Nature Biotechnology

High-throughput generation, optimization and analysis of genome-scale metabolic models.

Publication Date: 2010 Aug 29 PMID: 20802497
Authors: Henry, C. S. - Dejongh, M. - Best, A. A. - Frybarger, P. M. - Linsay, B. - Stevens, R. L.
Journal: Nat Biotechnol

Genome-scale metabolic models have proven to be valuable for predicting organism phenotypes from genotypes. Yet efforts to develop new models are failing to keep pace with genome sequencing. To address this problem, we introduce the Model SEED, a web-based resource for high-throughput generation, optimization and analysis of genome-scale metabolic models. The Model SEED integrates existing methods and introduces techniques to automate nearly every step of this process, taking approximately 48 h to reconstruct a metabolic model from an assembled genome sequence. We apply this resource to generate 130 genome-scale metabolic models representing a taxonomically diverse set of bacteria. Twenty-two of the models were validated against available gene essentiality and Biolog data, with the average model accuracy determined to be 66% before optimization and 87% after optimization.

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De novo identification and biophysical characterization of transcription-factor binding sites with microfluidic affinity analysis.

Publication Date: 2010 Aug 29 PMID: 20802496
Authors: Fordyce, P. M. - Gerber, D. - Tran, D. - Zheng, J. - Li, H. - Derisi, J. L. - Quake, S. R.
Journal: Nat Biotechnol

Gene expression is regulated in part by protein transcription factors that bind target regulatory DNA sequences. Predicting DNA binding sites and affinities from transcription factor sequence or structure is difficult; therefore, experimental data are required to link transcription factors to target sequences. We present a microfluidics-based approach for de novo discovery and quantitative biophysical characterization of DNA target sequences. We validated our technique by measuring sequence preferences for 28 Saccharomyces cerevisiae transcription factors with a variety of DNA-binding domains, including several that have proven difficult to study by other techniques. For each transcription factor, we measured relative binding affinities to oligonucleotides covering all possible 8-bp DNA sequences to create a comprehensive map of sequence preferences; for four transcription factors, we also determined absolute affinities. We expect that these data and future use of this technique will provide information essential for understanding transcription factor specificity, improving identification of regulatory sites and reconstructing regulatory interactions.

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Monoclonal antibodies isolated without screening by analyzing the variable-gene repertoire of plasma cells.

Publication Date: 2010 Aug 29 PMID: 20802495
Authors: Reddy, S. T. - Ge, X. - Miklos, A. E. - Hughes, R. A. - Kang, S. H. - Hoi, K. H. - Chrysostomou, C. - Hunicke-Smith, S. P. - Iverson, B. L. - Tucker, P. W. - Ellington, A. D. - Georgiou, G.
Journal: Nat Biotechnol

Isolation of antigen-specific monoclonal antibodies (mAbs) and antibody fragments relies on high-throughput screening of immortalized B cells or recombinant antibody libraries. We bypassed the screening step by using high-throughput DNA sequencing and bioinformatic analysis to mine antibody variable region (V)-gene repertoires from bone marrow plasma cells (BMPC) of immunized mice. BMPCs, which cannot be immortalized, produce the vast majority of circulating antibodies. We found that the V-gene repertoire of BMPCs becomes highly polarized after immunization, with the most abundant sequences represented at frequencies between approximately 1% and >10% of the total repertoire. We paired the most abundant variable heavy (V(H)) and variable light (V(L)) genes based on their relative frequencies, reconstructed them using automated gene synthesis, and expressed recombinant antibodies in bacteria or mammalian cells. Antibodies generated in this manner from six mice, each immunized with one of three antigens were overwhelmingly antigen specific (21/27 or 78%). Those generated from a mouse with high serum titers had nanomolar binding affinities.

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Draft genome sequence of the oilseed species Ricinus communis.

Publication Date: 2010 Aug 22 PMID: 20729833
Authors: Chan, A. P. - Crabtree, J. - Zhao, Q. - Lorenzi, H. - Orvis, J. - Puiu, D. - Melake-Berhan, A. - Jones, K. M. - Redman, J. - Chen, G. - Cahoon, E. B. - Gedil, M. - Stanke, M. - Haas, B. J. - Wortman, J. R. - Fraser-Liggett, C. M. - Ravel, J. - Rabinowicz, P. D.
Journal: Nat Biotechnol

Castor bean (Ricinus communis) is an oilseed crop that belongs to the spurge (Euphorbiaceae) family, which comprises approximately 6,300 species that include cassava (Manihot esculenta), rubber tree (Hevea brasiliensis) and physic nut (Jatropha curcas). It is primarily of economic interest as a source of castor oil, used for the production of high-quality lubricants because of its high proportion of the unusual fatty acid ricinoleic acid. However, castor bean genomics is also relevant to biosecurity as the seeds contain high levels of ricin, a highly toxic, ribosome-inactivating protein. Here we report the draft genome sequence of castor bean (4.6-fold coverage), the first for a member of the Euphorbiaceae. Whereas most of the key genes involved in oil synthesis and turnover are single copy, the number of members of the ricin gene family is larger than previously thought. Comparative genomics analysis suggests the presence of an ancient hexaploidization event that is conserved across the dicotyledonous lineage.

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Deconvolution of complex G protein-coupled receptor signaling in live cells using dynamic mass redistribution measurements.

Publication Date: 2010 Aug 15 PMID: 20711173
Authors: Schroder, R. - Janssen, N. - Schmidt, J. - Kebig, A. - Merten, N. - Hennen, S. - Muller, A. - Blattermann, S. - Mohr-Andra, M. - Zahn, S. - Wenzel, J. - Smith, N. J. - Gomeza, J. - Drewke, C. - Milligan, G. - Mohr, K. - Kostenis, E.
Journal: Nat Biotechnol

Label-free biosensor technology based on dynamic mass redistribution (DMR) of cellular constituents promises to translate GPCR signaling into complex optical 'fingerprints' in real time in living cells. Here we present a strategy to map cellular mechanisms that define label-free responses, and we compare DMR technology with traditional second-messenger assays that are currently the state of the art in GPCR drug discovery. The holistic nature of DMR measurements enabled us to (i) probe GPCR functionality along all four G-protein signaling pathways, something presently beyond reach of most other assay platforms; (ii) dissect complex GPCR signaling patterns even in primary human cells with unprecedented accuracy; (iii) define heterotrimeric G proteins as triggers for the complex optical fingerprints; and (iv) disclose previously undetected features of GPCR behavior. Our results suggest that DMR technology will have a substantial impact on systems biology and systems pharmacology as well as for the discovery of drugs with novel mechanisms.

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