Open access peer-reviewed chapter

A Real-Time PCR-Based Diagnostic Test for Organisms in Respiratory Tract Infection

By Takashi Hirama

Submitted: April 14th 2016Reviewed: September 13th 2016Published: December 14th 2016

DOI: 10.5772/65740

Downloaded: 1162

Abstract

Respiratory tract infection, especially pneumonia, is a significant cause of morbidity and mortality worldwide. Although rapid and accurate identification of the pathogens and the corresponding treatment, which is based on the microbiological results, is required in the healthcare setting, the current clinical tests lack high sensitivity and flexibility. As of yet, a comprehensive approach has not been able to work these issues out. Meanwhile, the development of molecular techniques enables the detection of organisms from respiratory specimens speedily as well as precisely and aids the settlement of such issues. With our novel approach that employs relative quantification, we successfully set the cutoff value to discriminate the causative pathogen from colonizing commensal organisms by real-time PCR. In this way, a diagnostic system for respiratory pathogens was devised and validated through clinical sample testing. In this chapter, a real-time PCR-based test capable of differentiating causative pathogens in respiratory specimens is described, and also its principle and the utility of this approach are illustrated.

Keywords

  • PCR
  • real-time PCR
  • pneumonia
  • respiratory tract infection
  • sputum
  • HIRA-TAN
  • commensal organism
  • foreign organism
  • non-commensal organism
  • nucleic acid amplification test
  • NAAT

1. Introduction

Pneumonia is a common disease in healthcare settings and is a significant cause of morbidity and mortality worldwide. Despite there being a mere two-dozen species of pathogens responsible for most cases of pneumonia, the causative pathogen cannot be identified by clinical tests involving smear and culture of sputum, antigen tests, and serological assays in up to 50% of the cases [14]. Therefore, the identification of the causative pathogen(s) with high detection power will allow for the selection of targeted antibiotics. Accordingly, timely identification has been shown to reduce the mortality rate and, on a long-term basis, the emergence of drug-resistant pathogens [5]. To identify such pathogens and thus to obtain the desired benefits, a clinical test is required that is sensitive, rapid, accurate, easily performed, and cost-effective.

Figure 1.

The overall strategy and assessment of pneumonia. The current consensus for diagnosis through treatment of pneumonia was summarized by STEP1 through STEP6 [3, 4]. Although examples of assessment, such as CURB-65 for CAP and ATS consensus for HAP, were illustrated in STEP3, an appropriate index ought to be chosen in each situation.

The development and wide usage of molecular techniques, such as polymerase chain reaction (PCR), brought about remarkable advances in clinical medicine. Detection of causative pathogens in pneumonia has been optimized in the last few decades, and from that point, PCR has played a principal role in laboratory medicine [68]. Currently PCR-based approaches, however, are mainly used to identify foreign organisms (such as Mycoplasma pneumoniae or Legionella pneumophila) in respiratory specimens [2, 911]. Even if such clinical tests have a multiplex detection system, identification of foreign organisms alone shows limited clinical value since the majority of pneumonia is caused by commensal organisms (such as Streptococcus pneumoniae or Haemophilus influenzae). Thus, distinguishing causative pathogens from among detected commensal organisms in respiratory samples, which contain colonizing organisms, has been challenging due to the highly sensitive detection power of PCR.

In this chapter, a real-time PCR-based test which is capable of differentiating therapeutic targets from detected colonizing commensal organisms in respiratory samples is described, and also its principle and the utility of this approach are illustrated.

2. Clinical approach and current strategy for pneumonia

Pneumonias are as of now classified as either the community-acquired pneumonia (CAP) or the hospital-acquired pneumonia (HAP), depending on the place pneumonia is acquired (Figure 1). Each has a specific spectrum of causative pathogens and allows medical professionals to speculate on the causative pathogen and initiate empirical antimicrobial therapy covering most of the speculated pathogens (Figure 2). Following confirmation of the causative pathogen, the antimicrobial should be switched/de-escalated to more specific and appropriate medication.

Figure 2.

