Zucker, Jeremy Daniel Hofeld

Person:
Zucker, Jeremy Daniel Hofeld

Last Name

Zucker

First Name

Jeremy Daniel Hofeld

Name

Zucker, Jeremy Daniel Hofeld

Full item page

Search Results

Now showing 1 - 8 of 8

Reconstruction and Validation of a Genome-Scale Metabolic Model for the Filamentous Fungus Neurospora crassa Using FARM
(Public Library of Science, 2013) Dreyfuss, Jonathan M.; Zucker, Jeremy Daniel Hofeld; Hood, Heather M.; Ocasio, Linda R.; Sachs, Matthew S.; Galagan, James E.
The filamentous fungus Neurospora crassa played a central role in the development of twentieth-century genetics, biochemistry and molecular biology, and continues to serve as a model organism for eukaryotic biology. Here, we have reconstructed a genome-scale model of its metabolism. This model consists of 836 metabolic genes, 257 pathways, 6 cellular compartments, and is supported by extensive manual curation of 491 literature citations. To aid our reconstruction, we developed three optimization-based algorithms, which together comprise Fast Automated Reconstruction of Metabolism (FARM). These algorithms are: LInear MEtabolite Dilution Flux Balance Analysis (limed-FBA), which predicts flux while linearly accounting for metabolite dilution; One-step functional Pruning (OnePrune), which removes blocked reactions with a single compact linear program; and Consistent Reproduction Of growth/no-growth Phenotype (CROP), which reconciles differences between in silico and experimental gene essentiality faster than previous approaches. Against an independent test set of more than 300 essential/non-essential genes that were not used to train the model, the model displays 93% sensitivity and specificity. We also used the model to simulate the biochemical genetics experiments originally performed on Neurospora by comprehensively predicting nutrient rescue of essential genes and synthetic lethal interactions, and we provide detailed pathway-based mechanistic explanations of our predictions. Our model provides a reliable computational framework for the integration and interpretation of ongoing experimental efforts in Neurospora, and we anticipate that our methods will substantially reduce the manual effort required to develop high-quality genome-scale metabolic models for other organisms.
A Framework for Human Microbiome Research
(Nature Publishing Group, 2012) Methé, Barbara A.; Nelson, Karen E.; Pop, Mihai; Creasy, Heather H.; Giglio, Michelle G.; Gevers, Dirk; Petrosino, Joseph F.; Abubucker, Sahar; Badger, Jonathan H.; Chinwalla, Asif T.; Earl, Ashlee M.; Fulton, Robert S.; Hallsworth-Pepin, Kymberlie; Lobos, Elizabeth A.; Madupu, Ramana; Magrini, Vincent; Mitreva, Makedonka; Muzny, Donna M.; Sodergren, Erica J.; Versalovic, James; Wollam, Aye M.; Worley, Kim C.; Wortman, Jennifer R.; Zeng, Qiandong; Aagaard, Kjersti M.; Abolude, Olukemi O.; Allen-Vercoe, Emma; Alm, Eric J.; Alvarado, Lucia; Andersen, Gary L.; Appelbaum, Elizabeth; Arachchi, Harindra M.; Armitage, Gary; Arze, Cesar A.; Ayvaz, Tulin; Baker, Carl C.; Begg, Lisa; Belachew, Tsegahiwot; Bhonagiri, Veena; Bihan, Monika; Blaser, Martin J.; Bloom, Toby; Vivien Bonazzi, J.; Brooks, Paul; Buck, Gregory A.; Buhay, Christian J.; Busam, Dana A.; Campbell, Joseph L.; Canon, Shane R.; Cantarel, Brandi L.; Chain, Patrick S.; Chen, I-Min A.; Chen, Lei; Chhibba, Shaila; Ciulla, Dawn M.; Clemente, Jose C.; Clifton, Sandra W.; Conlan, Sean; Crabtree, Jonathan; Cutting, Mary A.; Davidovics, Noam J.; Davis, Catherine C.; DeSantis, Todd Z.; Deal, Carolyn; Delehaunty, Kimberley D.; Deych, Elena; Dooling, David J.; Dugan, Shannon P.; Farmer, Candace N.; Faust, Karoline; Feldgarden, Michael; Felix, Victor M.; Fisher, Sheila; Fodor, Anthony A.; Forney, Larry; Foster, Leslie; Di Francesco, Valentina; Friedman, Jonathan; Friedrich, Dennis C.; Fronick, Catrina C.; Fulton, Lucinda L.; Gao, Hongyu; Garcia, Nathalia; Giannoukos, Georgia; Giblin, Christina; Giovanni, Maria Y.; Goll, Johannes; Gonzalez, Antonio; Griggs, Allison; Gujja, Sharvari; Haas, Brian J.; Hamilton, Holli A.; Hepburn, Theresa