Whole-genome sequencing analysis of suicide deaths integrating brain-regulatory eQTLs data to identify risk loci and genes.

Recent large-scale genome-wide association studies (GWAS) have started to identify potential genetic risk loci associated with risk of suicide; however, a large portion of suicide-associated genetic factors affecting gene expression remain elusive. Dysregulated gene expression, not assessed by GWAS, may play a significant role in increasing the risk of suicide death. We performed the first comprehensive genomic association analysis prioritizing brain expression quantitative trait loci (eQTLs) within regulatory regions in suicide deaths from the Utah Suicide Genetic Risk Study (USGRS). 440,324 brain-regulatory eQTLs were obtained by integrating brain eQTLs, histone modification ChIP-seq, ATAC-seq, DNase-seq, and Hi-C results from publicly available data. Subsequent genomic analyses were conducted in whole-genome sequencing (WGS) data from 986 suicide deaths of non-Finnish European (NFE) ancestry and 415 ancestrally matched controls. Additional independent USGRS suicide deaths with genotyping array data (n = 4657) and controls from the Genome Aggregation Database were explored for WGS result replication. One significant eQTL locus, rs926308 (p = 3.24e-06), was identified. The rs926308-T is associated with lower expression of RFPL3S, a gene important for neocortex development and implicated in arousal. Gene-based analyses performed using Sherlock Bayesian statistical integrative analysis also detected 20 genes with expression changes that may contribute to suicide risk. From analyzing publicly available transcriptomic data, ten of these genes have previous evidence of differential expression in suicide death or in psychiatric disorders that may be associated with suicide, including schizophrenia and autism (ZNF501, ZNF502, CNN3, IGF1R, KLHL36, NBL1, PDCD6IP, SNX19, BCAP29, and ARSA). Electronic health records (EHR) data was further merged to evaluate if there were clinically relevant subsets of suicide deaths associated with genetic variants. In summary, our study identified one risk locus and ten genes associated with suicide risk via gene expression, providing new insight into possible genetic and molecular mechanisms leading to suicide.

Extended familial risk of suicide death is associated with younger age at death and elevated polygenic risk of suicide.

Suicide accounts for >800,000 deaths annually worldwide; prevention is an urgent public health issue. Identification of risk factors remains challenging due to complexity and heterogeneity. The study of suicide deaths with increased extended familial risk provides an avenue to reduce etiological heterogeneity and explore traits associated with increased genetic liability. Using extensive genealogical records, we identified high-risk families where distant relatedness of suicides implicates genetic risk. We compared phenotypic and polygenic risk score (PRS) data between suicides in high-risk extended families (high familial risk (HFR), n = 1,634), suicides linked to genealogical data not in any high-risk families (low familial risk (LFR), n = 147), and suicides not linked to genealogical data with unknown familial risk (UFR, n = 1,865). HFR suicides were associated with lower age at death (mean = 39.34 years), more suicide attempts, and more PTSD and trauma diagnoses. For PRS tests, we included only suicides with >90% European ancestry and adjusted for residual ancestry effects. HFR suicides showed markedly higher PRS of suicide death (calculated using cross-validation), supporting specific elevation of genetic risk of suicide in this subgroup, and also showed increased PRS of PTSD, suicide attempt, and risk taking. LFR suicides were substantially older at death (mean = 49.10 years), had fewer psychiatric diagnoses of depression and pain, and significantly lower PRS of depression. Results suggest extended familiality and trauma/PTSD may provide specificity in identifying individuals at genetic risk for suicide death, especially among younger ages, and that LFR of suicide warrants further study regarding the contribution of demographic and medical risks.

A support vector machine for identification of single-nucleotide polymorphisms from next-generation sequencing data.

MOTIVATION: Accurate determination of single-nucleotide polymorphisms (SNPs) from next-generation sequencing data is a significant challenge facing bioinformatics researchers. Most current methods use mechanistic models that assume nucleotides aligning to a given reference position are sampled from a binomial distribution. While such methods are sensitive, they are often unable to discriminate errors resulting from misaligned reads, sequencing errors or platform artifacts from true variants. RESULTS: To enable more accurate SNP calling, we developed an algorithm that uses a trained support vector machine (SVM) to determine variants from .BAM or .SAM formatted alignments of sequence reads. Our SVM-based implementation determines SNPs with significantly greater sensitivity and specificity than alternative platforms, including the UnifiedGenotyper included with the Genome Analysis Toolkit, samtools and FreeBayes. In addition, the quality scores produced by our implementation more accurately reflect the likelihood that a variant is real when compared with those produced by the Genome Analysis Toolkit. While results depend on the model used, the implementation includes tools to easily build new models and refine existing models with additional training data. AVAILABILITY: Source code and executables are available from github.com/brendanofallon/SNPSVM/

The Biotinidase Gene Variants Registry: A Paradigm Public Database.