Etiology of three types of pneumonia diagnosed by conventional methods in clinical study. Five-hundred and sixty eight patients were enrolled in the prospective study where seven institutions participated [12]. Three-hundred and forty nine, 147, and 72 patients who met the criteria of pneumonia shown in Figure 1 were categorized as community-acquired pneumonia (CAP), healthcare-associated pneumonia (HCAP), and hospital-acquired pneumonia (HAP), respectively, in which causative pathogen(s) were summarized in pie charts. In this study, all patients tested Gram stain and culture of expectorated sputum, urine antigen for Streptococcus pneumoniae, and real-time PCR for Mycoplasma pneumoniae, Chlamydophila pneumoniae, Chlamydophila psittaci, Coxiella burnetii, Legionella pneumophila, Bordetella pertussis, Mycobacterium tuberculosis, Mycobacterium intracellulare, Mycobacterium avium, Mycobacterium kansasii, and Pneumocystis jiroveci. Urine antigen was tested for in the case of L. pneumophila for admitted cases only and influenza antigen for flu season only. Mixed infection indicates the cases in which two or more causative pathogens were identified at one time.

3. Organisms in respiratory tract infection

3.1. Conventional diagnostic tools for causative pathogens

A wide variety of laboratory tests including culture-based methods, antigen tests, and serology have been available for diagnosis and treatment of pneumonia (Table 1). Nevertheless, despite comprehensive evaluations with a range of different tests, as many as 40% of the causative pathogens causing CAP, HCAP, and HAP remain undiagnosed (Figure 2). While defining the pathogenic role of the respiratory organisms during pneumonia is still difficult, a commensal organism and foreign organism are often considered to be a causative pathogen when the below criteria are met. These criteria have provided legitimate results and thus have been used in clinical practice.

3.2. Commensal organism

Commensal organisms form part of human normal flora in the respiratory system (e.g., Streptococcus pneumoniae, Haemophilus influenzae, or Pseudomonas aeruginosa). However, in specific situations as when the host defense is weakened, the number of organisms grows beyond their typical ranges and then causes pneumonia. Since the majority of them reside in the respiratory tract, when they are detected from respiratory sample, there is no unequivocal criterion to discriminate causative pathogen from colonizing one by the sputum examination.

3.3. Foreign organism (non-commensal organism)

Foreign organisms account for a small portion of pneumonias (e.g., Mycoplasma pneumoniae, Legionella pneumophila, or Mycobacterium tuberculosis) (Figure 2). They do not reside in the respiratory tract or are rarely identified from healthy individuals, and thus their detection indicates that they are the causative pathogens.

3.4. Criteria for commensal organisms to be the causative pathogen

A commensal organism that fulfills at least one of the following three criteria is considered to be a causative pathogen when (1) an organism is identified from the normally sterile site (blood or pleural effusion); (2) a morphologically compatible organism, coexisting with abundant neutrophils, is observed through Gram staining and later confirmed by sputum culture; or (3) for Streptococcus pneumoniae only, the urine antigen test is positive.

3.5. Criteria for foreign organisms to be the causative pathogen

Foreign organisms that fulfill at least one of the following two criteria are considered to be a causative pathogen when (1) an organism is identified by culture, antigen test (involving serum, urine or nasopharyngeal specimen), or PCR test or (2) a paired serological test reveals a significant increase in antibody titer (more than four times).

Rapid testConfirmed diagnosis
Commensal organisms
Gram-positive cocci
Streptococcus pneumoniaeGram stain, urine antigenCulture
Streptococcus milleri groupCulture
Staphylococcus aureusGram stainCulture
Gram-negative cocci
Moraxella catarrhalisGram stainCulture
Gram-negative rods
Haemophilus influenzaeGram stainChocolate agar culture
(Non-fermenter)
Pseudomonas aeruginosaGram stainCulture
Acinetobacter baumanniiCulture
Stenotrophomonas maltophiliaCulture
Burkholderia cepaciaSelective agar culture
(Enterobacteria)
Klebsiella pneumoniaeGram stainCulture
Escherichia coliCulture
Serratia marcescensCulture
Anaerobes
AnaerobesGram stain from sterile sampleAnaerobic culture
Foreign organisms
Atypical organisms
Mycoplasma pneumoniaeNAATSerology CF, PPLO culture
Legionella pneumophilaNAAT, urine antigenBCYE culture
Chlamydophila pneumoniaeNAATSerology MIF
Chlamydophila psittaciNAATSerology MIF
Coxiella burnetiiNAATSerology IIF
Mycobacterium TuberculosisNAAT, AFB smearLowenstein-Jensen culture
Opportunistic organisms
Actinomyces israeliiAnaerobic culture, microscopy for sulfur granules
Nocardia asteroidesGram stain, AFB smearCulture
Pneumocystis jiroveciNAAT, Giemsa stain
Fungi
Aspergillus fumigatusGMS stain, Galactomannan testSabouraud agar culture
Cryptococcus neoformansAntigen testSabouraud agar culture
Histoplasma capsulatumAntigen testSerology CF, Sabouraud agar culture
Coccidioides immitisSerology ID, Sabouraud agar culture
Viruses
Influenza virusNAAT, rapid antigen
Parainfluenza virusNAATSerology EIA
RS virusNAAT, rapid antigen
Human metapneumovirusNAATSerology EIA
AdenovirusNAAT, antigen test
Cytomegaloviruspp65 antigen
Parasites
Paragonimus westermaniMicroscopy for ovaMicroscopy for ova or worms

