Bonnie L Hurwitz
- Associate Professor, Biosystems Engineering
- Clinical Instructor, Pharmacy Practice-Science
- Associate Professor, Genetics - GIDP
- Associate Professor, Statistics-GIDP
- Associate Professor, BIO5 Institute
Dr. Bonnie Hurwitz is an Associate Professor of Agricultural and 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 earth and human microbiome incorporates large-scale –omics datasets, high-throughput computing, and big data analytics to answer questions in systems biology. In particular, Dr. Hurwitz is interested how viruses re-engineer host metabolism and the implications on host-driven processes. Dr. Hurwitz is well-cited for her work in computational biology in diverse areas from plant genomics to viral metagenomics with over 2600 citations.
- Ph.D. Ecology and Evolutionary Biology
- University of Arizona, Tucson, Arizona, United States
- Viral community dynamics and functional specialization in the Pacific Ocean
- B.S. Biochemistry and Molecular Biology
- University of California, Santa Cruz, Santa Cruz, California, United States
- University of Arizona, Tucson, Arizona (2014 - Ongoing)
- University of Arizona, Tucson, Arizona (2012 - 2014)
- Cold Spring Harbor Laboratory (2004 - 2008)
- Third Wave Technologies (2002 - 2004)
- Accelrys (2001 - 2002)
- Incyte Genomics (1997 - 2001)
- Highly accessed publication in FEMS Microbiology Reviews
- FEMS Microbiology Reviews, Summer 2016
- Highly Accessed Publication in BMC Medicine
- BMC Medicine, Spring 2014
Licensure & Certification
- Bioinformatics, University of California Santa Cruz (1999)
metagenomics, genomics, microbes, viruses, bioinformatics, computing, high performance computing
metagenomics, genomics, microbes, viruses, bioinformatics, computing, big data, high performance computing
DissertationBE 920 (Fall 2020)
DissertationMCB 920 (Fall 2020)
Independent StudyBE 199 (Fall 2020)
Lab Presentations & DiscussionMCB 696A (Fall 2020)
MetagenomicsBE 487 (Fall 2020)
MetagenomicsBE 587 (Fall 2020)
Biosystems AnalyticsBE 434 (Spring 2020)
Biosystems AnalyticsBE 534 (Spring 2020)
DissertationBE 920 (Spring 2020)
DissertationMCB 920 (Spring 2020)
Honors Independent StudyBE 299H (Spring 2020)
InternshipBE 693 (Spring 2020)
Lab Presentations & DiscussionMCB 696A (Spring 2020)
DissertationBE 920 (Fall 2019)
DissertationMCB 920 (Fall 2019)
Independent StudyBE 299 (Fall 2019)
Lab Presentations & DiscussionMCB 696A (Fall 2019)
MetagenomicsBE 587 (Fall 2019)
Biosystems AnalyticsBE 434 (Spring 2019)
Biosystems AnalyticsBE 534 (Spring 2019)
Directed ResearchBE 492 (Spring 2019)
DissertationBE 920 (Spring 2019)
Lab Presentations & DiscussionMCB 696A (Spring 2019)
ResearchMCB 900 (Spring 2019)
ThesisBE 910 (Spring 2019)
DissertationABE 920 (Fall 2018)
Honors ThesisMIC 498H (Fall 2018)
InternshipABE 493 (Fall 2018)
InternshipABE 693 (Fall 2018)
Lab Presentations & DiscussionMCB 696A (Fall 2018)
MetagenomicsABE 487 (Fall 2018)
MetagenomicsABE 587 (Fall 2018)
ResearchMCB 900 (Fall 2018)
ThesisABE 910 (Fall 2018)
DissertationABE 920 (Spring 2018)
Introduction to ResearchMCB 795A (Spring 2018)
DissertationABE 920 (Fall 2017)
Introduction to ResearchMCB 795A (Fall 2017)
MetagenomicsABE 487 (Fall 2017)
MetagenomicsABE 587 (Fall 2017)
ThesisABE 910 (Fall 2017)
DissertationABE 920 (Spring 2017)
DissertationMCB 920 (Spring 2017)
InternshipABE 393 (Spring 2017)
InternshipABE 693 (Spring 2017)
Lab Presentations & DiscussionMCB 696A (Spring 2017)
ThesisABE 910 (Spring 2017)
DissertationABE 920 (Fall 2016)
DissertationMCB 920 (Fall 2016)
Independent StudyABE 599 (Fall 2016)
InternshipABE 393 (Fall 2016)
InternshipABE 593 (Fall 2016)
InternshipABE 693 (Fall 2016)
MetagenomicsABE 487 (Fall 2016)
MetagenomicsABE 587 (Fall 2016)
ThesisABE 910 (Fall 2016)
InternshipMCB 693 (Summer I 2016)
DissertationABE 920 (Spring 2016)
DissertationMCB 920 (Spring 2016)
Lab Presentations & DiscussionMCB 696A (Spring 2016)
ThesisSTAT 910 (Spring 2016)
- Spichler-Moffrah, A., Al Mohajer, M., Hurwitz, B. L., & Armstrong, D. G. (2016). Skin and Soft Tissue Infection. In ASM: Diagnostic Microbiology of the Immunocompromised Host, Hayden RT, Carroll KC, Tang TW, Wolk DM (eds). Washington, DC: American Society of Microbiology Press.
