Advertisement
  • Loading metrics

DNA Microarray Detection of 18 Important Human Blood Protozoan Species

  • Mu-Xin Chen ,

    Contributed equally to this work with: Mu-Xin Chen, Lin Ai

    Affiliations Department of Microbiology and Microbial Engineering, School of Life Sciences, Fudan University, Shanghai, PR China, National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, WHO Collaborating Center for Tropical Diseases, Key Laboratory of Parasite and Vector Biology, National Health and Family Planning Commission, Shanghai, PR China

  • Lin Ai ,

    Contributed equally to this work with: Mu-Xin Chen, Lin Ai

    Affiliation National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, WHO Collaborating Center for Tropical Diseases, Key Laboratory of Parasite and Vector Biology, National Health and Family Planning Commission, Shanghai, PR China

  • Jun-Hu Chen,

    Affiliation National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, WHO Collaborating Center for Tropical Diseases, Key Laboratory of Parasite and Vector Biology, National Health and Family Planning Commission, Shanghai, PR China

  • Xin-Yu Feng,

    Affiliation National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, WHO Collaborating Center for Tropical Diseases, Key Laboratory of Parasite and Vector Biology, National Health and Family Planning Commission, Shanghai, PR China

  • Shao-Hong Chen,

    Affiliation National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, WHO Collaborating Center for Tropical Diseases, Key Laboratory of Parasite and Vector Biology, National Health and Family Planning Commission, Shanghai, PR China

  • Yu-Chun Cai,

    Affiliation National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, WHO Collaborating Center for Tropical Diseases, Key Laboratory of Parasite and Vector Biology, National Health and Family Planning Commission, Shanghai, PR China

  • Yan Lu,

    Affiliation National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, WHO Collaborating Center for Tropical Diseases, Key Laboratory of Parasite and Vector Biology, National Health and Family Planning Commission, Shanghai, PR China

  • Xiao-Nong Zhou,

    Affiliation National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, WHO Collaborating Center for Tropical Diseases, Key Laboratory of Parasite and Vector Biology, National Health and Family Planning Commission, Shanghai, PR China

  • Jia-Xu Chen ,

    huw@fudan.edu.cn (WH); chenjiaxu1962@163.com (J-XC)

    Affiliation National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, WHO Collaborating Center for Tropical Diseases, Key Laboratory of Parasite and Vector Biology, National Health and Family Planning Commission, Shanghai, PR China

  • Wei Hu

    huw@fudan.edu.cn (WH); chenjiaxu1962@163.com (J-XC)

    Affiliations Department of Microbiology and Microbial Engineering, School of Life Sciences, Fudan University, Shanghai, PR China, National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, WHO Collaborating Center for Tropical Diseases, Key Laboratory of Parasite and Vector Biology, National Health and Family Planning Commission, Shanghai, PR China

DNA Microarray Detection of 18 Important Human Blood Protozoan Species

  • Mu-Xin Chen, 
  • Lin Ai, 
  • Jun-Hu Chen, 
  • Xin-Yu Feng, 
  • Shao-Hong Chen, 
  • Yu-Chun Cai, 
  • Yan Lu, 
  • Xiao-Nong Zhou, 
  • Jia-Xu Chen, 
  • Wei Hu
PLOS
x

Abstract

Background

Accurate detection of blood protozoa from clinical samples is important for diagnosis, treatment and control of related diseases. In this preliminary study, a novel DNA microarray system was assessed for the detection of Plasmodium, Leishmania, Trypanosoma, Toxoplasma gondii and Babesia in humans, animals, and vectors, in comparison with microscopy and PCR data. Developing a rapid, simple, and convenient detection method for protozoan detection is an urgent need.

Methodology/Principal Findings

The microarray assay simultaneously identified 18 species of common blood protozoa based on the differences in respective target genes. A total of 20 specific primer pairs and 107 microarray probes were selected according to conserved regions which were designed to identify 18 species in 5 blood protozoan genera. The positive detection rate of the microarray assay was 91.78% (402/438). Sensitivity and specificity for blood protozoan detection ranged from 82.4% (95%CI: 65.9% ~ 98.8%) to 100.0% and 95.1% (95%CI: 93.2% ~ 97.0%) to 100.0%, respectively. Positive predictive value (PPV) and negative predictive value (NPV) ranged from 20.0% (95%CI: 2.5% ~ 37.5%) to 100.0% and 96.8% (95%CI: 95.0% ~ 98.6%) to 100.0%, respectively. Youden index varied from 0.82 to 0.98. The detection limit of the DNA microarrays ranged from 200 to 500 copies/reaction, similar to PCR findings. The concordance rate between microarray data and DNA sequencing results was 100%.

Conclusions/Significance

Overall, the newly developed microarray platform provides a convenient, highly accurate, and reliable clinical assay for the determination of blood protozoan species.

Author Summary

More than 1 billion people are infected with blood protozoan diseases worldwide. The most common blood protozoa in humans, animals, and vectors include Plasmodium, Leishmania, Trypanosoma, Toxoplasma gondii and Babesia. Due to similar morphology among different blood protozoan species, misdiagnosis always occurs. Most molecular techniques are only carried out in laboratories, with a small number of samples detected simultaneously. Meanwhile, common detection methods may not be convenient for field investigation of large amounts of samples. In order to better manage blood protozoan infection, proper tools are required for the monitoring of these pathogens. Here, a comprehensive and sensitive DNA microarray was developed and tested, which allowed the parallel detection of 18 blood protozoan species.

Introduction

Blood protozoa are single-cell organisms that often have flagella, cilia or other structures that help them move [1]. They sometimes form parasitic relationships with humans and cause diseases or infections. The most common blood protozoa in humans, animals, and vectors include Plasmodium, Leishmania, Trypanosoma, Toxoplasma gondii and Babesia [26].

