publications

2020

1. CRISPR Journal

   Evaluation of Homology-Independent CRISPR-Cas9 Off-Target Assessment Methods

   Hemangi G Chaudhari, Jon Penterman, Holly J Whitton, and 12 more authors

   The CRISPR Journal, 2020

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   The ability to alter genomes specifically by CRISPR-Cas gene editing has revolutionized biological research, biotechnology, and medicine. Broad therapeutic application of this technology, however, will require thorough preclinical assessment of off-target editing by homology-based prediction coupled with reliable methods for detecting off-target editing. Several off-target site nomination assays exist, but careful comparison is needed to ascertain their relative strengths and weaknesses. In this study, HEK293T cells were treated with Streptococcus pyogenes Cas9 and eight guide RNAs with varying levels of predicted promiscuity in order to compare the performance of three homology-independent off-target nomination methods: the cell-based assay, GUIDE-seq, and the biochemical assays CIRCLE-seq and SITE-seq. The three methods were benchmarked by sequencing 75,000 homology-nominated sites using hybrid capture followed by high-throughput sequencing, providing the most comprehensive assessment of such methods to date. The three methods performed similarly in nominating sequence-confirmed off-target sites, but with large differences in the total number of sites nominated. When combined with homology-dependent nomination methods and confirmation by sequencing, all three off-target nomination methods provide a comprehensive assessment of off-target activity. GUIDE-seq’s low false-positive rate and the high correlation of its signal with observed editing highlight its suitability for nominating off-target sites for ex vivo CRISPR-Cas therapies.

2019

1. Scientific Reports

   Uncovering biomarker genes with enriched classification potential from Hallmark gene sets

   Colin A Targonski, Courtney A Shearer, Benjamin T Shealy, and 2 more authors

   Scientific reports, 2019

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   Given the complex relationship between gene expression and phenotypic outcomes, computationally efficient approaches are needed to sift through large high-dimensional datasets in order to identify biologically relevant biomarkers. In this report, we describe a method of identifying the most salient biomarker genes in a dataset, which we call “candidate genes”, by evaluating the ability of gene combinations to classify samples from a dataset, which we call “classification potential”. Our algorithm, Gene Oracle, uses a neural network to test user defined gene sets for polygenic classification potential and then uses a combinatorial approach to further decompose selected gene sets into candidate and non-candidate biomarker genes. We tested this algorithm on curated gene sets from the Molecular Signatures Database (MSigDB) quantified in RNAseq gene expression matrices obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) data repositories. First, we identified which MSigDB Hallmark subsets have significant classification potential for both the TCGA and GTEx datasets. Then, we identified the most discriminatory candidate biomarker genes in each Hallmark gene set and provide evidence that the improved biomarker potential of these genes may be due to reduced functional complexity.

2018

1. Animal Science

   Poor feed efficiency in sheep is associated with several structural abnormalities in the community metabolic network of their ruminal microbes

   Rocky D Patil, Melinda J Ellison, Sara M Wolff, and 7 more authors

   Journal of Animal Science, 2018

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   Ruminant animals have a symbiotic relationship with the microorganisms in their rumens. In this relationship, rumen microbes efficiently degrade complex plant-derived compounds into smaller digestible compounds, a process that is very likely associated with host animal feed efficiency. The resulting simpler metabolites can then be absorbed by the host and converted into other compounds by host enzymes. We used a microbial community metabolic network inferred from shotgun metagenomics data to assess how this metabolic system differs between animals that are able to turn ingested feedstuffs into body mass with high efficiency and those that are not. We conducted shotgun sequencing of microbial DNA from the rumen contents of 16 sheep that differed in their residual feed intake (RFI), a measure of feed efficiency. Metagenomic reads from each sheep were mapped onto a database-derived microbial metabolic network, which was linked to the sheep metabolic network by interface metabolites (metabolites transferred from microbes to host). No single enzyme was identified as being significantly different in abundance between the low and high RFI animals (P > 0.05, Wilcoxon test). However, when we analyzed the metabolic network as a whole, we found several differences between efficient and inefficient animals. Microbes from low RFI (efficient) animals use a suite of enzymes closer in network space to the host’s reactions than those of the high RFI (inefficient) animals. Similarly, low RFI animals have microbial metabolic networks that, on average, contain reactions using shorter carbon chains than do those of high RFI animals, potentially allowing the host animals to extract metabolites more efficiently. Finally, the efficient animals possess community networks with greater Shannon diversity among their enzymes than do inefficient ones. Thus, our system approach to the ruminal microbiome identified differences attributable to feed efficiency in the structure of the microbes’ community metabolic network that were undetected at the level of individual microbial taxa or reactions.