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/

Absence of nonfatal suicidal behavior preceding suicide death reveals differences in clinical risks.

Nonfatal suicidality is the most robust predictor of suicide death. However, only ~10% of those who survive an attempt go on to die by suicide. Moreover, ~50% of suicide deaths occur in the absence of prior known attempts, suggesting risks other than nonfatal suicide attempt need to be identified. We studied data from 4,000 population-ascertained suicide deaths and 26,191 population controls to improve understanding of risks leading to suicide death. This study included 2,253 suicide deaths and 3,375 controls with evidence of nonfatal suicidality (SUI_SI/SB and CTL_SI/SB) from diagnostic codes and natural language processing of electronic health records notes. Characteristics of these groups were compared to 1,669 suicides with no prior nonfatal SI/SB (SUI_None) and 22,816 controls with no lifetime suicidality (CTL_None). The SUI_None and CTL_None groups had fewer diagnoses and were older than SUI_SI/SB and CTL_SI/SB. Mental health diagnoses were far less common in both the SUI_None and CTL_None groups; mental health problems were less associated with suicide death than with presence of SI/SB. Physical health diagnoses were conversely more often associated with risk of suicide death than with presence of SI/SB. Pending replication, results indicate highly significant clinical differences among suicide deaths with versus without prior nonfatal SI/SB.

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.

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.

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 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.

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.

Large scale mass spectrometric profiling of peptides eluted from HLA molecules reveals N-terminal-extended peptide motifs.

The majority of >2000 HLA class I molecules can be clustered according to overlapping peptide binding specificities or motifs recognized by CD8(+) T cells. HLA class I motifs are classified based on the specificity of residues located in the P2 and the C-terminal positions of the peptide. However, it has been suggested that other positions might be relevant for peptide binding to HLA class I molecules and therefore be used for further characterization of HLA class I motifs. In this study we performed large-scale sequencing of endogenous peptides eluted from K562 cells (HLA class I null) made to express a single HLA molecule from HLA-B*3501, -B*3502, -B*3503, -B*3504, -B*3506, or -B*3508. Using sequence data from >1,000 peptides, we characterized novel peptide motifs that include dominant anchor residues extending to all positions in the peptide. The length distribution of HLA-B35-bound peptides included peptides of up to 15 residues. Remarkably, we determined that some peptides longer than 11 residues represented N-terminal-extended peptides containing an appropriate HLA-B35 peptide motif. These results provide evidence for the occurrence of endogenous N-terminal-extended peptide-HLA class I configurations. In addition, these results expand the knowledge about the identity of anchor positions in HLA class I-associated peptides that can be used for characterization of HLA class I motifs.

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.

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.

Analysis of gene expression profile of TPM3-ALK positive anaplastic large cell lymphoma reveals overlapping and unique patterns with that of NPM-ALK positive anaplastic large cell lymphoma.

Anaplastic large cell lymphoma (ALCL) comprises a group of non-Hodgkin lymphomas characterized by the expression of the CD30/Ki-1 antigen. A subset of ALCL is characterized by chromosomal translocations involving the anaplastic lymphoma kinase (ALK) gene on chromosome 2. While the most common translocation is the t(2;5)(p23;q35) involving the nucleophosmin (NPM) gene on chromosome 5, up to 12 other translocations partners of the ALK gene have been identified. One of these is the t(1;2)(q25;p23) which results in the formation of the chimeric protein TPM3-ALK. While several of the signaling pathways induced by NPM-ALK have been elucidated, those involved in ALCLs harboring TPM3-ALK are largely unknown. In order to investigate the expression profiles of ALCLs carrying the NPM-ALK and TPM3-ALK fusions, we carried out cDNA microarray analysis of two ALCL tissue samples, one expressing the NPM-ALK fusion protein and the other the TPM3-ALK fusion protein. RNA was extracted from snap-frozen tissues, labeled with fluorescent dyes and analyzed using cDNAs microarray containing approximately 9,200 genes and expressed sequence tags (ESTs). Quantitative fluorescence RT-PCR was performed to validate the cDNA microarray data on nine selected gene targets. Our results show a significant overlap of genes deregulated in the NPM-ALK and TPM-ALK positive lymphomas. These deregulated genes are involved in diverse cellular functions, such as cell cycle regulation, apoptosis, proliferation, and adhesion. Interestingly, a subset of the genes was distinct in their expression pattern in the two types of lymphomas. More importantly, many genes that were not previously associated with ALK positive lymphomas were identified. Our results demonstrate the overlapping and unique transcriptional patterns associated with the NPM-ALK and TPM3-ALK fusions in ALCL.

