Comparative procedures for sample processing and development of qPCR assays for the rapid detection of grapevine and citrus pathogens

Emir Hodzic 1,
1School of Veterinary Medicine, University of California at Davis, United States; 

Different instruments and methods were used for tissue homog- enization, RNA extraction and qPCR based detection of grapevine and citrus RNA viruses were evaluated. Semi-automated and auto- mated homogenization techniques were compared to process samples from grapevine and citrus. Four different high throughput automated nucleic acid extraction platforms were compared with the RNeasy plant extraction kit for their capacity and efficiency of extracting viral RNA from grapevine and citrus infected tissues. The RNA prepared from each extraction platform was then used as tem- plate for a comparative analysis. The study showed that a thorough homogenization of grapevine and citrus tissues using the Tissue Lyser as well as DNase digestion of the purified RNA prior to cDNA synthesis improved the virus detection and yielded the lowest Cq values in RT-qPCR. Comparison of different RNA extraction meth- ods showed that methods implementing the magnetic bead-based technology were superior to other methods used.
The development of diagnostic systems to effectively detect multiple targets in a single assay has provided a constant techno- logical challenge. The main goal of this study was to develop a rapid, sensitive and specific multiplex RT-qPCR assays for the detection and quantification of viruses in grapevines and citrus that can be incorporated into routine virus detection protocols. The proposed approach can then be used as a robust diagnostic tool for grapevine and citrus germplasm screening programs and for the certification program to ensure clean plant material for propagation.
A single real-time multiplex qPCR assay for the simultaneous detection of Grapevine virus A, B and D (GVA, GVB and GVD) was developed, using three different fluorescently labeled minor groove binding probes. This multiplex RT-qPCR was compared to singleplex RT-qPCR designed specifically for each virus and a con- ventional multiplex RT-PCR. The results showed that the developed multiplex RT-qPCR assay was a cost-effective diagnostic tool that could streamline the testing of grapevine viruses, and replace the singleplex RT-qPCR assays, thus reducing time and labor while retaining the same sensitivity and specificity.
For detection of Citrus tristeza virus (CTV), Citrus psorosis virus (CPsV), and Citrus leaf blotch virus (CLBV) a single multiplex RT-qPCR was developed and validated using the same approach as above. To increase the detection reliability, coat protein from large number of different isolates of CTV, CPsV and CLBV were sequenced and multi- ple sequence alignments were generated. No significant difference in detection limits was found and specificity was not affected by the inclusion of the three assays in a multiplex RT-qPCR reaction.
Adopting compatible multiplex RT-qPCR testing protocols for grapevine and citrus viruses as well as other RNA and DNA regulated pathogens will provide a valuable alternative tool for pathogen detection and efficient program implementation.

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COMplementary Primer ASymmetric PCR (COMPASTM-PCR), a counter intuitive primer design for PCR

Marc Anglès d’Auriac 1,
1Norwegian Institute for Water Research, Norway; 

Since it was developed in the mid 80’, the Polymerase Chain Reaction (PCR) has established itself as a central tool for molec- ular biology. Instrumentation, reagents as well as methodology have regularly improved and expanded the capabilities of this technology. Limitations inherent to DNA and kit chemistries have generated guidelines for PCR-assay development. For instance, non-target amplification and in particular primer complementarity leading to Primer Dimer formation is a well-known limiting fac- tor to be reckoned with. Although various strategies have helped improved general robustness of PCR assays, primer complemen- tarity usually is carefully avoided when designing PCR by using ad hoc software. Indeed, 3′-complementarity will extend the primers during PCR elongation using one another as template, conse- quently disabling any possible further involvement in traditional target amplification. However, a 5′-complementarity will leave the primers unchanged during PCR cycles, albeit sequestered to one another, therefore also competing with target amplification.
In this work we show that 5′-complementarity between primers may be exploited in a new asymmetric PCR method, the COMplementary-Primer-Asymmetric (COMPASTM )-PCR, to achieve effective double strand target PCR amplification. More- over, such a design may paradoxically reduce spurious non-target amplification by actively sequestering the limiting primer. Using asymmetric primer concentration is not a new approach but has, to our knowledge, only been previously used for enhancing the production of single strand amplicon from a target sequence, i.e. for probe detection assays. In the presence of the target sequence, COMPAS-PCR initiates target linear amplification with the excess primer, hence progressively changing the stoichiometry of the reac- tion so that priming of the limiting primer to the target strand and target extension of the complementary strand is gradually favored. This general principle was developed using 5S rDNA direct repeats as target sequences to design a species-specific assay for identify- ing Salmo salar and Salmo trutta using almost fully complementary primers overlapping the same target sequence. This initial applica- tion, published in PLoS One (doi: 10.1371/journal.pone.0165468), was designed so that both primers are complementary to the same genomic sequence target. This approach may in principle be applied to any tandem direct repeats DNA motifs of interest as target sequences. Ribosomal genes and in particular the 5S rDNA tandem direct repeats are found in all eukaryotic cells and are there- fore suitable for developing new specific complementary-primer assays. We believe that further understanding and modelling of these COMPAS-PCR principles could be incorporated in primer design software.
This small paradigm shift, using highly complementary primers for PCR, should help develop or improve PCR assays, increasing design possibilities available to the molecular scientist.

