Highly accurate sigmoidal fitting of real-time PCR data by introducing a parameter for asymmetry

High-Yield α-Synuclein Purification and Ionic Strength Modification Pivotal to Seed Amplification Assay Performance and Reproducibility

Alpha-synuclein seed amplification assays (αSyn-SAAs) have emerged as promising diagnostic tools for Parkinson's disease (PD) by detecting misfolded αSyn and amplifying the signal through cyclic shaking and resting in vitro. Recently, our group and others have shown that multiple biospecimens, including CSF, skin, and submandibular glands (SMGs), can be used to seed the aggregation reaction and robustly distinguish between patients with PD and non-disease controls. The ultrasensitivity of the assay affords the ability to detect minute quantities of αSyn in peripheral tissues, but it also produces various technical challenges of variability. To address the problem of variability, we present a high-yield αSyn protein purification protocol for the efficient production of monomers with a low propensity for self-aggregation. We expressed wild-type αSyn in BL21 Escherichia coli, lysed the cells using osmotic shock, and isolated αSyn using acid precipitation and fast protein liquid chromatography (FPLC). Following purification, we optimized the ionic strength of the reaction buffer to distinguish the fluorescence maximum (Fmax) separation between disease and healthy control tissues for enhanced assay performance. Our protein purification protocol yielded high quantities of αSyn (average: 68.7 mg/mL per 1 L of culture) and showed highly precise and robust αSyn-SAA results using brain, skin, and SMGs with inter-lab validation.

A cobalt concentration sensitive Btu-like system facilitates cobalamin uptake in Anabaena sp. PCC 7120

Metal homeostasis is central to all forms of life, as metals are essential micronutrients with toxic effects at elevated levels. Macromolecular machines facilitate metal uptake into the cells and their intracellular level is regulated by multiple means, which can involve RNA elements and proteinaceous components. While the general principles and components for uptake and cellular content regulation of, e.g., cobalt have been identified for proteobacteria, the corresponding mechanism in other Gram-negative bacteria such as cyanobacteria remain to be established. Based on their photosynthetic activity, cyanobacteria are known to exhibit a special metal demand in comparison to other bacteria. Here, the regulation by cobalt and cobalamin as well as their uptake is described for Anabaena sp. PCC 7120, a model filamentous heterocyst-forming cyanobacterium. Anabaena contains at least three cobalamin riboswitches in its genome, for one of which the functionality is confirmed here. Moreover, two outer membrane-localized cobalamin TonB-dependent transporters, namely BtuB1 and BtuB2, were identified. BtuB2 is important for fast uptake of cobalamin under conditions with low external cobalt, whereas BtuB1 appears to function in cobalamin uptake under conditions of sufficient cobalt supply. While the general function is comparable, the specific function of the two genes differs and mutants thereof show distinct phenotypes. The uptake of cobalamin depends further on the TonB and a BtuFCD machinery, as mutants of tonB3 and btuD show reduced cobalamin uptake rates. Thus, our results provide novel information on the uptake of cobalamin and the regulation of the cellular cobalt content in cyanobacteria.

An Algebraic Blueprint for Predicting Turnover Numbers and Endpoints in Photocatalysis

Photocatalysis is a contemporary research field given that the world's fossil energy resources including coal, mineral oil and natural gas are finite. The vast variety of photocatalytic systems demands for standardized protocols facilitating an objective comparison. While there are commonly accepted performance indicators such as the turnover number (TON) that are usually reported, to date there is no unified concept for the determination of TONs and the endpoint of the reaction during continuous measurements. Herein, we propose an algebraic approach using defined parameters and boundary conditions based on partial-least squares regression for generically calculating and predicting the turnover number and the endpoint of a photocatalytic experiment. Furthermore, the impact of the analysis period was evaluated with respect to the fidelity of the obtained TON, and the influence of the data point density along critical segments of the obtained fitting function is demonstrated.

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 .

Quantitative mRNA expression measurement at home

mRNA measurement is dominated by RT-PCR, which requires expensive laboratory equipment and personnel with advanced degrees. Loop-mediated isothermal amplification (LAMP) is a versatile technique for detecting target DNA and RNA. The sensitivity of LAMP in early reports has been below that of the standard RT-PCR tests. Here, we report the use of a fluorescence-based RT-LAMP protocol to measure CDX2 expression patterns, which match extremely well to the standards of sophisticated RT-PCR techniques (r = 0.99, p < 0.001). The assay works on diverse sample types such as cDNA, mRNA, and direct tissue sample testing in 25 min compared to more than 3 h for RT-PCR. We have developed a new protocol for designing RT-LAMP primers that reduce false positives due to self-amplification and improve quantification. A simple device with a 3D-printed box enables the measurement of mRNA expression at home, outdoors, and point-of-care setting.

Were public interventions relevant for containing the covid-19 pandemic in Brazil in 2020?

OBJECTIVE

Flattening the curve was the most promoted public health strategy worldwide, during the pandemic, to slow down the spread of the SARS-CoV-2 virus, and, consequently, to avoid overloading the healthcare systems. In Brazil, a relative success of public policies was evidenced. However, the association between public policies and the "flatten the curve" objectives remain unclear, as well as the association of different policies to reach this aim. This study aims to verify if the adoption of different public policies was associated with the flattening of the infection and death curves by covid-19 first wave in 2020.

METHODS

Data from the Sistema de Informação da Vigilância Epidemiológica da Gripe (Influenza Epidemiological Surveillance Information System - SIVEP-Gripe) and the Instituto Brasileiro de Geografia e Estatística (Brazilian Institute of Geography and Statistics - IBGE) were used to compute standardized incidence and mortality rates. The Oxford Covid-19 Government Response Tracker (OxCGRT) was used to obtain information about governmental responses related to the mitigation of pandemic effects, and the Human Development Index (HDI) was used as a measure of socioeconomic status. A non-linear least-square method was used to estimate parameters of the five-parameter sigmoidal curve, obtaining the time to reach the peak and the incremental rate of the curves. Additionally, ordinary least-square linear models were used to assess the correlation between the curves and the public policies adopted.

RESULTS

Out of 51 municipalities, 261,326 patients had SARS-CoV-2 infection. Stringency Index was associated with reducing covid-19 incremental incidence and death rates,in addition to delaying the time to reach the peak of both pandemic curves. Considering both parameters, economic support policies did not affect the incidence nor the mortality rate curves.

CONCLUSION

The evidence highlighted the importance and effectiveness of social distancing policies during the first year of the pandemic in Brazil, flattening the curves of mortality and incidence rates. Other policies, such as those focused on economic support, were not effective in flattening the curves but met humanitarian and social outcomes.

A novel metric to improve mismatched primer selection and quantification accuracy in amplifying DNA repeats for quantitative polymerase chain reactions

In quantitative polymerase chain reaction (qPCR) experiments, primers containing mismatches with respect to the template are widely used in measuring repetitive DNA elements. Primer-template mismatches may lead to underestimation of the input sample quantity due to inefficient annealing and amplification. But how primer-template mismatches affect quantification accuracy has not been rigorously investigated. In this study, we performed a series of qPCR experiments in which we tested three pairs of mismatched telomere primers (tel1/tel2, tel1b/tel2b and telg/telc) and two pairs of perfect-match reference gene primers (36B4-F/-R and IFNB1-F/-R) at three different primer concentrations under four cycling conditions. Templates used were genomic DNA from two human cell lines and oligo duplexes which contained telomere sequences, reference gene sequences, or both. We demonstrated that the underestimation of input sample quantity from reactions containing mismatched primers was not due to lower amplification efficiency (E), but due to ineffective usage of the input sample. We defined a novel concept of amplification efficacy (f) which quantifies the effectiveness of input sample amplification by primers. We have modified the conventional qPCR kinetic formula to include f, which corrects the effects of primer mismatches. We demonstrated that reactions containing mismatched telomere primer pairs had similar efficiency (E), but varying degrees of reduced efficacy (f) in comparison to those with the perfect-match gene primer pairs. Using the quantitative parameter f, underestimation of initial target by telomere primers can be adjusted to provide a more accurate measurement. Additionally, we found that the tel1b/tel2b primer set at concentration of 500 nM and 900 nM exhibited the best amplification efficacy f. This study provides a novel way to incorporate an evaluation of amplification efficacy into qPCR analysis. In turn, it improves mismatched primer selection and quantification accuracy in amplifying DNA repeats using qPCR methods.