- Daniel, S. G., Ball, C. L., Besselsen, D. G., Doetschman, T., & Hurwitz, B. L. (2019). Functional Changes in the Gut Microbiome Contribute to Transforming Growth Factor β-Deficient Colon Cancer. mSystems, 2(5).More infoColorectal cancer (CRC) is one of the most treatable cancers, with a 5-year survival rate of ~64%, yet over 50,000 deaths occur yearly in the United States. In 15% of cases, deficiency in mismatch repair leads to null mutations in transforming growth factor β (TGF-β) type II receptor, yet genotype alone is not responsible for tumorigenesis. Previous work in mice shows that disruptions in TGF-β signaling combined with cause tumorigenesis, indicating a synergistic effect between genotype and microbial environment. Here, we examine functional shifts in the gut microbiome in CRC using integrated -omics approaches to untangle the role of host genotype, inflammation, and microbial ecology. We profile the gut microbiome of 40 mice with/without deficiency in TGF-β signaling from a (mothers against decapentaplegic homolog-3) knockout and with/without inoculation with . Clear functional differences in the microbiome tied to specific bacterial species emerge from four pathways related to human colon cancer: lipopolysaccharide (LPS) production, polyamine synthesis, butyrate metabolism, and oxidative phosphorylation (OXPHOS). Specifically, an increase in drives LPS production, which is associated with an inflammatory response. We observe a commensurate decrease in butyrate production from bacterium A4, which could promote tumor formation. causes an increase in OXPHOS that may increase DNA-damaging free radicals. Finally, multiple bacterial species increase polyamines that are associated with colon cancer, implicating not just diet but also the microbiome in polyamine levels. These insights into cross talk between the microbiome, host genotype, and inflammation could promote the development of diagnostics and therapies for CRC. Most research on the gut microbiome in colon cancer focuses on taxonomic changes at the genus level using 16S rRNA gene sequencing. Here, we develop a new methodology to integrate DNA and RNA data sets to examine functional shifts at the species level that are important to tumor development. We uncover several metabolic pathways in the microbiome that, when perturbed by host genetics and inoculation, contribute to colon cancer. The work presented here lays a foundation for improved bioinformatics methodologies to closely examine the cross talk between specific organisms and the host, important for the development of diagnostics and pre/probiotic treatment.
- Ponsero, A. J., & Hurwitz, B. L. (2019). The Promises and Pitfalls of Machine Learning for Detecting Viruses in Aquatic Metagenomes. Frontiers in microbiology, 10, 806.More infoTools allowing for the identification of viral sequences in host-associated and environmental metagenomes allows for a better understanding of the genetics and ecology of viruses and their hosts. Recently, new approaches using machine learning methods to distinguish viral from bacterial signal using k-mer sequence signatures were published for identifying viral contigs in metagenomes. The promise of these content-based approaches is the ability to discover new viruses, with no or few known relatives. In this perspective paper, we examine the use of the content-based machine learning tool VirFinder for the identification of viral sequences in aquatic metagenomes and explore the possibility of using ecosystem-focused models targeted to marine metagenomes. We discuss the impact of the training set composition on the tool performance and the current limitation for the retrieval of low abundance viral sequences in metagenomes. We identify potential biases that could arise from machine learning approaches for viral hunting in real-world datasets and suggest possible avenues to overcome them.
- Watts, G. S., Thornton, J. E., Youens-Clark, K., Ponsero, A. J., Slepian, M. J., Menashi, E., Hu, C., Deng, W., Armstrong, D. G., Reed, S., Cranmer, L. D., & Hurwitz, B. L. (2019). Identification and quantitation of clinically relevant microbes in patient samples: Comparison of three k-mer based classifiers for speed, accuracy, and sensitivity. PLoS computational biology, 15(11), e1006863.More infoInfections are a serious health concern worldwide, particularly in vulnerable populations such as the immunocompromised, elderly, and young. Advances in metagenomic sequencing availability, speed, and decreased cost offer the opportunity to supplement or even replace culture-based identification of pathogens with DNA sequence-based diagnostics. Adopting metagenomic analysis for clinical use requires that all aspects of the workflow are optimized and tested, including data analysis and computational time and resources. We tested the accuracy, sensitivity, and resource requirements of three top metagenomic taxonomic classifiers that use fast k-mer based algorithms: Centrifuge, CLARK, and KrakenUniq. Binary mixtures of bacteria showed all three reliably identified organisms down to 1% relative abundance, while only the relative abundance estimates of Centrifuge and CLARK were accurate. All three classifiers identified the organisms present in their default databases from a mock bacterial community of 20 organisms, but only Centrifuge had no false positives. In addition, Centrifuge required far less computational resources and time for analysis. Centrifuge analysis of metagenomes obtained from samples of VAP, infected DFUs, and FN showed Centrifuge identified pathogenic bacteria and one virus that were corroborated by culture or a clinical PCR assay. Importantly, in both diabetic foot ulcer patients, metagenomic sequencing identified pathogens 4-6 weeks before culture. Finally, we show that Centrifuge results were minimally affected by elimination of time-consuming read quality control and host screening steps.
- Youens-Clark, K., Bomhoff, M., Ponsero, A. J., Wood-Charlson, E. M., Lynch, J., Choi, I., Hartman, J. H., & Hurwitz, B. L. (2019). Erratum to: iMicrobe: Tools and data-driven discovery platform for the microbiome sciences. GigaScience, 8(8).