Global proteome profiling of NPM/ALK-positive anaplastic large cell lymphoma.

OBJECTIVE: Constitutive overexpression of nucleophosmin/anaplastic lymphoma kinase (NPM/ALK) is a key oncogenic event in anaplastic large cell lymphomas (ALCL) that carry the t(2;5)(p23;q35) translocation. Global proteomic analysis of NPM/ALK-positive ALCL would improve understanding of the disease pathogenesis and yield new candidate targets for novel treatment and diagnostic strategies. MATERIALS AND METHODS: To comprehensively determine the inventory of proteins from NPM/ALK-positive ALCL SUDHL-1 cells, the membrane, cytoplasm, and nuclear subcellular fractions were resolved by one-dimensional sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The MS spectra were interpreted using SEQUEST to search the electronic UniProt protein database, and analyzed by ProteinProphet and INTERACT. RESULTS: A total of 623 proteins consisting of 210 membrane, 229 cytoplasm, and 184 nuclear proteins were identified with a

Mutation database for the galactose-1-phosphate uridyltransferase (GALT) gene.

Classical galactosemia is an autosomal recessive disorder caused by mutations in the galactose-1-phosphate uridyltransferase (GALT) gene. Our group developed a disease-specific database containing all of the reported sequence variants in GALT (Available at: http://arup.utah.edu/database/galactosemia/GALT_welcome.php; Last accessed: 13 April 2007). Currently the database contains a total of 229 sequence variants, of which 196 are mutations (including nine novel mutations identified in our laboratory), 31 polymorphisms in both introns and exons, and two variants of unknown or uncertain significance. All sequence variants have been verified for their position within the GALT gene and named following standard nomenclature. Sequence variants are reported with accompanying information on protein effect, classification of mutation vs. polymorphism, mutation type (when applicable) based on how each was first described in the literature, and accompanying link to pertinent publication. Unpublished variants are described with relevant clinical information that supports their classification as causative of the disease vs. polymorphisms. Other features of this database include disease information, relevant links for galactosemia and literature, reference sequences, ability to query by various criteria, and submit of novel variations to the database. This free online scientific resource was developed with the clinical laboratory in mind to serve as a reference and repository for novel findings that are periodically collected, verified, and updated into the database.

Comprehensive identification of proteins in Hodgkin lymphoma-derived Reed-Sternberg cells by LC-MS/MS.

Mass spectrometry-based proteomics in conjunction with liquid chromatography and bioinformatics analysis provides a highly sensitive and high-throughput approach for the identification of proteins. Hodgkin lymphoma is a form of malignant lymphoma characterized by the proliferation of Reed-Sternberg cells and background reactive lymphocytes. Comprehensive analysis of proteins expressed and released by Reed-Sternberg cells would assist in the discovery of potential biomarkers and improve our understanding of its pathogenesis. The subcellular proteome of the three cellular compartments from L428 and KMH2 Hodgkin lymphoma-derived cell lines were fractionated, and analyzed by reverse-phase liquid chromatography coupled with electrospray ionization tandem mass spectrometry. Additionally, proteins released by Hodgkin lymphoma-derived L428 cells were extracted from serum-free culture media and analyzed. Peptide spectra were analyzed using TurboSEQUEST against the UniProt protein database (5.26.05; 188 712 entries). A subset of the identified proteins was validated by Western blot analysis, immunofluorescence microscopy and immunohistochemistry. A total of 1945 proteins were identified with 785 from the cytosolic fraction, 305 from the membrane fraction, 441 from the nuclear fraction and 414 released proteins using a minimum of two peptide identifications per protein and an error rate of <5.0%. Identification of proteins from diverse functional groups reflected the functional complexity of the Reed-Sternberg proteome. Proteins with previously reported oncogenic function in other cancers and from signaling pathways implicated in Hodgkin lymphoma were identified. Selected proteins without previously demonstrated expression in Hodgkin lymphoma were validated by Western blot analysis (B-RAF, Erb-B3), immunofluorescence microscopy (Axin1, Tenascin-X, Mucin-2) and immunohistochemistry using a tissue microarray (BRAF, PIM1). This study represents the first comprehensive inventory of proteins expressed by Reed-Sternberg cells of Hodgkin lymphoma and demonstrates the utility of combining cellular subfractionation, protein precipitation, tandem mass spectrometry and bioinformatics analysis for comprehensive identification of proteins that may represent potential biomarkers of the disease.

