Corepressor-dependent silencing of fetal hemoglobin expression by BCL11A.

Reactivation of fetal hemoglobin (HbF) in adults ameliorates the severity of the common ß-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 chromatin-modifying 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 ß-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 ß-hemoglobinopathies.

Rapid and efficient clathrin-mediated endocytosis revealed in genome-edited mammalian cells.

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.

Enhancing zinc-finger-nuclease activity with improved obligate heterodimeric architectures.

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.

Transient cold shock enhances zinc-finger nuclease-mediated gene disruption.

Zinc-finger nucleases (ZFNs) are powerful tools for editing the genomes of cell lines and model organisms. Given the breadth of their potential application, simple methods that increase ZFN activity, thus ensuring genome modification, are highly attractive. Here we show that transient hypothermia generally and robustly increased the level of stable, ZFN-induced gene disruption, thereby providing a simple technique to enhance the experimental efficacy of ZFNs.

In silico analysis of SNPs and other high-throughput data.

The availability and accessibility of high-throughput and biological legacy data have allowed mathematical analyses of genome-scale metabolic networks and models. Model formulation is centered on the conservation principles of mass and charge. Thermodynamic information is generally incorporated by means of reaction reversibility. If further experimental data are available, such as kinetic parameters, models describing system evolution over time can be developed. The type of data available largely determines the type of model (and subsequently the type of analysis) that can be performed. Different modeling approaches offer different advantages. Detailed kinetic models can make specific predictions about network functional states given knowledge about the enzyme parameter variations resulting from single-nucleotide polymorphisms (SNPs). They also require a large amount of experimental data, which is rarely available. On the other hand, although current formulations using the constraint-based optimization framework do not offer information about metabolite concentrations or time-dependent changes, it is a remarkably flexible modeling framework and permits the integration of a large amount of very different data types.

Systems analysis of energy metabolism elucidates the affected respiratory chain complex in Leigh's syndrome.

Leigh's syndrome is a complex neurological disease with little known correlation between causes and symptoms. Mutations in pyruvate dehydrogenase and electron transport chain complexes have been associated with this syndrome, although the identification of affected enzymes is difficult, if not impossible, with non-invasive clinical tests. In this study, isotopomer analysis is used to characterize the metabolic phenotype of normal and Leigh's syndrome fibroblasts (GM01503), thereby identifying affected enzymes in the diseased cells. Fibroblasts are grown with DMEM media enriched with (13)C labeled glucose. Amino acids from media and proteins as well as lactate are analyzed with GC-MS to identify their label distributions. A computational model accounting for all major pathways in fibroblast metabolism (including 430 metabolites and 508 reactions) is built to determine the metabolic steady states of the normal and Leigh's cell lines based on measured substrate uptake and secretion rates and isotopomer data. Results show that (i) Leigh's syndrome affected cells have slower metabolism than control fibroblasts as evidenced by their overall slower substrate utilization and lower secretion of end products; (ii) intracellular fluxes predicted by the models, some of which are validated by biochemical studies published in the literature, show that the respiratory chain in Leigh's affected cells can produce ATP at a similar rate as the controls, but with a more restricted flux range; and (iii) mutations causing the defects observed in the Leigh's cells are likely to be in succinate cytochrome c reductase.

Global reconstruction of the human metabolic network based on genomic and bibliomic data.

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.

Building the power house: recent advances in mitochondrial studies through proteomics and systems biology.

The emerging field of systems biology seeks to develop novel approaches to integrate heterogeneous data sources for effective analysis of complex living systems. Systemic studies of mitochondria have generated a large number of proteomic data sets in numerous species, including yeast, plant, mouse, rat, and human. Beyond component identification, mitochondrial proteomics is recognized as a powerful tool for diagnosing and characterizing complex diseases associated with these organelles. Various proteomic techniques for isolation and purification of proteins have been developed; each tailored to preserve protein properties relevant to study of a particular disease type. Examples of such techniques include immunocapture, which minimizes loss of posttranslational modification, 4-iodobutyltriphenylphosphonium labeling, which quantifies protein redox states, and surface-enhanced laser desorption ionization-time-of-flight mass spectrometry, which allows sequence-specific binding. With the rapidly increasing number of discovered molecular components, computational models are also being developed to facilitate the organization and analysis of such data. Computational models of mitochondria have been accomplished with top-down and bottom-up approaches and have been steadily improved in size and scope. Results from top-down methods tend to be more qualitative but are unbiased by prior knowledge about the system. Bottom-up methods often require the incorporation of a large amount of existing data but provide more rigorous and quantitative information, which can be used as hypotheses for subsequent experimental studies. Successes and limitations of the studies reviewed here provide opportunities and challenges that must be addressed to facilitate the application of systems biology to larger systems.

