Sean D. Mooney

Automated inference of molecular mechanisms of disease from amino acid substitutions

MOTIVATION Advances in high-throughput genotyping and next generation sequencing have generated a vast amount of human genetic variation data. Single nucleotide substitutions within protein coding regions are of particular importance owing to their potential to give rise to amino acid substitutions that affect protein structure and function which may ultimately lead to a disease state. Over the last decade, a number of computational methods have been developed to predict whether such amino acid substitutions result in an altered phenotype. Although these methods are useful in practice, and accurate for their intended purpose, they are not well suited for providing probabilistic estimates of the underlying disease mechanism. RESULTS We have developed a new computational model, MutPred, that is based upon protein sequence, and which models changes of structural features and functional sites between wild-type and mutant sequences. These changes, expressed as probabilities of gain or loss of structure and function, can provide insight into the specific molecular mechanism responsible for the disease state. MutPred also builds on the established SIFT method but offers improved classification accuracy with respect to human disease mutations. Given conservative thresholds on the predicted disruption of molecular function, we propose that MutPred can generate accurate and reliable hypotheses on the molecular basis of disease for approximately 11% of known inherited disease-causing mutations. We also note that the proportion of changes of functionally relevant residues in the sets of cancer-associated somatic mutations is higher than for the inherited lesions in the Human Gene Mutation Database which are instead predicted to be characterized by disruptions of protein structure. AVAILABILITY http://mutdb.org/mutpred CONTACT predrag@indiana.edu; smooney@buckinstitute.org.

Splicing factor SFRS1 recognizes a functionally diverse landscape of RNA transcripts

Metazoan genes are encrypted with at least two superimposed codes: the genetic code to specify the primary structure of proteins and the splicing code to expand their proteomic output via alternative splicing. Here, we define the specificity of a central regulator of pre-mRNA splicing, the conserved, essential splicing factor SFRS1. Cross-linking immunoprecipitation and high-throughput sequencing (CLIP-seq) identified 23,632 binding sites for SFRS1 in the transcriptome of cultured human embryonic kidney cells. SFRS1 was found to engage many different classes of functionally distinct transcripts including mRNA, miRNA, snoRNAs, ncRNAs, and conserved intergenic transcripts of unknown function. The majority of these diverse transcripts share a purine-rich consensus motif corresponding to the canonical SFRS1 binding site. The consensus site was not only enriched in exons cross-linked to SFRS1 in vivo, but was also enriched in close proximity to splice sites. mRNAs encoding RNA processing factors were significantly overrepresented, suggesting that SFRS1 may broadly influence the post-transcriptional control of gene expression in vivo. Finally, a search for the SFRS1 consensus motif within the Human Gene Mutation Database identified 181 mutations in 82 different genes that disrupt predicted SFRS1 binding sites. This comprehensive analysis substantially expands the known roles of human SR proteins in the regulation of a diverse array of RNA transcripts.

Label-free quantitative proteomics of the lysine acetylome in mitochondria identifies substrates of SIRT3 in metabolic pathways

Large-scale proteomic approaches have identified numerous mitochondrial acetylated proteins; however in most cases, their regulation by acetyltransferases and deacetylases remains unclear. Sirtuin 3 (SIRT3) is an NAD+-dependent mitochondrial protein deacetylase that has been shown to regulate a limited number of enzymes in key metabolic pathways. Here, we use a rigorous label-free quantitative MS approach (called MS1 Filtering) to analyze changes in lysine acetylation from mouse liver mitochondria in the absence of SIRT3. Among 483 proteins, a total of 2,187 unique sites of lysine acetylation were identified after affinity enrichment. MS1 Filtering revealed that lysine acetylation of 283 sites in 136 proteins was significantly increased in the absence of SIRT3 (at least twofold). A subset of these sites was independently validated using selected reaction monitoring MS. These data show that SIRT3 regulates acetylation on multiple proteins, often at multiple sites, across several metabolic pathways including fatty acid oxidation, ketogenesis, amino acid catabolism, and the urea and tricarboxylic acid cycles, as well as mitochondrial regulatory proteins. The widespread modification of key metabolic pathways greatly expands the number of known substrates and sites that are targeted by SIRT3 and establishes SIRT3 as a global regulator of mitochondrial protein acetylation with the capability of coordinating cellular responses to nutrient status and energy homeostasis.

