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
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.

Hurwitz, B. L., Deng, L., Poulos, B. T., & Sullivan, M. B. (2013). Evaluation of methods to concentrate and purify ocean virus communities through comparative, replicated metagenomics. Environmental microbiology, 15(5), 1428-40.

Viruses have global impact through mortality, nutrient cycling and horizontal gene transfer, yet their study is limited by complex methodologies with little validation. Here, we use triplicate metagenomes to compare common aquatic viral concentration and purification methods across four combinations as follows: (i) tangential flow filtration (TFF) and DNase + CsCl, (ii) FeCl3 precipitation and DNase, (iii) FeCl3 precipitation and DNase + CsCl and (iv) FeCl3 precipitation and DNase + sucrose. Taxonomic data (30% of reads) suggested that purification methods were statistically indistinguishable at any taxonomic level while concentration methods were significantly different at family and genus levels. Specifically, TFF-concentrated viral metagenomes had significantly fewer abundant viral types (Podoviridae and Phycodnaviridae) and more variability among Myoviridae than FeCl3 -precipitated viral metagenomes. More comprehensive analyses using protein clusters (66% of reads) and k-mers (100% of reads) showed 50-53% of these data were common to all four methods, and revealed trace bacterial DNA contamination in TFF-concentrated metagenomes and one of three replicates concentrated using FeCl3 and purified by DNase alone. Shared k-mer analyses also revealed that polymerases used in amplification impact the resulting metagenomes, with TaKaRa enriching for 'rare' reads relative to PfuTurbo. Together these results provide empirical data for making experimental design decisions in culture-independent viral ecology studies.

Hurwitz, B. L., Ponsero, A., Thornton, J., & U'Ren, J. M. (2017). Phage hunters: Computational strategies for finding phages in large-scale 'omics datasets. Virus research, 244, 110-115.

A plethora of tools exist for identifying phage sequences in bacterial genomes, single cell amplified genomes, and host-associated and environmental metagenomes. Yet because the genetics of phages and their hosts are closely intertwined, distinguishing viral from bacterial signal remains an ongoing challenge. Further the size, quantity and fragmentary nature of modern 'omics datasets ushers in a new set of computational challenges. Here, we detail the promises and pitfalls of using currently available gene-centric or k-mer based tools for identifying prophage sequences in genomes and prophage and viral contigs in metagenomes. Each of these methods offers a unique piece of the puzzle to elucidating the intriguing signatures of phage-host coevolution.