Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus ‘metabolic reconstruction’, which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ~2× more reactions and ~1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type–specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.

Here we describe the insights gained from sequencing the whole genomes of 2,636 Icelanders to a median depth of 20×. We found 20 million SNPs and 1.5 million insertions-deletions (indels). We describe the density and frequency spectra of sequence variants in relation to their functional annotation, gene position, pathway and conservation score. We demonstrate an excess of homozygosity and rare protein-coding variants in Iceland. We imputed these variants into 104,220 individuals down to a minor allele frequency of 0.1% and found a recessive frameshift mutation in MYL4 that causes early-onset atrial fibrillation, several mutations in ABCB4 that increase risk of liver diseases and an intronic variant in GNAS associating with increased thyroid-stimulating hormone levels when maternally inherited. These data provide a study design that can be used to determine how variation in the sequence of the human genome gives rise to human diversity.

, Melissa Yssel, MB ChB, FC Path(SA) Chem 139, and Wendy M. Zakowicz, BS 79 Purpose: To achieve clinical validation of cutoff values for newborn screening by tandem mass spectrometry through a worldwide collaborative effort. Methods: Cumulative percentiles of amino acids and acylcarnitines in dried blood spots of approximately 25-30 million normal newborns and 10,742 deidentified true positive cases are compared to assign clinical significance, which is achieved when the median of a disorder range is, and usually markedly outside, either the 99th or the 1st percentile of the normal population. The cutoff target ranges of analytes and ratios are then defined as the interval between selected percentiles of the two populations. When overlaps occur, adjustments are made to maximize sensitivity and specificity taking all available factors into consideration.

Autosomal dominant polycystic kidney disease (ADPKD) is typically a late-onset disease caused by mutations in PKD1 or PKD2, but about 2% of patients with ADPKD show an early and severe phenotype that can be clinically indistinguishable from autosomal recessive polycystic kidney disease (ARPKD). The high recurrence risk in pedigrees with early and severe PKD strongly suggests a common familial modifying background, but the mechanisms underlying the extensive phenotypic variability observed among affected family members remain unknown. Here, we describe severely affected patients with PKD who carry, in addition to their expected familial germ-line defect, additional mutations in PKD genes, including HNF-1␤, which likely aggravate the phenotype. Our findings are consistent with a common pathogenesis and dosage theory for PKD and may propose a general concept for the modification of disease expression in other so-called monogenic disorders.

Purpose: To improve quality of newborn screening by tandem mass spectrometry with a novel approach made possible by the collaboration of 154 laboratories in 49 countries. Methods: A database of 767,464 results from 12,721 cases affected with 60 conditions was used to build multivariate pattern recognition software that generates tools integrating multiple clinically significant results into a single score. This score is determined by the overlap between normal and disease ranges, penetration within the disease range, differences between conditions, and weighted correction factors. Results: Ninety tools target either a single condition or the differential diagnosis between multiple conditions. Scores are expressed as the percentile rank among all cases with the same condition and are compared to interpretation guidelines. Retrospective evaluation of past cases suggests that these tools could have avoided at least half of 279 false-positive outcomes caused by carrier status for fatty-acid oxidation disorders and could have prevented 88% of known false-negative events. Conclusions: Application of this computational approach to raw data is independent from single analyte cutoff values. In Minnesota, the tools have been a major contributing factor to the sustained achievement of a false-positive rate below 0.1% and a positive predictive value above 60%

Inter-individual and regional variability in recombination rates cannot be fully explained by the DNA sequence itself. Epigenetic mechanisms might be one additional factor affecting recombination. A biochemical approach to studying human germline methylation is difficult. We used the density of the 434,198 nonredundant methylation-associated SNPs (mSNPs) in the derived allele HapMap data set as a surrogate marker for germline DNA methylation. We validated our methodology by demonstrating that the mSNP density confirmed known patterns of genomic methylation, including the hypermutability of methylated cytosine and hypomethylation of CpG islands. Using this approach, we found a genomewide positive correlation between germline methylation and regional recombination rate (500-kb windows: r = 0.622, P < 10 À15). This remained significant with multiple correlations correcting for sequence features known to affect recombination, such as GC content and CpG dinucleotides (500-kb windows: r = 0.172, P < 10 À15). Using the ENCODE data set for increased resolution, we found a positive correlation between germline DNA methylation and recombination rate (50-kb windows: r = 0.301, P = 0.002). This correlation was further strengthened when corrected for sequence features affecting recombination (50-kb windows: r = 0.445, P < 0.0001). In the Human Epigenome Project data set there was increased DNA methylation in regions within recombination hot spots in male germ cells (0.632 vs. 0.557, P = 0.007). The relationship we observed between germline DNA methylation and recombination could be explained in two ways that are not mutually exclusive: DNA methylation could indicate preferred sites for recombination, or methylation following recombination could inhibit further recombination, perhaps by being part of the enigmatic molecular pathway mediating crossover interference.

Inborn errors of metabolism (IEMs) are hereditary metabolic defects, which are encountered in almost all major metabolic pathways occurring in man. Many IEMs are screened for in neonates through metabolomic analysis of dried blood spot samples. To enable the mapping of these metabolomic data onto the published human metabolic reconstruction, we added missing reactions and pathways involved in acylcarnitine (AC) and fatty acid oxidation (FAO) metabolism. Using literary data, we reconstructed an AC/FAO module consisting of 352 reactions and 139 metabolites. When this module was combined with the human metabolic reconstruction, the synthesis of 39 acylcarnitines and 22 amino acids, which are routinely measured, was captured and 235 distinct IEMs could be mapped. We collected phenotypic and clinical features for each IEM enabling comprehensive classification. We found that carbohydrate, amino acid, and lipid metabolism were most affected by the IEMs, while the brain was the most commonly affected organ. Furthermore, we analyzed the IEMs in the context of metabolic network topology to gain insight into common features between metabolically connected IEMs. While many known examples were identified, we discovered some surprising IEM pairs that shared reactions as well as clinical features but not necessarily causal genes. Moreover, we could also re-confirm that acetyl-CoA acts as a central metabolite. This network based analysis leads to further insight of hot spots in human metabolism with respect to IEMs. The presented comprehensive knowledge base of IEMs will provide a valuable tool in studying metabolic changes involved in inherited metabolic diseases.

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