The BTD gene codes for production of biotinidase, the enzyme responsible for helping the body reuse and recycle the biotin found in foods. Biotinidase deficiency is an autosomal recessively inherited disorder resulting in the inability to recycle the vitamin biotin and affects approximately 1 in 60,000 newborns. If untreated, the depletion of intracellular biotin leads to impaired activities of the biotin-dependent carboxylases and can result in cutaneous and neurological abnormalities in individuals with the disorder. Mutations in the biotinidase gene (BTD) alter enzymatic function. To date, more than 165 mutations in BTD have been reported. Our group has developed a database that characterizes the known mutations and sequence variants in BTD (http://arup.utah.edu/database/BTD/BTD_welcome.php). All sequence variants have been verified for their positions within the BTD gene and designated according to standard nomenclature suggested by Human Genome Variation Society (HGVS). In addition, we describe the change in the protein, indicate whether the variant is a known or likely mutation vs. a benign polymorphism, and include the reference that first described the alteration. We also indicate whether the alteration is known to be clinically pathological based on an observation of a known symptomatic individual or predicted to be pathological based on enzymatic activity or putative disruption of the protein structure. We incorporated the published phenotype to help establish genotype-phenotype correlations and facilitate this process for those performing mutation analysis and/or interpreting results. Other features of this database include disease information, relevant links about biotinidase deficiency, reference sequences, ability to query by various criteria, and the process for submitting novel variations. This database is free to the public and will be updated quarterly. This database is a paradigm for formulating databases for other inherited metabolic disorders.

Protein and steroid profiles in follicular fluid after ovarian hyperstimulation as potential biomarkers of IVF outcome.

Controlled ovarian hyperstimulation is performed to assist with generation of multiple mature oocytes for use in in vitro fertilization (IVF). The goal of our study was to evaluate differences in protein and steroid profiles in ovarian follicular fluid (hFF) samples obtained during oocyte retrieval from women undergoing IVF treatment and to identify physiological pathways associated with the proteins. The hFF samples were depleted of abundant proteins, fractionated by ultrafiltration, digested, and analyzed by nano-LC-QTOF. Concentrations of 15 endogenous steroids were determined in the samples using LC-MS/MS methods. The total number of proteins identified in the samples was 75, of which 4, 7, and 2 were unique to the samples from women with viable pregnancy, miscarriage, and no pregnancy, respectively. Identified proteins were associated with the acute response signaling, coagulation system, intrinsic and extrinsic prothrombin activation, complement system, neuroprotective role of THOP1, FXR/RXR activation, role of tissue factor, and growth hormone pathways. A greater number of proteins associated with biosynthesis was found in hFF samples corresponding to the oocytes resulting in pregnancy. The abundance of seven proteins was found to be associated with steroidogenesis. The obtained data will contribute to better understanding of the pathogenesis and development of noninvasive markers for assessment of oocytes viability.

Consensus: a framework for evaluation of uncertain gene variants in laboratory test reporting.

Accurate interpretation of gene testing is a key component in customizing patient therapy. Where confirming evidence for a gene variant is lacking, computational prediction may be employed. A standardized framework, however, does not yet exist for quantitative evaluation of disease association for uncertain or novel gene variants in an objective manner. Here, complementary predictors for missense gene variants were incorporated into a weighted Consensus framework that includes calculated reference intervals from known disease outcomes. Data visualization for clinical reporting is also discussed.

Identification of histoplasma-specific peptides in human urine.