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A new paradigm in genetic data analysis on the thermo fisher cloud

Nivedita Majumdar *,Puneet Suri, Gloria Lam, David Woo, Shakila Pothini

Thermofisher Scientific, USA; 

In this day and age, computation, storage and collation of information, collation of analysis results from the large variety of experiments, are becoming vital to any biologically relevant discov- ery. This translates to millions of records in databases, requiring sophisticated algorithmic processing, cross-application analysis, interactive visualizations and infrastructure for collaboration. The Thermo Fisher Cloud platform offers such a solution with more than ten qPCR (quantitative polymerase chain reaction) and CE (capil- lary electrophoresis) apps for gene expression, genotyping, melt analysis and Sanger sequencing. We are building an ecosystem to eventually connect across all types of genetic analysis including NGS (next generation sequencing) and MS (mass spectrometry) on a single platform, with a data or scientific question centric organi- zation of your information.
This talk will demonstrate the capabilities of our current plat- form with a case study doing gene expression and genotyping analysis. We will demonstrate the tools we provide for reviewing and making sense of large volumes of data, with emphasis on ampli- fication curves. We will present tools such as the outlier wheel that come together to enable the user to identify outliers, to identify trends and patterns in their data, in order to make scientifically relevant and reliable conclusions.

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Development of an HRM-based tool for the automated identification of nucleotide sequences in large datasets

Jean-Christophe Avarre 1,*,Matthieu Vignoles2, Mathieu Laffont2, Lise Grewis2, Christelle Reynes 3,4
1Institut des Sciences de l’Evolution de Montpellier, UMR IRD-CNRS-UM-EPHE, Montpellier, France
2 IDVet Genetics, Montpellier, France
3 Institute of Functional Genomics, UMR CNRS-INSERM-UM, Montpellier, France
4 Laboratory of Biostatistics, Informatics and Pharmaceutical Physics, UFR Pharmacy, University of Montpellier, France; 

