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.

Listeria floridensis sp. nov., Listeria aquatica sp. nov., Listeria cornellensis sp. nov., Listeria riparia sp. nov. and Listeria grandensis sp. nov., from agricultural and natural environments.

Sampling of agricultural and natural environments in two US states (Colorado and Florida) yielded 18 Listeria-like isolates that could not be assigned to previously described species using traditional methods. Using whole-genome sequencing and traditional phenotypic methods, we identified five novel species, each with a genome-wide average BLAST nucleotide identity (ANIb) of less than 85% to currently described species. Phylogenetic analysis based on 16S rRNA gene sequences and amino acid sequences of 31 conserved loci showed the existence of four well-supported clades within the genus Listeria; (i) a clade representing Listeria monocytogenes, L. marthii, L. innocua, L. welshimeri, L. seeligeri and L. ivanovii, which we refer to as Listeria sensu stricto, (ii) a clade consisting of Listeria fleischmannii and two newly described species, Listeria aquatica sp. nov. (type strain FSL S10-1188(T) = DSM 26686(T) = LMG 28120(T) = BEI NR-42633(T)) and Listeria floridensis sp. nov. (type strain FSL S10-1187(T) = DSM 26687(T) = LMG 28121(T) = BEI NR-42632(T)), (iii) a clade consisting of Listeria rocourtiae, L. weihenstephanensis and three novel species, Listeria cornellensis sp. nov. (type strain TTU A1-0210(T) = FSL F6-0969(T) = DSM 26689(T) = LMG 28123(T) = BEI NR-42630(T)), Listeria grandensis sp. nov. (type strain TTU A1-0212(T) = FSL F6-0971(T) = DSM 26688(T) = LMG 28122(T) = BEI NR-42631(T)) and Listeria riparia sp. nov. (type strain FSL S10-1204(T) = DSM 26685(T) = LMG 28119(T) = BEI NR- 42634(T)) and (iv) a clade containing Listeria grayi. Genomic and phenotypic data suggest that the novel species are non-pathogenic.

VIPR HMM: a hidden Markov model for detecting recombination with microbial detection microarrays.

MOTIVATION: Current methods in diagnostic microbiology typically focus on the detection of a single genomic locus or protein in a candidate agent. The presence of the entire microbe is then inferred from this isolated result. Problematically, the presence of recombination in microbial genomes would go undetected unless other genomic loci or protein components were specifically assayed. Microarrays lend themselves well to the detection of multiple loci from a given microbe; furthermore, the inherent nature of microarrays facilitates highly parallel interrogation of multiple microbes. However, none of the existing methods for analyzing diagnostic microarray data has the capacity to specifically identify recombinant microbes. In previous work, we developed a novel algorithm, VIPR, for analyzing diagnostic microarray data. RESULTS: We have expanded upon our previous implementation of VIPR by incorporating a hidden Markov model (HMM) to detect recombinant genomes. We trained our HMM on a set of non-recombinant parental viruses and applied our method to 11 recombinant alphaviruses and 4 recombinant flaviviruses hybridized to a diagnostic microarray in order to evaluate performance of the HMM. VIPR HMM correctly identified 95% of the 62 inter-species recombination breakpoints in the validation set and only two false-positive breakpoints were predicted. This study represents the first description and validation of an algorithm capable of detecting recombinant viruses based on diagnostic microarray hybridization patterns. AVAILABILITY: VIPR HMM is freely available for academic use and can be downloaded from http://ibridgenetwork.org/wustl/vipr. CONTACT: davewang@borcim.wustl.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

VIPR: A probabilistic algorithm for analysis of microbial detection microarrays.

BACKGROUND: All infectious disease oriented clinical diagnostic assays in use today focus on detecting the presence of a single, well defined target agent or a set of agents. In recent years, microarray-based diagnostics have been developed that greatly facilitate the highly parallel detection of multiple microbes that may be present in a given clinical specimen. While several algorithms have been described for interpretation of diagnostic microarrays, none of the existing approaches is capable of incorporating training data generated from positive control samples to improve performance. RESULTS: To specifically address this issue we have developed a novel interpretive algorithm, VIPR (Viral Identification using a PRobabilistic algorithm), which uses Bayesian inference to capitalize on empirical training data to optimize detection sensitivity. To illustrate this approach, we have focused on the detection of viruses that cause hemorrhagic fever (HF) using a custom HF-virus microarray. VIPR was used to analyze 110 empirical microarray hybridizations generated from 33 distinct virus species. An accuracy of 94% was achieved as measured by leave-one-out cross validation. CONCLUSIONS: VIPR outperformed previously described algorithms for this dataset. The VIPR algorithm has potential to be broadly applicable to clinical diagnostic settings, wherein positive controls are typically readily available for generation of training data.

Metagenomic analysis of human diarrhea: viral detection and discovery.

Worldwide, approximately 1.8 million children die from diarrhea annually, and millions more suffer multiple episodes of nonfatal diarrhea. On average, in up to 40% of cases, no etiologic agent can be identified. The advent of metagenomic sequencing has enabled systematic and unbiased characterization of microbial populations; thus, metagenomic approaches have the potential to define the spectrum of viruses, including novel viruses, present in stool during episodes of acute diarrhea. The detection of novel or unexpected viruses would then enable investigations to assess whether these agents play a causal role in human diarrhea. In this study, we characterized the eukaryotic viral communities present in diarrhea specimens from 12 children by employing a strategy of "micro-mass sequencing" that entails minimal starting sample quantity (<100 mg stool), minimal sample purification, and limited sequencing (384 reads per sample). Using this methodology we detected known enteric viruses as well as multiple sequences from putatively novel viruses with only limited sequence similarity to viruses in GenBank.