A.; Herter, Brandi; Hoffmann, Diane E.; Holder, Michael E.; Howarth, Clinton; Huse, Susan M.; Jansson, Janet K.; Jiang, Huaiyang; Jordan, Catherine; Joshi, Vandita; Katancik, James A.; Keitel, Wendy A.; Kelley, Scott T.; Kells, Cristyn; Kinder-Haake, Susan; King, Nicholas B.; Knight, Rob; Kong, Heidi H.; Koren, Omry; Koren, Sergey; Kota, Karthik C.; Kovar, Christie L.; Kyrpides, Nikos C.; La Rosa, Patricio S.; Lewis, Cecil M.; Lewis, Lora; Ley, Ruth E.; Li, Kelvin; Liolios, Konstantinos; Lo, Chien-Chi; Lozupone, Catherine A.; Lunsford, R. Dwayne; Madden, Tessa; Mahurkar, Anup A.; Mannon, Peter J.; Mardis, Elaine R.; Markowitz, Victor M.; Mavrommatis, Konstantinos; McCorrison, Jamison M.; McEwen, Jean; McGuire, Amy L.; McInnes, Pamela; Mehta, Teena; Mihindukulasuriya, Kathie A.; Minx, Patrick J.; Newsham, Irene; Nusbaum, Chad; O’Laughlin, Michelle; Orvis, Joshua; Pagani, Ioanna; Palaniappan, Krishna; Patel, Shital M.; Peterson, Jane; Podar, Mircea; Pohl, Craig; Pollard, Katherine S.; Priest, Margaret E.; Proctor, Lita M.; Qin, Xiang; Raes, Jeroen; Ravel, Jacques; Reid, Jeffrey G.; Rho, Mina; Rhodes, Rosamond; Riehle, Kevin P.; Rivera, Maria C.; Rodriguez-Mueller, Beltran; Rogers, Yu-Hui; Ross, Matthew C.; Russ, Carsten; Sanka, Ravi K.; Pamela Sankar, J.; Sathirapongsasuti, Fah; Schloss, Jeffery A.; Schloss, Patrick D.; Scholz, Matthew; Schriml, Lynn; Schubert, Alyxandria M.; Segata, Nicola; Segre, Julia A.; Shannon, William D.; Sharp, Richard R.; Sharpton, Thomas J.; Shenoy, Narmada; Sheth, Nihar U.; Simone, Gina A.; Singh, Indresh; Sobel, Jack D.; Sommer, Daniel D.; Spicer, Paul; Sutton, Granger G.; Tabbaa, Diana G.; Thiagarajan, Mathangi; Tomlinson, Chad M.; Torralba, Manolito; Treangen, Todd J.; Truty, Rebecca M.; Vishnivetskaya, Tatiana A.; Walker, Jason; Wang, Zhengyuan; Ward, Doyle V.; Warren, Wesley; Watson, Mark A.; Wellington, Christopher; Wetterstrand, Kris A.; Wilczek-Boney, Katarzyna; Wu, Yuan Qing; Wylie, Kristine M.; Wylie, Todd; Yandava, Chandri; Ye, Yuzhen; Yooseph, Shibu; Youmans, Bonnie P.; Zhou, Yanjiao; Zhu, Yiming; Zoloth, Laurie; Birren, Bruce W.; Gibbs, Richard A.; Highlander, Sarah K.; Weinstock, George M.; White, Owen; Huttenhower, Curtis; FitzGerald, Michael G.; Martin, John C.; Young, Sarah K.; Anderson, Scott; Chu, Ken; Dewhirst, Floyd; Ding, Yan; Dunne, Wm. Michael; Durkin, A. Scott; Edgar, Robert C.; Erlich, R; Farrell, Ruth M.; Goldberg, Jonathan M.; Harris, Emily L.; Huang, Katherine H.; Izard, Jacques Georges; Knights, Dan; Lee, Sandra L.; Lemon, Katherine; Lennon, Niall; Liu, Bo; Liu, Yue; McDonald, Daniel; Miller, Jason R.; Pearson, Matthew; Schmidt, Thomas M.; Smillie, Chris; Sykes, Sean M.; Wang, Lu; White, James R.; Ye, Liang; Zhang, Lan; Zucker, Jeremy Daniel Hofeld; Wilson, Richard K.
A variety of microbial communities and their genes (microbiome) exist throughout the human body, playing fundamental roles in human health and disease. The NIH funded Human Microbiome Project (HMP) Consortium has established a population-scale framework which catalyzed significant development of metagenomic protocols resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 to 18 body sites up to three times, which to date, have generated 5,177 microbial taxonomic profiles from 16S rRNA genes and over 3.5 Tb of metagenomic sequence. In parallel, approximately 800 human-associated reference genomes have been sequenced. Collectively, these data represent the largest resource to date describing the abundance and variety of the human microbiome, while providing a platform for current and future studies.
Inferring Carbon Sources from Gene Expression Profiles Using Metabolic Flux Models
(Public Library of Science, 2012) Brandes, Aaron; Lun, Desmond S.; Ip, Kuhn; Zucker, Jeremy Daniel Hofeld; Colijn, Caroline; Weiner, Brian; Galagan, James E.