- Youens-Clark, K., Bomhoff, M., Ponsero, A. J., Wood-Charlson, E. M., Lynch, J., Choi, I., Hartman, J. H., & Hurwitz, B. L. (2019). iMicrobe: Tools and data-dreaiven discovery platform for the microbiome sciences. GigaScience, 8(7).More infoScientists have amassed a wealth of microbiome datasets, making it possible to study microbes in biotic and abiotic systems on a population or planetary scale; however, this potential has not been fully realized given that the tools, datasets, and computation are available in diverse repositories and locations. To address this challenge, we developed iMicrobe.us, a community-driven microbiome data marketplace and tool exchange for users to integrate their own data and tools with those from the broader community.
- Choi, I., Ponsero, A. J., Bomhoff, M., Youens-Clark, K., Hartman, J. H., & Hurwitz, B. L. (2018). Libra: scalable k-mer based tool for massive all-vs-all metagenome comparisons. GigaScience.More infoShotgun metagenomics provides powerful insights into microbial community biodiversity and function. Yet, inferences from metagenomic studies are often limited by dataset size and complexity and are restricted by the availability and completeness of existing databases. De novo comparative metagenomics enables the comparison of metagenomes based on their total genetic content.
- Roux, S., Adriaenssens, E. M., Dutilh, B. E., Koonin, E. V., Kropinski, A. M., Krupovic, M., Kuhn, J. H., Lavigne, R., Brister, J. R., Varsani, A., Amid, C., Aziz, R. K., Bordenstein, S. R., Bork, P., Breitbart, M., Cochrane, G. R., Daly, R. A., Desnues, C., Duhaime, M. B., , Emerson, J. B., et al. (2018). Minimum Information about an Uncultivated Virus Genome (MIUViG). Nature biotechnology.More infoWe present an extension of the Minimum Information about any (x) Sequence (MIxS) standard for reporting sequences of uncultivated virus genomes. Minimum Information about an Uncultivated Virus Genome (MIUViG) standards were developed within the Genomic Standards Consortium framework and include virus origin, genome quality, genome annotation, taxonomic classification, biogeographic distribution and in silico host prediction. Community-wide adoption of MIUViG standards, which complement the Minimum Information about a Single Amplified Genome (MISAG) and Metagenome-Assembled Genome (MIMAG) standards for uncultivated bacteria and archaea, will improve the reporting of uncultivated virus genomes in public databases. In turn, this should enable more robust comparative studies and a systematic exploration of the global virosphere.
- Alberti, A., Poulain, J., Engelen, S., Labadie, K., Romac, S., Ferrera, I., Albini, G., Aury, J. M., Belser, C., Bertrand, A., Cruaud, C., Da Silva, C., Dossat, C., Gavory, F., Gas, S., Guy, J., Haquelle, M., Jacoby, E., Jaillon, O., , Lemainque, A., et al. (2017). Viral to metazoan marine plankton nucleotide sequences from the Tara Oceans expedition. Scientific data, 4, 170093.More infoA unique collection of oceanic samples was gathered by the Tara Oceans expeditions (2009-2013), targeting plankton organisms ranging from viruses to metazoans, and providing rich environmental context measurements. Thanks to recent advances in the field of genomics, extensive sequencing has been performed for a deep genomic analysis of this huge collection of samples. A strategy based on different approaches, such as metabarcoding, metagenomics, single-cell genomics and metatranscriptomics, has been chosen for analysis of size-fractionated plankton communities. Here, we provide detailed procedures applied for genomic data generation, from nucleic acids extraction to sequence production, and we describe registries of genomics datasets available at the European Nucleotide Archive (ENA, www.ebi.ac.uk/ena). The association of these metadata to the experimental procedures applied for their generation will help the scientific community to access these data and facilitate their analysis. This paper complements other efforts to provide a full description of experiments and open science resources generated from the Tara Oceans project, further extending their value for the study of the world's planktonic ecosystems.
- Brum, J. R., Hurwitz, B. L., Schofield, O., Ducklow, H. W., & Sullivan, M. B. (2017). Seasonal time bombs: dominant temperate viruses affect Southern Ocean microbial dynamics. The ISME journal, 11(2), 588.
- Eizenga, G. C., Sanchez, P. L., Jackson, A. K., Edwards, J. D., Hurwitz, B. L., Wing, R. A., & Kudrna, D. (2017). Genetic variation for domestication-related traits revealed in a cultivated rice, Nipponbare (Oryza sativa ssp. japonica) x ancestral rice, O-nivara, mapping population. MOLECULAR BREEDING, 37(11).
- 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.More infoA 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.
- Watts, G. S., Youens-Clark, K., Slepian, M. J., Wolk, D. M., Oshiro, M. M., Metzger, G. S., Dhingra, D., Cranmer, L. D., & Hurwitz, B. L. (2017). 16S rRNA gene sequencing on a benchtop sequencer: accuracy for identification of clinically important bacteria. Journal of applied microbiology, 123(6), 1584-1596.More infoTest the choice of 16S rRNA gene amplicon and data analysis method on the accuracy of identification of clinically important bacteria utilizing a benchtop sequencer.
