Bonnie L Hurwitz
Assistant Professor, Agricultural-Biosystems Engineering
Assistant Professor, BIO5 Institute
Assistant Professor, Genetics - GIDP
Assistant Professor, Statistics-GIDP
Clinical Instructor, Pharmacy Practice-Science
Primary Department
Department Affiliations
(520) 626-9819
Work Summary
Our lab focuses on large-scale –omics datasets, high-throughput computing, and big data analytics. We leverage these technologies to answer questions related to the relationship between microbes, their hosts, and the environment. In particular, we focus on viral-host interactions and co-evolution given environmental factors (i) in aquatic systems and (ii) for phage treatment of diabetic foot ulcers.
Research Interest
Dr. Bonnie Hurwitz is an Assistant Professor of Biosystems Engineering at the University of Arizona and BIO5 Research Institute Fellow. She has worked as a computational biologist for nearly two decades on interdisciplinary projects in both industry and academia. Her research on the human/earth microbiome incorporates large-scale –omics datasets, high-throughput computing, and big data analytics towards research questions in “One Health”. In particular, Dr. Hurwitz is interested in the relationship between the environment, microbial communities, and their hosts. Dr. Hurwitz is well-cited for her work in computational biology in diverse areas from plant genomics to viral metagenomics with over 1200 citations

Publications

Ball, C. L., Daniel, S. G., Besselsen, D. G., Hurwitz, B. L., & Doetschman, T. C. (2017). Functional changes in the gut microbiome contribute to Transforming Growth Factor β-deficient colon cancer. mSystems, 2(5), 1-17.
BIO5 Collaborators
David G Besselsen, Thomas C Doetschman, Bonnie L Hurwitz
Gore, M. A., Chia, J., Elshire, R. J., Sun, Q., Ersoz, E. S., Hurwitz, B. L., Peiffer, J. A., McMullen, M. D., Grills, G. S., Ross-Ibarra, J., Ware, D. H., & Buckler, E. S. (2009). A first-generation haplotype map of maize. Science (New York, N.Y.), 326(5956), 1115-7.

Maize is an important crop species of high genetic diversity. We identified and genotyped several million sequence polymorphisms among 27 diverse maize inbred lines and discovered that the genome was characterized by highly divergent haplotypes and showed 10- to 30-fold variation in recombination rates. Most chromosomes have pericentromeric regions with highly suppressed recombination that appear to have influenced the effectiveness of selection during maize inbred development and may be a major component of heterosis. We found hundreds of selective sweeps and highly differentiated regions that probably contain loci that are key to geographic adaptation. This survey of genetic diversity provides a foundation for uniting breeding efforts across the world and for dissecting complex traits through genome-wide association studies.

Hurwitz, B. L., Kudrna, D., Yu, Y., Sebastian, A., Zuccolo, A., Jackson, S. A., Ware, D., Wing, R. A., & Stein, L. (2010). Rice structural variation: a comparative analysis of structural variation between rice and three of its closest relatives in the genus Oryza. The Plant journal : for cell and molecular biology, 63(6), 990-1003.

Rapid progress in comparative genomics among the grasses has revealed similar gene content and order despite exceptional differences in chromosome size and number. Large- and small-scale genomic variations are of particular interest, especially among cultivated and wild species, as they encode rapidly evolving features that may be important in adaptation to particular environments. We present a genome-wide study of intermediate-sized structural variation (SV) among rice (Oryza sativa) and three of its closest relatives in the genus Oryza (Oryza nivara, Oryza rufipogon and Oryza glaberrima). We computationally identified regional expansions, contractions and inversions in the Oryza species genomes relative to O. sativa by combining data from paired-end clone alignments to the O. sativa reference genome and physical maps. A subset of the computational predictions was validated using a new approach for BAC size determination. The result was a confirmed catalog of 674 expansions (25-38 Mb) and 611 (4-19 Mb) contractions, and 140 putative inversions (14-19 Mb) between the three Oryza species and O. sativa. In the expanded regions unique to O. sativa we found enrichment in transposable elements (TEs): long terminal repeats (LTRs) were randomly located across the chromosomes, and their insertion times corresponded to the date of the A genome radiation. Also, rice-expanded regions contained an over-representation of single-copy genes related to defense factors in the environment. This catalog of confirmed SV in reference to O. sativa provides an entry point for future research in genome evolution, speciation, domestication and novel gene discovery.

Choi, I., Ponsero, A., Bomhoff, M., Youens-Clark, K., Watts, G. S., Hartman, J. H., & Hurwitz, B. L. (2017). Libra: Comparative Metagenomics with MapReduce. Genome Biology.

Metagenomics promises insight into uncultured microbes across space and time. Yet, the tsunami of low-cost sequencing meant to enable these discoveries is leaving scientists drowning in data. We present Libra, a comparative metagenomics algorithm, that considers genetic distance and microbial abundance simultaneously using a vector-space model, and scales using Apache Hadoop. We compare Libra to other tools to examine effects of data reduction and distance metrics using simulated metagenomes, controlled bacterial mixtures, and metagenomes from the Human Microbiome Project and Tara Oceans Expedition. We show that Libra provides accurate, efficient, and scalable compute for discerning global patterns in microbial ecology.

Hurwitz, B. L., U'Ren, J. M., & Youens-Clark, K. (2016). Computational prospecting the great viral unknown. FEMS microbiology letters, 363(10).

Bacteriophages play an important role in host-driven biological processes by controlling bacterial population size, horizontally transferring genes between hosts and expressing host-derived genes to alter host metabolism. Metagenomics provides the genetic basis for understanding the interplay between uncultured bacteria, their phage and the environment. In particular, viral metagenomes (viromes) are providing new insight into phage-encoded host genes (i.e. auxiliary metabolic genes; AMGs) that reprogram host metabolism during infection. Yet, despite deep sequencing efforts of viral communities, the majority of sequences have no match to known proteins. Reference-independent computational techniques, such as protein clustering, contig spectra and ecological profiling are overcoming these barriers to examine both the known and unknown components of viromes. As the field of viral metagenomics progresses, a critical assessment of tools is required as the majority of algorithms have been developed for analyzing bacteria. The aim of this paper is to offer an overview of current computational methodologies for virome analysis and to provide an example of reference-independent approaches using human skin viromes. Additionally, we present methods to carefully validate AMGs from host contamination. Despite computational challenges, these new methods offer novel insights into the diversity and functional roles of phages in diverse environments.