Nucleic acid characterization by High Resolution Melting (HRM) is a simple, flexible, low-cost and powerful technique for identify- ing sequence variations, making it attractive for a broad range of diagnostic and research applications including infectious diseases, oncology, epigenetics and even metabarcoding. Current procedures for analyzing HRM curves mostly rely on unsupervised methods, principally via subtraction (difference) plots against a known con- trol sample, and less commonly on supervised methods through the use of discrimination analyses. If these procedures have proven useful for discriminating a small number of variants, they are yet limiting for analyzing large HRM data sets and do not provide pre- cise feedback to the user.
In this context, we have developed an innovative method that enables the simultaneous discrimination of a large number of variants from their HRM profile. This method relies on the estab- lishment of a melting profile library, computes new descriptors from the HRM curves for an optimal discrimination and offers a fully automated analysis of melting profiles. The output consists in the possibility to assign a given melting profile to an existing group included in the reference library (assorted with a confidence index) or to reject any assignment in case of an unknown profile.
This method was first validated on a set of 19 nontuberculous mycobacterial species. Each species was represented by 3–20 bio- logical samples consisting of genomic DNA extracted either from animal tissues or from cultivated isolates. Each sample was ampli- fied with a unique pair of primers targeting the 16S-ITS region and yielding amplicons with sizes ranging from ∼230 to ∼350 bp. Melt- ing profiles of the corresponding amplicons were generated using 3-9 replicates per sample. On a total of 95 samples, 91 were allot- ted to the right species. Automatic group rationalization led to split two species into two subgroups, suggesting that this method is able to integrate intraspecific sequence variations.
The method was then applied to develop a diagnostic tool tar- geting five different pathogens responsible for abortive diseases in cattle. Each pathogen was represented by 10 to 22 different biological samples consisting of genomic DNA extracted from dif- ferent matrices (e.g. feces, swabs, whole blood). Each biological sample was amplified and melted in 4 replicates with a ready-to- use mastermix containing 5 sets of primers, specific to the targeted pathogens. The limit of detection of this multiplex test was equiva- lent to that of the current individual tests using hydrolysis probes, around 10 copies/PCR. Moreover, all samples containing at least 10 copies of pathogen(s) were correctly identified with high confi- dence.
These results underline the high potential of this novel HRM- based method for the simultaneous detection and identification of a large number of nucleotide sequences, in both simplex and multiplex formats.

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Methods and technologies for analysis digital PCR experiments – Lessons we learned from the qPCR technology

Stefan Rödiger 1,*,Andrej-Nikolai Spiess 2 , Michał Burdukiewicz 3
1Brandenburg University of Technology Cottbus–Senftenberg, Senftenberg, Germany
2 University Medical Center Hamburg-Eppendorf, Hamburg, Germany
3 University of Wroclaw, Wroclaw, Poland; 

Quantitative PCR (qPCR) and digital PCR (dPCR) are widely employed DNA quantification methods in diagnostic and foren- sic applications. Both have proven to be precise and robust. In qPCR, both parameters quantification cycle (Cq) and amplification efficiency (AE) are estimated from the amplification curve data to quantify the DNA amount [1,2]. dPCR, which astonishingly predates qPCR, allows copy number quantification based on parti- tioning a PCR bulk reaction into multiple smaller reaction entities. Quantification is then achieved by assessing the distribution of tar- get molecules within the sample by Poisson statistics [3]. Unlike qPCR, dPCR does not require a calibration curve for quantifica- tion. Moreover, according to recent reports, dPCR offers a more precise and reproducible quantification as well as sensitive detec- tion of minority genetic targets. Numerous platform technologies (e. g., droplet-based, chamber-based, microbead-based) have been developed so far, however their accompanying dPCR software mod- ules are exclusively tied to the platform and therefore lack general applicability [4]. As the analysis of the dPCR and qPCR data is still matter of ongoing research, others and we have recently investi- gated different software frameworks to fill this gap [5,6]. This talk will discuss novel and specific advances within the field of dPCR and qPCR data analysis, which have been made available to the sci- entific community. The talk will focus on open source software and selected algorithms that can be used to build customized desktop and remote browser applications.
[1] S. Pabinger, et al., A survey of tools for the analysis of quantitative PCR (qPCR) data, Biomol. Detect. Quantif. 1 (2014) 23–33, 2014.08.002.
[2] A.-N. Spiess, et al., Impact of smoothing on parameter estimation in quantitative DNA amplification experiments, Clin. Chem. 61 (2015) 379–388,
[3] M. Burdukiewicz, et al., Methods for comparing multiple digital PCR experiments, Biomol. Detect. Quantif. 9 (2016) 14–19, 1016/j.bdq.2016.06.004.
[4] S. Rödiger, et al., R as an Environment for the Reproducible Analysis of DNA Amplification Experiments, R J. 7 (2015) 127–150,
[5] A.-N. Spiess, et al., System-specific periodicity in quantitative real-time polymerase chain reaction data questions threshold-based quantitation, Sci. Rep. 6 (2016) 38951,
[6] S. Rödiger, et al., chipPCR: an R package to pre-process raw data of amplification curves, Bioinformatics. 31 (2015) 2900–2902, 10.1093/bioinformatics/btv205.

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