Smart-Plexer: a breakthrough workflow for hybrid development of multiplex PCR assays

Developing multiplex PCR assays requires extensive experimental testing, the number of which exponentially increases by the number of multiplexed targets. Dedicated efforts must be devoted to the design of optimal multiplex assays ensuring specific and sensitive identification of multiple analytes in a single well reaction. Inspired by data-driven approaches, we reinvent the process of developing and designing multiplex assays using a hybrid, simple workflow, named Smart-Plexer, which couples empirical testing of singleplex assays and computer simulation to develop optimised multiplex combinations. The Smart-Plexer analyses kinetic inter-target distances between amplification curves to generate optimal multiplex PCR primer sets for accurate multi-pathogen identification. In this study, the Smart-Plexer method is applied and evaluated for seven respiratory infection target detection using an optimised multiplexed PCR assay. Single-channel multiplex assays, together with the recently published data-driven methodology, Amplification Curve Analysis (ACA), were demonstrated to be capable of classifying the presence of desired targets in a single test for seven common respiratory infection pathogens.

Robust and Precise Quantitative Real-Time Polymerase Chain Reaction with an Amplification Efficiency-Aware Reaction Kinetics Model

Quantitative real-time PCR (qPCR) is a method extensively used in nucleic acid testing for plants and animals. During the coronavirus disease 2019 (COVID-19) pandemic, high-precision qPCR analysis was urgently needed since quantitative results obtained from conventional qPCR methods were not accurate and precise, causing misdiagnoses and high rates of false-negative. To achieve more accurate results, we propose a new qPCR data analysis method with an amplification efficiency-aware reaction kinetics model (AERKM). Our reaction kinetics model (RKM) mathematically describes the tendency of the amplification efficiency during the whole qPCR process inferred by biochemical reaction dynamics. Amplification efficiency (AE) was introduced to rectify the fitted data so as to match the real reaction process for individual tests, thus reducing errors. The 5-point 10-fold gradient qPCR tests of 63 genes have been verified. The results of a 0.9% slope bias and an 8.2% ratio bias using AERKM exceed 4.1 and 39.4%, respectively, of the best performance of existing models, which demonstrates higher precision, less fluctuation, and better robustness among different nucleic acids. AERKM also provides a better understanding of the real qPCR process and gives insights into the detection, treatment, and prevention of severe diseases.

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.

The effect of the immunoassay curve fitting routine on bias in troponin

OBJECTIVES

Unlike many dose-response curves used in clinical chemistry, the immunoassay curve used to quantitate measurands is often sigmoidal rather than linear. Consequently, a more complex curve fitting model is required. Various models are available, but they can introduce bias, and there can be little awareness of why this error can be introduced.

CONTENT

These curve-fitting models include those based on the law of mass-action, empirical models such as splines or linearization models such as the log/logit function. All these models involve assumptions, which can introduce bias as the dose-response curve is 'forced' to fit or minimize the distance between the standard concentration points to the theoretical curve. The most common curve fitting model is the four or five parameter model, which uses four or five parameters to fit a sigmoidal curve to a set of standard points.

SUMMARY AND OUTLOOK

Measurement of cardiac troponin is an important element in establishing a diagnosis of acute myocardial infarction. We use troponin, a cardiac biomarker, to demonstrate the potential effect of the bias that the curve fit could introduce. Troponin is used for both rule-in and rule-out decisions at different concentrations and at either end of the dose-response curve. The curve fitting process can cause lot-to-lot reagent (and calibrator) variation in immunoassay. However, laboratory staff need to be aware of this potential source of error and why it occurs. Understanding how the error occurs leads to a greater awareness of the importance of validating new reagent/calibrator assessment using patient samples with concentrations at crucial decision points.

Alleviating Cell Lysate-Induced Inhibition to Enable RT-PCR from Single Cells in Picoliter-Volume Double Emulsion Droplets

Microfluidic droplet assays enable single-cell polymerase chain reaction (PCR) and sequencing analyses at unprecedented scales, with most methods encapsulating cells within nanoliter-sized single emulsion droplets (water-in-oil). Encapsulating cells within picoliter double emulsion (DE) (water-in-oil-in-water) allows sorting droplets with commercially available fluorescence-activated cell sorter (FACS) machines, making it possible to isolate single cells based on phenotypes of interest for downstream analyses. However, sorting DE droplets with standard cytometers requires small droplets that can pass FACS nozzles. This poses challenges for molecular biology, as prior reports suggest that reverse transcription (RT) and PCR amplification cannot proceed efficiently at volumes below 1 nL due to cell lysate-induced inhibition. To overcome this limitation, we used a plate-based RT-PCR assay designed to mimic reactions in picoliter droplets to systematically quantify and ameliorate the inhibition. We find that RT-PCR is blocked by lysate-induced cleavage of nucleic acid probes and primers, which can be efficiently alleviated through heat lysis. We further show that the magnitude of inhibition depends on the cell type, but that RT-PCR can proceed in low-picoscale reaction volumes for most mouse and human cell lines tested. Finally, we demonstrate one-step RT-PCR from single cells in 20 pL DE droplets with fluorescence quantifiable via FACS. These results open up new avenues for improving picoscale droplet RT-PCR reactions and expanding microfluidic droplet-based single-cell analysis technologies.

Adaptive Filtering Framework to Remove Nonspecific and Low-Efficiency Reactions in Multiplex Digital PCR Based on Sigmoidal Trends

Real-time digital polymerase chain reaction (qdPCR) coupled with machine learning (ML) methods has shown the potential to unlock scientific breakthroughs, particularly in the field of molecular diagnostics for infectious diseases. One promising application of this emerging field explores single fluorescent channel PCR multiplex by extracting target-specific kinetic and thermodynamic information contained in amplification curves, also known as data-driven multiplexing. However, accurate target classification is compromised by the presence of undesired amplification events and not ideal reaction conditions. Therefore, here, we proposed a novel framework to identify and filter out nonspecific and low-efficient reactions from qdPCR data using outlier detection algorithms purely based on sigmoidal trends of amplification curves. As a proof-of-concept, this framework is implemented to improve the classification performance of the recently reported data-driven multiplexing method called amplification curve analysis (ACA), using available published data where the ACA is demonstrated to screen carbapenemase-producing organisms in clinical isolates. Furthermore, we developed a novel strategy, named adaptive mapping filter (AMF), to adjust the percentage of outliers removed according to the number of positive counts in qdPCR. From an overall total of 152,000 amplification events, 116,222 positive amplification reactions were evaluated before and after filtering by comparing against melting peak distribution, proving that abnormal amplification curves (outliers) are linked to shifted melting distribution or decreased PCR efficiency. The ACA was applied to assess classification performance before and after AMF, showing an improved sensitivity of 1.2% when using inliers compared to a decrement of 19.6% when using outliers (p-value < 0.0001), removing 53.5% of all wrong melting curves based only on the amplification shape. This work explores the correlation between the kinetics of amplification curves and the thermodynamics of melting curves, and it demonstrates that filtering out nonspecific or low-efficient reactions can significantly improve the classification accuracy for cutting-edge multiplexing methodologies.