Malaria, caused by one of four Plasmodium species (P. falciparum, P. vivax, P. malariae, and P. ovale) and spread through anopheles mosquitoes. It is the deadliest protozoan diseases with nearly 800,000 deaths yearly [2]. Once Plasmodium enters into human body, it matures in the liver and blood cells. Symptoms include fever with chills and rigor followed by excessive sweating. If not detected promptly, it can cause cerebral malaria and even death [7]. Hemoflagellates constitute another important group of blood protozoa, whose family includes two genera, Leishmania and Trypanosoma, both of which require a blood feeding insect vector for transmission, and can infect humans [8, 9]. Leishmaniasis is caused by one of 20 species of protozoa of the Leishmania genus. Individuals infected with leishmaniasis often show signs and symptoms such as sores and/or ulcers, swollen glands, weight loss, fever and/or an enlarged spleen [3]. Trypanosomes are distributed in specific areas mainly dictated by vector distribution. Trypanosoma brucei rhodesiense and T. b. gambiense cause Human African Trypanosomiasis (HAT), which is transmitted by the tsetse fly. The disease causes fever, headaches, itching, coordination and balance problems, confusion and/or reduced mental abilities. Sometimes, it affects the central nervous system; without treatment, it can be fatal [10, 11]. Trypanosoma cruzi, the causative agent of American trypanosomiasis (Chagas’ disease), is characterized by cardiomyopathy, which may present with arrhythmias, conduction defects, cardiomegaly, thromboembolic events, or congestive heart failure [12, 13]. Toxoplasmosis is caused by Toxoplasma gondii, which is caused by consumption of contaminated meat [14]. Sometimes, accidental ingestion of cat stool or possibly unwashed vegetables also can infect this disease [14]. Toxoplasmosis is usually asymptomatic and self-limiting but can have serious or fatal effects on fetuses whose mothers contract the disease during pregnancy or in immuno-compromised individuals [15]. For instance, it may be fatal in individuals infected with HIV, due to encephalitis or necrotizing retinochoroiditis [16, 17]. Babesia causes a hemolytic disease known as babesiosis, which causes malaria-like symptoms and is often misdiagnosed as malaria. Approximately 100 species of Babesia have been identified, but only a few are documented as humans pathogens [18, 19]. Babesia microti is the most common strain associated with human infections [20].

Blood protozoan infection is usually diagnosed by microscopic examination of blood smears [1]. Since such detection depends on operator experience, misdiagnosis is common. To overcome this drawback, conventional PCR assays in combination with amplicon sequencing have been used for sensitive and specific detection of several blood protozoan species [2125]. Molecular techniques have undergone great improvements in recent years. For example, DNA microarrays have been proposed, as high-density microscopic arrangement of immobilized nucleic acid samples on a glass slide; hybridization with fluorescent probes permits evaluation of gene expression at the genome level [26, 27]. The DNA microarray technique has been successfully applied for a range of biological questions, including human cancer[28], metamorphosis of fruit flies [29], and helminth parasites [30]. Recently, DNA microarrays have also been developed and applied for the assessment of major protozoa that cause human and animals’ diseases [3133].

DNA microarray is an advanced, large-scale, and high output detection technology, which is able to provide a decent platform to genomics and functional genomics research on blood parasites. Currently, gene microarray is mainly applied for functional gene screening [34], exploring the relationships between blood protozoan and their hosts [35], pathogenesis mechanisms [36], drug resistance and targets [37], diagnostic antigens [38], and molecular vaccine screening [39], all of which have yielded excellent results. However, only few of the multitude of microarray technologies are suitable for clinical application and large-scale epidemiological investigations of blood protozoan infections. For example, Li et al. (2005) [40] designed a DNA array for rapid detection and genotyping of pathogenic microbes responsible for epidemic hemorrhagic fever, Tsutsugamushi disease, leptospirosis, malaria, schistosomiasis, cholera, and hemorrhagic colitis. El-Ashker et al. (2015) [41] reported Babesia, Theileria, and Anaplasma species in cattle using PCR assays, gene sequence analysis and a novel DNA microarray. The above studies only included few species of blood protozoa. Thus, the present study aimed to assess the potential diagnostic value of a novel DNA microarray chip in comparison with microscopy and PCR for the diagnosis of common blood protozoan infections.

Materials and Methods

STARD Checklist and flowchart could be found in S1 and S2 Figs, respectively.

Ethical Statement

Ethical clearance for the collection and detection of human samples was obtained from the Ethics Committee of the National Institute of Parasitic Diseases (NIPD), Chinese Center for Disease Control and Prevention (China CDC). The objectives, procedures and potential risks were verbally explained to all participants. Signed written informed consent was obtained from all study participants. The study approval notice is found in (S3 Fig). Animals were handled in accordance with good animal practice strictly according to the Animal Ethics Procedures and Guidelines of the People’s Republic of China. The protocol for sampling from animals had been approved by the Animal Welfare& Ethics Committee of the National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention in Shanghai (Permit No: IPD-2012-5) (S3 Fig).

Samples and blood protozoan specimen

Reference blood protozoan samples were either stored in our laboratory or kindly provided by different partner laboratories (S1 Table). A total of 438 samples from humans, animals and vectors (S2 Table) collected from August 2012 to December 2014 were obtained in Myanmar-Yunnan border, Hainan Province, Xinjiang Uygur Autonomous Region, Gansu Province, Guangzhou and Shenzhen in Guangdong Province, and Shanghai (China), as well as 100 blood samples from healthy individuals (S2 Table). B. microti was from patients in Yunnan and Shanghai, and B. venatorum from tick in Heilongjiang; P. vivax, P. falciparum, P. knowlesi, P. malariae, P. ovale and mixed species Plasmodium were from patients in Yuannan; L. donovani was from human, sand fly and dog in Xingjiang and Sichuan; L. infantum was from humans, sand fly and dog in Xingjiang, Sichuan and Gansu; T. gondii was from goat, human, cougar, cat, deer, and toucan in the USA, France, Canada, Brazil, Costa Rica and China (Qinghai, Yunnan and Guangdong). All blood samples were collected according to patient consent as well as medical ethics norms.

Blood smears were prepared for each clinical blood sample. After drying the slides at ambient, the blood smears were quickly fixed in methanol (99%) for 5 min, then stained with 10% Giemsa staining solution (Sigma–Aldrich Chemie GmbH, Taufkirchen, Germany) for 30 min. The slides were examined under an oil immersion lens at a total magnification of 1000 for presence of blood protozoa. After examining more than 50 microscopic fields, blood protozoa were quantified and expressed as a percentage of infected erythrocytes. Blood samples were stored at -80°C until further processing.

In order to detect Leishmania infection, smears of marrow and materials from spleen puncture were dried, following fixed in methanol (99%) for 5 min, then stained with 10% Giemsa staining solution (Sigma–Aldrich Chemie GmbH, Taufkirchen, Germany) for 30 min. The slides were examined under an oil immersion lens for observing amastigotes [42]. Meanwhile, the whole blood (suspected infected Leishmania from human and canine) with also have been tested by the dipstick (recombinant k39 antigen-based immunochromatographic strip) to detect anti-Leishmania antibody (the Kalazar Detect, batch JL1019; InBios, Seattle, WA) [43].

Tick and sandfly samples were preserved in 75% ethanol for subsequent DNA extracting and PCR amplification.

DNA extraction from samples

DNA was extracted from whole blood of human and animals using QIAmp DNA Mini kit 250 (QIAGEN) according to the manufacturer’s instructions. Genomic DNA from marrow, materials from spleen puncture and the vectors (ticks and sandflies) were also extracted by the above kit, which can be used for DNA extracting from both tissue and blood specimens. Positive control samples were used as described in S1 Table. The concentration of DNA was measured on a NanoDrop ND-1000 Spectrophotometer (peQLab Biotechnologie GmbH, Erlangen, Germany).