- Bolduc, B., Youens-Clark, K., Roux, S., Hurwitz, B. L., & Sullivan, M. B. (2017). iVirus: facilitating new insights in viral ecology with software and community data sets imbedded in a cyberinfrastructure. The ISME journal, 11(1), 7-14.More infoMicrobes affect nutrient and energy transformations throughout the world's ecosystems, yet they do so under viral constraints. In complex communities, viral metagenome (virome) sequencing is transforming our ability to quantify viral diversity and impacts. Although some bottlenecks, for example, few reference genomes and nonquantitative viromics, have been overcome, the void of centralized data sets and specialized tools now prevents viromics from being broadly applied to answer fundamental ecological questions. Here we present iVirus, a community resource that leverages the CyVerse cyberinfrastructure to provide access to viromic tools and data sets. The iVirus Data Commons contains both raw and processed data from 1866 samples and 73 projects derived from global ocean expeditions, as well as existing and legacy public repositories. Through the CyVerse Discovery Environment, users can interrogate these data sets using existing analytical tools (software applications known as 'Apps') for assembly, open reading frame prediction and annotation, as well as several new Apps specifically developed for analyzing viromes. Because Apps are web based and powered by CyVerse supercomputing resources, they enable scalable analyses for a broad user base. Finally, a use-case scenario documents how to apply these advances toward new data. This growing iVirus resource should help researchers utilize viromics as yet another tool to elucidate viral roles in nature.
- Brum, J. R., Hurwitz, B. L., Schofield, O., Ducklow, H. W., & Sullivan, M. B. (2016). Seasonal time bombs: dominant temperate viruses affect Southern Ocean microbial dynamics. The ISME journal, 10(2), 437-49.More infoRapid warming in the highly productive western Antarctic Peninsula (WAP) region of the Southern Ocean has affected multiple trophic levels, yet viral influences on microbial processes and ecosystem function remain understudied in the Southern Ocean. Here we use cultivation-independent quantitative ecological and metagenomic assays, combined with new comparative bioinformatic techniques, to investigate double-stranded DNA viruses during the WAP spring-summer transition. This study demonstrates that (i) temperate viruses dominate this region, switching from lysogeny to lytic replication as bacterial production increases, and (ii) Southern Ocean viral assemblages are genetically distinct from lower-latitude assemblages, primarily driven by this temperate viral dominance. This new information suggests fundamentally different virus-host interactions in polar environments, where intense seasonal changes in bacterial production select for temperate viruses because of increased fitness imparted by the ability to switch replication strategies in response to resource availability. Further, temperate viral dominance may provide mechanisms (for example, bacterial mortality resulting from prophage induction) that help explain observed temporal delays between, and lower ratios of, bacterial and primary production in polar versus lower-latitude marine ecosystems. Together these results suggest that temperate virus-host interactions are critical to predicting changes in microbial dynamics brought on by warming in polar marine systems.
- Hurwitz, B. L., & U'Ren, J. M. (2016). Viral metabolic reprogramming in marine ecosystems. Current opinion in microbiology, 31, 161-8.More infoMarine viruses often contain host-derived metabolic genes (i.e., auxiliary metabolic genes; AMGs), which are hypothesized to increase viral replication by augmenting key steps in host metabolism. Currently described AMGs encompass a wide variety of metabolic functions, including amino acid and carbohydrate metabolism, energy production, and iron-sulfur cluster assembly and modification, and their community-wide gene content and abundance vary as a function of environmental conditions. Here, we describe different AMGs classes, their hypothesized role in redirecting host carbon metabolism, and their ecological importance. Focusing on metagenomic ocean surveys, we propose a new model where a suite of phage-encoded genes activate host pathways that respond rapidly to environmental cues, presumably resulting in rapid changes to host metabolic flux for phage production.
- Hurwitz, B. L., U'Ren, J. M., & Youens-Clark, K. (2016). Computational prospecting the great viral unknown. FEMS microbiology letters, 363(10).More infoBacteriophages 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.
- Kindler, L., Stoliartchouk, A., Teytelman, L., & Hurwitz, B. L. (2016). Method-centered digital communities on protocols.io for fast-paced scientific innovation. F1000Research, 5, 2271.More infoThe Internet has enabled online social interaction for scientists beyond physical meetings and conferences. Yet despite these innovations in communication, dissemination of methods is often relegated to just academic publishing. Further, these methods remain static, with subsequent advances published elsewhere and unlinked. For communities undergoing fast-paced innovation, researchers need new capabilities to share, obtain feedback, and publish methods at the forefront of scientific development. For example, a renaissance in virology is now underway given the new metagenomic methods to sequence viral DNA directly from an environment. Metagenomics makes it possible to "see" natural viral communities that could not be previously studied through culturing methods. Yet, the knowledge of specialized techniques for the production and analysis of viral metagenomes remains in a subset of labs. This problem is common to any community using and developing emerging technologies and techniques. We developed new capabilities to create virtual communities in protocols.io, an open access platform, for disseminating protocols and knowledge at the forefront of scientific development. To demonstrate these capabilities, we present a virology community forum called VERVENet. These new features allow virology researchers to share protocols and their annotations and optimizations, connect with the broader virtual community to share knowledge, job postings, conference announcements through a common online forum, and discover the current literature through personalized recommendations to promote discussion of cutting edge research. Virtual communities in protocols.io enhance a researcher's ability to: discuss and share protocols, connect with fellow community members, and learn about new and innovative research in the field. The web-based software for developing virtual communities is free to use on protocols.io. Data are available through public APIs at protocols.io.