Intramolecular vibrational energy redistribution in nucleobases: Excitation of NH stretching vibrations in adenine–uracil + H2O

Redistribution of vibrational energy in the adenine–uracil base pair is studied when the base pair undergoes an intermolecular interaction with an overtone-bending vibration excited H2O(2[Formula: see text]bend) molecule. Energy transfer is calculated using the structural information obtained from density functional theory in the solution of the equations of motion. Intermolecular vibrational energy transfer (VET) from H2O(2[Formula: see text]bend) to the uracil–NH stretching mode is efficient and rapidly followed by intramolecular vibrational energy redistribution (IVR) resulting from coupling between vibrational modes. An important pathway is IVR carrying energy to the NH-stretching mode of the adenine moiety in a subpicosecond scale, the energy build-up being sigmoidal, when H2O interacts with the uracil–NH bond. The majority of intermolecular hydrogen bonds between the base pair and H2O are weakened but unbroken during the ultrafast energy redistribution period. Lifetimes of intermolecular HB are on the order of 0.5 ps. The efficiency of IVR in the base pair is due to near-resonance between coupled CC and CN vibrations. The resonance also exists between the frequencies of H2O bend and NH stretch, thus facilitating VET. When H2O interacts with the NH bond at the adenine end of the base pair, energy flow in the reverse direction to the uracil–NH stretch is negligible, the unidirectionality discussed in terms of the effects of uracil CH stretches. The energy distributed in the CH bonds is found to be significant. The IVR process is found to be nearly temperature independent between 200 and 400 K.

Evaluation validation of a qPCR curve analysis method and conventional approaches

BACKGROUND

Reverse Transcription quantitative polymerase chain reaction (RT-qPCR) is a sensitive and reliable method for mRNA quantification and rapid analysis of gene expression from a large number of starting templates. It is based on the statistical significance of the beginning of exponential phase in real-time PCR kinetics, reflecting quantitative cycle of the initial target quantity and the efficiency of the PCR reaction (the fold increase of product per cycle).

RESULTS

We used the large clinical biomarker dataset and 94-replicates-4-dilutions set which was published previously as research tools, then proposed a new qPCR curve analysis method--CqMAN, to determine the position of quantitative cycle as well as the efficiency of the PCR reaction and applied in the calculations. To verify algorithm performance, 20 genes from biomarker and partial data with concentration gradients from 94-replicates-4-dilutions set of MYCN gene were used to compare our method with various publicly available methods and established a suitable evaluation index system.

CONCLUSIONS

The results show that CqMAN method is comparable to other methods and can be a feasible method which applied to our self-developed qPCR data processing and analysis software, providing a simple tool for qPCR analysis.

Web-based LinRegPCR: application for the visualization and analysis of (RT)-qPCR amplification and melting data

BACKGROUND

The analyses of amplification and melting curves have been shown to provide valuable information on the quality of the individual reactions in quantitative PCR (qPCR) experiments and to result in more reliable and reproducible quantitative results.

IMPLEMENTATION

The main steps in the amplification curve analysis are (1) a unique baseline subtraction, not using the ground phase cycles, (2) PCR efficiency determination from the exponential phase of the individual reactions, (3) setting a common quantification threshold and (4) calculation of the efficiency-corrected target quantity with the common threshold, efficiency per assay and C_q per reaction. The melting curve analysis encompasses smoothing of the observed fluorescence data, normalization to remove product-independent fluorescence loss, peak calling and assessment of the correct peak by comparing its melting temperature with the known melting temperature of the intended amplification product.

RESULTS

The LinRegPCR web application provides visualization and analysis of a single qPCR run. The user interface displays the analysis results on the amplification curve analysis and melting curve analysis in tables and graphs in which deviant reactions are highlighted. The annotated results in the tables can be exported for calculation of gene-expression ratios, fold-change between experimental conditions and further statistical analysis. Web-based LinRegPCR addresses two types of users, wet-lab scientists analyzing the amplification and melting curves of their own qPCR experiments and bioinformaticians creating pipelines for analysis of series of qPCR experiments by splitting its functionality into a stand-alone back-end RDML (Real-time PCR Data Markup Language) Python library and several companion applications for data visualization, analysis and interactive access. The use of the RDML data standard enables machine independent storage and exchange of qPCR data and the RDML-Tools assist with the import of qPCR data from the files exported by the qPCR instrument.

CONCLUSIONS

The combined implementation of these analyses in the newly developed web-based LinRegPCR ( https://www.gear-genomics.com/rdml-tools/ ) is platform independent and much faster than the original Windows-based versions of the LinRegPCR program. Moreover, web-based LinRegPCR includes a novel statistical outlier detection and the combination of amplification and melting curve analyses allows direct validation of the amplification product and reporting of reactions that amplify artefacts.

Perturbation of miR-146b and relevant inflammatory elements in esophageal carcinoma patients supports an immune downregulatory mechanism

BACKGROUND

Esophageal Cancer is known as one of the deadliest cancers worldwide with the squamous cell carcinoma (ESCC) being the predominant subtype. There is a growing body of evidence linking the dysregulated microRNA (miRNA) pathway of immune cells to the progression of several tumors. In a previous study, we investigated molecular alterations pertaining to miR-146a and some components of NF-kB signaling pathway and proposed a possible immune downregulatory mechanism in peripheral blood mononuclear cells (PBMCs) of ESCC patients. Here, we further scrutinized other components of this pathway by evaluating PBMC levels of miR-146b, TLR4, IL10, and TNFA.

METHODS

Gene expressions were quantified using RT-qPCR assays. To prevent the vulnerability of results to the expression instability of reference genes, nine additional transcripts were quantified, and stable reference genes for normalizing qPCR data were identified using the NormFinder and the geNorm algorithms. The efficiency-corrected normalized relative quantity values were used to compare gene expressions among study groups.

RESULTS

The PBMC expression of miR-146b and TNFA was downregulated in ESCC patients as compared to healthy subjects. While the level of TLR4 was not different among the study groups, the PBMC level of IL10 was upregulated in ESCC patients. Logistic regression analyses coupled with the ROC curve and cross-validation analysis suggested that PBMC expression may serve as potential candidate biomarker for discriminating ESCC patients from healthy subjects.

CONCLUSION

The present findings, in line with our previous report, propose a particular gene expression pattern in PBMCs of ESCC patients, providing evidence in support of an immune downregulatory mechanism.

Estimating Real-Time qPCR Amplification Efficiency from Single-Reaction Data

Methods for estimating the qPCR amplification efficiency E from data for single reactions are tested on six multireplicate datasets, with emphasis on their performance as a function of the range of cycles n₁–n₂ included in the analysis. The two-parameter exponential growth (EG) model that has been relied upon almost exclusively does not allow for the decline of E(n) with increasing cycle number n through the growth region and accordingly gives low-biased estimates. Further, the standard procedure of “baselining”—separately estimating and subtracting a baseline before analysis—leads to reduced precision. The three-parameter logistic model (LRE) does allow for such decline and includes a parameter E₀ that represents E through the baseline region. Several four-parameter extensions of this model that accommodate some asymmetry in the growth profiles but still retain the significance of E₀ are tested against the LRE and EG models. The recursion method of Carr and Moore also describes a declining E(n) but tacitly assumes E₀ = 2 in the baseline region. Two modifications that permit varying E₀ are tested, as well as a recursion method that directly fits E(n) to a sigmoidal function. All but the last of these can give E₀ estimates that agree fairly well with calibration-based estimates but perform best when the calculations are extended to only about one cycle below the first-derivative maximum (FDM). The LRE model performs as well as any of the four-parameter forms and is easier to use. Its proper implementation requires fitting to it plus a suitable baseline function, which typically requires four–six adjustable parameters in a nonlinear least-squares fit.