Genetic Correction of Huntington's Disease Phenotypes in Induced Pluripotent Stem Cells

Huntingtons disease (HD) is caused by a CAG expansion in the huntingtin gene. Expansion of the polyglutamine tract in the huntingtin protein results in massive cell death in the striatum of HD patients. We report that human induced pluripotent stem cells (iPSCs) derived from HD patient fibroblasts can be corrected by the replacement of the expanded CAG repeat with a normal repeat using homologous recombination, and that the correction persists in iPSC differentiation into DARPP-32-positive neurons in vitro and in vivo. Further, correction of the HD-iPSCs normalized pathogenic HD signaling pathways (cadherin, TGF-β, BDNF, and caspase activation) and reversed disease phenotypes such as susceptibility to cell death and altered mitochondrial bioenergetics in neural stem cells. The ability to make patient-specific, genetically corrected iPSCs from HD patients will provide relevant disease models in identical genetic backgrounds and is a critical step for the eventual use of these cells in cell replacement therapy.

An integrated approach to inferring gene-disease associations in humans.

One of the most important tasks of modern bioinformatics is the development of computational tools that can be used to understand and treat human disease. To date, a variety of methods have been explored and algorithms for candidate gene prioritization are gaining in their usefulness. Here, we propose an algorithm for detecting gene–disease associations based on the human protein–protein interaction network, known gene–disease associations, protein sequence, and protein functional information at the molecular level. Our method, PhenoPred, is supervised: first, we mapped each gene/protein onto the spaces of disease and functional terms based on distance to all annotated proteins in the protein interaction network. We also encoded sequence, function, physicochemical, and predicted structural properties, such as secondary structure and flexibility. We then trained support vector machines to detect gene–disease associations for a number of terms in Disease Ontology and provided evidence that, despite the noise/incompleteness of experimental data and unfinished ontology of diseases, identification of candidate genes can be successful even when a large number of candidate disease terms are predicted on simultaneously. Availability: www.phenopred.org. Proteins 2008.

Late-life rapamycin treatment reverses age-related heart dysfunction

Rapamycin has been shown to extend lifespan in numerous model organisms including mice, with the most dramatic longevity effects reported in females. However, little is known about the functional ramifications of this longevity‐enhancing paradigm in mammalian tissues. We treated 24‐month‐old female C57BL/6J mice with rapamycin for 3 months and determined health outcomes via a variety of noninvasive measures of cardiovascular, skeletal, and metabolic health for individual mice. We determined that while rapamycin has mild transient metabolic effects, there are significant benefits to late‐life cardiovascular function with a reversal or attenuation of age‐related changes in the heart. RNA‐seq analysis of cardiac tissue after treatment indicated inflammatory, metabolic, and antihypertrophic expression changes in cardiac tissue as potential mechanisms mediating the functional improvement. Rapamycin treatment also resulted in beneficial behavioral, skeletal, and motor changes in these mice compared with those fed a control diet. From these findings, we propose that late‐life rapamycin therapy not only extends the lifespan of mammals, but also confers functional benefits to a number of tissues and mechanistically implicates an improvement in contractile function and antihypertrophic signaling in the aged heart with a reduction in age‐related inflammation.

Proteomic analysis of age-dependent changes in protein solubility identifies genes that modulate lifespan

While it is generally recognized that misfolding of specific proteins can cause late‐onset disease, the contribution of protein aggregation to the normal aging process is less well understood. To address this issue, a mass spectrometry‐based proteomic analysis was performed to identify proteins that adopt sodium dodecyl sulfate (SDS)‐insoluble conformations during aging in Caenorhabditis elegans. SDS‐insoluble proteins extracted from young and aged C. elegans were chemically labeled by isobaric tagging for relative and absolute quantification (iTRAQ) and identified by liquid chromatography and mass spectrometry. Two hundred and three proteins were identified as being significantly enriched in an SDS‐insoluble fraction in aged nematodes and were largely absent from a similar protein fraction in young nematodes. The SDS‐insoluble fraction in aged animals contains a diverse range of proteins including a large number of ribosomal proteins. Gene ontology analysis revealed highly significant enrichments for energy production and translation functions. Expression of genes encoding insoluble proteins observed in aged nematodes was knocked down using RNAi, and effects on lifespan were measured. 41% of genes tested were shown to extend lifespan after RNAi treatment, compared with 18% in a control group of genes. These data indicate that genes encoding proteins that become insoluble with age are enriched for modifiers of lifespan. This demonstrates that proteomic approaches can be used to identify genes that modify lifespan. Finally, these observations indicate that the accumulation of insoluble proteins with diverse functions may be a general feature of aging.

