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.

Escherichia coli strains are classified based on O-antigens that are components of the lipopolysaccharide (LPS) in the cell envelope. O-antigens are important virulence factors, targets of both the innate and adaptive immune system, and play a role in host-pathogen interactions. Because they are highly immunogenic and display antigenic specificity unique for each strain, O-antigens are the biomarkers for designating O-types. Immunologically, 185 O-serogroups and 11 OX-groups exist for classification. Conventional serotyping for O-typing entails agglutination reactions between the O-antigen and antisera generated against each O-group. The procedure is labor intensive, not always accurate, and exhibits equivocal results. In this report, we present the sequences of 71 O-antigen gene clusters (O-AGC) and a comparison of all 196 O- and OX-groups. Many of the designated O-types, applied for classification over several decades, exhibited similar nucleotide sequences of the O-AGCs and cross-reacted serologically. Some O-AGCs carried insertion sequences and others had only a few nucleotide differences between them. Thus, based on these findings, it is proposed that several of the E. coli O-groups may be merged. Knowledge of the O-AGC sequences facilitates the development of molecular diagnostic platforms that are rapid, accurate, and reliable that can replace conventional serotyping. Additionally, with the scientific knowledge presented, new frontiers in the discovery of biomarkers, understanding the roles of O-antigens in the innate and adaptive immune system and pathogenesis, the development of glycoconjugate vaccines, and other investigations, can be explored.

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 S10-1188T = DSM 26686T = LMG 28120T = BEI NR-42633T) and Listeria floridensis sp. nov. (type strain FSL S10-1187T = DSM 26687T = LMG 28121T = BEI NR-42632T), (iii) a clade consisting of Listeria rocourtiae , L. weihenstephanensis and three novel species, Listeria cornellensis sp. nov. (type strain TTU A1-0210T = FSL F6-0969T = DSM 26689T = LMG 28123T = BEI NR-42630T), Listeria grandensis sp. nov. (type strain TTU A1-0212T = FSL F6-0971T = DSM 26688T = LMG 28122T = BEI NR-42631T) and Listeria riparia sp. nov. (type strain FSL S10-1204T = DSM 26685T = LMG 28119T = BEI NR- 42634T) and (iv) a clade containing Listeria grayi . Genomic and phenotypic data suggest that the novel species are non-pathogenic.

BackgroundHistorically, 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.ResultsIn 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.ConclusionsThe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13104-015-1530-0) contains supplementary material, which is available to authorized users.

BackgroundAll 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.ResultsTo 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. ConclusionsVIPR 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.

Supporting Information S1 Fig. Structure of O-AGC of all 196 O-serogroups. The O-AGCs of all 196 O-and OX-groups are diagrammatically represented. The nucleotide sequences of 71 O-groups marked with asterisk and in bold font were determined in the present investigation.

Supplementary data are available at Bioinformatics online.

The gold standard method for serotyping Escherichia coli has relied on antisera-based typing of the O- and H-antigens, which is labor intensive and often unreliable. In the post-genomic era, sequence-based assays are potentially faster to provide results, could combine O-serogrouping and H-typing in a single test, and could simultaneously screen for the presence of other genetic markers of interest such as virulence factors. Whole genome sequencing is one approach; however, this method has limited multiplexing capabilities, and only a small fraction of the sequence is informative for subtyping or identifying virulence potential. A targeted, sequence-based assay and accompanying software for data analysis would be a great improvement over the currently available methods for serotyping. The purpose of this study was to develop a high-throughput, molecular method for serotyping E. coli by sequencing the genes that are required for production of O- and H-antigens, as well as to develop software for data analysis and serotype identification. To expand the utility of the assay, targets for the virulence factors, Shiga toxins (stx1, and stx2) and intimin (eae) were included. To validate the assay, genomic DNA was extracted from O-serogroup and H-type standard strains and from Shiga toxin-producing E. coli, the targeted regions were amplified, and then sequencing libraries were prepared from the amplified products followed by sequencing of the libraries on the Ion S5™ sequencer. The resulting sequence files were analyzed via the SeroType Caller™ software for identification of O-serogroup, H-type, and presence of stx1, stx2, and eae. We successfully identified 169 O-serogroups and 41 H-types. The assay also routinely detected the presence of stx1a,c,d (3 of 3 strains), stx2c−e,g (8 of 8 strains), stx2f (1 strain), and eae (6 of 6 strains). Taken together, the high-throughput, sequence-based method presented here is a reliable alternative to antisera-based serotyping methods for E. coli.