Establishment of multiple PCR microarray

Specific primers and microarray probe design.

18S small subunit ribosomal DNA (18S rDNA), mitochondrial cytochrome c oxidase subunit 1 (pcox1), Internal Transcribed Spacer (ITS) gene sequences, fumarate hydratase (fh) gene, lysosomal/endosomal membrane protein p67 gene and hypothetical protein gene (hp), etc. of 18 blood protozoan species were obtained from GenBank. In Babesia species (B. microti, B. divergens, B. duncani, and B. venatorum), the 18S rDNA conserved region was selected. For Plasmodium, 18S rDNA+ITS were used to amplify P. vivax and P. falciparum; pcox1 was used for P. knowlesi, P. malariae, and P. ovale. In Leishmania spp., 18S rDNA was selected for L. donovani, L. gerbilli / L. tropica, L. aethiopica and L. infantum; fh was for L. gerbilli; mspC was used for L. tropica. 18S rDNA was also used to identify T. cruzi and T. brucei. Moreover, T. b. rhodesiense and T. b. gambiense were distinguished by p67 and hp gene, respectively. T. gondii was amplified by primers designed according to the conserved region of pcox1. Then, Primer 5.0 and Array Designer 4.2 was used to design specific primers and probes for each gene fragment (S3 and S4 Tables).

The microarray target probes were selected according to ⊿G (hybridization thermodynamics) which was calculated by the nearest-neighbor method [44] and BLAST sequence analysis. For a probe to be considered as “good” candidate for microarrays, ⊿G must be at least -70 kcal/mol for homologous sequences and more than -40 kcal/mol for heterologous ones. A minimum of 6 non-overlapping probes designed from conserved regions were selected for each genus, and each species of blood protozoa was recognized by at least two probes. Due to variability within species, if necessary, two or more source sequences were selected and processed as described above.

Specific PCR amplification.

The specific primers in S3 Table were used for PCR amplification, in a total volume of 25 μL containing 2.5 μL of 10×buffer (Mg2+ free), 2 μL mixed dNTPs (2.5 mM each) (Takara), 10 pmol of each primer (Sangon Biotech Corporation, Shanghai, China) one unit Taq DNA polymerase (Takara), and 3μL of extracted DNA. PCR assays were performed on a thermocycler (Biometra) at 94°C for 5 min (pre-denaturation), followed by 35 cycles of 94°C for 30 s (denaturation), 55°C for 30 s (annealing), and 72°C for 1 min 30 s (extension), and a final extension of 72°C for 10 min. Samples without genomic DNA (distilled water) were included in each PCR run as ‘negative’ controls. An aliquot (5 μL) of each PCR product was examined by 1% agarose gel electrophoresis; gels were stained with ethidium bromide and photographed on a gel documentation system (UVItec). One amplicon representing each of the species was sequenced to confirm their identity.

Multiple PCR DNA microarray preparation and detection.

20 pairs of specific primers (S3 Table) were used in DNA fragment amplification of reference strains of P. falciparum, P. vivax, P. ovale, P. malariae, P. knowlesi, L. donovani, L. infantum, L. tropica, L. aethiopica, L. gerbilli, T. cruzi, T. b. rhodesiense, T. b. gambiense, T. gondii, B. microti, B. divergens, B. venatorum, and B. duncani. Positive amplicons were sequenced directly on an ABI 377 automated DNA sequencer (using Big Dye Terminator Chemistry) employing the above PCR primers (individually).

DNA detection microarray preparation

Oligonucleotide probes were designed in accordance with multiple-sequence alignment analysis of sequences available in GenBank with the Array Designer 4.2 program. Probes were selected in several species-specific sequence regions of the 18S rDNA, cox1, ITS genes, for differentiation among species. An artificial mismatch was introduced into specific oligonucleotide probes to distinguish the similar species and provide better thermal differentiation between the sequences. The probes were between 40 and 58 nucleotides, with melting temperatures (Tm) of 60–65°C. The 5 end of each probe was modified by addition of a spacer with 25 consecutive thymine residues and an amino-linker group for covalent immobilization on the aldehyde-coated glass surface. Oligonucleotide probes were contact printed onto OPAldehyde Slide aldehyde-activated slides at a concentration of 10μM in DNA spotting solution on a Smart Arrayer-96 Microarrayer (both from CapitalBio, Beijing, China), and covalently immobilized on the slides via an amino group at their 5’ ends [4547] to create biochips (Fig 1). All oligonucleotide primers and probes listed in S3 and S4 Tables were obtained from CapitalBio Corporation, Beijing, China. In each array, three types of controls (a surface chemistry control: fluorescent dye HEX-labeled oligonucleotide, a hybridization positive control to monitor the hybridization process: oligonucleotide complementary to a synthetic template, and the negative control for background signal corrections: oligonucleotide designed to not hybridize to any sequence present) were printed.

thumbnail
Fig 1. Probe arrangement of the DNA microarray system.

Babesia 1–11: Babesia genus probes; B. microti 1 & 2: B. microti species probes; B. divergens 1–5: B. divergens species probes; B. venatorum 1–4: B. venatorum species probes; B. duncani: B. duncani species probes; Plasmodium 1–10: Plasmodium genus probes; P. vivax 1 & 2: P. vivax species probes; P. falciparum 1 & 2: P. falciparum species probes; P. knowlesi 1–4: P. knowlesi species probes; P. malariae 1 & 2: P. malariae species probes; P. ovale 1 & 2: P. ovale species probes; Leishmania 1–8: Leishmania genus probes; L. gerbilli 1–6: L. gerbilli species probes; L. donovani 1–10: L. donovani species probes; L. infantum 1–4: L. infantum species probes; L. tropica 1–3: L. tropica species probes; L. aethiopica 1–6: L. aethiopica species probes; Trypanosoma 1–12: Trypanosoma genus probes; T. cruzi 1–3: T. cruzi species probes; T. b. rhodesiense 1–3: T. b. rhodesiense species probes; T. b. gambiense 1–3: T. b. gambiense species probes; T. gondii 1–4: T. gondii species probes. QC: quality control; PC: positive control; NC: negative control.

https://doi.org/10.1371/journal.pntd.0005160.g001

Microarray probe in silico analysis

The hybridization thermodynamics of probes have been evaluated in silico with reference blood protozoan isolates shown in Table 1. The hybridization ⊿G (kcal/mol) between probe and target was calculated by the nearest-neighbor method. Detection of a target was achieved at⊿G < -50 kcal/mol [48].

thumbnail
Table 1. Reference blood protozoan species used in microarray validation.

https://doi.org/10.1371/journal.pntd.0005160.t001

Microarray production

Illumina Oligator (Illumina Inc., CA, USA) was used to synthesize probes. Then, oligonucleotides were resuspended to 400 pmol in 3×SSC buffer (0.45 M NaCl, 45 mM sodium citrate, pH 7.0), following spotted onto epoxide-coated glass slides in the Microarray Facility of CapitalBio Corporation. One specific probe was contained in each spot that can detect one blood protozoan species. Slides were stored in a humidity-free chamber until use.

