Amplicon identification using SparsE representation of multiplex PYROsequencing signal (AdvISER-M-PYRO): application to bacterial resistance genotyping

Using a constraint-based regression method for relative quantification of somatic mutations in pyrosequencing signals: a case for NRAS analysis

BACKGROUND

Pyrosequencing Allele Quantification (AQ) is a cost-effective DNA sequencing method that can be used for detecting somatic mutations in formalin-fixed paraffin-embedded (FFPE) samples. The method displays a low turnaround time and a high sensitivity. Pyrosequencing suffers however from two main drawbacks including (i) low specificity and (ii) difficult signal interpretation when multiple mutations are reported in a hotspot genomic region.

RESULTS

Using a constraint-based regression method, the new AdvISER-PYRO-SMQ algorithm was developed in the current study and implemented into an R package. As a proof-of-concept, AdvISER-PYRO-SMQ was used to identify a set of 9 distinct point mutations affecting codon 61 of the NRAS oncogene. In parallel, a pyrosequencing assay using the Qiagen software and its AQ module was used to assess selectively the presence of a single point mutation (NRAS[Formula: see text] - Q61R-1) among the set of codon 61 mutations, and to analyze related pyrosequencing signals. AdvISER-PYRO-SMQ produced a lower limit of blank (0 %) than the AQ module of Qiagen software (5.1 %) and similar limit of detection were obtained for both software (5.6 vs 4.8 %). AdvISER-PYRO-SMQ was able to screen for the presence of 9 distinct mutations with a single pyrosequencing reaction whereas the AQ module was limited to screen a single mutation per reaction.

CONCLUSION

Using a constraint-based regression method enables to analyze pyrosequencing signal and to detect multiple mutations within a hotspot genomic region with an optimal compromise between sensitivity and specificity. The AdvISER-PYRO-SMQ R package provides a generic tool which can be applied on a wide range of somatic mutations. Its implementation in a Shiny web interactive application (available at https://ucl-irec-ctma.shinyapps.io/Pyrosequencing-NRAS-61/) enables its use in research or clinical routine applications.

A qPCR and multiplex pyrosequencing assay combined with automated data processing for rapid and unambiguous detection of ESBL-producers Enterobacteriaceae

Rapid and specific detection of extended-spectrum β-lactamase-producing (ESBL) bacteria is crucial both for timely antibiotic therapy when treating infected patients as well as for appropriate infection control measures aimed at curbing the spread of ESBL-producing isolates. Whereas a variety of phenotypic methods are currently available for ESBL detection, they remain time consuming and sometimes difficult to interpret while being also affected by a lack of sensitivity and specificity. Considering the longer turnaround time (TAT) of susceptibility testing and culture results, DNA-based ESBL identification would be a valuable surrogate for phenotypic-based methods. Putative ESBL-positive Enterobacteriaceae isolates (n = 330) from clinical specimen were prospectively collected in Bulgaria, Romania and Democratic Republic of Congo and tested in this study. All isolates were assessed for ESBL-production by the E-test method and those giving undetermined ESBL status were re-tested using the combination disk test. A genotypic assay successively combining qPCR detection of blaCTX-M, blaTEM and blaSHV genes with a multiplex pyrosequencing of blaTEM and blaSHV genes was developed in order to detect the most common ESBL-associated TEM and SHV single nucleotides polymorphisms, irrespective of their plasmid and/or chromosomal location. This assay was applied on all Enterobacteriaceae isolates (n = 330). Phenotypic and genotypic results matched in 324/330 (98.2%). Accordingly, real-time PCR combined with multiplex pyrosequencing appears to be a reliable and easy-to-perform assay with high-throughput identification and fast TAT (~5 h).

Multiplex pyrosequencing assay using AdvISER-MH-PYRO algorithm: a case for rapid and cost-effective genotyping analysis of prostate cancer risk-associated SNPs

Background

Single Nucleotide Polymorphisms (SNPs) identified in Genome Wide Association Studies (GWAS) have generally moderate association with related complex diseases. Accordingly, Multilocus Genetic Risk Scores (MGRSs) have been computed in previous studies in order to assess the cumulative association of multiple SNPs. When several SNPs have to be genotyped for each patient, using successive uniplex pyrosequencing reactions increases analytical reagent expenses and Turnaround Time (TAT). While a set of several pyrosequencing primers could theoretically be used to analyze multiplex amplicons, this would generate overlapping primer-specific pyro-signals that are visually uninterpretable.

Methods

In the current study, two multiplex assays were developed consisting of a quadruplex (n=4) and a quintuplex (n=5) polymerase chain reaction (PCR) each followed by multiplex pyrosequencing analysis. The aim was to reliably but rapidly genotype a set of prostate cancer-related SNPs (n=9). The nucleotide dispensation order was selected using SENATOR software. Multiplex pyro-signals were analyzed using the new AdvISER-MH-PYRO software based on a sparse representation of the signal. Using uniplex assays as gold standard, the concordance between multiplex and uniplex assays was assessed on DNA extracted from patient blood samples (n = 10).

Results

All genotypes (n=90) generated with the quadruplex and the quintuplex pyroquencing assays were perfectly (100 %) concordant with uniplex pyrosequencing. Using multiplex genotyping approach for analyzing a set of 90 patients allowed reducing TAT by approximately 75 % (i.e., from 2025 to 470 min) while reducing reagent consumption and cost by approximately 70 % (i.e., from ∼229 US$ /patient to ∼64 US$ /patient).

Conclusions

This combination of quadruplex and quintuplex pyrosequencing and PCR assays enabled to reduce the amount of DNA required for multi-SNP analysis, and to lower the global TAT and costs of SNP genotyping while providing results as reliable as uniplex analysis. Using this combined multiplex approach also substantially reduced the production of waste material. These genotyping assays appear therefore to be biologically, economically and ecologically highly relevant, being worth to be integrated in genetic-based predictive strategies for better selecting patients at risk for prostate cancer. In addition, the same approach could now equally be transposed to other clinical/research applications relying on the computation of MGRS based on multi-SNP genotyping.