Table 1.

Current laboratory diagnosis of pathogens causing pneumonia.

Representative laboratory tools to diagnose clinically significant pathogens which cause pneumonia are listed [13].

NAAT, nucleic acid amplification test; CF, complement fixation; PPLO, pleuropneumonia-like organism; BCYE, buffered charcoal yeast extract; MIF, micro-immunofluorescence; IIF, indirect immunofluorescence; AFB, acid-fast Bacillus; GMS, Gomori methenamine silver; ID, immunodiffusion; EIA, enzyme immunoassay.

3.6. Issues with applying conventional diagnostic tools for identifying causative pathogens in respiratory samples

Although an organism requires specific agar or a unique detection assay, applying such different kinds of diagnostic tools for all patients with pneumonia is not practical and feasible from the standpoint of labor required. Moreover, the most significant issue is the culture-based method, which is a standard test for respiratory samples containing colonizing organisms or normal flora. It is not capable of discriminating a causative pathogen from isolated commensal organisms.

4. A real-time PCR for respiratory samples

4.1. Applying molecular techniques to identifying the causative pathogens in respiratory tract infection

Nucleic acid amplification test (NAAT), such as a PCR-based test or reverse transcription-PCR (RT-PCR), for purulent sputum is a logical and beneficial strategy. Firstly, if PCR cannot detect a suspected pathogen, the pathogen is less likely to be the causative pathogen due to the highly sensitive nature of PCR which can amplify even small numbers of pathogens. Secondly, PCR is capable of identifying foreign organisms that cannot grow on the standard culture agar. Thus, their detection conclusively yields the causative pathogens. Thirdly, PCR is a speedy test, and the result can be delivered to medical professionals in an early phase of the treatment. Finally, from the standpoint of NAAT, organisms causing respiratory tract infections can be simply divided into two categories; commensal organism and foreign organism (see Sections 3.2 and 3.3). Therefore, PCR does not require the pathogen-specific agar or growth conditions since all organisms are dealt with at the nucleic acid (DNA or RNA) level in the laboratory.

4.2. Issues with applying molecular diagnostic tools for identifying causative pathogens in respiratory samples

A major problem associated with a PCR-based test, similar to conventional methods, is its inability to discriminate a commensal organism causing pneumonia from the same organism colonizing in the airway.

Given the unique aspect of sputum, we assumed that, although setting a cutoff value by the direct quantification of bacterial cell number in respiratory samples would fluctuate and thus provide indistinct discrimination between causative pathogen and colonizing organism, using the relative quantification would be more stable even using the sputum which lacks homogeneity and reproductivity. To overcome this challenge, we proposed the “battlefield hypothesis,” in which the ratio of pathogen to human cells in the respiratory samples would be an indicator for the dominant pathogen in the “pneumonia battlefield.” The principal of this hypothesis is that the relative number of combatants (i.e., pathogens) causing the current state of pneumonia is considered a major determinant.

Figure 3.