Coupling reactions of sample DNA with Cy3 as well as probes with Cy5 dyes (GE HealthCare, USA) were carried out as described elsewhere [49]. Fluorophore-labeled DNA was purified with a Zymo DNA Clean & Concentrator-5 kit, and label incorporation was quantified on a NanoDrop system.

Slide preparation, hybridization and scanning

Before use, the microarray slides were washed with an ethanolamine washing solution (50 mM ethanolamine, 0.1% SDS, 0.1 M Tris, pH 9) for 15 min at 50°C, then washed twice with distilled water, and dried by centrifugation for 5 min at 500 rpm. The processed slides were loaded with 30 μL of a combination of Cy3- and Cy5-labeled DNA in 3×SSC buffer, and hybridization was allowed in a sealed chamber which submerged in a water bath at 65°C for 8-12 h. Next step, the slides were washed consecutively in 2×SSC (65°C), 2×SSC, 1×SSC, and 0.2×SSC after incubation, and dried for 5 min at 500 rpm. Hybridization images were acquired with an AxonGenePix 4000B scanner (Molecular Devices, USA) and synchronized with the GenePix Pro 6.0 software for spot intensity assessment.

Data analysis

The intensity of each hybridization spot was first filtered by spot size and shape (denoted as good/bad/absent), foreground signal saturation percentage on channel 532 (denoted as F532, <5 was considered to be satisfactory), background signal saturation percentage on channel 532 (denoted as B532), and the F532 to B532 ratio [(% >B532 + 2 standard deviations), >50%] to determine hybridization quality. Good quality spots were used to generate microarray level background values. Normalization of intensity values was performed according to the formula (F532i/F532m)—(B532i-B532m), where i represents each individual spot, m is the sum of all spots. Thus, F532i and B532i represented as foreground and background signals of a spot “i,” respectively, while F532m and B532m are the sums of all foreground or background spots, respectively.

In reference samples, statistical significance of probe intensities was assessed by the rank products algorithm [50], using a minimum of three technical replicates. A “spot rank value” from negative-controls was used in values analyzed with the versatile R package [51] which can estimate local and tail area-based false discovery rates (FDR) [52]. Positive blood protozoan species were defined as having at least two probes with P<0.05 and FDR<0.01.

Limit-of-detection assays

The purified corresponding gene fragments (S3 Table) of Plasmodium, Leishmania, Trypanosoma, T. gondii, and Babesia, among others, were ligated into the pMD-18 T vector, and transformed into Escherichia coli JM109. Positive clones were screened, and the positive plasmid extracted. The amounts of gene-positive plasmids were detected on a nucleic acid micro-analyzer (CapitalBio Corporation, Beijing, China); copy number was calculated according to the following formula: (6.02×1023) × (ng/μL×10−9)/ (length of DNA fragment×660) = copy/μL [53]. Dilutions of positive plasmids corresponding to 1×106 to 10 particles were analyzed alone and amplified, labeled, and processed using the DNA microarray protocol described above.

Results

An advantage of the microarray technology is its capacity to assess hundreds and even thousands of targets in a single detection. The main goal of this research was to develop a chip for the detection of 18 species of blood protozoa including Plasmodium, Leishmania, Trypanosoma, T. gondii, and Babesia found in humans, animals, and vectors, which should be convenient used in clinical and epidemiological studies in humans and animals.

Screening and verification of specific primers and probes

A set of 20 pairs of specific primers (S3 Table) and 107 microarray probes (S4 Table) were selected and designed to identify 18 species in 5 genera of blood protozoa associated with humans, animals and vectors. The electrophoretograms of representative PCR products and multiple PCR products for the 18 species of blood protozoa are shown in S4 and S5 Figs. The highest number of probes covered Plasmodium (22 probes), Leishmania (37 probes), Trypanosoma (21probes), T. gondii (4 probes), and Babesia (23 probes) (S4 Table).

Reference strains for 18 species of blood protozoa were available for probe validation. These species represent 5 genera, and included 5 human Plasmodium, 4 human Leishmania, 2 human Trypanosoma, 1 human Babesia, 1 sand rat Leishmania, 1 rat Trypanosoma, 1 cat T. gondii, 1 field mouse Babesia and 2 tick Babesia (S1 Table and Table 1). All reference strains were detected as expected; the number of positive species and probes in each genus are shown in Table 1. Microarray showed no hybridization fluorescence signal, indicating a good specificity for this microarray assay (Fig 2).

thumbnail
Fig 2. Blood protozoan detection DNA microarray results for 18 blood protozoan species and samples from healthy adults.

a-t: Blood protozoan detection DNA microarray data for B. microti, B. divergens, B. venatorum, B. duncani, P. vivax, P. falciparum, P. knowlesi, P. malariae, P. ovale, L. gerbilli, L. donovani, L. infantum, L. tropica, L. aethiopica, T. cruzi, T. b. rhodesiense, T. b. gambiense, and T. gondii, healthy adult and negative control.

https://doi.org/10.1371/journal.pntd.0005160.g002

Assay sensitivity and specificity

To further assess the value of the developed microarray assay in detecting blood protozoa, 438 blood samples (including 4 samples from cases with combined infection, i.e. 3 combined P. malariae and P. ovale, and one case with combined infection of P. falciparum and P. ovale), collected from 2012 to 2014 in China, were analyzed (S2 Table) alongside and 100 samples from healthy adults. Blood protozoan reference isolates were detected by DNA microarray, while performing parallel evaluation using gold standard methods such as morphology, PCR or multi-locus enzyme electrophoresis (MLEE). The results are shown in Fig 3. Compared to the “gold standard methods”, the positive detection rate of the microarray assay was 91.78% (402/438). Detection rates were 100.0% (3/3), 100.0% (4/4), 93.3% (126/135), and 92.7% (152/164, including 151 cases only infected with P. falciparum; one with the combined infection of P. falciparum and P. ovale), 87.5% (7/8), 92.9% (26/28, including 23 cases only infected with P. malariae; 3 cases with the combined infection of P. malariae and P. ovale), 90.0% (36/40, including 32 cases only infected with P. ovale; 4 with combined infection of P. ovale and P. malariae or P. falciparum), 82.4% (14/17), 84.0% (21/25), and 94.4% (17/18) in the samples for B. microti, B. venatorum, P. vivax, P. falciparum, P. knowlesi, P. malariae, P. ovale, L. donovani, L. infantum, and T. gondii, respectively (Figs 3, 4 and Table 2).