Use and Misuse of Cq in qPCR Data Analysis and Reporting

In the analysis of quantitative PCR (qPCR) data, the quantification cycle (C_q) indicates the position of the amplification curve with respect to the cycle axis. Because C_q is directly related to the starting concentration of the target, and the difference in C_q values is related to the starting concentration ratio, the only results of qPCR analysis reported are often C_q, ΔC_q or ΔΔC_q values. However, reporting of C_q values ignores the fact that C_q values may differ between runs and machines, and, therefore, cannot be compared between laboratories. Moreover, C_q values are highly dependent on the PCR efficiency, which differs between assays and may differ between samples. Interpreting reported C_q values, assuming a 100% efficient PCR, may lead to assumed gene expression ratios that are 100-fold off. This review describes how differences in quantification threshold setting, PCR efficiency, starting material, PCR artefacts, pipetting errors and sampling variation are at the origin of differences and variability in C_q values and discusses the limits to the interpretation of observed C_q values. These issues can be avoided by calculating efficiency-corrected starting concentrations per reaction. The reporting of gene expression ratios and fold difference between treatments can then easily be based on these starting concentrations.

Efficiency Correction Is Required for Accurate Quantitative PCR Analysis and Reporting

BACKGROUND

Quantitative PCR (qPCR) aims to measure the DNA or RNA concentration in diagnostic and biological samples based on the quantification cycle (Cq) value observed in the amplification curves. Results of qPCR experiments are regularly calculated as if all assays are 100% efficient or reported as just Cq, ΔCq, or ΔΔCq values.

CONTENTS

When the reaction shows specific amplification, it should be deemed to be positive, regardless of the observed Cq. Because the Cq is highly dependent on amplification efficiency that can vary among targets and samples, accurate calculation of the target quantity and relative gene expression requires that the actual amplification efficiency be taken into account in the analysis and reports. PCR efficiency is frequently derived from standard curves, but this approach is affected by dilution errors and hampered by properties of the standard and the diluent. These factors affect accurate quantification of clinical and biological samples used in diagnostic applications and collected in challenging conditions. PCR efficiencies determined from individual amplification curves avoid these confounders. To obtain unbiased efficiency-corrected results, we recommend absolute quantification with a single undiluted calibrator with a known target concentration and efficiency values derived from the amplification curves of the calibrator and the unknown samples.

SUMMARY

For meaningful diagnostics or biological interpretation, the reported results of qPCR experiments should be efficiency corrected. To avoid ambiguity, the Minimal Information for Publications on Quantitative Real-Time PCR Experiments (MIQE) guidelines checklist should be extended to require the methods that were used (1) to determine the PCR efficiency and (2) to calculate the reported target quantity and relative gene expression value.

Autoregressive modeling and diagnostics for qPCR amplification

MOTIVATION

Current methods used to analyze real-time quantitative polymerase chain reaction (qPCR) data exhibit systematic deviations from the assumed model over the progression of the reaction. Slight variations in the amount of the initial target molecule or in early amplifications are likely responsible for these deviations. Commonly used 4- and 5-parameter sigmoidal models appear to be particularly susceptible to this issue, often displaying patterns of autocorrelation in the residuals. The presence of this phenomenon, even for technical replicates, suggests that these parametric models may be misspecified. Specifically, they do not account for the sequential dependent nature of the amplification process that underlies qPCR fluorescence measurements.

RESULTS

We demonstrate that a Smooth Transition Autoregressive (STAR) model addresses this limitation by explicitly modeling the dependence between cycles and the gradual transition between amplification regimes. In summary, application of a STAR model to qPCR amplification data improves model fit and reduces autocorrelation in the residuals.

AVAILABILITY AND IMPLEMENTATION

R scripts to reproduce all the analyses and results described in this manuscript can be found at: https://github.com/bhsu4/GAPDH.SO.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A rationally engineered decoder of transient intracellular signals

Cells can encode information about their environment by modulating signaling dynamics and responding accordingly. Yet, the mechanisms cells use to decode these dynamics remain unknown when cells respond exclusively to transient signals. Here, we approach design principles underlying such decoding by rationally engineering a synthetic short-pulse decoder in budding yeast. A computational method for rapid prototyping, TopoDesign, allowed us to explore 4122 possible circuit architectures, design targeted experiments, and then rationally select a single circuit for implementation. This circuit demonstrates short-pulse decoding through incoherent feedforward and positive feedback. We predict incoherent feedforward to be essential for decoding transient signals, thereby complementing proposed design principles of temporal filtering, the ability to respond to sustained signals, but not to transient signals. More generally, we anticipate TopoDesign to help designing other synthetic circuits with non-intuitive dynamics, simply by assembling available biological components.

Progress curve analysis of microtitre plate plasma clotting assays. Assessment of tissue factor levels

MTP plasma clotting assays monitor the time course of fibrin formation in re-calcified plasma by absorbance measurements and are increasingly used as alternatives to traditional one-point clot time assays employed in clinical laboratories to detect thrombotic disorders. The parameters derived from these analyses are analogous to thromboelastography viz. time, rate and maximum extent of clot formation. The derived parameters, based on the whole course of the clotting reaction are more robust, informative and quantitative than single-point clot time assays. However, the parameters themselves are usually obtained arbitrarily by crude graphical analysis of subjectively selected points of progress curves. The current work aimed to investigate the sensitivity and reproducibility of an MTP clotting assay and examine its suitability for measuring tissue factor (TF) levels in cell culture medium and patient urine. The results demonstrate that progress curves can be analysed by fitting a logistic equation, derived from a simplified autocatalytic clot formation model. The parameters, maximum amplitude (Fm), rate constant (k), time to half-maximum amplitude (tm) and maximum rate of clot formation (vm), fit a power curve showing limiting effects with increasing TF concentration. Log/log plots of tm and k against TF concentration provide standard curves for assessment of unknowns.

Multiple imputation and direct estimation for qPCR data with non-detects

BACKGROUND

Quantitative real-time PCR (qPCR) is one of the most widely used methods to measure gene expression. An important aspect of qPCR data that has been largely ignored is the presence of non-detects: reactions failing to exceed the quantification threshold and therefore lacking a measurement of expression. While most current software replaces these non-detects with a value representing the limit of detection, this introduces substantial bias in the estimation of both absolute and differential expression. Single imputation procedures, while an improvement on previously used methods, underestimate residual variance, which can lead to anti-conservative inference.

RESULTS

We propose to treat non-detects as non-random missing data, model the missing data mechanism, and use this model to impute missing values or obtain direct estimates of model parameters. To account for the uncertainty inherent in the imputation, we propose a multiple imputation procedure, which provides a set of plausible values for each non-detect. We assess the proposed methods via simulation studies and demonstrate the applicability of these methods to three experimental data sets. We compare our methods to mean imputation, single imputation, and a penalized EM algorithm incorporating non-random missingness (PEMM). The developed methods are implemented in the R/Bioconductor package nondetects.

CONCLUSIONS

The statistical methods introduced here reduce discrepancies in gene expression values derived from qPCR experiments in the presence of non-detects, providing increased confidence in downstream analyses.

Assessing a novel, lab-free, point-of-care test for SARS-CoV-2 (CovidNudge): a diagnostic accuracy study

BACKGROUND

Access to rapid diagnosis is key to the control and management of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Laboratory RT-PCR testing is the current standard of care but usually requires a centralised laboratory and significant infrastructure. We describe our diagnostic accuracy assessment of a novel, rapid point-of-care real time RT-PCR CovidNudge test, which requires no laboratory handling or sample pre-processing.

METHODS

Between April and May, 2020, we obtained two nasopharyngeal swab samples from individuals in three hospitals in London and Oxford (UK). Samples were collected from three groups: self-referred health-care workers with suspected COVID-19; patients attending emergency departments with suspected COVID-19; and hospital inpatient admissions with or without suspected COVID-19. For the CovidNudge test, nasopharyngeal swabs were inserted directly into a cartridge which contains all reagents and components required for RT-PCR reactions, including multiple technical replicates of seven SARS-CoV-2 gene targets (rdrp1, rdrp2, e-gene, n-gene, n1, n2 and n3) and human ribonuclease P (RNaseP) as sample adequacy control. Swab samples were tested in parallel using the CovidNudge platform, and with standard laboratory RT-PCR using swabs in viral transport medium for processing in a central laboratory. The primary analysis was to compare the sensitivity and specificity of the point-of-care CovidNudge test with laboratory-based testing.