Battlefield hypothesis. (A) When pneumonia occurs, the number of human inflammatory cells (gray) increases at the inflammation site where that of the causative pathogen (blue) outnumbers the human cells. Meanwhile, the colonizing pathogens (red) lag behind. Thus, the ratio of causative pathogen cells to human cells was considered as a practical indicator for the discrimination of the causative pathogen from the colonizing organisms. (B) The ratio of cell numbers between two cell types in sputum is measureable by quantitative PCR. The Ct (threshold cycle) is the number of PCR cycles at which the fluorescent signal passes the threshold. Cthuman is the Ct for the human-specific gene, Ctpathogen is the Ct for the pathogen-specific gene, and both are log proportional to the number of the cells during PCR cycles, which is accordingly formulated to ΔCtpathogen = −(Ctpathogen − Cthuman). (C) Since ΔCtpathogen indicates the ratio of pathogen cells to human cells, the ΔCtpathogen cutoff can be determined. Two examples of pneumonia cases were displayed; case A is Streptococcus pneumoniae (S. pneumoniae) pneumonia (Gram-positive cocci in purple), and case B is Haemophilus influenzae (H. influenzae) pneumonia (Gram-negative bacilli in red). In case A, the ΔCtS. pneumoniae value above which a pathogenic role of S. pneumoniae in pneumonia was confirmed should show a higher value, while a ΔCtS. pneumoniae value would demonstrate lower value in case B in which H. influenzae dominated human cells and S. pneumoniae played a colonizing role. (D) Determination of the ΔCtpathogen cutoff in a case of S. pneumoniae pneumonia. ΔCtpathogen was measured for commensal organisms in a pilot study (n = 223). Respiratory samples from patients with pneumonia in which S. pneumoniae was identified using commensal organism criteria (1)–(3) (see Section 3.4) were illustrated as blue circles, and samples in which none of criteria (1)–(3) was met were depicted as white circles. The ΔCtS. pneumoniae cutoff (a red line) was determined as the smallest ΔCtS. pneumoniae for the blue circles. Sputum in which S. pneumoniae was not detected and ΔCtS. pneumoniae was not applied is shown at the bottom (shown as “not detected”).

4.3. Battlefield hypothesis

With the battlefield hypothesis, the ratio of the cell number of a commensal organism to human cell numbers is assumed to be an index of the organism’s pathogenic role. When pneumonia occurs, the number of human cells, mostly inflammatory cells, drastically climbs at the site of infection where that of causative pathogen exceeds the human cells (Figure 3A). On the other hand, pathogens that merely colonize the affected area do not proliferate. In a real-time PCR-based system specializing in quantification, the specific primers and probe can amplify the target sequence log proportionally, in which the ratio of pathogen to human cells is formulated as ΔCtpathogen = −(Ctpathogen − Cthuman) (Figure 3B). As indicated by the battlefield hypothesis, a threshold value that discriminates commensal organisms from organisms colonizing the airway would be set up as ΔCtpathogen cutoff (Figure 3C).

Figure 4.

Determination of ΔCtpathogen cutoff. The ΔCtpathogen was measured for seven representative commensal organisms from the past clinical study (n = 533, from May 2007 to January 2009 at the Saitama Medical University Hospital and the participating institutes). Samples from patients with pneumonia in which a causative pathogen was identified using criteria (1)–(3) (see Section 3.4) are shown as blue circles. The ΔCtpathogen cutoff (a red line) was defined as the smallest ΔCtpathogen for the blue circles. The ΔCtpathogen cutoff was defined below: (A) −6 for S. pneumoniae, (B) −1 for H. influenzae, (C) 0 for M. catarrhalis, (D) −4 for P. aeruginosa, (E) −6 for K. pneumoniae; (F) −8 for MRSA, and (G) −7 for E. coli. Brackets indicate the target gene specific for each pathogen.

In order to verify the hypothesis, we first screened S. pneumoniae by specific real-time PCR (targeted to the pneumolysin gene) in 223 pneumonia patients (Figure 3D). By setting the cutoff as the smallest value of ΔCtpathogen, the samples were classified into three groups: (i) samples with ΔCtpathogen > cutoff (−4), in which case the organism was considered to have a high chance of being a causative pathogen which was called a “therapeutic target”; (ii) samples with ΔCtpathogen < cutoff, in which case the detected organism had little chance of being a causative pathogen since the pathogen-to-human ratio has never been shown in a causative pathogen in pneumonia; and (iii) samples lacking organism detection, in which case the pathogen was unlikely to be a causative pathogen.