thumbnail
Fig 3. Microarray detection of blood protozoa isolates or clinical samples from infected blood protozoan diseases.

a. Microarray detection for 438 blood protozoan samples from infected blood from cases with protozoa and 100 healthy adults; b. A parallel detection by morphology, PCR sequencing and multi-locus enzyme electrophoresis was achieved.

https://doi.org/10.1371/journal.pntd.0005160.g003

thumbnail
Fig 4. Sensitivity and specificity of DNA microarray for detecting blood protozoa.

https://doi.org/10.1371/journal.pntd.0005160.g004

thumbnail
Table 2. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and Youden index of blood protozoan detection DNA microarrays.

https://doi.org/10.1371/journal.pntd.0005160.t002

Sensitivity and specificity of the blood protozoa detection were 82.4% (95%CI 65.9% ~ 98.8%) to 100.0% and 95.1%CI (95% CI 93.2%~97.0%) to 100.0%, respectively. Positive predictive value (PPV) and negative predictive value (NPV) were 20.0% (95% CI 2.5% ~ 37.5%) to 100.0% and 96.8% (95% CI: 95.0% ~ 98.6%) to 100.0%, respectively. Youden index values were from 0.82 to 0.97 (Figs 4, 5 and Table 2).

thumbnail
Fig 5. Youden index of DNA microarray for the detection of blood protozoa.

https://doi.org/10.1371/journal.pntd.0005160.g005

Detection limit

To determine the detection limit of the PCR and DNA microarray assay, the concentrations of specific gene-positive plasmids of Plasmodium, Leishmania, Trypanosoma, T. gondii, and Babesia were assessed. The detection limit of the DNA microarray was between 200 to 500 copies/reaction, similar to PCR data (Table 3).

Repeatability of the DNA microarray assay

A total of 5 microarrays from each batch (20150925, 20151026, 20151125, 20151224, and 20160125) were selected randomly for the detection of reference blood protozoa in the same conditions as described above. Detection results of the reference isolates were positive (S6 and S7 Figs), indicating that the DNA microarray assay developed had good repeatability.

Discussion

The current routine blood protozoa testing method is morphology. Due to similar morphology among different species of blood protozoa (for example Plasmodium and Babesia) and the technical variations for detection operators, misdiagnosis is common [1]. Recently, the number of blood protozoa identified has increased considerably for applying the genomic technologies, e.g. next-generation sequencing technologies with the principle of Sequencing by Synthesis, including the platform of Roche/454FLX, Illumina/Solexa Genome Analyzer, and Applied Biosystems SOLID system in studies of genetic, fatal or rare cases of diseases affecting humans and animals [41, 54]. Most molecular technologies are only carried out in laboratories, with only a few samples detected simultaneously. Therefore, common detection methods may not be convenient in field investigation for large amounts of samples [1]. To better assess infections by blood protozoa, proper detection methods are required for pathogenic monitoring. Here, a sensitive and comprehensive DNA microarray assay was developed with the purpose of the parallel detection of 18 blood protozoan species.

A PCR microarray is a solid carrier with several primers of known genes, and used to assess gene expression by the PCR technique. The PCR microarray technology is a high-throughput method with accuracy and sensitivity, and different from the microarray technology based on hybridization. The advantages of PCR microarray over conventional PCR are: (a). Integrity. The operating system of PCR microarrays applies multi-gene amplification with the integration of result analysis and easy operation. However, traditional PCR employs single gene amplification and data analysis, and its operation is complicated. (b). Time. Time of PCR microarray detection is greatly reduced. (c). Regents. Higher amounts of reagents are required in conventional PCR, and it is difficult to obtain the desired amplification under 15 μl reaction volume. Meanwhile, PCR microarrays can amplify 10 μL sample or less. This could reduce the amounts of reagents, e.g. Taq polymerase, saving experimental cost and reducing waste pollution[55, 56]. Overall, the PCR microarray technology has the advantages of small size, fast response, simple operation, low cost, sample saving, no pollution, and easy integration[57, 58].

Design and implementation of a DNA microarrayto detect and identify blood protozoa is not an easy task. Probe design and experimental conditions are two important parameters in careful consideration. Resequencing microarrays, which permit the identification of mutations, require numerous probes for a single gene, increasing the overall cost [59]. In this research, gene sequences of 18 blood protozoan species in the GenBank were analyzed. A total of 20 pairs of specific primers and 107 probes of blood protozoa were successfully designed according to the specific gene fragments. Conventional PCR verification was carried out by amplifying the target genes of reference blood protozoan isolates, with results similar to microarray findings.

Some DNA microarrays have been previously reported in the identification of main blood protozoa [3133]. However, the latter targeted mostly the identification of one or two species. The microarray method described in this work was validated with 18 species of reference blood protozoa. Importantly, 438 samples corresponding to the infected areas were detected. The microarray assay can readily accommodate numerous probes and could easily increase the information to resolve more protozoa species. Considering the prevalence of blood protozoan species found in Chinese patients, the present DNA microarray technique could easily meet the requirements for clinical detection. However, with further development, additional probes could be included to detect more species to further the scope of CDC epidemiological investiagtions.

Moreover, the microarray system presented in this research has many major advantages that could be used in clinical practice greatly, particularly the ample quality controls (three control types as discussed in Materials and Methods), global signal uniformity which can emerges higher quality hybridization results, and semi-automation and total solution-based procedures, which also meet the requirement of high-throughput from busy clinics.

One of the important parameters in blood protozoan detection is assay sensitivity, which can be affected by several factors. In the case of microarrays, nucleic acids in samples are processed by random-primed amplification before hybridization to ensure amplification of various blood protozoa [60]. The specificyt of random PCR is lower than that of specific PCR, decreasing assay sensitivity, as all as genetic materials are amplified, and diluted the positive signals [60]. However, the limit of detection of the current blood protozoan detection method was 200 to 500 copies/reaction, indicating a good sensitivity.

In this research, a group of clinical samples collected from patients with fever, as well as animals and vectors were subsequently analyzed. Firstly, the clinical samples were tested by morphology, which led to the identification of Plasmodium and Babesia; the microarray technique could distinguish them. it suggested that the latter platform owns a higher specificity than traditional methods.