FINDINGS

We obtained 386 paired samples: 280 (73%) from self-referred health-care workers, 15 (4%) from patients in the emergency department, and 91 (23%) hospital inpatient admissions. Of the 386 paired samples, 67 tested positive on the CovidNudge point-of-care platform and 71 with standard laboratory RT-PCR. The overall sensitivity of the point-of-care test compared with laboratory-based testing was 94% (95% CI 86-98) with an overall specificity of 100% (99-100). The sensitivity of the test varied by group (self-referred healthcare workers 94% [95% CI 85-98]; patients in the emergency department 100% [48-100]; and hospital inpatient admissions 100% [29-100]). Specificity was consistent between groups (self-referred health-care workers 100% [95% CI 98-100]; patients in the emergency department 100% [69-100]; and hospital inpatient admissions 100% [96-100]). Point of care testing performance was similar during a period of high background prevalence of laboratory positive tests (25% [95% 20-31] in April, 2020) and low prevalence (3% [95% 1-9] in inpatient screening). Amplification of viral nucleocapsid (n1, n2, and n3) and envelope protein gene (e-gene) were most sensitive for detection of spiked SARS-CoV-2 RNA.

INTERPRETATION

The CovidNudge platform was a sensitive, specific, and rapid point of care test for the presence of SARS-CoV-2 without laboratory handling or sample pre-processing. The device, which has been implemented in UK hospitals since May, 2020, could enable rapid decisions for clinical care and testing programmes.

FUNDING

National Institute of Health Research (NIHR) Imperial Biomedical Research Centre, NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at Oxford University in partnership with Public Health England, NIHR Biomedical Research Centre Oxford, and DnaNudge.

Affine analysis for quantitative PCR measurements

Motivated by the current COVID-19 health crisis, we consider data analysis for quantitative polymerase chain-reaction (qPCR) measurements. We derive a theoretical result specifying the conditions under which all qPCR amplification curves (including their plateau phases) are identical up to an affine transformation, i.e. a multiplicative factor and horizontal shift. We use this result to develop a data analysis procedure for determining when an amplification curve exhibits characteristics of a true signal. The main idea behind this approach is to invoke a criterion based on constrained optimization that assesses when a measurement signal can be mapped to a master reference curve. We demonstrate that this approach: (i) can decrease the fluorescence detection threshold by up to a decade; and (ii) simultaneously improve confidence in interpretations of late-cycle amplification curves. Moreover, we demonstrate that the master curve is transferable reference data that can harmonize analyses between different labs and across several years. Application to reverse-transcriptase qPCR measurements of a SARS-CoV-2 RNA construct points to the usefulness of this approach for improving confidence and reducing limits of detection in diagnostic testing of emerging diseases.

Graphical Abstract

Quantification of colorimetric isothermal amplification on the smartphone and its open-source app for point-of-care pathogen detection

The increasing risk of infectious pathogens, especially in the under-developed countries, is demanding the development of point-of-care (POC) nucleic acid testing in the low-resource setting conditions. Here, we describe a methodology for colorimetric quantitative analysis of nucleic acid using an easy-to-build smartphone-based platform, offering low-cost, portability, simplicity in operation, and user-friendliness. The whole system consists of a hand-held box equipped with a smartphone, a film heater, a white LED, a loop-mediated isothermal amplification (LAMP) chip, and a DC converter, and all the processes were powered by a portable battery of 5 V. Upon the amplification of the target gene by an Eriochrome Black T-mediated LAMP reaction, the color of the LAMP reaction was changed from violet to blue that was real-time recorded by a smartphone camera. To keep track of the progress of the color change, we developed a novel mobile app in which a hue value was accepted as an indicator for color transition and for determining the threshold time of the amplification reaction. A calibration curve could be generated by plotting the logarithm of the known concentration of the DNA templates versus the threshold time, and it can be used to predict the copy number of nucleic acids in the test samples. Thus, the proposed mobile platform can inform us of not only qualitative but also quantitative results of the pathogens. We believe that this advanced colorimetric approach and the mobile app can expand the potentials of the smartphone for the future POCT system in the bio-diagnostic fields.

Effect of Quantitative Polymerase Chain Reaction Data Analysis Using Sample Amplification Efficiency on Microbial Source Tracking Assay Performance and Source Attribution

The widely used microbial source tracking (MST) technique, quantitative polymerase chain reaction (qPCR), quantifies host-specific gene abundance in polluted water to identify and prioritize contamination sources. This study characterized the effects of a qPCR data analysis using the sample PCR efficiencies (the LinRegPCR model) on gene abundance and compared them with the standard curve-based method (the mixed model). Five qPCR assays were evaluated: the universal GenBac3, human-specific HF183/BFDrev and CPQ_056, swine-specific Pig-2-Bac, and cattle-specific Bac3qPCR assays. The LinRegPCR model increased the low-copy amplification, especially in the HF183/BFDrev assay, thus lowering the specificity to 0.34. Up to 1.41 log₁₀ copies/g and 0.41 log₁₀ copies/100 mL differences were observed for composite fecal and sewage samples (n = 147) by the LinRegPCR approach, corresponding to an 18.2% increase and 6.4% decrease, respectively. Freshwater samples (n = 48) demonstrated a maximum of 1.95 log₁₀ copies/100 mL difference between the two models. Identical attributing sources by both models were shown in 54.55% of environmental samples; meanwhile, the LinRegPCR approach improved the inability to identify sources by the mixed model in 29.55% of the samples. This study emphasizes the need for a standardized data analysis protocol for qPCR MST assays for interlaboratory consistency and comparability.

Biostimulant Action of Dissolved Humic Substances From a Conventionally and an Organically Managed Soil on Nitrate Acquisition in Maize Plants

Conversion of conventional farming (CF) to organic farming (OF) is claimed to allow a sustainable management of soil resources, but information on changes induced on dissolved organic matter (DOM) are scarce. Among DOM components, dissolved humic substances (DHS) were shown to possess stimulatory effects on plant growth. DHS were isolated from CF and OF soil leacheates collected from soil monolith columns: first in November (bare soils) and then in April and June (bare and planted soils). DHS caused an enhancement of nitrate uptake rates in maize roots and modulated several genes involved in nitrogen acquisition. The DHS from OF soil exerted a stronger biostimulant action on the nitrate uptake system, but the first assimilatory step of nitrate was mainly activated by DHS derived from CF soil. To validate the physiological response of plants to DHS exposure, real-time RT-PCR analyses were performed on those genes most involved in nitrate acquisition, such as ZmNRT2.1, ZmNRT2.2, ZmMHA2 (coding for two high-affinity nitrate transporters and a PM H⁺-proton pump), ZmNADH:NR, ZmNADPH:NR, and ZmNiR (coding for nitrate reductases and nitrite reductase). All tested DHS fractions induced the upregulation of nitrate reductase (NR), and in particular the OF2 DHS stimulated the expression of both tested transcripts encoding for two NR isoforms. Characteristics of DHS varied during the experiment in both OF and CF soils: a decrease of high molecular weight fractions in the OF soil, a general increase in the carboxylic groups content, as well as diverse structural modifications in OF vs. CF soils were observed. These changes were accelerated in planted soils. Similarity of chemical properties of DHS with the more easily obtainable water-soluble humic substance extracted from peat (WEHS) and the correspondence of their biostimulant actions confirm the validity of studies which employ WEHS as an easily available source of DHS to investigate biostimulant actions on agricultural crops.