Histoplasmosis is a severe dimorphic fungus infection, which is often difficult to diagnose due to similarity in symptoms to other diseases and lack of specific diagnostic tests. Urine samples from histoplasma-antigen-positive patients and appropriate controls were prepared using various sample preparation strategies including immunoenrichment, ultrafiltration, high-abundant protein depletion, deglycosylation, reverse-phase fractions, and digest using various enzymes. Samples were then analyzed by nanospray tandem mass spectrometry. Accurate mass TOF scans underwent molecular feature extraction and statistical analysis for unique disease makers, and acquired MS/MS data were searched against known human and histoplasma proteins. In human urine, some 52 peptides from 37 Histoplasma proteins were identified with high confidence. This is the first report of identification of a large number of Histoplasma-specific peptides from immunoassay-positive patient samples using tandem mass spectrometry and bioinformatics techniques. These findings may lead to novel diagnostic markers for histoplasmosis in human urine.

Exploratory study of proteins in urine of patients with histoplasma antigenuria.

Disseminated histoplasmosis is an invasive fungal infection that can be fatal in patients with weak immune system. The goal of our exploratory study was to evaluate differences in urinary protein profiles among samples of healthy individuals, patients with proteinuria (PRU), and histoplasma antigenuria (HIS), and to identify physiological pathways associated with the excreted proteins. Urine samples were depleted of abundant proteins, deglycosylated, digested with trypsin, fractionated and analyzed by nano-LC-QTOF. The total number of human proteins identified in the samples was 117, of which 20 and 23 were unique to the samples from patients with PRU and HIS, respectively. Pathway analysis of proteins identified in samples of PRU and HIS patients suggested increased levels of proteins associated with acute response signaling, coagulation system, prothrombin activation, glucocorticoid regulation and the lipid antigen presentation signaling pathway networks. The obtained data provide information on protein expression associated with HIS, and suggest that further more rigorous studies aimed at the identification of proteins associated with proteinuria of different causes are feasible.

Utility of gene-specific algorithms for predicting pathogenicity of uncertain gene variants.

The rapid advance of gene sequencing technologies has produced an unprecedented rate of discovery of genome variation in humans. A growing number of authoritative clinical repositories archive gene variants and disease phenotypes, yet there are currently many more gene variants that lack clear annotation or disease association. To date, there has been very limited coverage of gene-specific predictors in the literature. Here the evaluation is presented of "gene-specific" predictor models based on a naïve Bayesian classifier for 20 gene-disease datasets, containing 3986 variants with clinically characterized patient conditions. The utility of gene-specific prediction is then compared with "all-gene" generalized prediction and also with existing popular predictors. Gene-specific computational prediction models derived from clinically curated gene variant disease datasets often outperform established generalized algorithms for novel and uncertain gene variants.

Predicting phenotypic severity of uncertain gene variants in the RET proto-oncogene.

Although reported gene variants in the RET oncogene have been directly associated with multiple endocrine neoplasia type 2 and hereditary medullary thyroid carcinoma, other mutations are classified as variants of uncertain significance (VUS) until the associated clinical phenotype is made clear. Currently, some 46 non-synonymous VUS entries exist in curated archives. In the absence of a gold standard method for predicting phenotype outcomes, this follow up study applies feature selected amino acid physical and chemical properties feeding a Bayes classifier to predict disease association of uncertain gene variants into categories of benign and pathogenic. Algorithm performance and VUS predictions were compared to established phylogenetic based mutation prediction algorithms. Curated outcomes and unpublished RET gene variants with known disease association were used to benchmark predictor performance. Reliable classification of RET uncertain gene variants will augment current clinical information of RET mutations and assist in improving prediction algorithms as knowledge increases.

Utility of characteristic QTOF MS/MS fragmentation for MHC class I peptides.

Systematic investigation of cellular process by mass spectrometric detection of peptides obtained from proteins digestion or directly from immuno-purification can be a powerful tool when used appropriately. The true sequence of these peptides is defined by the interpretation of spectral data using a variety of available algorithms. However peptide match algorithm scoring is typically based on some, but not all, of the mechanisms of peptide fragmentation. Although algorithm rules for soft ionization techniques generally fit very well to tryptic peptides, manual validation of spectra is often required for endogenous peptides such as MHC class I molecules where traditional trypsin digest techniques are not used. This study summarizes data mining and manual validation of hundreds of peptide sequences from MHC class I molecules in publically available data files. We herein describe several important features to improve and quantify manual validation for these endogenous peptides--post automated algorithm searching. Important fragmentation patterns are discussed for the studied MHC Class I peptides. These findings lead to practical rules that are helpful when performing manual validation. Furthermore, these observations may be useful to improve current peptide search algorithms or development of novel software tools.

The SPRED1 Variants Repository for Legius Syndrome.