- Moffarah, A. S., Al Mohajer, M., Hurwitz, B. L., & Armstrong, D. G. (2016). Skin and Soft Tissue Infections. Microbiology spectrum, 4(4).More infoThe skin is colonized by a diverse collection of microorganisms which, for the most part, peacefully coexist with their hosts. Skin and soft tissue infections (SSTIs) encompass a variety of conditions; in immunocompromised hosts, SSTIs can be caused by diverse microorganisms-most commonly bacteria, but also fungi, viruses, mycobacteria, and protozoa. The diagnosis of SSTIs is difficult because they may commonly masquerade as other clinical syndromes or can be a manifestation of systemic disease. In immunocompromised hosts, SSTI poses a major diagnostic challenge, and clinical dermatological assessment should be initially performed; to better identify the pathogen and to lead to appropriate treatment, etiology should include cultures of lesions and blood, biopsy with histology, specific microbiological analysis with special stains, molecular techniques, and antigen-detection methodologies. Here, we reviewed the epidemiology, pathophysiology, clinical presentation, and diagnostic techniques, including molecular biological techniques, used for SSTIs, with a focus on the immunocompromised host, such as patients with cellular immunodeficiency, HIV, and diabetic foot infection.
- Teytelman, L., Stoliartchouk, A., Kindler, L., & Hurwitz, B. L. (2016). Protocols.io: Virtual Communities for Protocol Development and Discussion. PLoS biology, 14(8), e1002538.More infoThe detailed know-how to implement research protocols frequently remains restricted to the research group that developed the method or technology. This knowledge often exists at a level that is too detailed for inclusion in the methods section of scientific articles. Consequently, methods are not easily reproduced, leading to a loss of time and effort by other researchers. The challenge is to develop a method-centered collaborative platform to connect with fellow researchers and discover state-of-the-art knowledge. Protocols.io is an open-access platform for detailing, sharing, and discussing molecular and computational protocols that can be useful before, during, and after publication of research results.
- Armstrong, D. G., Hurwitz, B. L., & Lipsky, B. A. (2015). Set Phages to Stun: Reducing the Virulence of Staphylococcus aureus in Diabetic Foot Ulcers. Diabetes, 64(8), 2701-3.
- Armstrong, D. G., Lew, E. J., Hurwitz, B. L., & Wild, T. (2015). The quest for tissue repair’s holy grail: the promise of wound diagnostics or just another fishing expedition?. Wound Medicine, 8, 1-5.
- Hurwitz, B. L., Brum, J. R., & Sullivan, M. B. (2015). Depth-stratified functional and taxonomic niche specialization in the 'core' and 'flexible' Pacific Ocean Virome. The ISME journal, 9(2), 472-84.More infoMicrobes drive myriad ecosystem processes, and their viruses modulate microbial-driven processes through mortality, horizontal gene transfer, and metabolic reprogramming by viral-encoded auxiliary metabolic genes (AMGs). However, our knowledge of viral roles in the oceans is primarily limited to surface waters. Here we assess the depth distribution of protein clusters (PCs) in the first large-scale quantitative viral metagenomic data set that spans much of the pelagic depth continuum (the Pacific Ocean Virome; POV). This established 'core' (180 PCs; one-third new to science) and 'flexible' (423K PCs) community gene sets, including niche-defining genes in the latter (385 and 170 PCs are exclusive and core to the photic and aphotic zones, respectively). Taxonomic annotation suggested that tailed phages are ubiquitous, but not abundant (
- Roux, S., Enault, F., Hurwitz, B. L., & Sullivan, M. B. (2015). VirSorter: mining viral signal from microbial genomic data. PeerJ, 3, e985.More infoViruses of microbes impact all ecosystems where microbes drive key energy and substrate transformations including the oceans, humans and industrial fermenters. However, despite this recognized importance, our understanding of viral diversity and impacts remains limited by too few model systems and reference genomes. One way to fill these gaps in our knowledge of viral diversity is through the detection of viral signal in microbial genomic data. While multiple approaches have been developed and applied for the detection of prophages (viral genomes integrated in a microbial genome), new types of microbial genomic data are emerging that are more fragmented and larger scale, such as Single-cell Amplified Genomes (SAGs) of uncultivated organisms or genomic fragments assembled from metagenomic sequencing. Here, we present VirSorter, a tool designed to detect viral signal in these different types of microbial sequence data in both a reference-dependent and reference-independent manner, leveraging probabilistic models and extensive virome data to maximize detection of novel viruses. Performance testing shows that VirSorter's prophage prediction capability compares to that of available prophage predictors for complete genomes, but is superior in predicting viral sequences outside of a host genome (i.e., from extrachromosomal prophages, lytic infections, or partially assembled prophages). Furthermore, VirSorter outperforms existing tools for fragmented genomic and metagenomic datasets, and can identify viral signal in assembled sequence (contigs) as short as 3kb, while providing near-perfect identification (>95% Recall and 100% Precision) on contigs of at least 10kb. Because VirSorter scales to large datasets, it can also be used in "reverse" to more confidently identify viral sequence in viral metagenomes by sorting away cellular DNA whether derived from gene transfer agents, generalized transduction or contamination. Finally, VirSorter is made available through the iPlant Cyberinfrastructure that provides a web-based user interface interconnected with the required computing resources. VirSorter thus complements existing prophage prediction softwares to better leverage fragmented, SAG and metagenomic datasets in a way that will scale to modern sequencing. Given these features, VirSorter should enable the discovery of new viruses in microbial datasets, and further our understanding of uncultivated viral communities across diverse ecosystems.
