MicroRNA expression in mice infected with seasonal H1N1, swine H1N1 or highly pathogenic H5N1.

Influenza virus infections in humans remain a healthcare concern, and the need for vaccines, therapeutics and prophylactics remains a high priority. Understanding the molecular events associated with influenza-virus-induced pathology may lead to the identification of clinical disease biomarkers and novel antiviral targets. MicroRNAs (miRNAs) are well-conserved endogenous non-coding RNAs known to regulate post-transcriptional gene expression as well as play a major role in many biological processes and pathways. Animal studies have demonstrated that miRNAs are involved in viral disease and controlling inflammation. In this study, we examined the differences in the miRNA expression profiles associated with the lung in mice infected with influenza viruses that varied in virulence and pathogenicity. A statistical model was employed that utilized changes in miRNA expression to identify the virus that was used to infect the mice. This study identified a unique fingerprint of viral pathogenicity associated with seasonal H1N1, swine H1N1 and highly pathogenic H5N1 in the mouse model, and may lead to the identification of novel therapeutic and prophylactic targets.

Short-read, high-throughput sequencing technology for STR genotyping.

DNA-based methods for human identification principally rely upon genotyping of short tandem repeat (STR) loci. Electrophoretic-based techniques for variable-length classification of STRs are universally utilized, but are limited in that they have relatively low throughput and do not yield nucleotide sequence information. High-throughput sequencing technology may provide a more powerful instrument for human identification, but is not currently validated for forensic casework. Here, we present a systematic method to perform high-throughput genotyping analysis of the Combined DNA Index System (CODIS) STR loci using short-read (150 bp) massively parallel sequencing technology. Open source reference alignment tools were optimized to evaluate PCR-amplified STR loci using a custom designed STR genome reference. Evaluation of this approach demonstrated that the 13 CODIS STR loci and amelogenin (AMEL) locus could be accurately called from individual and mixture samples. Sensitivity analysis showed that as few as 18,500 reads, aligned to an in silico referenced genome, were required to genotype an individual (>99% confidence) for the CODIS loci. The power of this technology was further demonstrated by identification of variant alleles containing single nucleotide polymorphisms (SNPs) and the development of quantitative measurements (reads) for resolving mixed samples.