Background: Bacteria have evolved the ability to efficiently and resourcefully adapt to changing environments. A key means by which they optimize their use of available nutrients is through adjustments in gene expression with consequent changes in enzyme activity. We report a new method for drawing environmental inferences from gene expression data. Our method prioritizes a list of candidate carbon sources for their compatibility with a gene expression profile using the framework of flux balance analysis to model the organism’s metabolic network. Principal Findings: For each of six gene expression profiles for Escherichia coli grown under differing nutrient conditions, we applied our method to prioritize a set of eighteen different candidate carbon sources. Our method ranked the correct carbon source as one of the top three candidates for five of the six expression sets when used with a genome-scale model. The correct candidate ranked fifth in the remaining case. Additional analyses show that these rankings are robust with respect to biological and measurement variation, and depend on specific gene expression, rather than general expression level. The gene expression profiles are highly adaptive: simulated production of biomass averaged 94.84% of maximum when the in silico carbon source matched the in vitro source of the expression profile, and 65.97% when it did not. Conclusions: Inferences about a microorganism’s nutrient environment can be made by integrating gene expression data into a metabolic framework. This work demonstrates that reaction flux limits for a model can be computed which are realistic in the sense that they affect in silico growth in a manner analogous to that in which a microorganism’s alteration of gene expression is adaptive to its nutrient environment.
Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome
(Public Library of Science, 2012) Abubucker, Sahar; Goll, Johannes; Schubert, Alyxandria M.; Cantarel, Brandi L.; Rodriguez-Mueller, Beltran; Thiagarajan, Mathangi; Henrissat, Bernard; White, Owen; Kelley, Scott T.; Methé, Barbara; Schloss, Patrick D.; Gevers, Dirk; Mitreva, Makedonka; Segata, Nicola; Izard, Jacques Georges; Zucker, Jeremy Daniel Hofeld; Huttenhower, Curtis
Microbial communities carry out the majority of the biochemical activity on the planet, and they play integral roles in processes including metabolism and immune homeostasis in the human microbiome. Shotgun sequencing of such communities' metagenomes provides information complementary to organismal abundances from taxonomic markers, but the resulting data typically comprise short reads from hundreds of different organisms and are at best challenging to assemble comparably to single-organism genomes. Here, we describe an alternative approach to infer the functional and metabolic potential of a microbial community metagenome. We determined the gene families and pathways present or absent within a community, as well as their relative abundances, directly from short sequence reads. We validated this methodology using a collection of synthetic metagenomes, recovering the presence and abundance both of large pathways and of small functional modules with high accuracy. We subsequently applied this method, HUMAnN, to the microbial communities of 649 metagenomes drawn from seven primary body sites on 102 individuals as part of the Human Microbiome Project (HMP). This provided a means to compare functional diversity and organismal ecology in the human microbiome, and we determined a core of 24 ubiquitously present modules. Core pathways were often implemented by different enzyme families within different body sites, and 168 functional modules and 196 metabolic pathways varied in metagenomic abundance specifically to one or more niches within the microbiome. These included glycosaminoglycan degradation in the gut, as well as phosphate and amino acid transport linked to host phenotype (vaginal pH) in the posterior fornix. An implementation of our methodology is available at http://huttenhower.sph.harvard.edu/humann. This provides a means to accurately and efficiently characterize microbial metabolic pathways and functional modules directly from high-throughput sequencing reads, enabling the determination of community roles in the HMP cohort and in future metagenomic studies.