Changes in the NK Cell Repertoire Related to Initiation of TB Treatment and Onset of Immune Reconstitution Inflammatory Syndrome in TB/HIV Co-infected Patients in Rio de Janeiro, Brazil-ANRS 12274

Tuberculosis (TB) is the most common comorbidity and the leading cause of death among HIV-infected individuals. Although the combined antiretroviral therapy (cART) during TB treatment improves the survival of TB/HIV patients, the occurrence of immune reconstitution inflammatory syndrome (IRIS) in some patients poses clinical and scientific challenges. This work aimed to evaluate blood innate lymphocytes during therapeutic intervention for both diseases and their implications for the onset of IRIS. Natural killer (NK) cells, invariant NKT cells (iNKT), γδ T cell subsets, and in vitro NK functional activity were characterized by multiparametric flow cytometry in the following groups: 33 TB/HIV patients (four with paradoxical IRIS), 27 TB and 25 HIV mono-infected subjects (prior to initiation of TB treatment and/or cART and during clinical follow-up to 24 weeks), and 25 healthy controls (HC). Concerning the NK cell repertoire, several activation and inhibitory receptors were skewed in the TB/HIV patients compared to those in the other groups, especially the HCs. Significantly higher expression of CD158a (p = 0.025), NKp80 (p = 0.033), and NKG2C (p = 0.0076) receptors was detected in the TB/HIV IRIS patients than in the non-IRIS patients. Although more NK degranulation was observed in the TB/HIV patients than in the other groups, the therapeutic intervention did not alter the frequency during follow-up (weeks 2-24). A higher frequency of the γδ T cell population was observed in the TB/HIV patients with inversion of the Vδ2⁺/Vδ2^- ratio, especially for those presenting pulmonary TB, suggesting an expansion of particular γδ T subsets during TB/HIV co-infection. In conclusion, HIV infection impacts the frequency of circulating NK cells and γδ T cell subsets in TB/HIV patients. Important modifications of the NK cell repertoire were observed after anti-TB treatment (week 2) but not during the cART/TB follow-up (weeks 6-24). An increase of CD161⁺ NK cells was related to an unfavorable outcome. Despite the low number of cases, a more preserved NK cell profile was detected in IRIS patients previous to treatment, suggesting a role for these cells in IRIS onset. Longitudinal evaluation of the NK repertoire showed the impact of TB treatment and implicated these cells in TB pathogenesis in TB/HIV co-infected patients.

Isothermal amplification of specific DNA molecules inside giant unilamellar vesicles

An isothermal amplification circuit for specific DNA molecules was implemented in giant unilamellar vesicles. Using this circuit, over 5000-fold amplification of output DNAs was achieved, and the amplification behaviour depended on the concentration of input signal DNAs in a cell-sized compartment. Moreover, initiation of the amplification by photo-stimulation was demonstrated.

Dysregulated expression of STAT1, miR-150, and miR-223 in peripheral blood mononuclear cells of coronary artery disease patients with significant or insignificant stenosis

Coronary artery disease (CAD) is a multicellular disease characterized by chronic inflammation. Peripheral blood-mononuclear cells (PBMCs), as a critical component of immune system, actively cross-talk with pathophysiological conditions induced by endothelial cell injury, reflecting in perturbed PBMC expression. STAT1 is believed to be relevant to CAD pathogenesis through regulating key inflammatory processes and modulating STAT1 expression play key roles in fine-tuning CAD-related inflammatory processes. This study evaluated PBMC expressions of STAT1, and its regulators (miR-150 and miR-223) in a cohort including 72 patients with CAD with significant ( ≥ 50%) stenosis, 30 patients with insignificant ( < 50%) coronary stenosis (ICAD), and 74 healthy controls, and assessed potential of PBMC expressions to discriminate between patients and controls. We designed quantitative real-time polymerase chain reaction (RT-qPCR) assays and identified stable reference genes for normalizing PBMC quantities of miR-150, miR-223, and STAT1 applying geNorm algorithm to six small RNAs and five mRNAs. There was no significant difference between CAD and ICAD patients regarding STAT1 expression. However, both groups of patients had higher levels of STAT1 than healthy controls. miR-150 and miR-223 were differently expressed across three groups of subjects and were downregulated in patients compared with healthy controls, with the lowest expression levels being observed in patients with ICAD. ROC curves suggested that PBMC expressions may separate between different groups of study subjects. PBMC expressions also discriminated different clinical manifestations of CAD from ICADs or healthy controls. In conclusion, the present study reported PBMC dysregulations of STAT1, miR-150, and miR-223, in patients with significant or insignificant coronary stenosis and suggested that these changes may have diagnostic implications.

qPCR data analysis: Better results through iconoclasm

The standard approach for quantitative estimation of genetic materials with qPCR is calibration with known concentrations for the target substance, in which estimates of the quantification cycle (C_q ) are fitted to a straight-line function of log(N ₀), where N ₀ is the initial number of target molecules. The location of C_q for the unknown on this line then yields its N ₀. The most widely used definition for C_q is an absolute threshold that falls in the early growth cycles. This usage is flawed as commonly implemented: threshold set very close to the baseline level, which is estimated separately, from designated "baseline cycles." The absolute threshold is especially poor for dealing with the scale variability often observed for growth profiles. Scale-independent markers, like the first derivative maximum (FDM) and a relative threshold (C_r ) avoid this problem. We describe improved methods for estimating these and other C_q markers and their standard errors, from a nonlinear algorithm that fits growth profiles to a 4-parameter log-logistic function plus a baseline function. Further, by examining six multidilution, multireplicate qPCR data sets, we find that nonlinear expressions are often preferred statistically for the dependence of C_q on log(N ₀). This means that the amplification efficiency E depends on N ₀, in violation of another tenet of qPCR analysis. Neglect of calibration nonlinearity leads to biased estimates of the unknown. By logic, E estimates from calibration fitting pertain to the earliest baseline cycles, not the early growth cycles used to estimate E from growth profiles for single reactions. This raises concern about the use of the latter in lengthy extrapolations to estimate N ₀. Finally, we observe that replicate ensemble standard deviations greatly exceed predictions, implying that much better results can be achieved from qPCR through better experimental procedures, which likely include reducing pipette volume uncertainty.

Pairwise efficiency: a new mathematical approach to qPCR data analysis increases the precision of the calibration curve assay

Background

The real-time quantitative polymerase chain reaction (qPCR) is routinely used for quantification of nucleic acids and is considered the gold standard in the field of relative nucleic acid measurements. The efficiency of the qPCR reaction is one of the most important parameters in data analysis in qPCR experiments. The Minimum Information for publication of Quantitative real-time PCR Experiments (MIQE) guidelines recommends the calibration curve as the method of choice for estimation of qPCR efficiency. The precision of this method has been reported to be between SD = 0.007 (three replicates) and SD = 0.022 (no replicates).

Results

In this article, we present a novel approach to the analysis of qPCR data which has been obtained by running a dilution series. Unlike previously developed methods, our method, Pairwise Efficiency, involves a new formula that describes pairwise relationships between data points on separate amplification curves and thus enables extensive statistics. The comparison of Pairwise Efficiency with the calibration curve by Monte Carlo simulation shows the two-folds improvement in the precision of estimations of efficiency and gene expression ratios on the same dataset.

Conclusions

The Pairwise Efficiency nearly doubles the precision in qPCR efficiency determinations compared to standard calibration curve method. This paper demonstrates that applications of combinatorial treatment of data provide the improvement of the determination.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2911-5) contains supplementary material, which is available to authorized users.

