Quantitative PCR (qPCR) is one of the most common techniques for quantification of nucleic acid molecules in biological and environmental samples. Although the methodology is perceived to be relatively simple, there are a number of steps and reagents that require optimization and validation to ensure reproducible data that accurately reflect the biological question(s) being posed. This review article describes and illustrates the critical pitfalls and sources of error in qPCR experiments, along with a rigorous, stepwise process to minimize variability, time, and cost in generating reproducible, publication quality data every time. Finally, an approach to make an informed choice between qPCR and digital PCR technologies is described.qPCR Technique: The Perception and Reality qPCR (see Glossary) is generally viewed by researchers as a powerful technique that can provide precise and quantitative data reflecting the biology of the tested experimental parameters. However, without following strict guidelines, validation and data analysis procedures, the results can be far from valid [1,2]. Unfortunately, the adoption and transfer of inadequate and varied protocols between individual laboratory members and laboratories throughout the scientific community have led to frustration in reproducing data [3][4][5]. This has driven the production of the minimum information for publication of quantitative real-time PCR experiments (MIQE) guidelines and related methodology articles to help the scientific community in augmenting experimental rigor and uniformity to produce more reliable and consistent data [6][7][8]. Nevertheless, there remain concerns regarding the quality of qPCR results in the published literature [1,2].When designing experiments for qPCR, all protocols, such as sample handling, harvesting, nucleic acid extraction, reverse transcription, and qPCR should be described and vetted in detail. Mistakes or assumptions can be made in the planning process, resulting in a flawed experimental design with results and conclusions based on artefacts of pre and/or post sample handling procedures as opposed to the true effect of the tested experimental parameters [7]. Poorly optimized reactions can result in data that are consequent to a combination of sample contaminants and/or poor annealing temperature, leading to misinterpreted results and conclusions that are difficult or even impossible to reproduce [9,10].Despite the MIQE guidelines and other methodology articles, the variability and reproducibility pitfalls associated with qPCR remain elusive for many laboratories [7,11]. This review article describes the major sources of error associated with a qPCR experiment and strategies for their Highlights qPCR is more complex than perceived by many scientists.

Human coronaviruses (HuCV) are recognized respiratory pathogens. Data accumulated by different laboratories suggest their neurotropic potential. For example, primary cultures of human astrocytes and microglia were shown to be susceptible to an infection by the OC43 strain of HuCV (A. Bonavia, N. Arbour, V. W. Yong, and P. J. Talbot, J. Virol. 71:800–806, 1997). We speculate that the neurotropism of HuCV will lead to persistence within the central nervous system, as was observed for murine coronaviruses. As a first step in the verification of our hypothesis, we have characterized the susceptibility of various human neural cell lines to infection by HuCV-OC43. Viral antigen, infectious virus progeny, and viral RNA were monitored during both acute and persistent infections. The astrocytoma cell lines U-87 MG, U-373 MG, and GL-15, as well as neuroblastoma SK-N-SH, neuroglioma H4, oligodendrocytic MO3.13, and the CHME-5 immortalized fetal microglial cell lines, were all susceptible to an acute infection by HuCV-OC43. Viral antigen and RNA and release of infectious virions were observed during persistent HuCV-OC43 infections (∼130 days of culture) of U-87 MG, U-373 MG, MO3.13, and H4 cell lines. Nucleotide sequences of RNA encoding the putatively hypervariable viral S1 gene fragment obtained after 130 days of culture were compared to that of initial virus input. Point mutations leading to amino acid changes were observed in all persistently infected cell lines. Moreover, an in-frame deletion was also observed in persistently infected H4 cells. Some point mutations were observed in some molecular clones but not all, suggesting evolution of the viral population and the emergence of viral quasispecies during persistent infection of H4, U-87 MG, and MO3.13 cell lines. These results are consistent with the potential persistence of HuCV-OC43 in cells of the human nervous system, accompanied by the production of infectious virions and molecular variation of viral genomic RNA.

