Evaluation validation of a qPCR curve analysis method and conventional approaches

Detection of the chuA gene encoding the invasive enterohemorrhagic species Escherichia coli 0157:H7 using qPCR in horse feces samples on Sumbawa Island, Indonesia

Background

Bacterial identification can be done using various testing techniques. Molecular techniques are often used to research dangerous diseases, an approach using genetic information on the pathogenic agent. The enterohemorrhagic invasive species Escherichia coli 0157:H7 was identified from the feces of working horses on the island of Sumbawa. Another advance in molecular technology is genome amplification with qPCR which is the gold standard for detecting E. coli.

Aim

This study aims to detect and identify the invasive species E. coli 0157:H7 using the gene encoding chuA with the qPCR method sourced from horse feces.

Methods

Fresh fecal samples from horses on Sumbawa Island were isolated and identified, then continued with molecular examination using the gene encoding chuA using the qPCR method.

Results

qPCR testing in this study showed that six sample isolates that were positive for E. coli 0157:H7 were detected for the presence of the chuA gene, which is a gene coding for an invasive species of E. coli bacteria. The highest to lowest Cq values and Tm from the qPCR results of the sample isolates were 15.98 (4KJ), 14.90 (19KG), 14.6 (3KJ), 13.77 (20KG), 12.56 (5KGB), and 12.20 (6KJ). Tm values are 86.7 (4KJ), 86.69 (3KJ), 86.56 (5KGB), 85.88 (20KGB), 85.81 (19KG), and 85.74 (6KJ).

Conclusion

Validation, standardization of the development, and modification of qPCR technology must be carried out to harmonize testing throughout to avoid wrong interpretation of the test results so that the determination of actions to eradicate and control diseases originating from animals in the field does not occur.

Introducing the f0% method: a reliable and accurate approach for qPCR analysis

BACKGROUND

qPCR is a widely used technique in scientific research as a basic tool in gene expression analysis. Classically, the quantitative endpoint of qPCR is the threshold cycle (CT) that ignores differences in amplification efficiency among many other drawbacks. While other methods have been developed to analyze qPCR results, none has statistically proven to perform better than the CT method. Therefore, we aimed to develop a new qPCR analysis method that overcomes the limitations of the CT method. Our f0% [eff naught percent] method depends on a modified flexible sigmoid function to fit the amplification curve with a linear part to subtract the background noise. Then, the initial fluorescence is estimated and reported as a percentage of the predicted maximum fluorescence (f0%).

RESULTS

The performance of the new f0% method was compared against the CT method along with another two outstanding methods-LinRegPCR and Cy0. The comparison regarded absolute and relative quantifications and used 20 dilution curves obtained from 7 different datasets that utilize different DNA-binding dyes. In the case of absolute quantification, f0% reduced CV%, variance, and absolute relative error by 1.66, 2.78, and 1.8 folds relative to CT; and by 1.65, 2.61, and 1.71 folds relative to LinRegPCR, respectively. While, regarding relative quantification, f0% reduced CV% by 1.76, 1.55, and 1.25 folds and variance by 3.13, 2.31, and 1.57 folds regarding CT, LinRegPCR, and Cy0, respectively. Finally, f0% reduced the absolute relative error caused by LinRegPCR by 1.83 folds.

CONCLUSIONS

We recommend using the f0% method to analyze and report qPCR results based on its reported advantages. Finally, to simplify the usage of the f0% method, it was implemented in a macro-enabled Excel file with a user manual located on https://github.com/Mahmoud0Gamal/F0-perc/releases .

Prolonged Linear Amplification of qPCR for the Correction of Amplification Variation and the Absolute Quantification without Standard Curves

The variable amplification efficiency of each thermal cycle of qPCR obeys the Poisson distribution, and the qPCR system dynamically changes, so there must be a detection error in its quantitative analysis. Here, more than 20 cycles of the linear amplification of qPCR can be produced as the BSA hydrogel is introduced to achieve the controlled release of Taq DNA polymerase. There is a significant negative correlation between the slope of linear amplification and Ct values (r = -0.9455), and it is well evident that the slope can reflect the amplification efficiency and a linear positive correlation exists between them. Through the change in the concentration of primers in the qPCR system, an exponential equation between Ct values and the slopes can be fitted (R2 = 0.9995). The slopes and Ct values of each qPCR system can be corrected by using this equation to guarantee that there will be significant consistency in their amplification efficiency because the degree of linear fitting (R2) between Ct values and the logarithm of their corresponding concentration of the DNA template increased significantly. By this time, the accurate amplification efficiency can be calculated in a known multiple of two initial concentrations of DNA templates. With the aid of the relationship between the known primer concentration and the fluorescence intensity at the end of PCR (End RFU), the initial concentrations of DNA templates can be reversely calculated in the absence of standard curves.

Next-generation molecular diagnostics: Leveraging digital technologies to enhance multiplexing in real-time PCR

Real-time polymerase chain reaction (qPCR) enables accurate detection and quantification of nucleic acids and has become a fundamental tool in biological sciences, bioengineering and medicine. By combining multiple primer sets in one reaction, it is possible to detect several DNA or RNA targets simultaneously, a process called multiplex PCR (mPCR) which is key to attaining optimal throughput, cost-effectiveness and efficiency in molecular diagnostics, particularly in infectious diseases. Multiple solutions have been devised to increase multiplexing in qPCR, including single-well techniques, using target-specific fluorescent oligonucleotide probes, and spatial multiplexing, where segregation of the sample enables parallel amplification of multiple targets. However, these solutions are mostly limited to three or four targets, or highly sophisticated and expensive instrumentation. There is a need for innovations that will push forward the multiplexing field in qPCR, enabling for a next generation of diagnostic tools which could accommodate high throughput in an affordable manner. To this end, the use of machine learning (ML) algorithms (data-driven solutions) has recently emerged to leverage information contained in amplification and melting curves (AC and MC, respectively) - two of the most standard bio-signals emitted during qPCR - for accurate classification of multiple nucleic acid targets in a single reaction. Therefore, this review aims to demonstrate and illustrate that data-driven solutions can be successfully coupled with state-of-the-art and common qPCR platforms using a variety of amplification chemistries to enhance multiplexing in qPCR. Further, because both ACs and MCs can be predicted from sequence data using thermodynamic databases, it has also become possible to use computer simulation to rationalize and optimize the design of mPCR assays where target detection is supported by data-driven technologies. Thus, this review also discusses recent work converging towards the development of an end-to-end framework where knowledge-based and data-driven software solutions are integrated to streamline assay design, and increase the accuracy of target detection and quantification in the multiplex setting. We envision that concerted efforts by academic and industry scientists will help advance these technologies, to a point where they become mature and robust enough to bring about major improvements in the detection of nucleic acids across many fields.