Metabolism is a vital cellular process, and its malfunction is a major contributor to human disease. Metabolic networks are complex and highly interconnected, and thus systems-level computational approaches are required to elucidate and understand metabolic genotype-phenotype relationships. We have manually reconstructed the global human metabolic network based on Build 35 of the genome annotation and a comprehensive evaluation of >50 years of legacy data (i.e., bibliomic data). Herein we describe the reconstruction process and demonstrate how the resulting genome-scale (or global) network can be used (i) for the discovery of missing information, (ii) for the formulation of an in silico model, and (iii) as a structured context for analyzing high-throughput biological data sets. Our comprehensive evaluation of the literature revealed many gaps in the current understanding of human metabolism that require future experimental investigation. Mathematical analysis of network structure elucidated the implications of intracellular compartmentalization and the potential use of correlated reaction sets for alternative drug target identification. Integrated analysis of high-throughput data sets within the context of the reconstruction enabled a global assessment of functional metabolic states. These results highlight some of the applications enabled by the reconstructed human metabolic network. The establishment of this network represents an important step toward genome-scale human systems biology.constraint based ͉ metabolism ͉ model ͉ systems biology A n individual's metabolism is determined by one's genetics, environment, and nutrition. With the available human genome sequence and its annotation (1-3), we can hope to define the human body's complement of metabolic enzymes. In addition, numerous metabolic genes and enzymes have been individually studied for decades, resulting in a collective knowledge base, or ''bibliome,'' that includes reaction mechanisms and well characterized interactions. Manual component-bycomponent (bottom-up) reconstruction of genomic and bibliomic data leads to a biochemically, genetically, and genomically structured (BiGG) reconstruction (4) that can be mathematically represented as an in silico model for computing allowable network states under governing chemical and genetic constraints (5). The procedure for integrating these diverse data types to form a network reconstruction and predictive model is well established for microorganisms (4) and has recently been applied to mouse hybridomas (6). Such in silico models have enabled hypothesis-driven biology, including the prediction of the outcome of adaptive evolution (7-11) and the identification and discovery of candidates for missing metabolic functions that were subsequently experimentally verified (12). Because metabolic networks are more complex in mammals than in singlecelled organisms, there is likely to be an even greater opportunity for the use of computational models to understand the basis of normal and abnormal cellular function.He...

An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR) Diverse datasets, including genomic, transcriptomic, proteomic and metabolomic data, are becoming readily available for specific organisms. There is currently a need to integrate these datasets within an in silico modeling framework. Constraint-based models of Escherichia coli K-12 MG1655 have been developed and used to study the bacterium's metabolism and phenotypic behavior. The most comprehensive E. coli model to date (E. coli iJE660a GSM) accounts for 660 genes and includes 627 unique biochemical reactions. AbstractBackground: Diverse datasets, including genomic, transcriptomic, proteomic and metabolomic data, are becoming readily available for specific organisms. There is currently a need to integrate these datasets within an in silico modeling framework. Constraint-based models of Escherichia coli K-12 MG1655 have been developed and used to study the bacterium's metabolism and phenotypic behavior. The most comprehensive E. coli model to date (E. coli iJE660a GSM) accounts for 660 genes and includes 627 unique biochemical reactions.

Zinc-finger nucleases (ZFNs) drive efficient genome editing by introducing a double-strand break into the targeted gene. Cleavage is induced when two custom-designed ZFNs heterodimerize upon binding DNA to form a catalytically active nuclease complex. The importance of this dimerization event for subsequent cleavage activity has stimulated efforts to engineer the nuclease interface to prevent undesired homodimerization. Here we report the development and application of a yeast-based selection system designed to functionally interrogate the ZFN dimer interface. We identified critical residues involved in dimerization through the isolation of cold-sensitive nuclease domains. We used these residues to engineer ZFNs that have superior cleavage activity while suppressing homodimerization. The improvements were portable to orthogonal domains, allowing the concomitant and independent cleavage of two loci using two different ZFN pairs. These ZFN architectures provide a general means for obtaining highly efficient and specific genome modification.

Clathrin-mediated endocytosis (CME) is the best-studied pathway by which cells selectively internalize molecules from the plasma membrane and surrounding environment. Previous live-cell imaging studies using ectopically overexpressed fluorescent fusions of endocytic proteins indicated that mammalian CME is a highly dynamic but inefficient and heterogeneous process. In contrast, studies of endocytosis in budding yeast using fluorescent protein fusions expressed at physiological levels from native genomic loci have revealed a process that is very regular and efficient. To analyse endocytic dynamics in mammalian cells in which endogenous protein stoichiometry is preserved, we targeted zinc finger nucleases (ZFNs) to the clathrin light chain A and dynamin-2 genomic loci and generated cell lines expressing fluorescent protein fusions from each locus. The genome-edited cells exhibited enhanced endocytic function, dynamics and efficiency when compared with previously studied cells, indicating that CME is highly sensitive to the levels of its protein components. Our study establishes that ZFN-mediated genome editing is a robust tool for expressing protein fusions at endogenous levels to faithfully report subcellular localization and dynamics.

Reactivation of fetal hemoglobin (HbF) in adults ameliorates the severity of the common β-globin disorders. The transcription factor BCL11A is a critical modulator of hemoglobin switching and HbF silencing, yet the molecular mechanism through which BCL11A coordinates the developmental switch is incompletely understood. Particularly, the identities of BCL11A cooperating protein complexes and their roles in HbF expression and erythroid development remain largely unknown. Here we determine the interacting partner proteins of BCL11A in erythroid cells by a proteomic screen. BCL11A is found within multiprotein complexes consisting of erythroid transcription factors, transcriptional corepressors, and chromatinmodifying enzymes. We show that the lysine-specific demethylase 1 and repressor element-1 silencing transcription factor corepressor 1 (LSD1/CoREST) histone demethylase complex interacts with BCL11A and is required for full developmental silencing of mouse embryonic β-like globin genes and human γ-globin genes in adult erythroid cells in vivo. In addition, LSD1 is essential for normal erythroid development. Furthermore, the DNA methyltransferase 1 (DNMT1) is identified as a BCL11A-associated protein in the proteomic screen. DNMT1 is required to maintain HbF silencing in primary human adult erythroid cells. DNMT1 haploinsufficiency combined with BCL11A deficiency further enhances γ-globin expression in adult animals. Our findings provide important insights into the mechanistic roles of BCL11A in HbF silencing and clues for therapeutic targeting of BCL11A in β-hemoglobinopathies.gene regulation | globin switching | hematopoiesis