Gene (accession #) PCR productSequence (5ʹ–3ʹ)Gene (accession #) PCR productSequence (5ʹ–3ʹ)
ControlForeign organisms
1Homo sapiens11Mycoplasma pneumoniae
SFTPCFwGCAGTGCCTACGTCTAAGCTG16S rRNAFwAGTAATACTTTAGAGGCGAACGGGTGA
(U02948.1)RvTAGATGTAGTAGAGCGGCACCTC(NC_000912.1) RvTCTACTTCTCAGCATAGCTACACGTCA
130 bpDpCGAGATGCAGGCTCAGCACCCTC227 bpDpACCAACTAGCTGATATGGCGCA
12Chlamydophila pneumoniae
53KD-antigenFwGCAACCACGGTAGCAACACAAATTA
(E12535)RvAATTGAGCGACGTTTTGTTGCATCT
Commensal organisms364 bpDpAGCGGCTGTCAAATCTGGAATAAAAG
2Streptococcus pneumoniae13Chlamydophila psittaci
lytAFwACGCAATCTAGCAGATGAAGCAompAFwGTATGTTCATGCTTAAGGCTGTTTTCAC
(AE005672)RvTCGTGCGTTTTAATTCCAGCT(X56980.1)RvTCCCACATAGTGCCATCGATTAATAAAC
75 bpDpTGCCGAAAACGCTTGATACAGGGAG291 bpDpCCAGAAGAGCAAATTAGAATAGCGAGCA
3Haemophilus influenzae14Coxiella burnetii
16S rRNAFwTTGACATCCTAAGAAGAGCTCAGAGATransposaseFwGTCTTAAGGTGGGCTGCGTG
(Z22806.1)RvCTTCCCTCTGTATACGCCATTGTAGC(M80806)RvCCCCGAATCTCATTGATCAGC
267 bpDpATGGCTGTCGTCAGCTCGTGTT295 bpDpAGCGAACCATTGGTATCGGACGTTTATGG
4Moraxella catarrhalis15Legionella spp.
copBFwGACGGGTGAGTAATGCCTAGGA16S rRNAFwAGGCTAATCTTAAAGCGCCAGGCC
(U69982.1)RvCCACTGGTGTTCCTTCCTATATCT(FR799709)RvGCATGCTTAACACATGCAAGTCGAAC
298 bpDpAGTGGGGGATCTTCGGACCTCA198 bpDpCATATTCCTACGCGTTACTCACCCGT
5Pseudomonas aeruginosa16Legionella pneumophila
23S rRNAFwTCCAAGTTTAAGGTGGTAGGCTGmipFwTAACCGAACAGCAAATGAAAGACG
(AJ549386)RvACCACTTCGTCATCTAAAAGACGAC(S72442.1)RvAAAACGGTACCATCAATCAGACGA
94 bpDpAGGTAAATCCGGGGTTTCAAGGCC264 bpDpTGATGGCAAAGCGTACTGCTGAA
6Klebsiella pneumonia17Bordetella pertussis
gapAFwTGAAGTATGACTCCACTCACGGTBP485FwCGAGCCACTGTTTCTATTGATTGA
(M66869)RvCTTCAGAAGCGGCTTTGATGGCTT(BX640412)RvCGGGCCTCATCTTCGTTCAG
670 bpDpCCGGTATCTTCCTGACCGACGA118 bpDpTGTGCGTGTTTTCCCCAGAGCCCC
7Staphylococcus aureus18Mycobacterium tuberculosis
femBFwTGGCCACTATGAGTTAAAGCTTGCMPB64FwATCCGCTGCCAGTCGTCTTCC
(DQ352467)RvTCATAATCAATCACTGGACCGCGA(NC_000962)RvCTCGCGAGTCTAGGCCAGCAT
162 bpDpCGAGGTCATTGCAGCTTGCTTACTTA238 bpDpCCGGACAACAGGTATCGATAGCGCC
8Escherichia coli19Mycobacterium intracellulare
phoAFwCGAAGAGGATTCACAAGAACATACCITS 16-23S rRNAFwAGCACCACGAAAAGCACTCCAATT
(M29670)RvGGTCTGGTCGGTCAGTCCAA(AM709724)RvCGAACGCATCAGCCCTAAGGACTA
94 bpDpCGGGCCATACGCCGCAATACGCA243 bpDpCCTGAGACAACACTCGGTCGATCC
20Mycobacterium avium
16S rRNAFwCAAGTCGAACGGAAAGGCCTCT
(M29572)RvGCCGTATCTCAGTCCCAGTGTG
Drug resistance-related genes257 bpDpTACCGGATAGGACCTCAAGACGC
9Metallo-beta-lactamase21Mycobacterium kansasii
IMPFwGGCAGYATTTCCTCTCATTTTCATAGCdnaJFwACCCGTGTGATGAGTGCAAAGGC
(AY625689)RvAATTTGTRGCTTGAACCTTACCGTCTT(AB292544.1)RvGTAAAGCTGACCGGAACTGTGACG
134 bpDpATTCTCGATCTATCCCCACGTATGCA231 bpDpAGGACGGACAGCGGATCAGACT
10Methicillin-resistant S. aureus22Pneumocystis jiroveci
mecAFwAACTACGGTAACATTGATCGCAAC5S rRNAFwGTGTACGTTGCAAAGTACTCAGAAGA
(AY786579)RvCTTTGGTCTTTCTGCATTCCTGGA(AF461782)RvGATGGCTGTTTCCAAGCCCA
112 bpDpAGATGGTATGTGGAAGTTAGATTGGGA346 bpDpCTAGGATATAGCTGGTTTTCTGCGAA
23Nocardia spp.
16S rRNAFwCCTTCGGGTTGTAAACCTCTTTCGAC
(DQ659898)RvTTGGGGTTGAGCCCCAAGTTTTCA
191 bpDpAAGAAGCACCGGCCAACTACGTGC