Parallel detection of blood protozoan species should increase understanding of their etiologies, as it can increase the detection rate of positive cases. Furthermore, it may close the diagnostic gap that represents an important factor. Additional data from case-control studies and parameters from other hosts, such as serological data, would help provide evidence for blood protozoan pathogenicity. Moreover, more epidemiological studies in animals and vectors should be considered.

In addition to high specificity, accuracy, sensitivity, and increased information, we believe that the present microarray system will have several other critical advantages. Automatic chip washing procedures as well as biochip scanner and data software were introduced with the aim to reduce operators’ manual work burden and ease implementation of the technique into routine workflow. The amplified target DNA fragments were detected in the newly developed assay after fluorescently labeled. Furthermore, there was only a single washing step needed after hybridization by automatic chip cleanup instrument. At last, For the data acquisition from biochip was analyzed by a dedicated software, with results printed automatically.

Although the newly designed microarray platform has a number of overt advantages, It was also recognized that there is room for further improvement and development. We will pay attention to the more discriminatory loci assessed to ensure that individual species are unequivocally identified. Further development of the workstation is required to fully automatize the procedure; this would also have direct benefits to workflows, particularly in larger test institutes. In conclusion, the microarray platform can provide a convenient, accurate and reliable diagnostic tool for the identification of 18 most common blood protozoan species. The system should be widely used in future detection assay and management of blood protozoan infections in busy hospitals and research institutes, and would help in disease control and prevention plans.

Supporting Information

S3 Fig. Ethical clearance for the collection and examination of human and animal serum.

https://doi.org/10.1371/journal.pntd.0005160.s003

(PDF)

S4 Fig. Representative PCR products for 18 blood protozoan species.

a-v: Representative PCR products for B. microti, B. divergens, B. duncani, B. venatorum, P. vivax, P. falciparum, P. knowlesi, P. malariae, P. ovale, L. donovani, L. gerbilli (18S rDNA gene), L. gerbilli (fumarate hydratase gene), L. tropica (18S rDNA gene), L. tropica (mspC gene), L. infantum, L. aethiopica, T. b. rhodesiense (18S rDNA gene), T. b. rhodesiense (lysosomal/endosomal membrane protein p67 gene), T. b. gambiense (18S rDNA gene), T. b. gambiense (hypothetical protein gene), T. cruzi and T. gondii. M: Marker; 1: positive control; 2: negative; 3: blood of health human; 4: reference sample.

https://doi.org/10.1371/journal.pntd.0005160.s004

(TIF)

S5 Fig. Representative multiple PCR products for 18 blood protozoan species.

M: Marker; 1: positive control; 2: negative; 3: B. microti; 4: B. divergens; 5: B. duncani; 6: B. venatorum; 7: P. vivax; 8: P. falciparum; 9: P. knowlesi; 10: P. malariae; 11: P. ovale; 12: L. donovani; 13: L. gerbilli; 14: L. tropica; 15: L. infantum; 16: L. aethiopica; 17: T. b. rhodesiense; 18: T. b. gambiense; 19: T. cruzi; 20: T. gondii; 21: healthy adult.

https://doi.org/10.1371/journal.pntd.0005160.s005

(TIF)

S6 Fig. Repeatability of DNA microarray of Babesia microti.

1–5: 5 times repeatability of DNA microarray of B. microti with the array of 20150925 batche; 6–10: 5 times repeatability of DNA microarray of B. microti with the array of 20151026 batche; 11–15: 5 times repeatability of DNA microarray of B. microti with the array of 20151125 batche; 16–20: 5 times repeatability of DNA microarray of B. microti with the array of 20151224 batche; 21–25: 5 times repeatability of DNA microarray of B. microti with the array of 20160125 batche.

https://doi.org/10.1371/journal.pntd.0005160.s006

(TIF)

S7 Fig. Repeatability of DNA microarray of Plasmodium falciparum.

1–5: 5 times repeatability of DNA microarray of P. falciparum with the array of 20150925 batche; 6–10: 5 times repeatability of DNA microarray of P. falciparum with the array of 20151026 batche; 11–15: 5 times repeatability of DNA microarray of P. falciparum with the array of 20151125 batche; 16–20: 5 times repeatability of DNA microarray of P. falciparum with the array of 20151224 batche; 21–25: 5 times repeatability of DNA microarray of P. falciparum with the array of 20160125 batche.

https://doi.org/10.1371/journal.pntd.0005160.s007

(TIF)

S1 Table. Reference blood protozoan samples.

https://doi.org/10.1371/journal.pntd.0005160.s008

(DOCX)

S2 Table. Clinical protozoan isolates from reference blood.

https://doi.org/10.1371/journal.pntd.0005160.s009

(DOCX)

S3 Table. Specific primers for blood protozoan amplification.

https://doi.org/10.1371/journal.pntd.0005160.s010

(DOCX)

S4 Table. Probes for blood protozoan detection.

https://doi.org/10.1371/journal.pntd.0005160.s011

(DOCX)

Acknowledgments

We express our special thanks to Profs. Xun-Jia Cheng, Huai-Min Zhu, Jin-Lin Zhou for their kind help in providing the samples. We also would like to thank Professor Ren-Li Zhang, Shenzhen Center for Diseases Control and Prevention, China, for valuable comments on the manuscript.

Author Contributions

  1. Conceived and designed the experiments: MXC LA JXC WH.
  2. Performed the experiments: MXC LA XYF SHC YCC YL.
  3. Analyzed the data: MXC LA JHC JXC.
  4. Contributed reagents/materials/analysis tools: MXC LA JHC XYF.
  5. Wrote the paper: MXC LA XNZ JXC WH.