Legius syndrome (LS) is an autosomal dominant disorder caused by germline loss-of-function mutations in the sprouty-related, EVH1 domain containing 1 (SPRED1) gene. The phenotype of LS is multiple café au lait macules (CALM) with other commonly reported manifestations, including intertriginous freckling, lipomas, macrocephaly, and learning disabilities including ADHD and developmental delays. Since the earliest signs of LS and neurofibromatosis type 1 (NF1) syndrome are pigmentary findings, the two are indistinguishable and individuals with LS may meet the National Institutes of Health diagnostic criteria for NF1 syndrome. However, individuals are not known to have an increased risk for developing tumors (compared with NF1 patients). It is therefore important to fully characterize the phenotype differences between NF1 and LS because the prognoses of these two disorders differ greatly. We have developed a mutation database that characterizes the known variants in the SPRED1 gene in an effort to facilitate this process for testing and interpreting results. This database is free to the public and will be updated quarterly.

The Alport syndrome COL4A5 variant database.

Alport Syndrome is a progressive renal disease with cochlear and ocular involvement. The most common form ( approximately 80%) is inherited in an X-linked pattern. X-linked Alport Syndrome (XLAS) is caused by mutations in the type IV collagen alpha chain 5 (COL4A5). We have developed a curated disease-specific database containing reported sequence variants in COL4A5. Currently the database archives a total of 520 sequence variants, verified for their position within the COL4A5 gene and named following standard nomenclature. Sequence variants are reported with accompanying information on protein effect, classification of mutation vs. polymorphism, mutation type based on the first description in the literature, and links to pertinent publications. In addition, features of this database include disease information, relevant links for Alport syndrome literature, reference sequence information, and ability to query by various criteria. On-line submission for novel gene variants or updating information on existing database entries is also possible. This free online scientific resource was developed with the clinical laboratory in mind to serve as a reference and repository for COL4A5 variants.

Endoplasmic reticulum aminopeptidase associated with antigen processing defines the composition and structure of MHC class I peptide repertoire in normal and virus-infected cells.

The MHC class I (MHC-I) molecules ferry a cargo of peptides to the cell surface as potential ligands for CD8(+) cytotoxic T cells. For nearly 20 years, the cargo has been described as a collection of short 8-9 mer peptides, whose length and sequences were believed to be primarily determined by the peptide-binding groove of MHC-I molecules. Yet the mechanisms for producing peptides of such optimal length and composition have remained unclear. In this study, using mass spectrometry, we determined the amino acid sequences of a large number of naturally processed peptides in mice lacking the endoplasmic reticulum aminopeptidase associated with Ag processing (ERAAP). We find that ERAAP-deficiency changed the oeuvre and caused a marked increase in the length of peptides normally presented by MHC-I. Furthermore, we observed similar changes in the length of viral peptides recognized by CD8(+) T cells in mouse CMV-infected ERAAP-deficient mice. In these mice, a distinct CD8(+) T cell population was elicited with specificity for an N-terminally extended epitope. Thus, the characteristic length, as well as the composition of MHC-I peptide cargo, is determined not only by the MHC-I peptide-binding groove but also by ERAAP proteolysis in the endoplasmic reticulum.

A depletion strategy for improved detection of human proteins from urine.

With rapidly growing interest in the urine proteome, methods for reducing sample complexity are becoming increasingly important. Depletion strategies for removal of high-abundance proteins from human urine have not been reported. A commercial kit designed for depletion of abundant proteins from plasma was evaluated for removing top proteins from urine of patients with proteinuria. The number of low-abundance proteins identified in urine after depletion increased nearly 2.5-fold.

The proteomic signature of NPM/ALK reveals deregulation of multiple cellular pathways.