Comparative analysis of mycobacterium and related actinomycetes yields insight into the evolution of mycobacterium tuberculosis pathogenesis
(BioMed Central, 2012) Weiner, Brian; Raman, Sahadevan; Dolganov, Gregory; Peterson, Matthew; Riley, Robert; Abeel, Thomas; White, Jared; Sisk, Peter; Stolte, Christian; Koehrsen, Mike; Yamamoto, Robert T; Iacobelli-Martinez, Milena; Kidd, Matthew J; Maer, Andreia M; Schoolnik, Gary K; Regev, Aviv; McGuire, Abigail Manson; Park, Sang T.; Wapinski, Ilan; Zucker, Jeremy Daniel Hofeld; Galagan, James E.
Background: The sequence of the pathogen Mycobacterium tuberculosis (Mtb) strain H37Rv has been available for over a decade, but the biology of the pathogen remains poorly understood. Genome sequences from other Mtb strains and closely related bacteria present an opportunity to apply the power of comparative genomics to understand the evolution of Mtb pathogenesis. We conducted a comparative analysis using 31 genomes from the Tuberculosis Database (TBDB.org), including 8 strains of Mtb and M. bovis, 11 additional Mycobacteria, 4 Corynebacteria, 2 Streptomyces, Rhodococcus jostii RHA1, Nocardia farcinia, Acidothermus cellulolyticus, Rhodobacter sphaeroides, Propionibacterium acnes, and Bifidobacterium longum. Results: Our results highlight the functional importance of lipid metabolism and its regulation, and reveal variation between the evolutionary profiles of genes implicated in saturated and unsaturated fatty acid metabolism. It also suggests that DNA repair and molybdopterin cofactors are important in pathogenic Mycobacteria. By analyzing sequence conservation and gene expression data, we identify nearly 400 conserved noncoding regions. These include 37 predicted promoter regulatory motifs, of which 14 correspond to previously validated motifs, as well as 50 potential noncoding RNAs, of which we experimentally confirm the expression of four. Conclusions: Our analysis of protein evolution highlights gene families that are associated with the adaptation of environmental Mycobacteria to obligate pathogenesis. These families include fatty acid metabolism, DNA repair, and molybdopterin biosynthesis. Our analysis reinforces recent findings suggesting that small noncoding RNAs are more common in Mycobacteria than previously expected. Our data provide a foundation for understanding the genome and biology of Mtb in a comparative context, and are available online and through TBDB.org.
Microbial Community Function and Biomarker Discovery in the Human Microbiome
(BioMed Central, 2011) Abubucker, Sahar; Goll, Johannes; Schubert, Alyxandria M; Cantarel, Brandi L; Rodriguez-Mueller, Beltran; Thiagarajan, Mathangi; Henrissat, Bernard; White, Owen; Kelley, Scott T; Methé, Barbara; Schloss, Patrick D; Gevers, Dirk; Mitreva, Makedonka; Izard, Jacques Georges; Waldron, Levi; Zucker, Jeremy Daniel Hofeld; Garrett, Wendy; Huttenhower, Curtis; Segata, Nicola
Patterns and Implications of Gene Gain and Loss in the Evolution of Prochlorococcus
(Public Library of Science, 2007) Kettler, Gregory C; Martiny, Adam C; Coleman, Maureen L; Rodrigue, Sebastien; Lapidus, Alla; Ferriera, Steven; Steglich, Claudia; Chisholm, Sallie W; Huang, Katherine; Zucker, Jeremy Daniel Hofeld; Chen, Feng; Johnson, Justin; Church, George; Richardson, Paul
Prochlorococcus is a marine cyanobacterium that numerically dominates the mid-latitude oceans and is the smallest known oxygenic phototroph. Numerous isolates from diverse areas of the world's oceans have been studied and shown to be physiologically and genetically distinct. All isolates described thus far can be assigned to either a tightly clustered high-light (HL)-adapted clade, or a more divergent low-light (LL)-adapted group. The 16S rRNA sequences of the entire Prochlorococcus group differ by at most 3%, and the four initially published genomes revealed patterns of genetic differentiation that help explain physiological differences among the isolates. Here we describe the genomes of eight newly sequenced isolates and combine