Global Reconstruction
Atila Kalina
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Above all, we help ensure that progress in reducing poverty and broadening prosperity can be sustained. We place special emphasis on supporting lower-middle-income countries as they move up the economic chain, graduating from IDA to become clients of IBRD. We are also expanding capacity to help countries dealing with fragility and conflict situations. And as a long-term partner, we step up our support to all MICs in times of crisis.
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IBRD earns income every year from the return on its equity and from the small margin it makes on lending. This pays for World Bank operating expenses, goes into reserves to strengthen the balance sheet, and provides an annual transfer of funds to IDA, the fund for the poorest countries.
Abstract. Some recent compilations of proxy data both on land and ocean (MARGO Project Members, 2009; Bartlein et al., 2011; Shakun et al., 2012), have provided a new opportunity for an improved assessment of the overall climatic state of the Last Glacial Maximum. In this paper, we combine these proxy data with the ensemble of structurally diverse state of the art climate models which participated in the PMIP2 project (Braconnot et al., 2007) to generate a spatially complete reconstruction of surface air (and sea surface) temperatures. We test a variety of approaches, and show that multiple linear regression performs well for this application. Our reconstruction is significantly different to and more accurate than previous approaches and we obtain an estimated global mean cooling of 4.0 0.8 C (95% CI).
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An understanding of metabolism is fundamental to comprehending the phenotypic behavior of all living organisms, including humans, where metabolism is integral to health and is involved in much of human disease. High quality, genome-scale 'metabolic reconstructions' are at the heart of bottom-up systems biology analyses and represent the entire network of metabolic reactions that a given organism is known to exhibit1. The metabolic-network reconstruction procedure is now well-established2 and has been applied to a growing number of model organisms3. Metabolic reconstructions allow for the conversion of biological knowledge into a mathematical format and the subsequent computation of physiological states1,4,5 to address a variety of scientific and applied questions3,6. Reconstructions enable network-wide mechanistic investigations of the genotype-phenotype relationship. A high-quality reconstruction of the metabolic network is thus of interest to the community of researchers focused on the systems biology of metabolism of a target organism.
Of the reconstructions of human metabolism that have appeared to date, perhaps the most widely used is Recon 1 (ref. 7), which represents a knowledgebase and has also been converted into many predictive models. These models have been used for various biomedical applications, including the prediction of biomarkers for inborn errors of metabolism (IEMs)8, cancer drug targets9,10 and off-target drug effects11. Moreover, they have been used to evaluate missing metabolic functions systematically12,13 and to model host-microbe interactions14,15. These studies demonstrated the potential of metabolic modeling to advance understanding of human metabolism in health and disease.
It is clear that 'competing' (that is, different) reconstructions and reconstruction approaches coexist, but all have the common goal of providing an up-to-date, comprehensive and high-quality reconstruction, either at the global or cell-specific scale. Rather than continuing to duplicate efforts, a substantial fraction of the community has pooled resources to generate a consensus human metabolic reconstruction from many of the sources cited above.
Recon 2 accounts for 1,789 enzyme-encoding genes, 7,440 reactions and 2,626 unique metabolites distributed over eight cellular compartments, which is a large increase in comprehensiveness relative to Recon 1 (Table 1). Such an increase in scope does not necessarily constitute an improvement in utility over the previous version: expanding the reconstruction to resolve existing gaps and dead-end metabolites may introduce additional gaps and dead ends elsewhere. To demonstrate an improvement of the network, both coverage and functional improvements must be considered.
To quantify the overall improvements achieved through the community-driven expansion and refinement in the global human metabolic reconstruction, we compared the information coverage, topological and functional properties of Recon 2 with those of Recon 1 (Table 1). The reaction content was almost doubled, much of which belonged to one of the nine new pathways (Fig. 2). Moreover, 62% (61/99) of the existing pathways have been expanded in Recon 2, and reaction coverage in 29 pathways, accounting for 16.5% (1,231/7,440) of the reactions, remained unchanged. A total of 307 dead-end metabolites (metabolites that are either only produced or only consumed in the reconstruction) from Recon 1 were resolved in Recon 2, whereas 32 remained as participants in only one reaction. As a result of the expansion, 1,144 new dead-end metabolites, mostly from EHMN, were introduced. These will need to be connected to the rest of the network in subsequent efforts. Blocked reactions cannot carry a nonzero flux in any steady-state condition because they contain one or more dead-end metabolites or are in a linear pathway with such reactions. The expanded coverage of metabolic information resolved 827 blocked reactions present in Recon 1, and 443 blocked reactions remained (Table 1). The number of remaining and new blocked reactions and dead-end metabolites highlights that this current update is not intended to be the final compendium of human metabolism, but it is a major advance over Recon 1 and represents our current, continually evolving knowledge.
A metabolic task is defined as a nonzero flux through a reaction or through a pathway leading to the production of a metabolite B from a metabolite A. Examples of such tasks include the synthesis of all known precursors to produce a cell (biomass reaction; Supplementary Note 2) and the generation of energy via oxidative phosphorylation or fermentation (Supplementary Table 2). A total of 354 metabolic tasks were defined. Although a particular cell type is not capable of fulfilling all these metabolic tasks, Recon 2 should be able to fulfill these tasks because it is a global metabolic reconstruction. Recon 2 carried a nonzero flux for all tasks, compared with Recon 1, which achieved this functionality for only 83% of the tasks (Table 1).
To benchmark the models derived from both reconstructions against an independent data set, we used a manually assembled compendium of IEMs8 as a gold standard. This compendium accounts for 330 IEMs, such as phenylketonuria and orotic aciduria, along with their known metabolite biomarkers. As Recon 2 captured more metabolic genes, more IEMs could be mapped (Table 1). In Recon 2, almost all of the mapped IEMs affected the reaction activity, as no complementary isoenzymes are known for the absent enzymes (consistent with their occurrence as IEMs). We compared the predictive potential of Recon 2 and Recon 1 for associated biomarkers for the mapped IEMs (Fig. 3), in a process analogous to gene-deletion studies in microbial modeling. Recon 2 predicted 54 reported biomarkers for 49 different IEMs, with an accuracy of 77%. The coverage of predicted biomarkers and the accuracy was much lower for Recon 1, with 31 reported biomarkers for 29 IEMs and an accuracy of 63% (Fig. 3). This comparison demonstrates that the increased scope of Recon 2 led to a higher coverage of IEM-related biomarkers mapped and to an increase in predictive power.
Based on the accurate predictive capability of Recon 2 for biomarkers and for the metabolic tasks, the benchmarking demonstrated an increase in both scope and predictive accuracy of Recon 2 relative to its predecessor.
We also compared the Recon 2 exometabolome and the metabolites reported in the Human Metabolome Database (HMDB)36 as being detectable in biofluids (Fig. 4b). Biofluid information could be found for about half of the metabolites in Recon 2 identified in the HMDB. About 44% of these metabolites were also present in the extracellular compartment, indicating that there are still some transport and metabolic routes missing in Recon 2.
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