Tumor necrosis factor (TNF) is regulated post-transcriptionally by the AU-rich element (ARE) within the 3-untranslated region of its mRNA. This regulation modulates translational efficacy and mRNA stability. By using a cRNA probe containing the TNF ARE sequence, we screened a macrophage protein expression library and identified FXR1P. Macrophages that we generated from FXR1 knock-out mice had enhanced TNF protein production compared with wild type macrophages following activation. Expression of several other proteins that are regulated by ARE sequences was also affected by FXR1P deficiency. A GFP-ARE reporter that has green fluorescent protein (GFP) expression under control of the 3-untranslated region of TNF mRNA had enhanced expression in transfected macrophages deficient in FXR1P. Finally, we found that the ablation of FXR1P led to a dramatically enhanced association of the TNF mRNA with polyribosomes demonstrating the important role of FXR1P in the post-transcriptional regulation of TNF expression. Our data suggest that release of this repression by FXR1P occurs during lipopolysaccharideinduced macrophage activation. Finally, complementation of the knock-out macrophages with recombinant FXR1P resulted in decreased TNF protein production, supporting our findings that FXR1P operates as a repressor of TNF translation.

iWe developed a practical and easy two-step multiplex PCR assay to aid in serotyping of Streptococcus suis. The assay accurately typed almost all of the serotype reference strains and field isolates of various serotypes and also identified the genotypes of capsular polysaccharide synthesis gene clusters of some serologically nontypeable strains. Streptococcus suis is an important zoonotic pathogen that causes meningitis, septicemia, endocarditis, and other diseases in pigs and humans. S. suis strains have been classified into 35 serotypes (serotypes 1 to 34 and 1/2, which reacts with both serotype 1 and 2 typing sera) (1-4) on the basis of antigenic differences in their capsular polysaccharide (CP) (5). Serotyping of S. suis is one of the most useful methods to understand the epidemiology of a particular outbreak and monitor the prevalence of potentially hazardous strains. However, serotyping with all 35 typing antisera is time-consuming, and preparing the antisera is not easy due to the high cost and labor associated with its production. Additionally, cross-reactions in coagglutination tests with typing antisera and the presence of autoagglutinating strains increase the difficulty of serotyping in some cases. Therefore, the development of more practical and easier serotyping methods is desired.CP synthesis (cps) genes are clustered on a single locus of the chromosome in S. suis (6, 7). We recently sequenced and analyzed the cps gene clusters of all 35 serotype reference strains (8) and reported that 31 (serotypes 3 to 13 and 15 to 34) possessed serotype-specific genes, while the cps gene clusters of serotypes 1 and 14 and serotypes 2 and 1/2 were almost identical in each pair (8). Liu et al. (9) recently developed multiplex PCR assays to target serotype-specific cps genes for the molecular serotyping of S. suis. In their method, the reference strains of 33 serotypes (serotypes 1 to 31, 33, and 1/2) were sorted into their respective serotypes by three multiplex PCR assays, although serotypes 1 and 1/2 were not distinguished from serotypes 14 and 2, respectively (9). In addition, 84 isolates from a patient and clinically healthy pigs, which covered 20 serotypes, were correctly assigned serotypes predicted by coagglutination tests, except for those of a new serotype (serotype 21/29) (9). However, these methods have not yet been validated using field isolates from diseased pigs as well as nontypeable ones by the coagglutination test. Moreover, serotypes 32 and 34 were not included as targets for typing, because the reference strains of these serotypes have been reclassified as Streptococcus orisratti (10). In addition to the serotype 32 and 34 reference strains, those of serotypes 20, 22, 26, and 33 were also recently suggested to be removed from the taxon of S. suis (11). However, whether all isolates of these serotypes actually belong to a species that is different from S. suis remains unclear. From a diagnostic point of view, the isolates of these serotypes recovered from diseased pigs are still identified as S....

Streptococcus suis is a major swine pathogen and important zoonotic agent causing mainly septicemia and meningitis. However, the mechanisms involved in host innate and adaptive immune responses toward S. suis as well as the mechanisms used by S. suis to subvert these responses are unknown. Here, and for the first time, the ability of S. suis to interact with bone marrow-derived swine dendritic cells (DCs) was evaluated. In addition, the role of S. suis capsular polysaccharide in modulation of DC functions was also assessed. Well encapsulated S. suis was relatively resistant to phagocytosis, but it increased the relative expression of Toll-like receptors 2 and 6 and triggered the release of several cytokines by DCs, including IL-1β, IL-6, IL-8, IL-12p40 and TNF-α. The capsular polysaccharide was shown to interfere with DC phagocytosis; however, once internalized, S. suis was readily destroyed by DCs independently of the presence of the capsular polysaccharide. Cell wall components were mainly responsible for DC activation, since the capsular polysaccharide-negative mutant induced higher cytokine levels than the wild-type strain. The capsular polysaccharide also interfered with the expression of the co-stimulatory molecules CD80/86 and MHC-II on DCs. To conclude, our results show for the first time that S. suis interacts with swine origin DCs and suggest that these cells might play a role in the development of host innate and adaptive immunity during an infection with S. suis serotype 2.