Table 2.

Primers and probes for the real-time PCR.

A database search was used to select potential DNA sequences to identify microorganisms of interest. The primers and probes were validated using genomic DNA from the microorganism of interest as well as screened against 74 other microorganisms [12]. Fw, forward primer; Rv, reverse primer; Dp, detection probe (TaqMan).

4.4. Determination of the ΔCtpathogen cutoff

We then confirmed that the primers and probes were specific to seven representative commensal organisms (S. pneumoniae, H. influenzae, Moraxella catarrhalis (M. catarrhalis), Pseudomonas aeruginosa (P. aeruginosa), Klebsiella pneumoniae (K. pneumoniae), Methicillin-resistant Staphylococcus aureus (MRSA), and Escherichia coli (E. coli)) and the human genome and were able to quantify their cell numbers by counting the copy numbers (Ct value) for each in sputum. The pathogen-to-human cell number ratio was measured using 533 sputum samples collected in the past clinical study between May 2007 and January 2009 (Figure 4). Based on the microbiological examination and its criteria (see Section 3.4), the ΔCtpathogen cutoff to discriminate the causative pathogens was determined by the smallest ΔCtpathogen value in seven organisms (Figure 4).

4.5. HIRA-TAN system

Accordingly, we devised a PCR-based test for sputum samples that can distinguish causative pathogens from detected commensal organisms. Moreover, combining the described PCR system for “commensal organisms” with a PCR detection system for “foreign organisms” constitutes the HIRA-TAN (human cell-controlled identification of the respiratory agent from “TAN,” which means sputum in Japanese), which involved 23 PCR with organism-specific target genes for quantifying 7 commensal organisms, 13 foreign organisms, 2 drug resistance-related genes (DRRG), and the human specific gene (as the internal control) (Table 2). HIRA-TAN was capable of screening 23 target genes simultaneously and diagnosing the therapeutic targets among commensal and foreign organisms in a single assay, which was able to be completed within 4 h. The technical details in the real-time PCR and the HIRA-TAN system were discussed in more detail in Refs. [12, 14].

4.6. Criteria for detected organisms to be the therapeutic target by HIRA-TAN system

In the HIRA-TAN system, the cutoff value was determined for each commensal organism, which enabled us to discriminate the bona fide therapeutic target from other commensal organisms detected by real-time PCR,. with the assumption being that the high relative levels of any of the given candidate pathogens would increase its likelihood of being the causative pathogen. Thus, commensal organisms were diagnosed as the “therapeutic target” when Ctpathogen exceeded the given cutoff value. Foreign organisms were diagnosed as the “therapeutic target” when detected by real-time PCR.

Technically, since opportunistic organisms, such as Pneumocystis jiroveci and Nocardia spp., were not normally identified from healthy individuals, they were classified as foreign organisms. Methicillin-resistant S. aureus (MRSA) was judged as therapeutic target in HIRA-TAN both when S. aureus (femB gene) was detected above −7 in the ΔCtS. aureus cutoff and when DRRG (mecA gene) was also detected above −8 in the ΔCtMRSA cutoff altogether. IMP-producing P. aeruginosa (multidrug resistance P. aeruginosa, MDRP) was judged as therapeutic target in HIRA-TAN both when P. aeruginosa was detected above −4 in the ΔCtP. aeruginosa cutoff and IMP gene was detected altogether.