- Spichler, A., Hurwitz, B. L., Armstrong, D. G., & Lipsky, B. A. (2015). Microbiology of diabetic foot infections: from Louis Pasteur to 'crime scene investigation'. BMC medicine, 13, 2.More infoWere he alive today, would Louis Pasteur still champion culture methods he pioneered over 150 years ago for identifying bacterial pathogens? Or, might he suggest that new molecular techniques may prove a better way forward for quickly detecting the true microbial diversity of wounds? As modern clinicians faced with treating complex patients with diabetic foot infections (DFI), should we still request venerated and familiar culture and sensitivity methods, or is it time to ask for newer molecular tests, such as 16S rRNA gene sequencing? Or, are molecular techniques as yet too experimental, non-specific and expensive for current clinical use? While molecular techniques help us to identify more microorganisms from a DFI, can they tell us 'who done it?', that is, which are the causative pathogens and which are merely colonizers? Furthermore, can molecular techniques provide clinically relevant, rapid information on the virulence of wound isolates and their antibiotic sensitivities? We herein review current knowledge on the microbiology of DFI, from standard culture methods to the current era of rapid and comprehensive 'crime scene investigation' (CSI) techniques.
- U'Ren, J. M., Wisecaver, J. H., Paek, A. L., Dunn, B. L., & Hurwitz, B. L. (2015). Draft Genome Sequence of the Ale-Fermenting Saccharomyces cerevisiae Strain GSY2239. Genome announcements, 3(4).More infoSaccharomyces cerevisiae strain GSY2239 is derived from an industrial yeast strain used to ferment ale-style beer. We present here the 11.5-Mb draft genome sequence for this organism.
- Hurwitz, B. L., Westveld, A. H., Brum, J. R., & Sullivan, M. B. (2014). Modeling ecological drivers in marine viral communities using comparative metagenomics and network analyses. Proceedings of the National Academy of Sciences of the United States of America, 111(29), 10714-9.More infoLong-standing questions in marine viral ecology are centered on understanding how viral assemblages change along gradients in space and time. However, investigating these fundamental ecological questions has been challenging due to incomplete representation of naturally occurring viral diversity in single gene- or morphology-based studies and an inability to identify up to 90% of reads in viral metagenomes (viromes). Although protein clustering techniques provide a significant advance by helping organize this unknown metagenomic sequence space, they typically use only ∼75% of the data and rely on assembly methods not yet tuned for naturally occurring sequence variation. Here, we introduce an annotation- and assembly-free strategy for comparative metagenomics that combines shared k-mer and social network analyses (regression modeling). This robust statistical framework enables visualization of complex sample networks and determination of ecological factors driving community structure. Application to 32 viromes from the Pacific Ocean Virome dataset identified clusters of samples broadly delineated by photic zone and revealed that geographic region, depth, and proximity to shore were significant predictors of community structure. Within subsets of this dataset, depth, season, and oxygen concentration were significant drivers of viral community structure at a single open ocean station, whereas variability along onshore-offshore transects was driven by oxygen concentration in an area with an oxygen minimum zone and not depth or proximity to shore, as might be expected. Together these results demonstrate that this highly scalable approach using complete metagenomic network-based comparisons can both test and generate hypotheses for ecological investigation of viral and microbial communities in nature.
- Rankin, T. M., Giovinco, N. A., Cucher, D. J., Watts, G., Hurwitz, B., & Armstrong, D. G. (2014). Three-dimensional printing surgical instruments: are we there yet?. The Journal of surgical research, 189(2), 193-7.More infoThe applications for rapid prototyping have expanded dramatically over the last 20 y. In recent years, additive manufacturing has been intensely investigated for surgical implants, tissue scaffolds, and organs. There is, however, scant literature to date that has investigated the viability of three-dimensional (3D) printing of surgical instruments.
- Hurwitz, B. L., & Sullivan, M. B. (2013). The Pacific Ocean virome (POV): a marine viral metagenomic dataset and associated protein clusters for quantitative viral ecology. PloS one, 8(2), e57355.More infoBacteria and their viruses (phage) are fundamental drivers of many ecosystem processes including global biogeochemistry and horizontal gene transfer. While databases and resources for studying function in uncultured bacterial communities are relatively advanced, many fewer exist for their viral counterparts. The issue is largely technical in that the majority (often 90%) of viral sequences are functionally 'unknown' making viruses a virtually untapped resource of functional and physiological information. Here, we provide a community resource that organizes this unknown sequence space into 27 K high confidence protein clusters using 32 viral metagenomes from four biogeographic regions in the Pacific Ocean that vary by season, depth, and proximity to land, and include some of the first deep pelagic ocean viral metagenomes. These protein clusters more than double currently available viral protein clusters, including those from environmental datasets. Further, a protein cluster guided analysis of functional diversity revealed that richness decreased (i) from deep to surface waters, (ii) from winter to summer, (iii) and with distance from shore in surface waters only. These data provide a framework from which to draw on for future metadata-enabled functional inquiries of the vast viral unknown.