Gain and loss of phosphorylation sites in human cancer

MOTIVATION Coding-region mutations in human genes are responsible for a diverse spectrum of diseases and phenotypes. Among lesions that have been studied extensively, there are insights into several of the biochemical functions disrupted by disease-causing mutations. Currently, there are more than 60 000 coding-region mutations associated with inherited disease catalogued in the Human Gene Mutation Database (HGMD, August 2007) and more than 70 000 polymorphic amino acid substitutions recorded in dbSNP (dbSNP, build 127). Understanding the mechanism and contribution these variants make to a clinical phenotype is a formidable problem. RESULTS In this study, we investigate the role of phosphorylation in somatic cancer mutations and inherited diseases. Somatic cancer mutation datasets were shown to have a significant enrichment for mutations that cause gain or loss of phosphorylation when compared to our control datasets (putatively neutral nsSNPs and random amino acid substitutions). Of the somatic cancer mutations, those in kinase genes represent the most enriched set of mutations that disrupt phosphorylation sites, suggesting phosphorylation target site mutation is an active cause of phosphorylation deregulation. Overall, this evidence suggests both gain and loss of a phosphorylation site in a target protein may be important features for predicting cancer-causing mutations and may represent a molecular cause of disease for a number of inherited and somatic mutations.

Genome-wide DNA methylation changes with age in disease-free human skeletal muscle

A decline in skeletal muscle mass and function with aging is well recognized, but remains poorly characterized at the molecular level. Here, we report for the first time a genome‐wide study of DNA methylation dynamics in skeletal muscle of healthy male individuals during normal human aging. We predominantly observed hypermethylation throughout the genome within the aged group as compared to the young subjects. Differentially methylated CpG (dmCpG) nucleotides tend to arise intragenically and are underrepresented in promoters and are overrepresented in the middle and 3′ end of genes. The intragenic methylation changes are overrepresented in genes that guide the formation of the junction of the motor neuron and myofibers. We report a low level of correlation of gene expression from previous studies of aged muscle with our current analysis of DNA methylation status. For those genes that had both changes in methylation and gene expression with age, we observed a reverse correlation, with the exception of intragenic hypermethylated genes that were correlated with an increased gene expression. We suggest that a minimal number of dmCpG sites or select sites are required to be altered in order to correlate with gene expression changes. Finally, we identified 500 dmCpG sites that perform well in discriminating young from old samples. Our findings highlight epigenetic links between aging postmitotic skeletal muscle and DNA methylation.

Evaluation of features for catalytic residue prediction in novel folds

Structural genomics projects are determining the three‐dimensional structure of proteins without full characterization of their function. A critical part of the annotation process involves appropriate knowledge representation and prediction of functionally important residue environments. We have developed a method to extract features from sequence, sequence alignments, three‐dimensional structure, and structural environment conservation, and used support vector machines to annotate homologous and nonhomologous residue positions based on a specific training set of residue functions. In order to evaluate this pipeline for automated protein annotation, we applied it to the challenging problem of prediction of catalytic residues in enzymes. We also ranked the features based on their ability to discriminate catalytic from noncatalytic residues. When applying our method to a well‐annotated set of protein structures, we found that top‐ranked features were a measure of sequence conservation, a measure of structural conservation, a degree of uniqueness of a residues structural environment, solvent accessibility, and residue hydrophobicity. We also found that features based on structural conservation were complementary to those based on sequence conservation and that they were capable of increasing predictor performance. Using a family nonredundant version of the ASTRAL 40 v1.65 data set, we estimated that the true catalytic residues were correctly predicted in 57.0% of the cases, with a precision of 18.5%. When testing on proteins containing novel folds not used in training, the best features were highly correlated with the training on families, thus validating the approach to nonhomologous catalytic residue prediction in general. We then applied the method to 2781 coordinate files from the structural genomics target pipeline and identified both highly ranked and highly clustered groups of predicted catalytic residues.