Background: Phi29 polymerase based amplification methods provides amplified DNA with minimal changes in sequence and relative abundance for many biomedical applications. RNA virus detection using microarrays, however, can present a challenge because phi29 DNA polymerase cannot amplify RNA nor small cDNA fragments (<2000 bases) obtained by reverse transcription of certain viral RNA genomes. Therefore, ligation of cDNA fragments is necessary prior phi29 polymerase based amplification. We adapted the QuantiTect Whole Transcriptome Kit (Qiagen) to our purposes and designated the method as Whole Transcriptome Amplification (WTA).

The rapid and accurate identification of pathogens is critical in the control of infectious disease. To this end, we analyzed the capacity for viral detection and identification of a newly described high-density resequencing microarray (RMA), termed PathogenID, which was designed for multiple pathogen detection using database similarity searching. We focused on one of the largest and most diverse viral families described to date, the family Rhabdoviridae. We demonstrate that this approach has the potential to identify both known and related viruses for which precise sequence information is unavailable. In particular, we demonstrate that a strategy based on consensus sequence determination for analysis of RMA output data enabled successful detection of viruses exhibiting up to 26% nucleotide divergence with the closest sequence tiled on the array. Using clinical specimens obtained from rabid patients and animals, this method also shows a high species level concordance with standard reference assays, indicating that it is amenable for the development of diagnostic assays. Finally, 12 animal rhabdoviruses which were currently unclassified, unassigned, or assigned as tentative species within the family Rhabdoviridae were successfully detected. These new data allowed an unprecedented phylogenetic analysis of 106 rhabdoviruses and further suggest that the principles and methodology developed here may be used for the broad-spectrum surveillance and the broader-scale investigation of biodiversity in the viral world.

SummaryIdentification of microbial pathogens in clinical specimens is still performed by phenotypic methods that are often slow and cumbersome, despite the availability of more comprehensive genotyping technologies. We present an approach based on whole‐genome amplification and resequencing microarrays for unbiased pathogen detection. This 10 h process identifies a broad spectrum of bacterial and viral species and predicts antibiotic resistance and pathogenicity and virulence profiles. We successfully identify a variety of bacteria and viruses, both in isolation and in complex mixtures, and the high specificity of the microarray distinguishes between different pathogens that cause diseases with overlapping symptoms. The resequencing approach also allows identification of organisms whose sequences are not tiled on the array, greatly expanding the repertoire of identifiable organisms and their variants. We identify organisms by hybridization of their DNA in as little as 1–4 h. Using this method, we identified Monkeypox virus and drug‐resistant Staphylococcus aureus in a skin lesion taken from a child suspected of an orthopoxvirus infection, despite poor transport conditions of the sample, and a vast excess of human DNA. Our results suggest this technology could be applied in a clinical setting to test for numerous pathogens in a rapid, sensitive and unbiased manner.

BackgroundA resequencing microarray called PathogenID v2.0 has been developed and used to explore various strategies of sequence selection for its design. The part dedicated to influenza viruses was based on consensus sequences specific for one gene generated from global alignments of a large number of influenza virus sequences available in databanks.ResultsFor each HA (H1, H2, H3, H5, H7 and H9) and NA (N1, N2 and N7) molecular type chosen to be tested, 1 to 3 consensus sequences were computed and tiled on the microarray. A total of 12 influenza virus samples from different host origins (humans, pigs, horses and birds) and isolated over a period of about 50 years were used in this study. Influenza viruses were correctly identified, and in most cases with the accurate information of the time of their emergence.ConclusionsPathogenID v2.0 microarray demonstrated its ability to type and subtype influenza viruses, often to the level of viral variants, with a minimum number of tiled sequences. This validated the strategy of using consensus sequences, which do not exist in nature, for our microarray design. The versatility, rapidity and high discriminatory power of the PathogenID v2.0 microarray could prove critical to detect and identify viral genome reassortment events resulting in a novel virus with epidemic or pandemic potential and therefore assist health authorities to make efficient decisions about patient treatment and outbreak management.

Background and aims: Infection causes significant neonatal morbidity and mortality. Currently available methods for diagnosing infection are unreliable. We aimed to examine differences in host RNA expression profiles between infants with confirmed infection and control infants using microarray technology. Methods: RNA was extracted from neonatal whole blood taken from infants with confirmed infection and from controls using a modified PAXgene™ Blood RNA system protocol. High quality RNA was run on Illumina® Human Whole-Genome Expression BeadChip microarrays. Normalised, validated microarray data was analysed to examine differences between control and infected samples. Functional annotation according to gene ontology and pathway analysis was performed. Results: 28 infected and 35 control samples were examined. Differential gene expression between infected and control groups was analysed: 448 features had >2-fold up-regulation and 341 features >2-fold down-regulation (p< 0.001) in infected compared to control infants. There was significant immune-related differential gene expression. Up-regulated genes in the infected group included genes involved in cytokine, complement, interferon and Toll Like Receptor related processes. Down-regulated genes included genes involved in antigen processing, MHC II activity and T cell activation and signalling. Conclusions: There is immune-related differential gene expression between infected and control infants. Many of our results corroborate findings previously published for adult and paediatric populations. In addition, these results provide evidence that neonates are capable of mounting a substantial immune response to infection. It is likely that, with larger studies and, with examination of training sets of data, immune gene expression signatures for neonatal infection can be defined