5. Materials and methods

5.1. Respiratory specimen

In Section 5, samples and their treatment will be described. Sputum, induced sputum, or sputum obtained by intratracheal aspiration (sputum hereafter) was collected from patients with pneumonia. The sample was homogenized by pipetting and dispensed into two tubes; one was submitted for a standard microbiological test (microscopic examination and culture) and the other for nucleic acid extraction and real-time PCR analysis. To assess the pathogenic role with an appropriate sample, sputum with M2–P3 macroscopic appearance and a Cthuman <27 (the human-specific gene with Ct (threshold cycle) value by the real-time PCR) were studied [15]. Classification of the gross appearance of the sputum (M1, M2, P1, P2, and P3) was according to Miller and Jones [16].

5.2. DNA preparation from sputum

There are several kit options available for DNA extraction depending on the sample type; however, only column-based extraction has been used for most respiratory specimens due to their viscosity [14]. The sample was diluted with an equal volume of phosphate-buffered saline (PBS) and homogenized by vortexing. 200 μL of the homogenate was mixed with 200 μL AL buffer (Qiagen, Tokyo, Japan) containing 20 μL proteinase K (Takara Bio Inc., Shiga, Japan), and the resultant mixture was incubated at 56°C for 1 h. The DNA was extracted with 100 μL TE buffer using QIAamp DNA Blood Mini Kit (Qiagen, Tokyo, Japan). The DNA concentration, based on the absorbance, was determined in a spectrophotometer GeneQuant Pro (GE Healthcare, Tokyo, Japan). The ratio of nucleic acid to protein absorbance (260 nm/280 nm) was calculated as an index of the purity of DNA samples [14].

5.3. Real-time PCR

Although a variety of PCR methodologies and devices are available, an illustration of the details of our approach is given. The final solution of the PCR contained 12.5 μL of the Takara Premix Ex Taq (Takara Bio Inc., Shiga, Japan), 300 nM of each primer, 100–300 nM of the fluorescence-labeled TaqMan probe, 1.0 μL of purified DNA, and deionized distilled water up to 25.0 μL. The PCR for 23 target genes was multiplexed in 16 reactions and amplified using in a single assay. The PCR was performed by starting at 95°C for 30 s followed by 40 cycles at 95°C for 8 s, 61°C for 25 s, and 72°C for 20 s using the SmartCycler II (Cepheid, Sunnyvale, CA). The sequences of primer and probe were described in Table 2.

6. Practical application of HIRA-TAN system

6.1. Prospective study

We designed a prospective study to investigate the validity of the cutoff values we set up for the commensal organisms in the HIRA-TAN system. The aim of the study was the proportion of samples in which ΔCtpathogen was greater than the cutoff value (diagnosed as the therapeutic target by HIRA-TAN), compared to the proportion of the samples in which each commensal organism was shown to be the causative pathogen (diagnosed as the causative pathogen by microbiological methods (Table 1)). The study was performed between February 06, 2009, and October 14, 2010, at the Saitama Medical University Hospital and other six participating institutes. Five-hundred and sixty eight patients with pneumonia were enrolled, and the results of the microbiological examinations were summarized in Ref. [12] and Figure 2. The identification rates for S. pneumonia, H. influenzae, M. catarrhalis, P. aeruginosa, K. pneumoniae, MRSA, and E. coli by the HIRA-TAN were 91.6% (87/95; 95% CI 84.1–95.6%), 96.7% (29/30; 95% CI 82.8–99.2%), 90.9% (10/11; 95% CI 58.7–97.7%), 93.2% (41/44; 95% CI 81.3–97.5%), 80.6% (29/36; 95% CI 64.0–89.9%), 78.1% (25/32; 95% CI 60.0–88.5%) and 87.5% (14/16; 95% CI 61.6–96.0%), respectively (Figure 5A–G). The response of the antibiotics and the relief of pneumonia for the cases in which such commensal organisms were the causative pathogens showed a consistent clinical course as predicted from the results of the HIRA-TAN.

6.2. Overall identification capacity of therapeutic targets by real-time PCR-based test

Overall performance of the HIRA-TAN system to identify both the therapeutic targets (commensal organisms judged by the ∆Ct cutoff and foreign organisms detected by the real-time PCR) was altogether 60–70% en masse, which was comparable to what was attained by an extensive search using multiple detection methods [10, 18, 19]. However, it is supposed to reach its limit to identify the causative pathogens using primers and probe of only bacteria, and for a thorough investigation, incorporating PCR systems for viruses, anaerobes, and fungus will be required.