them with the first four genomes for a comprehensive analysis of the core (shared by all isolates) and flexible genes of the Prochlorococcus group, and the patterns of loss and gain of the flexible genes over the course of evolution. There are 1,273 genes that represent the core shared by all 12 genomes. They are apparently sufficient, according to metabolic reconstruction, to encode a functional cell. We describe a phylogeny for all 12 isolates by subjecting their complete proteomes to three different phylogenetic analyses. For each non-core gene, we used a maximum parsimony method to estimate which ancestor likely first acquired or lost each gene. Many of the genetic differences among isolates, especially for genes involved in outer membrane synthesis and nutrient transport, are found within the same clade. Nevertheless, we identified some genes defining HL and LL ecotypes, and clades within these broad ecotypes, helping to demonstrate the basis of HL and LL adaptations in Prochlorococcus. Furthermore, our estimates of gene gain events allow us to identify highly variable genomic islands that are not apparent through simple pairwise comparisons. These results emphasize the functional roles, especially those connected to outer membrane synthesis and transport that dominate the flexible genome and set it apart from the core. Besides identifying islands and demonstrating their role throughout the history of Prochlorococcus, reconstruction of past gene gains and losses shows that much of the variability exists at the “leaves of the tree,” between the most closely related strains. Finally, the identification of core and flexible genes from this 12-genome comparison is largely consistent with the relative frequency of Prochlorococcus genes found in global ocean metagenomic databases, further closing the gap between our understanding of these organisms in the lab and the wild.
Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production
(Public Library of Science, 2009) Colijn, Caroline; Brandes, Aaron; Zucker, Jeremy Daniel Hofeld; Lun, Desmond S.; Weiner, Brian; Farhat, Maha; Cheng, Tan-Yun; Moody, David; Murray, Megan; Galagan, James E.
Metabolism is central to cell physiology, and metabolic disturbances play a role in numerous disease states. Despite its importance, the ability to study metabolism at a global scale using genomic technologies is limited. In principle, complete genome sequences describe the range of metabolic reactions that are possible for an organism, but cannot quantitatively describe the behaviour of these reactions. We present a novel method for modeling metabolic states using whole cell measurements of gene expression. Our method, which we call E-Flux (as a combination of flux and expression), extends the technique of Flux Balance Analysis by modeling maximum flux constraints as a function of measured gene expression. In contrast to previous methods for metabolically interpreting gene expression data, E-Flux utilizes a model of the underlying metabolic network to directly predict changes in metabolic flux capacity. We applied E-Flux to Mycobacterium tuberculosis, the bacterium that causes tuberculosis (TB). Key components of mycobacterial cell walls are mycolic acids which are targets for several first-line TB drugs. We used E-Flux to predict the impact of 75 different drugs, drug combinations, and nutrient conditions on mycolic acid biosynthesis capacity in M. tuberculosis, using a public compendium of over 400 expression arrays. We tested our method using a model of mycolic acid biosynthesis as well as on a genome-scale model of M. tuberculosis metabolism. Our method correctly predicts seven of the eight known fatty acid inhibitors in this compendium and makes accurate predictions regarding the specificity of these compounds for fatty acid biosynthesis. Our method also predicts a number of additional potential modulators of TB mycolic acid biosynthesis. E-Flux thus provides a promising new approach for algorithmically predicting metabolic state from gene expression data.