Constitutive expression of the chimeric NPM/ALK fusion protein encoded by the t(2;5)(p32;q35) is a key oncogenic event in the pathogenesis of most anaplastic large cell lymphomas (ALCLs). The proteomic network alterations produced by this aberration remain largely uncharacterized. Using a mass spectrometry (MS)-driven approach to identify changes in protein expression caused by the NPM/ALK fusion, we identified diverse NPM/ALK-induced changes affecting cell proliferation, ribosome synthesis, survival, apoptosis evasion, angiogenesis, and cytoarchitectural organization. MS-based findings were confirmed using Western blotting and/or immunostaining of NPM/ALK-transfected cells and ALK-deregulated lymphomas. A subset of the proteins distinguished NPM/ALK-positive ALCLs from NPM/ALK-negative ALCLs and Hodgkin lymphoma. The multiple NPM/ALK-deregulated pathways identified by MS analysis also predicted novel biologic effects of NPM/ALK expression. In this regard, we showed loss of cell adhesion as a consequence of NPM/ALK expression in a kinase-dependent manner, and sensitivity of NPM/ALK-positive ALCLs to inhibition of the RAS, p42/44ERK, and FRAP/mTOR signaling pathways. These findings reveal that the NPM/ALK alteration affects diverse cellular pathways, and provide novel insights into NPM/ALK-positive ALCL pathobiology. Our studies carry important implications for the use of MS-driven approaches for the elucidation of neoplastic pathobiology, the identification of novel diagnostic biomarkers, and pathogenetically relevant therapeutic targets.

Identification of naturally processed ligands in the C57BL/6 mouse using large-scale mass spectrometric peptide sequencing and bioinformatics prediction.

Most major histocompatibility complex (MHC) class I-peptide-binding motifs are currently defined on the basis of quantitative in vitro MHC-peptide-binding assays. This information is used to develop bioinformatics-based tools to predict the binding of peptides to MHC class I molecules. To date few studies have analyzed the performance of these bioinformatics tools to predict the binding of peptides determined by sequencing of naturally processed peptides eluted directly from MHC class I molecules. In this study, we performed large-scale sequencing of endogenous peptides eluted from H2K(b) and H2D(b) molecules expressed in spleens of C57BL/6 mice. Using sequence data from 281 peptides, we identified novel preferred anchor residues located in H2K(b) and H2D(b)-associated peptides that refine our knowledge of these H2 class I peptide-binding motifs. The analysis comparing the performance of three bioinformatics methods to predict the binding of these peptides, including artificial neural network, stabilized matrix method, and average relative binding, revealed that 61% to 94% of peptides eluted from H2K(b) and H2D(b) molecules were correctly classified as binders by the three algorithms. These results suggest that bioinformatics tools are reliable and efficient methods for binding prediction of naturally processed MHC class I ligands.

Multiple endocrine neoplasia type 2 RET protooncogene database: repository of MEN2-associated RET sequence variation and reference for genotype/phenotype correlations.

Multiple endocrine neoplasia type 2 (MEN2) is an inherited, autosomal-dominant disorder caused by deleterious mutations within the RET protooncogene. MEN2 RET mutations are mainly heterozygous, missense sequence changes found in RET exons 10, 11, and 13-16. Our group has developed the publicly available, searchable MEN2 RET database to aid in genotype/phenotype correlations, using Human Genome Variation Society recommendations for sequence variation nomenclature and database content. The MEN2 RET database catalogs all RET sequence variation relevant to the MEN2 syndromes, with associated clinical information. Each database entry lists a RET sequence variation's location within the RET gene, genotype, pathogenicity classification, MEN2 phenotype, first literature reference, and comments (which may contain information on other clinical features, complex genotypes, and additional literature references). The MEN2 phenotype definitions were derived from the International RET Mutation Consortium guidelines for classification of MEN2 disease phenotypes. Although nearly all of the 132 RET sequence variation entries initially cataloged in the database were from literature reports, novel sequence variation and updated phenotypic information for any existing database entry can be submitted electronically on the database website. The database website also contains links to selected MEN2 literature reviews, gene and protein information, and RET reference sequences. The MEN2 RET database (www.arup.utah.edu/database/MEN2/MEN2_welcome.php) will serve as a repository for MEN2-associated RET sequence variation and reference for RET genotype/MEN2 phenotype correlations.

Quantitative proteomic analysis of follicular lymphoma cells in response to rituximab.

Rituximab is a monoclonal antibody that targets the uniquely expressed B-cell CD20 receptor. Although recently approved for treatment of follicular lymphomas, the intracellular events that occur when rituximab binds to CD20 are largely unknown. Quantitative proteomic analysis of B-cell lymphoma-derived cells exposed to rituximab was performed. Differentially expressed proteins belonged to functional groups involved in migration, adhesion, calcium-induced signaling, ubiquitination, and components of the phosphoinositol and NF-kappaB pathways. Our studies reveal the proteomic consequences of rituximab treatment and provide novel insights into the mechanism of action of the drug in susceptible B-cell lymphoproliferative disorders.