Isotopomer analysis of myocardial substrate metabolism: a systems biology approach.

The increasing accessibility of mass isotopomer data via GC-MS and NMR technology has necessitated the use of a systematic and reliable method to take advantage of such data for flux analysis. Here we applied a nonlinear, optimization-based method to study substrate metabolism in cardiomyocytes using (13)C data from perfused mouse hearts. The myocardial metabolic network used in this study accounts for 257 reactions and 240 metabolites, which are further compartmentalized into extracellular space, cytosol, and mitochondrial matrix. Analysis of the perfused mouse heart showed that the steady-state ATP production rate was 16.6 +/- 2.3 micromol/min . gww, with 30% of the ATP coming from glycolysis. Of the four substrates available in the perfusate (glucose, pyruvate, lactate, and oleate), exogenous glucose forms the majority of cytosolic pyruvate. Pyruvate decaboxylation is significantly higher than carboxylation, suggesting that anaplerosis is low in the perfused heart. Exchange fluxes were predicted to be high for reversible enzymes in the citric acid cycle (CAC), but low in the glycolytic pathway. Pseudoketogenesis amounted to approximately 50% of the net ketone body uptake. Sensitivity analysis showed that the estimated flux distributions were relatively insensitive to experimental errors. The application of isotopomer data drastically improved the estimation of reaction fluxes compared to results computed with respect to reaction stoichiometry alone. Further study of 12 commonly used (13)C glucose mixtures showed that the mixtures of 20% [U-(13)C(6)] glucose, 80% [3 (13)C] glucose and 20% [U-(13)C(6)] glucose, 80% [4 (13)C] were best for resolving fluxes in the current network.

Expanded metabolic reconstruction of Helicobacter pylori (iIT341 GSM/GPR): an in silico genome-scale characterization of single- and double-deletion mutants.

Helicobacter pylori is a human gastric pathogen infecting almost half of the world population. Herein, we present an updated version of the metabolic reconstruction of H. pylori strain 26695 based on the revised genome annotation and new experimental data. This reconstruction, iIT341 GSM/GPR, represents a detailed review of the current literature about H. pylori as it integrates biochemical and genomic data in a comprehensive framework. In total, it accounts for 341 metabolic genes, 476 intracellular reactions, 78 exchange reactions, and 485 metabolites. Novel features of iIT341 GSM/GPR include (i) gene-protein-reaction associations, (ii) elementally and charge-balanced reactions, (iii) more accurate descriptions of isoprenoid and lipopolysaccharide metabolism, and (iv) quantitative assessments of the supporting data for each reaction. This metabolic reconstruction was used to carry out in silico deletion studies to identify essential and conditionally essential genes in H. pylori. A total of 128 essential and 75 conditionally essential metabolic genes were identified. Predicted growth phenotypes of single knockouts were validated using published experimental data. In addition, in silico double-deletion studies identified a total of 47 synthetic lethal mutants involving 67 different metabolic genes in rich medium.

Candidate metabolic network states in human mitochondria. Impact of diabetes, ischemia, and diet.

The human mitochondrial metabolic network was recently reconstructed based on proteomic and biochemical data. Linear programming and uniform random sampling were applied herein to identify candidate steady states of the metabolic network that were consistent with the imposed physico-chemical constraints and available experimental data. The activity of the mitochondrion was studied under four metabolic conditions: normal physiologic, diabetic, ischemic, and dietetic. Pairwise correlations between steady-state reaction fluxes were calculated in each condition to evaluate the dependence among the reactions in the network. Applying constraints on exchange fluxes resulted in predictions for intracellular fluxes that agreed with experimental data. Analyses of the steady-state flux distributions showed that the experimentally observed reduced activity of pyruvate dehydrogenase in vivo could be a result of stoichiometric constraints and therefore would not necessarily require enzymatic inhibition. The observed changes in the energy metabolism of the mitochondrion under diabetic conditions were used to evaluate the impact of previously suggested treatments. The results showed that neither normalized glucose uptake nor decreased ketone body uptake have a positive effect on the mitochondrial energy metabolism or network flexibility. Taken together, this study showed that sampling of the steady-state flux space is a powerful method to investigate network properties under different conditions and provides a basis for in silico evaluations of effects of potential disease treatments.