References

  1. 1. Baron S. Medical Microbiology. 4th edition. Galveston (TX): University of Texas Medical Branch at Galveston.1996. http://www.ncbi.nlm.nih.gov/books/NBK7627/
  2. 2. WHO Malaria Policy Advisory Committee and Secretariat. Malaria Policy Advisory Committee to the WHO: conclusions and recommendations of eighth biannual meeting (September 2015). Malar J. 2016; 15:117. pmid:26911803
  3. 3. Babuadze G, Alvar J, Argaw D, de Koning HP, Iosava M, Kekelidze M, et al. Epidemiology of visceral leishmaniasis in Georgia. PLoS Negl Trop Dis. 2014; 8(3):e2725. pmid:24603768
  4. 4. Bern C. Chagas' Disease. N Engl J Med. 2015; 373(19):1882. pmid:26535522
  5. 5. Opsteegh M, Kortbeek TM, Havelaar AH, van der Giessen JW. Intervention strategies to reduce human Toxoplasma gondii disease burden. Clin Infect Dis. 2015; 60(1):101–7. pmid:25225234
  6. 6. Homer MJ, Aguilar-Delfin I, Telford SR 3rd, Krause PJ, Persing DH. Babesiosis. Clin Microbiol Rev. 2000; 13(3):451–69. pmid:10885987
  7. 7. Bell D, Wongsrichanalai C, Barnwell JW. Ensuring quality and access for malaria diagnosis: how can it be achieved? Nat Rev Microbiol. 2006; 4(9 Suppl):S7–20. pmid:17003770
  8. 8. Elmahallawy EK, Sampedro Martinez A, Rodriguez-Granger J, Hoyos-Mallecot Y, Agil A, Navarro Mari JM, et al. Diagnosis of leishmaniasis. J Infect Dev Ctries. 2014; 8(8):961–72. pmid:25116660
  9. 9. Keating J, Yukich JO, Sutherland CS, Woods G, Tediosi F. Human African trypanosomiasis prevention, treatment and control costs: a systematic review. Acta Trop. 2015; 150:4–13. pmid:26056739
  10. 10. Ilboudo H, Camara O, Ravel S, Bucheton B, Lejon V, Camara M, et al. Trypanosoma brucei gambiense Spliced Leader RNA Is a More Specific Marker for Cure of Human African Trypanosomiasis Than T.b. gambiense DNA. J Infect Dis. 2015; 212(12):1996–8. pmid:26080371
  11. 11. Mwanakasale V, Songolo P, Babaniyi O, Simarro P. Clinical presentation of human African trypanosomiasis in Zambia is linked to the existence of strains of Trypanosoma brucei rhodesiense with varied virulence: two case reports. J Med Case Rep. 2014; 8:53. pmid:24529084
  12. 12. Bonney KM. Chagas disease in the 21st century: a public health success or an emerging threat? Parasite. 2014; 21:11. pmid:24626257
  13. 13. Nouvellet P, Cucunubá ZM, Gourbière S. Ecology, evolution and control of Chagas disease: a century of neglected modelling and a promising future. Adv Parasitol. 2015; 87:135–91. pmid:25765195
  14. 14. Parlog A, Schlüter D, Dunay IR. Toxoplasma gondii-induced neuronal alterations. Parasite Immunol. 2015; 37(3):159–70. pmid:25376390
  15. 15. Gao XJ, Zhao ZJ, He ZH, Wang T, Yang TB, Chen XG, et al. Toxoplasma gondii infection in pregnant women in China. Parasitology. 2012; 139(2):139–47. pmid:22054357
  16. 16. Kijlstra A, Petersen E. Epidemiology, pathophysiology, and the future of ocular toxoplasmosis. Ocul Immunol Inflamm. 2014; 22(2):138–47. pmid:24131274
  17. 17. Park YH, Nam HW. Clinical features and treatment of ocular toxoplasmosis. Korean J Parasitol. 2013; 51(4):393–9. pmid:24039281
  18. 18. Vannier E, Gewurz BE, Krause PJ. Human babesiosis. Infect Dis Clin North Am. 2008; 22(3):469–88, viii–ix. pmid:18755385
  19. 19. Laha R, Das M, Sen A. Morphology, epidemiology, and phylogeny of Babesia: An overview. Trop Parasitol. 2015; 5(2):94–100. pmid:26629451
  20. 20. Zhou X, Xia S, Huang JL, Tambo E, Zhuge HX, Zhou XN. Human babesiosis, an emerging tick-borne disease in the People's Republic of China. Parasit Vectors. 2014; 7:509. pmid:25403908
  21. 21. Chua KH, Lee PC, Chai HC. Development of insulated isothermal PCR for rapid on-site malaria detection. Malar J. 2016; 15:134. pmid:26931146
  22. 22. de Morais RC, da Costa Oliveira CN, de Albuquerque Sda C, Mendonça Trajano Silva LA, Pessoa-E-Silva R, Alves da Cruz HL, et al. Real-time PCR for Leishmania species identification: Evaluation and comparison with classical techniques. Exp Parasitol. 2016; 165:43–50. pmid:26968776
  23. 23. Dunlop J, Thompson CK, Godfrey SS, Thompson RC. Sensitivity testing of trypanosome detection by PCR from whole blood samples using manual and automated DNA extraction methods. Exp Parasitol. 2014; 146:20–4. pmid:25124940
  24. 24. Yu H, Huang B, Zhuo X, Chen X, Du A. Evaluation of a real-time PCR assay based on the single-copy SAG1 gene for the detection of Toxoplasma gondii. Vet Parasitol. 2013; 197(3–4):670–3. pmid:23867074
  25. 25. Singh MN, Raina OK, Sankar M, Rialch A, Tigga MN, Kumar GR, et al. Molecular detection and genetic diversity of Babesia gibsoni in dogs in India. Infect Genet Evol. 2016; 41:100–6. pmid:27020545
  26. 26. Rathod PK, Ganesan K, Hayward RE, Bozdech Z, DeRisi JL. DNA microarrays for malaria. Trends Parasitol. 2002; 18(1):39–45. pmid:11850013
  27. 27. Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science. 1995; 270(5235): 467–70. pmid:7569999
  28. 28. Sun H. Identification of key genes associated with gastric cancer based on DNA microarray data. Oncol Lett. 2016; 11(1): 525–30. pmid:26870242
  29. 29. White KP, Rifkin SA, Hurban P, Hogness DS. Microarray analysis of Drosophila development during metamorphosis. Science. 1999; 286 (5447): 2179–84. pmid:10591654
  30. 30. Merrick JM, Osman A, Tsai J, Quackenbush J, LoVerde PT, Lee NH. The Schistosoma mansoni gene index: gene discovery and biology by reconstruction and analysis of expressed gene sequences. J Parasitol. 2003; 89(2):261–9. pmid:12760639
  31. 31. Hayward RE, Derisi JL, Alfadhli S, Kaslow DC, Brown PO, Rathod PK. Shotgun DNA microarrays and stage-specific gene expression in Plasmodium falciparum malaria. Mol Microbiol. 2000; 35(1):6–14. pmid:10632873
  32. 32. Quijada L, Soto M, Requena JM. Genomic DNA macroarrays as a tool for analysis of gene expression in Leishmania. Exp Parasitol. 2005; 111(1):64–70. pmid:15936759
  33. 33. Diehl S, Diehl F, El-Sayed NM, Clayton C, Hoheisel JD. Analysis of stage-specific gene expression in the bloodstream and the procyclic form of Trypanosoma brucei using a genomic DNA-microarray. Mol Biochem Parasitol. 2002; 123(2):115–23. pmid:12270627