- 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.More infoViruses 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., Hallam, S. J., & Sullivan, M. B. (2013). Metabolic reprogramming by viruses in the sunlit and dark ocean. Genome biology, 14(11), R123.More infoMarine ecosystem function is largely determined by matter and energy transformations mediated by microbial community interaction networks. Viral infection modulates network properties through mortality, gene transfer and metabolic reprogramming.
- Degnan, P. H., Leonardo, T. E., Cass, B. N., Hurwitz, B., Stern, D., Gibbs, R. A., Richards, S., & Moran, N. A. (2010). Dynamics of genome evolution in facultative symbionts of aphids. Environmental microbiology, 12(8), 2060-9.More infoAphids are sap-feeding insects that host a range of bacterial endosymbionts including the obligate, nutritional mutualist Buchnera plus several bacteria that are not required for host survival. Among the latter, 'Candidatus Regiella insecticola' and 'Candidatus Hamiltonella defensa' are found in pea aphids and other hosts and have been shown to protect aphids from natural enemies. We have sequenced almost the entire genome of R. insecticola (2.07 Mbp) and compared it with the recently published genome of H. defensa (2.11 Mbp). Despite being sister species the two genomes are highly rearranged and the genomes only have ∼55% of genes in common. The functions encoded by the shared genes imply that the bacteria have similar metabolic capabilities, including only two essential amino acid biosynthetic pathways and active uptake mechanisms for the remaining eight, and similar capacities for host cell toxicity and invasion (type 3 secretion systems and RTX toxins). These observations, combined with high sequence divergence of orthologues, strongly suggest an ancient divergence after establishment of a symbiotic lifestyle. The divergence in gene sets and in genome architecture implies a history of rampant recombination and gene inactivation and the ongoing integration of mobile DNA (insertion sequence elements, prophage and plasmids).
- 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.More infoRapid 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.
- Myles, S., Chia, J., Hurwitz, B., Simon, C., Zhong, G. Y., Buckler, E., & Ware, D. (2010). Rapid genomic characterization of the genus vitis. PloS one, 5(1), e8219.More infoNext-generation sequencing technologies promise to dramatically accelerate the use of genetic information for crop improvement by facilitating the genetic mapping of agriculturally important phenotypes. The first step in optimizing the design of genetic mapping studies involves large-scale polymorphism discovery and a subsequent genome-wide assessment of the population structure and pattern of linkage disequilibrium (LD) in the species of interest. In the present study, we provide such an assessment for the grapevine (genus Vitis), the world's most economically important fruit crop. Reduced representation libraries (RRLs) from 17 grape DNA samples (10 cultivated V. vinifera and 7 wild Vitis species) were sequenced with sequencing-by-synthesis technology. We developed heuristic approaches for SNP calling, identified hundreds of thousands of SNPs and validated a subset of these SNPs on a 9K genotyping array. We demonstrate that the 9K SNP array provides sufficient resolution to distinguish among V. vinifera cultivars, between V. vinifera and wild Vitis species, and even among diverse wild Vitis species. We show that there is substantial sharing of polymorphism between V. vinifera and wild Vitis species and find that genetic relationships among V. vinifera cultivars agree well with their proposed geographic origins using principal components analysis (PCA). Levels of LD in the domesticated grapevine are low even at short ranges, but LD persists above background levels to 3 kb. While genotyping arrays are useful for assessing population structure and the decay of LD across large numbers of samples, we suggest that whole-genome sequencing will become the genotyping method of choice for genome-wide genetic mapping studies in high-diversity plant species. This study demonstrates that we can move quickly towards genome-wide studies of crop species using next-generation sequencing. Our study sets the stage for future work in other high diversity crop species, and provides a significant enhancement to current genetic resources available to the grapevine genetic community.
- Cranston, K. A., Hurwitz, B., Ware, D., Stein, L., & Wing, R. A. (2009). Species trees from highly incongruent gene trees in rice. Systematic biology, 58(5), 489-500.More infoSeveral methods have recently been developed to infer multilocus phylogenies by incorporating information from topological incongruence of the individual genes. In this study, we investigate 2 such methods, Bayesian concordance analysis and Bayesian estimation of species trees. Our test data are a collection of genes from cultivated rice (genus Oryza) and the most closely related wild species, generated using a high-throughput sequencing protocol and bioinformatics pipeline. Trees inferred from independent genes display levels of topological incongruence that far exceed that seen in previous data sets analyzed with these species tree methods. We identify differences in phylogenetic results between inference methods that incorporate gene tree incongruence. Finally, we discuss the challenges of scaling these analyses for data sets with thousands of gene trees and extensive levels of missing data.
- 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.More infoMaize 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.
- Kim, H., Hurwitz, B., Yu, Y., Collura, K., Gill, N., SanMiguel, P., Mullikin, J. C., Maher, C., Nelson, W., Wissotski, M., Braidotti, M., Kudrna, D., Goicoechea, J. L., Stein, L., Ware, D., Jackson, S. A., Soderlund, C., & Wing, R. A. (2008). Construction, alignment and analysis of twelve framework physical maps that represent the ten genome types of the genus Oryza. Genome biology, 9(2), R45.More infoWe describe the establishment and analysis of a genus-wide comparative framework composed of 12 bacterial artificial chromosome fingerprint and end-sequenced physical maps representing the 10 genome types of Oryza aligned to the O. sativa ssp. japonica reference genome sequence. Over 932 Mb of end sequence was analyzed for repeats, simple sequence repeats, miRNA and single nucleotide variations, providing the most extensive analysis of Oryza sequence to date.
