidemiology of human cryptosporidiosis in England and Wales, 2000 to 2003. Euro Surveill 14: pii: 19086. [8] Essid R, Mousli M, Aoun K, Abdelmalek R, Mellouli F, Kanoun F, Derouin F, Bouratbine A (2008) Identification of Cryptosporidium species infecting humans in Tunisia. Am J Trop Med Hyg 79 (5):702-5.

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ACCEPTED MANUSCRIPT [9] Slapeta J (2013) Cryptosporidiosis and Cryptosporidium species in animals and humans: A thirty colour rainbow? Int J Parasitol 43(12-13): 957-70. [10] Coupe S, Sarfati C, Hamane S, Derouin F (2005) Detection of Cryptosporidium and identification to the species level by nested PCR and restriction fragment length

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polymorphism. J Clin Microbiol 43(3): 1017-1023.

[11] Gasser RB, Zhu X, Caccio S, Chalmers R, Widmer G, Morgan UM, Thompson RC, Pozio E, Browning GF (2001) Genotyping Cryptosporidium parvum by single-strand

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conformation polymorphism analysis of ribosomal and heat shock gene regions.

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Electrophoresis 22 (3): 433-7.

[12] Jex AR, Whipp M, Campbell BE, Caccio SM, Stevens M, Hogg G, et al. ( 2007) A practical and cost-effective mutation scanning-based approach for investigating genetic variation in Cryptosporidium. Electrophoresis 28: 3875–83.

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[13] Jex AR, Pangasa A, Campbell BE, Whipp M, Hogg G, Sinclair MI, et al. (2008) Classification of Cryptosporidium species from patients with sporadic cryptosporidiosis by use of sequence-based multilocus analysis following mutation scanning. J Clin Microbiol 46:

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2252–62.

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[14] Hadfield SJ, Pachebat JA, Swain MT, Robinson G, Cameron SJ, Alexander J, Hegarty MJ, Elwin K, Chalmers RM (2015) Generation of whole genome sequences of new Cryptosporidium hominis and Cryptosporidium parvum isolates directly from stool samples. BMC Genomics 16(1): 650. [15] Soliman RH and Othman AA (2009) Evaluation of DNA Melting Curve Analysis RealTime PCR for Detection and Differentiation of Cryptosporidium Species. Parasitologists United Journal (PUJ) 2; 47-54.

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ACCEPTED MANUSCRIPT [16] Safeukui I, Millet P, Boucher S, Melinard L, Fregeville F, Receveur MC et al. (2008) Evaluation of FRET real-time PCR assay for rapid detection and differentiation of Plasmodium species in returning travelers and migrants. Malar J 7:70-81. [17] Leone A, Ripabelli G, Sammarco ML (2009) Grasso GM. Detection of Cryptosporidium

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spp. from human faeces by PCR-RFLP, cloning and sequencing. Parasitol Res 104 (3):583-7.

[18] Morgan UM, Thompson RC (1998) PCR detection of Cryptosporidium: the way

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forward? Parasitol Today 14(6): 241-5.

[19] Le Blancq SM, Khramtsov NV, Zamani F, Upton SJ, Wu TW (1997) Ribosomal RNA

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gene organisation in Cryptosporidium parvum. Mol Biochem Parasitol 90(2): 463-78. [20] Pangasaa A, Jexa AR, Campbella B, Botta JN, Whippb M, Hoggb G, Stevensc AM, Gassera RB (2009) High-resolution melting-curve (HRM) analysis for the diagnosis of cryptosporidiosis in humans. Mol Cell Probes 23(1):10-5.

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[21] Gasser RB, Abs EL-Osta YG, Chalmers RM (2003) Electrophoretic analysis of genetic variability within Cryptosporidium parvum from imported and autochthonous cases of cryptosporidiosis in the United Kingdom. Appl Environ Microbiol 69: 2719–30.

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[22] Abs EL-Osta YG, Chalmers RM, Gasser RB (2003) Survey of Cryptosporidium parvum genotypes in humans from the UK by mutation scanning of a heat shock protein gene region.

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Mol Cell Probes 17: 127–34.

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ACCEPTED MANUSCRIPT Table 1: Molecular identification of Cryptosporidium spp. in the cases studied by qPCRHRM using designed primer. Negative -

C. parvum

C. meleagridis

C. hominis

Tm(°C)

-

77.6

77.8

77.2

Number

17

16

03

06

-

38

7.14

Percentage (%)

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C. spp.

Positive

14.28

PCR-RFLP(a) (b) Positive

PCR-HRM [15] (c)

Negative

n

n

%

n

Positive

13

10

0

Negative

29

11

21

a

Positive

negative

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Microscopy(a)

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Table 2: Comparison between microscopy, conventional PCR-RFLP and real-time PCRHRM assays (N=42)

%

n

%

n

%

PCR-HRM(d)

Positive n

%

Negative n

09

01

10

0

13

18

15

15

%

Details have been published elsewhere [8]. Sensitivity: 74.1%; Specificity: 100%. c Sensitivity: 84.1% Specificity: 98.1%. d Sensitivity: 100%; Specificity: 100%

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b

Table 3: Cryptosporidium species detected via both conventional nested PC-RFLP and realtime PCR-HRM assays (N = 25)

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PCR-RFLP (a)

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n

PCR-HRM

PCR-HRM

(Soliman and Othman, 2009)

%

n

%

n

%

C. parvum

11

40

12

48

16

64

C. hominis

02

08

03

12

06

24

C. meleagridis

02

04

0

0

03

12

Negative

10

48

-

40

0

0

Total

25

100

25

100

25

100

(a): details have been published elsewhere [8].

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Fig1: Melting curve analysis of real time PCR amplification product. Peaks at 77.6 represent the band of C. parvum, the peaks at 77.8 represent the band of C. hominis, and the peaks at 77.2 represent the band of C. meleagridis.

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a

b

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Fig 2: HRM analysis: Melt curves. Data were generated using the CFX96™ real-time PCR detection system and analyzed using Precision Melt Analysis™ software. (a) Highresolution melting profile in the normalized graph mode: Pre-melt (initial) and postmelt (final) fluorescence signals of all samples were normalized to relative values of 1.0 and 0. (b): High-resolution melting profile in difference graph mode: differences were magnified by subtracting each curve from the most abundant type or from a user-defined reference. The green and red boxes indicate the pre- and post-melt regions, respectively, used for data normalization. RFU, relative fluorescence units.

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Fig 3: Specificity of designed primer test: quantification curves demonstrate amplification of Cryptosporidium positive samples and no amplification of sample positive of others microorganisms.

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Highlights -

We sequenced the polymorphic sequence on the dihydrofolate reductase gene (DHFR) of three species of Cryptosporidium We designed new primers pair

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We developed a real time PCR coupled High-resolution melting-curve (HRM)

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analysis.

The new PCR-HRM allowing the detection for C. meleagridis, in addition of C.

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hominis and C. parvum.

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