PathoScope: a multi-omic approach to characterize microbiomes and identify pathogens Castro-Nallar E.1, Shen Y.2, Freishtat R.J.3, Pérez-Losada M.1, Manimaran S.2, Liu G.2, Spira A.2, Johnson W.E.2, and Crandall K.A.1 1Computational Biology Institute, George Washington University, Ashburn, VA 20147; 2Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118; 3Division of Emergency Medicine, Children’s National Medical Center, Washington, DC 20010 INTRODUCTION • The human microbiome plays a key role in a variety of human health issues from obesity to respiratory disease (Huang et al., 2013). • Assessing taxonomic and functional diversity of microbiomes across time, individuals, and tissues within individuals is crucial to understand infection and disease. • The affordable costs of high-throughput sequencing (HTS) and the limitations of single gene inferences, are pushing microbiome studies towards shotgun metagenomics. • Identifying causal pathogens and characterize microbiomes in metagenomic studies is a daunting task due to the large volume of data generated. • A variety of techniques have been developed to isolate potential pathogens for HTS targets using molecu­ lar biological approaches. However, this limits the inferences with respect to the host response. • We developed a computational solution combining metatranscriptomic microbial identification and host differential gene expression signatures. • We applied this approach to identify pathogens associated with pediatric asthma. METHODS Sample Collection Nasal epithelial cells from the nares were collected from 8 children with asthma and 6 controls. Participants were part of the Asth­ MaP (Asthma Severity Modifying Polymorphisms) Project at Children’s National Medical Center. Total RNA was extracted using Trizol (Life Technologies) reagent and puri­ fied in silica columns (Ambion, Carls­ bad CA). Total RNA was subjected to RiboZ­ ero ribosomal RNA reduction prior to library preparation using Illumina TrueSeq Stranded Total RNA kit (San Diego, CA) and sequenced on a HiS­ eq 2500 instrument on two separate ‘Rapid’ flow cells. Analyses Reads were preprocessed using PrinSeq-Lite and PathoQC (trim­ ming reads and bases < 25 PHRED). Target and filter genome libraries were built in PathoLib. Reads were aligned using Bowtie2 (Langmead and Salzberg, 2012) and filtered against the human genome (hg19) using PathoMap. The microbial communities in each patient were then characterized in PathoID. Raw counts identified in Pa­ thoScope were normalized using regularized-log transformation in DESeq2. Taxa abundance was tested us­ ing a negative binomial Wald test also as in DESeq2. Plots were created in R using Phyloseq, ggplot2, gplot packages. Gene differential expression analysis were performed in TopHat (Kim et al., 2013) and Cufflinks (Trapnell et al., 2012). Host response gene expression signatures between groups were compared in PathoSig using an adaptive Bayesian factor analysis model. CASE STUDY: ASTHMA IN CHILDREN Metatranscriptomic microbial identification • RNA-Seq runs generated an average of 41.4 million single-end 100bp sequencing reads per sample: 90- 95% human and 5-10% microbial. • Five of the 8 cases showed a high prevalence of the bacterial species Moraxella catarrhalis and Moraxella nonliquefaciens, while controls showed low prevalence. • These findings confirm previous metagenomic surveys which found large proportions of Proteobacteria in cases but not controls, speculating that this could be explained by Moraxella spp. and Hemophilus spp. (Hilty et al., 2010). PathoLib Raw Sequencing Reads (fastq) Quality Controlled Reads (fastq) NCBI nucleotide database (fasta) Host, Filter, and Target Genome Libraries (fasta) Filtered Read Alignments (sam) Reassigned Read Alignments (sam) Detailed Report (xml) 2. PathoMap 4. PathoID 5. PathoReport 6. PathoDB 1. PathoQC 3. PathoDB Core Module Optional Module Modules 1. PathoDB A mysql database con