- Liang, C., Jaiswal, P., Hebbard, C., Avraham, S., Buckler, E. S., Casstevens, T., Hurwitz, B., McCouch, S., Ni, J., Pujar, A., Ravenscroft, D., Ren, L., Spooner, W., Tecle, I., Thomason, J., Tung, C., Wei, X., Yap, I., Youens-Clark, K., , Ware, D., et al. (2008). Gramene: a growing plant comparative genomics resource. Nucleic acids research, 36(Database issue), D947-53.More infoGramene (www.gramene.org) is a curated resource for genetic, genomic and comparative genomics data for the major crop species, including rice, maize, wheat and many other plant (mainly grass) species. Gramene is an open-source project. All data and software are freely downloadable through the ftp site (ftp.gramene.org/pub/gramene) and available for use without restriction. Gramene's core data types include genome assembly and annotations, other DNA/mRNA sequences, genetic and physical maps/markers, genes, quantitative trait loci (QTLs), proteins, ontologies, literature and comparative mappings. Since our last NAR publication 2 years ago, we have updated these data types to include new datasets and new connections among them. Completely new features include rice pathways for functional annotation of rice genes; genetic diversity data from rice, maize and wheat to show genetic variations among different germplasms; large-scale genome comparisons among Oryza sativa and its wild relatives for evolutionary studies; and the creation of orthologous gene sets and phylogenetic trees among rice, Arabidopsis thaliana, maize, poplar and several animal species (for reference purpose). We have significantly improved the web interface in order to provide a more user-friendly browsing experience, including a dropdown navigation menu system, unified web page for markers, genes, QTLs and proteins, and enhanced quick search functions.
- Hass-Jacobus, B. L., Futrell-Griggs, M., Abernathy, B., Westerman, R., Goicoechea, J., Stein, J., Klein, P., Hurwitz, B., Zhou, B., Rakhshan, F., Sanyal, A., Gill, N., Lin, J., Walling, J. G., Luo, M. Z., Ammiraju, J. S., Kudrna, D., Kim, H. R., Ware, D., , Wing, R. A., et al. (2006). Integration of hybridization-based markers (overgos) into physical maps for comparative and evolutionary explorations in the genus Oryza and in Sorghum. BMC genomics, 7, 199.More infoWith the completion of the genome sequence for rice (Oryza sativa L.), the focus of rice genomics research has shifted to the comparison of the rice genome with genomes of other species for gene cloning, breeding, and evolutionary studies. The genus Oryza includes 23 species that shared a common ancestor 8-10 million years ago making this an ideal model for investigations into the processes underlying domestication, as many of the Oryza species are still undergoing domestication. This study integrates high-throughput, hybridization-based markers with BAC end sequence and fingerprint data to construct physical maps of rice chromosome 1 orthologues in two wild Oryza species. Similar studies were undertaken in Sorghum bicolor, a species which diverged from cultivated rice 40-50 million years ago.
- Choi, I., Ponsero, A., Youens-Clark, K., Bomhoff, M., Hurwitz, B. L., & Hartman, J. H. (2018, Spring). Libra: Improved Partitioning Strategies for Massive Comparative Metagenomics Analysis. In 9th Workshop on Scientific Cloud Computing.
- Hurwitz, B. L. (2016, January). iMicrobe: a Cyberinfrastrucutre for Microbial Ecology. Plant Animal Genome. San Diego, CA: PAG.
- Hurwitz, B. L. (2015, Fall). The New Molecular Microbiology: Impact on Understanding of Diabetic Foot Infections. ASM ICAAC. San Diego, CA: American Society for Microbiology.
- Hurwitz, B. L. (2015, February). Big Data for Viral Ecology. American Society for Limnology and Oceanography Conference. Granada, Spain: American Society for Limnology and Oceanography.
- Hurwitz, B. L. (2015, January). One Health Meet Big Data Analytics. Vet Sci Advisory Committee for Dean Burgess.
- Hurwitz, B. L. (2015, May). The Microbiome Revolution: new tools, new diabetic foot ulcer concepts?. 7th International Symposium on the Diabetic Foot. The Hague, Netherlands: International Symposium on the Diabetic Foot.
- Hurwitz, B. L. (2014, January). iMicrobe: Advancing Clinical and Environmental Microbial Research using the iPlant Cyberinfrastructure. Plant Animal Genome. San Diego, CA: PAG.
- Hurwitz, B. L. (2014, March). Wound Infection: Moving from Pasteur to CSI; and Panelist: Infection, DemisToephi’d. DFCon. Los Angeles, CA: DFCon Limb Salvage.
- Hurwitz, B. L. (2014, November). Metagenomics Meets Big Data Analytics. BME Departmental Seminar.
- Hurwitz, B. L. (2014, October). The Battle of Infection: Implant related biofilm, can we define it, treat it, and prevent it?. Diabetic Limb Salvage Conference. Washington, DC: Diabetic Limb Salvage Conference.
- Hurwitz, B. L. (2014, October). iMicrobe: A Cyberinfrastructure for Microbial Ecology. UA Plant Sciences Departmental Seminar.
- Hurwitz, B. L. (2014, October). Biological Research Innovation through Dynamic Graph Engineering. Biological Data Science. Cold Spring Harbor, NY.