Effect of the mutation rate and background size on the quality of pathogen identification

Targeted amplification for enhanced detection of biothreat agents by next-generation sequencing

BACKGROUND

Historically, identification of causal agents of disease has relied heavily on the ability to culture the organism in the laboratory and/or the use of pathogen-specific antibodies or sequence-based probes. However, these methods can be limiting: Even highly sensitive PCR-based assays must be continually updated due to signature degradation as new target strains and near neighbors are sequenced. Thus, there has been a need for assays that do not suffer as greatly from these limitations and/or biases. Recent advances in library preparation technologies for Next-Generation Sequencing (NGS) are focusing on the use of targeted amplification and targeted enrichment/capture to ensure that the most highly discriminating regions of the genomes of known targets (organism-unique regions and/or regions containing functionally important genes or phylogenetically-discriminating SNPs) will be sequenced, regardless of the complex sample background.

RESULTS

In the present study, we have assessed the feasibility of targeted sequence enhancement via amplification to facilitate detection of a bacterial pathogen present in low copy numbers in a background of human genomic material. Our results indicate that the targeted amplification of signature regions can effectively identify pathogen genomic material present in as little as 10 copies per ml in a complex sample. Importantly, the correct species and strain calls could be made in amplified samples, while this was not possible in unamplified samples.

CONCLUSIONS

The results presented here demonstrate the efficacy of a targeted amplification approach to biothreat detection, using multiple highly-discriminative amplicons per biothreat organism that provide redundancy in case of variation in some primer regions. Importantly, strain level discrimination was possible at levels of 10 genome equivalents. Similar results could be obtained through use of panels focused on the identification of amplicons targeted for specific genes or SNPs instead of, or in addition to, those targeted for specific organisms (ongoing gene-targeting work to be reported later). Note that without some form of targeted enhancement, the enormous background present in complex clinical and environmental samples makes it highly unlikely that sufficient coverage of key pathogen(s) present in the sample will be achieved with current NGS technology to guarantee that the most highly discriminating regions will be sequenced.

DNA signature-based approaches for bacterial detection and identification

During the late eighties, environmental microbiologists realized the potential of the polymerase chain reaction (PCR) for the design of innovative approaches to study microbial communities or to detect and identify microorganisms in diverse and complex environments. In contrast to long-established methods of cultivation-based microbial identification, PCR-based techniques allow for the identification of microorganisms regardless of their culturability. A large number of reports have been published that describe PCR-inspired methods, frequently complemented by sequencing or hybridization profiling, to infer taxonomic and clonal microbial diversity or to detect and identify microorganisms using taxa-specific genomic markers. Typing methods have been particularly useful for microbial ecology-driven studies; however, they are not suitable for diagnostic purposes, such as the detection of specific species, strains or clones. Recently, comprehensive reviews have been written describing the panoply of typing methods available and describing their advantages and limitations; however, molecular approaches for bacterial detection and identification were either not considered or only vaguely discussed. This review focuses on DNA-based methods for bacterial detection and identification, highlighting strategies for selecting taxa-specific loci and emphasizing the molecular techniques and emerging technological solutions for increasing the detection specificity and sensitivity. The massive and increasing number of available bacterial sequences in databases, together with already employed bioinformatics tools, hold promise of more reliable, fast and cost-effective methods for bacterial identification in a wide range of samples in coming years. This tendency will foster the validation and certification of these methods and their routine implementation by certified diagnostic laboratories.

The effects of glass surfaces and probe GC content on signal intensities of a 60-mer diagnostic microarray

The effects of glass surfaces and probe GC content on signal intensities of a 60-mer diagnostic microarray were studied. Twelve virus-specific oligonucleotide probes for severe acute respiratory syndrome coronavirus (SARS-CoV) were divided into a high GC content group (≥ 50%) and a low GC content group (< 50%), and spotted onto four different chemically-modified glass surfaces: a poly-amine coating activated by 1,4-phenylene diisothiocyanate (Poly-Amine surface), an acrylic acid-co-acrylamide copolymer coating activated by 1-(3-dimethylamino propyl)-3-ethylcarbodiimide hydrochloride and N-hydroxysuccinimide (AACA-Copolymer surface), a commercial Corning CMT-GAPS amino surface, and a Telechem SuperAmine amino surface. RNA samples from cultured SARS-CoV strain were labelled using direct cDNA labelling with restriction display in a single colour format. The background-subtracted signal intensities were analysed using two-way analysis of variance. The effects of glass surfaces on background-subtracted signal intensities were significant (p=0.003). Multiple comparisons showed that differences existed mainly between the AACA-Copolymer surface and the other glass surfaces, and that the AACA-Copolymer surface had the highest background-subtracted signal intensity. The probe GC content had no significant effect on signal intensities in the narrow range of GC content represented (p=0.07). The results suggested that the AACA-Copolymer surface may be a novel choice of microorganism survey based on long oligonucleotide microarray.

A high-throughput pipeline for designing microarray-based pathogen diagnostic assays

Background

We present a methodology for high-throughput design of oligonucleotide fingerprints for microarray-based pathogen diagnostic assays. The oligonucleotide fingerprints, or DNA microarray probes, are designed for identifying target organisms in environmental or clinical samples. The design process is implemented in a high-performance computing software pipeline that incorporates major algorithmic improvements over a previous version to both reduce computation time and improve specificity assessment.

Results

The algorithmic improvements result in significant reduction in runtimes, with the updated pipeline being nearly up to five-times faster than the previous version. The improvements in specificity assessment, based on multiple specificity criteria, result in robust and consistent evaluation of cross-hybridization with nontarget sequences. In addition, the multiple criteria provide finer control on the number of resulting fingerprints, which helps in obtaining a larger number of fingerprints with high specificity. Simulation tests for Francisella tularensis and Yersinia pestis, using a well-established hybridization model to estimate cross-hybridization with nontarget sequences, show that the improved specificity criteria yield a larger number of fingerprints as compared to using a single specificity criterion.

Conclusion

The faster runtimes, achieved as the result of algorithmic improvements, are critical for extending the pipeline to process multiple target genomes. The larger numbers of identified fingerprints, obtained by considering broader specificity criteria, are essential for designing probes for hard-to-distinguish target sequences.