Deriving site-specific water quality criteria for ammonia from national versus international toxicity data

A key question to be asked when developing regional water quality criteria with scarce toxicity data is whether such data need to be locally derived. To address this, ammonia toxicity data from local aquatic species in the Liao River were compared against data from species native and non-native to China, based on comparisons of the overall trends of species sensitivity distributions and derived water quality criteria. Liao River data were acquired by acute and chronic tests using five local freshwater invertebrate species, and then compiled alongside published data from Chinese national guidelines and international literature. Models of best fit using three species sensitivity distribution approaches (log-logistic, log-normal, and Burr III) did not vary markedly (r² >0.9), and no specific model provided a best fit across all data sets. The comparisons of the overall trend of species sensitivity distribution curves showed no significant differences at either a national (Chinese native taxa tested in China versus non-native taxa) or regional level (Liao River taxa versus non-Liao River taxa). The comparisons also revealed that the inclusion or exclusions of different ecological groups had little influence on the overall trends of species sensitivity distributions. These findings suggested data on non-local and non-native species, and data from local species tested elsewhere, could be appropriate for guiding the derivation of ammonia water quality criteria for regions such as Liao River. However, caution is needed when using hazardous concentration 5% values in the development of site-specific water quality criteria for a river basin due to the considerable variation observed for ammonia (16.8-56.6 mg/L), although these differences were not statistically significant. Based on the toxicity test evaluation, a preliminary acute value of 10.0 mg/L and chronic value of 1.7 mg/L (at pH of 7.0 and 20 °C) are proposed as site-specific ammonia water quality criteria for the Liao River, China.

Simultaneous Single-Channel Multiplexing and Quantification of Carbapenem-Resistant Genes Using Multidimensional Standard Curves

Multiplexing and quantification of nucleic acids, both have, in their own right, significant and extensive use in biomedical related fields. Currently, the ability to detect several nucleic acid targets in a single-reaction scales linearly with the number of targets; an expensive and time-consuming feat. Here, we propose a new methodology based on multidimensional standard curves that extends the use of real-time PCR data obtained by common qPCR instruments. By applying this novel methodology, we achieve simultaneous single-channel multiplexing and enhanced quantification of multiple targets using only real-time amplification data. This is obtained without the need of fluorescent probes, agarose gels, melting curves or sequencing analysis. Given the importance and demand for tackling challenges in antimicrobial resistance, the proposed method is applied to four of the most prominent carbapenem-resistant genes: bla_OXA-48, bla_NDM, bla_VIM, and bla_KPC, which account for 97% of the UK’s reported carbapenemase-producing Enterobacteriaceae.

Algorithms for automated detection of hook effect-bearing amplification curves

Amplification curves from quantitative Real-Time PCR experiments typically exhibit a sigmoidal shape. They can roughly be divided into a ground or baseline phase, an exponential amplification phase, a linear phase and finally a plateau phase, where in the latter, the PCR product concentration no longer increases. Nevertheless, in some cases the plateau phase displays a negative trend, e.g. in hydrolysis probe assays. This cycle-to-cycle fluorescence decrease is commonly referred to in the literature as the hook effect. Other detection chemistries also exhibit this negative trend, however the underlying molecular mechanisms are different.

In this study we present two approaches to automatically detect hook effect-like curvatures based on linear (hookreg) and nonlinear regression (hookregNL). As the hook effect is typical for qPCR data, both algorithms can be employed for the automated identification of regular structured qPCR curves. Therefore, our algorithms streamline quality control, but can also be used for assay optimization or machine learning.

Digital High-Resolution Melt Platform for Rapid and Parallelized Molecule-by-Molecule Genetic Profiling

This work presents a microfluidic Digital High-Resolution Melt platform for absolute quantitation and sensitive detection of locus-specific sequence variations on a molecule-by-molecule basis. The platform provides a facile means for assessment of hundreds to thousands of single DNA copies by digitizing template molecules in a 4096 1-nL array microfluidic device and observing the sequence-dependent fluorescence changes during temperature ramping. The analytical capability of this platform is demonstrated in several applications, such as digital assay characterization, detection and assessment of DNA methylation heterogeneity, and detection of rare biomarkers at frequencies as low as 0.0005% target to background molecules.

Specific serum and CSF microRNA profiles distinguish sporadic behavioural variant of frontotemporal dementia compared with Alzheimer patients and cognitively healthy controls

Information on circulating miRNAs in frontotemporal lobar degeneration is very limited and conflicting results have complicated an interpretation in Alzheimer's disease thus far. In the present study we I) collected samples from multiple clinical centers across Germany, II) defined 3 homogenous patient groups with high sample sizes (bvFTD n = 48, AD n = 48 and cognitively healthy controls n = 44), III) compared expression levels in both CSF and serum samples and IV) detected a limited set of miRNAs by using a MIQE compliant protocol based on SYBR-green miRCURY assays that have proven reliable to generate reproducible results. We included several quality controls that identified and reduced technical variation to increase the reliability of our data. We showed that the expression levels of circulating miRNAs measured in CSF did not correlate with levels in serum. Using cluster analysis we found expression pattern in serum that, in part, reflects the genomic organization and affiliation to a specific miRNA family and that were specifically altered in bvFTD, AD, and control groups. Applying factor analysis we identified a 3-factor model characterized by a miRNA signature that explained 80% of the variance classifying healthy controls with 97%, bvFTD with 77% and AD with 72% accuracy. MANOVA confirmed signals like miR-320a and miR-26b-5p at BH corrected significance that contributed most to discriminate bvFTD cases with 96% sensitivity and 90% specificity and AD cases with 89% sensitivity and specificity compared to healthy controls, respectively. Correlation analysis revealed that miRNAs from the 3-factor model also correlated with levels of protein biomarker amyloid-beta1-42 and phosphorylated neurofilament heavy chain, indicating their potential role in the monitoring of progressive neuronal degeneration. Our data show that miRNAs can be reproducibly measured in serum and CSF without pre-amplification and that serum includes higher expressed signals that demonstrate an overall better ability to classify bvFTD, AD and healthy controls compared to signals detected in CSF.

Improving the standardization of mRNA measurement by RT-qPCR

Human health and safety depend on reliable measurements in medical diagnosis and on tests that support the selection and evaluation of therapeutic intervention and newly discovered molecular biomarkers must pass a rigorous evaluation process if they are to be of benefit to patients. Measurement standardization helps to maximize data quality and confidence and ultimately improves the reproducibility of published research. Failure to consider how a given experiment may be standardized can be costly, both financially as well as in time and failure to perform and report pre-clinical research in an appropriately rigorous manner will hinder the development of diagnostic methods. Hence standardization is a crucial step in maintaining the integrity of scientific studies and is a key feature of robust investigation.

Inhibition mechanisms of hemoglobin, immunoglobulin G, and whole blood in digital and real-time PCR

Blood samples are widely used for PCR-based DNA analysis in fields such as diagnosis of infectious diseases, cancer diagnostics, and forensic genetics. In this study, the mechanisms behind blood-induced PCR inhibition were evaluated by use of whole blood as well as known PCR-inhibitory molecules in both digital PCR and real-time PCR. Also, electrophoretic mobility shift assay was applied to investigate interactions between inhibitory proteins and DNA, and isothermal titration calorimetry was used to directly measure effects on DNA polymerase activity. Whole blood caused a decrease in the number of positive digital PCR reactions, lowered amplification efficiency, and caused severe quenching of the fluorescence of the passive reference dye 6-carboxy-X-rhodamine as well as the double-stranded DNA binding dye EvaGreen. Immunoglobulin G was found to bind to single-stranded genomic DNA, leading to increased quantification cycle values. Hemoglobin affected the DNA polymerase activity and thus lowered the amplification efficiency. Hemoglobin and hematin were shown to be the molecules in blood responsible for the fluorescence quenching. In conclusion, hemoglobin and immunoglobulin G are the two major PCR inhibitors in blood, where the first affects amplification through a direct effect on the DNA polymerase activity and quenches the fluorescence of free dye molecules, and the latter binds to single-stranded genomic DNA, hindering DNA polymerization in the first few PCR cycles. Graphical abstract PCR inhibition mechanisms of hemoglobin and immunoglobulin G (IgG). Cq quantification cycle, dsDNA double-stranded DNA, ssDNA single-stranded DNA.