Active collaboration with primary care providers increases specialist referral in chronic renal disease.

BACKGROUND: Late referral to specialist nephrological care is associated with increased morbidity, mortality, and cost. Consequently, nephrologists' associations recommend early referral. The recommendations' effectiveness remains questionable: 22-51% of referrals need renal replacement therapy (RRT) within 3-4 months. This may be due to these recommendations addressing the specialist, rather than the primary care providers (PCP). The potential of specialist intervention aiming at slowing progression of chronic renal failure was introduced individually to some 250 local PCPs, and referral strategies were discussed. To overcome the PCPs' most often expressed fears, every referred patient was asked to report back to his PCP immediately after the initial specialist examination, and new medications were prescribed directly, and thus allotted to the nephrologist's budget. METHODS: In retrospective analysis, the stage of renal disease in patients referred within three months before the introductory round (group A, n = 18), was compared to referrals two years later (group B, n = 50). RESULTS: Relative number of patients remained stable (28%) for mild/ moderate chronic kidney disease (MMCKD), while there was a noticeable shift from patients referred severe chronic kidney disease (SCKD) (group A: 44%, group B: 20%) to patients referred in moderate chronic kidney disease (MCKD) (group A: 28%, group B: 52%). CONCLUSION: Individually addressing PCPs' ignorance and concerns noticeably decreased late referral. This stresses the importance of enhancing the PCPs' problem awareness and knowledge of available resources in order to ensure timely specialist referral.

Reconstruction and functional characterization of the human mitochondrial metabolic network based on proteomic and biochemical data.

Diverse datasets including genomic, proteomic, isotopomer, and DNA sequence variation are becoming available for human mitochondria. Thus there is a need to integrate these data within an in silico modeling framework where mitochondrial biology and related disorders can be studied and analyzed. This paper reports a reconstruction and characterization of the human mitochondrial metabolic network based on proteomic and biochemical data. The 189 reactions included in this reconstruction are both elementally and charge-balanced and are assigned to their respective cellular compartments (mitochondrial, cytosol, or extracellular). The capabilities of the reconstructed network to fulfill three metabolic functions (ATP production, heme synthesis, and mixed phospholipid synthesis) were determined. Network-based analysis of the mitochondrial energy conversion process showed that the overall ATP yield per glucose is 31.5. Network flexibility, characterized by allowable variation in reaction fluxes, was evaluated using flux variability analysis and analysis of all of the possible optimal flux distributions. Results showed that the network has high flexibility for the biosynthesis of heme and phospholipids but modest flexibility for maximal ATP production. A subset of all of the optimal network flux distributions, computed with respect to the three metabolic functions individually, was found to be highly correlated, suggesting that this set may contain physiological meaningful fluxes. Examinations of optimal flux distributions also identified correlated reaction sets that form functional modules in the network.

An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR).

BACKGROUND: 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. RESULTS: An expanded genome-scale metabolic model of E. coli (iJR904 GSM/GPR) has been reconstructed which includes 904 genes and 931 unique biochemical reactions. The reactions in the expanded model are both elementally and charge balanced. Network gap analysis led to putative assignments for 55 open reading frames (ORFs). Gene to protein to reaction associations (GPR) are now directly included in the model. Comparisons between predictions made by iJR904 and iJE660a models show that they are generally similar but differ under certain circumstances. Analysis of genome-scale proton balancing shows how the flux of protons into and out of the medium is important for maximizing cellular growth. CONCLUSIONS: E. coli iJR904 has improved capabilities over iJE660a. iJR904 is a more complete and chemically accurate description of E. coli metabolism than iJE660a. Perhaps most importantly, iJR904 can be used for analyzing and integrating the diverse datasets. iJR904 will help to outline the genotype-phenotype relationship for E. coli K-12, as it can account for genomic, transcriptomic, proteomic and fluxomic data simultaneously.