  34. 34. Gissot M, Refour P, Briquet S, Boschet C, Coupé S, Mazier D, et al. Transcriptome of 3D7 and its gametocyte-less derivative F12 Plasmodium falciparum clones during erythrocytic development using a gene-specific microarray assigned to gene regulation, cell cycle and transcription factors. Gene. 2004; 341:267–77. pmid:15474309
  35. 35. Dogra N, Warburton C, McMaster WR. Leishmania major abrogates gamma interferon-induced gene expression in human macrophages from a global perspective. Infect Immun. 2007; 75(7):3506–15. pmid:17470541
  36. 36. Luu VD, Brems S, Hoheisel JD, Burchmore R, Guilbride DL, Clayton C. Functional analysis of Trypanosoma brucei PUF1. Mol Biochem Parasitol. 2006; 150(2):340–9. pmid:17052765
  37. 37. Mbengue A, Bhattacharjee S, Pandharkar T, Liu H, Estiu G, Stahelin RV, et al. A molecular mechanism of artemisinin resistance in Plasmodium falciparum malaria. Nature. 2015; 520(7549):683–7. pmid:25874676
  38. 38. Pandey R, Mohmmed A, Pierrot C, Khalife J, Malhotra P, Gupta D. Genome wide in silico analysis of Plasmodium falciparum phosphatome. BMC Genomics. 2014; 15:1024. pmid:25425018
  39. 39. Gannavaram S, Dey R, Avishek K, Selvapandiyan A, Salotra P, Nakhasi HL. Biomarkers of safety and immune protection for genetically modified live attenuated leishmania vaccines against visceral leishmaniasis—discovery and implications. Front Immunol. 2014; 5:241. pmid:24904589
  40. 40. Li Y, Tao K, Jin H, Zhang J, Cao M, Zhou J, et al. Design and development of a DNA array for rapid detection and genotyping of seven kinds of pathogenic microbes. J Nanosci Nanotechnol. 2005; 5(8):1220–4. pmid:16193981
  41. 41. El-Ashker M, Hotzel H, Gwida M, El-Beskawy M, Silaghi C, Tomaso H. Molecular biological identification of Babesia, Theileria, and Anaplasma species in cattle in Egypt using PCR assays, gene sequence analysis and a novel DNA microarray. Vet Parasitol. 2015; 207(3–4):329–34. pmid:25591406
  42. 42. Giudice E, Passantino A. Detection of Leishmania amastigotes in peripheral blood from four dogs--Short communication. Acta Vet Hung. 2011; 59(2):205–13. pmid:21665574
  43. 43. Wang JY, Ha Y, Gao CH, Wang Y, Yang YT, Chen HT. The prevalence of canine Leishmania infantum infection in western China detected by PCR and serological tests. Parasit Vectors. 2011; 4:69. pmid:21554677
  44. 44. SantaLucia J Jr. A unified view of polymer, dumbbell, and oligonucleotide DNA nearest-neighbor thermodynamics. Proc Natl Acad Sci U S A. 1998; 95(4): 1460–5. Available: http://www.pnas.org/content/95/4/1460.long pmid:9465037
  45. 45. Guo Y, Guo H, Zhang L, Xie H, Zhao X, Wang F, et al. Genomic analysis of anti-hepatitis B virus (HBV) activity by small interfering RNA and lamivudine in stable HBV-producing cells. J Virol. 2005; 79(22): 14392–403. pmid:16254373
  46. 46. Guo Y, Chen Z, Zhang L, Zhou F, Shi S, Feng X, et al. Distinctive microRNA profiles relating to patient survival in esophageal squamous cell carcinoma. Cancer Res. 2008; 68 (1):26–33. pmid:18172293
  47. 47. Zhu L, Jiang G, Wang S, Wang C, Li Q, Yu H, et al. Biochip system for rapid and accurate identification of mycobacterial species from isolates and sputum. J Clin Microbiol. 2010; 48(10):3654–60. pmid:20686082
  48. 48. Martínez MA, Soto-Del Río Mde L, Gutiérrez RM, Chiu CY, Greninger AL, Contreras JF, et al. DNA microarray for detection of gastrointestinal viruses. J Clin Microbiol. 2015; 53 (1):136–45. pmid:25355758
  49. 49. Hughes TR, Mao M, Jones AR, Burchard J, Marton MJ, Shannon KW, et al. Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer. Nat Biotechnol. 2001; 19 (4):342–7. pmid:11283592
  50. 50. Breitling R, Armengaud P, Amtmann A, Herzyk P. Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett. 2004; 573 (1–3):83–92. pmid:15327980
  51. 51. Strimmer K. fdrtool: a versatile R package for estimating local and tail area-based false discovery rates. Bioinformatics. 2008; 24 (12):1461–2. pmid:18441000
  52. 52. R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2013, http://www.R-project.org
  53. 53. Teo J, Di Pietro P, San Biagio F, Capozzoli M, Deng YM, Barr I, et al. VereFlu: an integrated multiplex RT-PCR and microarray assay for rapid detection and identification of human influenza A and B viruses using lab-on-chip technology. Arch Virol. 2011; 156(8):1371–8. pmid:21503642
  54. 54. Llewellyn MS, Messenger LA, Luquetti AO, Garcia L, Torrico F, Tavares SB, et al. Deep sequencing of the Trypanosoma cruzi GP63 surface proteases reveals diversity and diversifying selection among chronic and congenital Chagas disease patients. PLoS Negl Trop Dis. 2015; 9(4):e0003458. pmid:25849488
  55. 55. Fakhari FD, Dittmer DP. Charting latency transcripts in Kaposi's sarcoma-associated herpesvirus by whole-genome real-time quantitative PCR. J Virol. 2002; 76 (12):6213–23. pmid:12021355
  56. 56. Wu J, Kodzius R, Xiao K, Qin J, Wen W. Fast detection of genetic information by an optimized PCR in an interchangeable chip. Biomed Microdevices. 2012; 14(1):179–86. pmid:21976029
  57. 57. Sun Y, Dhumpa R, Bang DD, Høgberg J, Handberg K, Wolff A. A lab-on-a-chip device for rapid identification of avian influenza viral RNA by solid-phase PCR. Lab Chip. 2011; 11(8):1457–63. pmid:21369571
  58. 58. Grubaugh ND, McMenamy SS, Turell MJ, Lee JS. Multi-gene detection and identification of mosquito-borne RNA viruses using an oligonucleotide microarray. PLoS Negl Trop Dis. 2013; 7(8):e2349. pmid:23967358
  59. 59. Lin B, Blaney KM, Malanoski AP, Ligler AG, Schnur JM, Metzgar D, et al. Using a resequencing microarray as a multiple respiratory pathogen detection assay. J Clin Microbiol. 2007; 45 (2): 443–52. pmid:17135438
  60. 60. Vora GJ, Meador CE, Stenger DA, Andreadis JD. Nucleic acid amplification strategies for DNA microarray-based pathogen detection. Appl Environ Microbiol. 2004; 70 (5):3047–54. pmid:15128566