6.3. Beneficial aspects of PCR-based system for identifying pathogen

The most prominent feature of the HIRA-TAN is its ability to identify the causative pathogen for the pneumonias from among the commensal organisms detected in the sputum. Clinically, without this ability, this system would have been only partially useful, since more than half of pneumonias are caused by commensal organisms (Figure 2). And this system does not require the use of pathogen-by-pathogen identification methods (unique agar or a specific antibody for an organism). The easily performed comprehensive test covers a wide variety of pathogens in a single assay, which will reduce the time and labor spent on cumbersome procedures. The HIRA-TAN procedure now provides a comprehensive detection system for causative pathogens of pneumonia.

The HIRA-TAN system can also be expanded to include more pathogens, thereby increasing its abilities. The addition of any respiratory viruses or particular fungi to the screening protocol is straightforward [6, 911, 20]. Likewise, the inclusion of other commensal organisms, such as Acinetobacter baumannii, Burkholderia cepacia, or Stenotrophomonas maltophilia, will also be beneficial in nosocomial pneumonia. In addition, the most challenging category is anaerobes. Since investigating the anaerobe’s contribution to respiratory tract infection by molecular techniques has not been attempted, indicating that setting a cutoff value for anaerobes with HIRA-TAN system will be beneficial. Thus, the commensal organisms that have conformed to the scheme shown in Figure 3A–D and all non-commensal organisms become candidates, which will make the greater detection rate by the HIRA-TAN than the current 60–70% performance.

Figure 5.

Evaluating ΔCtpathogen for each commensal organism in the prospective study (n = 568). Samples from patients with pneumonia in which a causative pathogen was identified using criteria (1)–(3) (see Section 3.4) are shown as blue circles, and samples from patients with pneumonia in which none of criteria (1)–(3) was met were shown as white circles. The HIRA-TAN identified the causative pathogens: (A) 91.6% (87/95; 95% CI 84.1–95.6%) for S. pneumoniae, (B) 96.7% (29/30; 95% CI 82.8–99.2%) for H. influenzae, (C) 90.9% (10/11; 95% CI 58.7–97.7%) for M. catarrhalis, (D) 93.2% (41/44; 95% CI 81.3–97.5%) for P. aeruginosa, (E) 80.6% (29/36; 95% CI 64.0–89.9%) for K. pneumoniae, (F) 78.1% (25/32; 95% CI 60.0–88.5%) for MRSA, and (G) 87.5% (14/16; 95% CI 61.6–96.0%) for E. coli. Brackets indicate the target gene specific for each pathogen. The 95% confidence interval (CI) was calculated using the formula for the binomial probabilities by the software Mathematica Ver.8TM (Wolfram Research, IL, USA) [17].

6.4. Drawback of newly emerged PCR system

Currently the ability of the HIRA-TAN system to determine if MRSA is the causative pathogen is lacking. This is largely due to the fact that the determination and establishment of the cutoff value for a given microorganism in the HIRA-TAN system still require the conventional sputum examination. To date we have not been able to determine the ΔCtMRSA cutoff, and this will take more time and the availability of properly analyzed clinical samples to be established. Likewise, the diagnosis of Pneumocystis jiroveci pneumonia is hardly confirmed since the definitive diagnosis for this organism by conventional method has not been established.

7. Conclusion

In this chapter, the principle and utility of a real-time PCR-based diagnostic test for the causative pathogen in respiratory samples was described. Although rapid and accurate identification of pathogens and corresponding treatment based on the microbiological results are required in the healthcare setting, the current clinical tests lacking high sensitivity and a comprehensive approach have not been able to work these issues out. Development of molecular techniques and their usefulness enables the detection of organisms from the clinical specimens speedily as well as precisely and aids the settlement of such issues. With our novel approach that employs the relative quantification, we successfully set up the cutoff value to differentiate the causative pathogen from colonizing commensal organisms by PCR, with which a real-time PCR-based diagnostic system was devised and validated through clinical sample testing. Although this may be only one instance among many comprehensive systems, innovating such systems will help patients struggling with these disorders in the future.

© 2016 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Takashi Hirama (December 14th 2016). A Real-Time PCR-Based Diagnostic Test for Organisms in Respiratory Tract Infection, Polymerase Chain Reaction for Biomedical Applications, Ali Samadikuchaksaraei, IntechOpen, DOI: 10.5772/65740. Available from:

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