Diel pattern of circadian clock and storage protein gene expression in leaves and during seed filling in cowpea (Vigna unguiculata)

BACKGROUND

Cowpea (Vigna unguiculata) is an important source of protein supply for animal and human nutrition. The major storage globulins VICILIN and LEGUMIN (LEG) are synthesized from several genes including LEGA, LEGB, LEGJ and CVC (CONVICILIN). The current hypothesis is that the plant circadian core clock genes are conserved in a wide array of species and that primary metabolism is to a large extent controlled by the plant circadian clock. Our aim was to investigate a possible link between gene expression of storage proteins and the circadian clock.

RESULTS

We identified cowpea orthologues of the core clock genes VunLHY, VunTOC1, VunGI and VunELF3, the protein storage genes VunLEG, VunLEGJ, and VunCVC as well as nine candidate reference genes used in RT-PCR. ELONGATION FACTOR 1-A (ELF1A) resulted the most suitable reference gene. The clock genes VunELF3, VunGI, VunTOC1 and VunLHY showed a rhythmic expression profile in leaves with a typical evening/night and morning/midday phased expression. The diel patterns were not completely robust and only VungGI and VungELF3 retained a rhythmic pattern under free running conditions of darkness. Under field conditions, rhythmicity and phasing apparently faded during early pod and seed development and was regained in ripening pods for VunTOC1 and VunLHY. Mature seeds showed a rhythmic expression of VunGI resembling leaf tissue under controlled growth chamber conditions. Comparing time windows during developmental stages we found that VunCVC and VunLEG were significantly down regulated during the night in mature pods as compared to intermediate ripe pods, while changes in seeds were non-significant due to high variance. The rhythmic expression under field conditions was lost under growth chamber conditions.

CONCLUSIONS

The core clock gene network is conserved in cowpea leaves showing a robust diel expression pattern except VunELF3 under growth chamber conditions. There appears to be a clock transcriptional reprogramming in pods and seeds compared to leaves. Storage protein deposition may be circadian regulated under field conditions but the strong environmental signals are not met under artificial growth conditions. Diel expression pattern in field conditions may result in better usage of energy for protein storage.

Temperature change rate actuated bubble mixing for homogeneous rehydration of dry pre-stored reagents in centrifugal microfluidics

In centrifugal microfluidics, dead volumes in valves downstream of mixing chambers can hardly be avoided. These dead volumes are excluded from mixing processes and hence cause a concentration gradient. Here we present a new bubble mixing concept which avoids such dead volumes. The mixing concept employs heating to create a temperature change rate (TCR) induced overpressure in the air volume downstream of mixing chambers. The main feature is an air vent with a high fluidic resistance, representing a low pass filter with respect to pressure changes. Fast temperature increase causes rapid pressure increase in downstream structures pushing the liquid from downstream channels into the mixing chamber. As air further penetrates into the mixing chamber, bubbles form, ascend due to buoyancy and mix the liquid. Slow temperature/pressure changes equilibrate through the high fluidic resistance air vent enabling sequential heating/cooling cycles to repeat the mixing process. After mixing, a complete transfer of the reaction volume into the downstream fluidic structure is possible by a rapid cooling step triggering TCR actuated valving. The new mixing concept is applied to rehydrate reagents for loop-mediated isothermal amplification (LAMP). After mixing, the reaction mix is aliquoted into several reaction chambers for geometric multiplexing. As a measure for mixing efficiency, the mean coefficient of variation (C[combining macron]V[combining macron], n = 4 LabDisks) of the time to positivity (t_p) of the LAMP reactions (n = 11 replicates per LabDisk) is taken. The C[combining macron]V[combining macron] of the t_p is reduced from 18.5% (when using standard shake mode mixing) to 3.3% (when applying TCR actuated bubble mixing). The bubble mixer has been implemented in a monolithic fashion without the need for any additional actuation besides rotation and temperature control, which are needed anyhow for the assay workflow.

Savitzky-Golay smoothing and differentiation for polymerase chain reaction quantification

In quantitative PCR (qPCR), replicates can minimize the impact of intra-assay variation; however, inter-assay variations must be minimized to obtain a robust quantification method. The method proposed in this study uses Savitzky-Golay smoothing and differentiation (SGSD) to identify a derivative-maximum-based cycle of quantification. It does not rely on curve modeling, as is the case with many existing techniques. PCR fluorescence data sets challenged for inter-assay variations (different thermocycler units, different reagents batches, different operators, different standard curves, and different labs) were used for the evaluation. The algorithm was compared with a four-parameter logistic model (4PLM) method, the C_y0 method, and the threshold method. The SGSD method compared favourably with all methods in terms of inter-assay variation. SGSD was statistically different from the 4PLM (P = 0.03), C_y0 (P = 0.05), and threshold (P = 0.004) methods on relative error comparison basis. For intra-assay variations, SGSD outperformed the threshold method (P = 0.005) and equalled the 4PLM and C_y0 methods (P > 0.05) on relative error basis. Our results demonstrate that the SGSD method could potentially be an alternative to sigmoid modeling based methods (4PLM and C_y0) when PCR data are challenged for inter-assay variations.

Modeling qRT-PCR dynamics with application to cancer biomarker quantification

Quantitative reverse transcription polymerase chain reaction (qRT-PCR) is widely used for molecular diagnostics and evaluating prognosis in cancer. The utility of mRNA expression biomarkers relies heavily on the accuracy and precision of quantification, which is still challenging for low abundance transcripts. The critical step for quantification is accurate estimation of efficiency needed for computing a relative qRT-PCR expression. We propose a new approach to estimating qRT-PCR efficiency based on modeling dynamics of polymerase chain reaction amplification. In contrast, only models for fluorescence intensity as a function of polymerase chain reaction cycle have been used so far for quantification. The dynamics of qRT-PCR efficiency is modeled using an ordinary differential equation model, and the fitted ordinary differential equation model is used to obtain effective polymerase chain reaction efficiency estimates needed for efficiency-adjusted quantification. The proposed new qRT-PCR efficiency estimates were used to quantify GUCY2C (Guanylate Cyclase 2C) mRNA expression in the blood of colorectal cancer patients. Time to recurrence and GUCY2C expression ratios were analyzed in a joint model for survival and longitudinal outcomes. The joint model with GUCY2C quantified using the proposed polymerase chain reaction efficiency estimates provided clinically meaningful results for association between time to recurrence and longitudinal trends in GUCY2C expression.

Tuned SMC Arms Drive Chromosomal Loading of Prokaryotic Condensin

SMC proteins support vital cellular processes in all domains of life by organizing chromosomal DNA. They are composed of ATPase “head” and “hinge“ dimerization domains and a connecting coiled-coil “arm.” Binding to a kleisin subunit creates a closed tripartite ring, whose ∼47-nm-long SMC arms act as barrier for DNA entrapment. Here, we uncover another, more active function of the bacterial Smc arm. Using high-throughput genetic engineering, we resized the arm in the range of 6–60 nm and found that it was functional only in specific length regimes following a periodic pattern. Natural SMC sequences reflect these length constraints. Mutants with improper arm length or peptide insertions in the arm efficiently target chromosomal loading sites and hydrolyze ATP but fail to use ATP hydrolysis for relocation onto flanking DNA. We propose that SMC arms implement force transmission upon nucleotide hydrolysis to mediate DNA capture or loop extrusion.

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Short and long but not intermediate-length Smc coiled-coil arms are functional

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Smc complexes with improper arms accumulate at chromosomal loading sites

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Smc arms are functional units linking ATP hydrolysis to an essential DNA transaction

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Pro- and eukaryotic SMC sequences reflect similar periodic length constraints