Evaluation of enrichment techniques for mass spectrometry: identification of tyrosine phosphoproteins in cancer cells.

Phosphorylation of tyrosine residues by protein tyrosine kinases mediates numerous cellular processes. Deregulated tyrosine phosphorylation underlies constitutive activation of signaling pathways leading to oncogenesis. Analytical techniques for evaluation of the global phosphoproteome level are challenging and can be improved on to enhance yields. Here, we evaluated several approaches to enrich for tyrosine phosphoproteins in cancer cells for subsequent liquid chromatography-tandem mass spectrometry analysis using lysates from SU-DHL-1 cells, which express the nucleophosmin-anaplastic lymphoma kinase tyrosine kinase as a model system. Cells were grown in the presence or absence of the phosphatase inhibitor sodium orthovanadate, and tyrosine phosphoproteins were subsequently enriched by immunoprecipitation or immunoaffinity chromatography and protein identification performed by liquid chromatography-tandem mass spectrometry. Our results show that sodium orthovanadate improves enrichment and thus detection of tyrosine phosphoproteins. Immunoprecipitation of tyrosine phosphoproteins using two different antiphosphotyrosine antibodies increased the number of protein identifications. Finally, peptides from proteins enriched by immunoprecipitation were more abundant (n=338) than those enriched by immunoaffinity chromatography (n=138), and relatively few proteins were found in common (n=43). Our data demonstrate the utility of an enrichment strategy for the mass spectrometry-based identification of tyrosine phosphoproteins and show the advantage of complementary techniques for greater protein identification.

Analytical characteristics of cleavable isotope-coded affinity tag-LC-tandem mass spectrometry for quantitative proteomic studies.

Quantitative proteomic studies using cleavable isotope-coded affinity tags (cICAT) in concert with tandem mass spectrometry (MS/MS) permit unbiased comparisons between biologically distinct samples. We sought to determine the analytical characteristics of cICAT-based studies by examining the cumulative results of multiple, separate cICAT-based experiments involving human lymphoma-derived cells. We found that the number of identified proteins increased with larger numbers of fractions analyzed. The majority of proteins were identified by single peptides. Only 24 to 41% of the peptides contained cysteine residues, but 85% of the cysteine-containing peptides yielded quantification data. Approximately 28% of all identified proteins yielded quantification data, with 57% of these being differentially expressed by at least 1.5-fold. The quantification ratios of peptides for proteins with multiple quantified peptides were concordant in trend in 87% of instances. cICAT-labeled peptides identified proteins in all subcellular compartments without significant bias. Analysis of the flow-through fraction did not increase the number of peptides identified per protein. Our studies indicate that cICAT-LC-MS/MS yields quantifications primarily based on single peptides, and analysis of flow-through peptides does not contribute signifi-cantly to the results. Nevertheless, identifications based on single cICAT-labeled peptides with tryptic ends provide sufficiently reliable protein identifications and quantification information in cICAT-LC-MS/MS-based proteomic studies.

Identification of NPM-ALK interacting proteins by tandem mass spectrometry.

Constitutive overexpression of nucleophosmin-anaplastic lymphoma kinase (NPM-ALK) is a key oncogenic event in anaplastic large-cell lymphomas with the characteristic chromosomal aberration t(2;5)(p23;q35). Proteins that interact with ALK tyrosine kinase play important roles in mediating downstream cellular signals, and are potential targets for novel therapies. Using a functional proteomic approach, we determined the identity of proteins that interact with the ALK tyrosine kinase by co-immunoprecipitation with anti-ALK antibody, followed by electrospray ionization and tandem mass spectrometry (MS/MS). A total of 46 proteins were identified as unique to the ALK immunocomplex using monoclonal and polyclonal antibodies, while 11 proteins were identified in the NPM immunocomplex. Previously reported proteins in the ALK signal pathway were identified including PI3-K, Jak2, Jak3, Stat3, Grb2, IRS, and PLCgamma1. More importantly, many proteins previously not recognized to be associated with NPM-ALK, but with potential NPM-ALK interacting protein domains, were identified. These include adaptor molecules (SOCS, Rho-GTPase activating protein, RAB35), kinases (MEK kinase 1 and 4, PKC, MLCK, cyclin G-associated kinase, EphA1, JNK kinase, MAP kinase 1), phosphatases (meprin, PTPK, protein phosphatase 2 subunit), and heat shock proteins (Hsp60 precursor). Proteins identified by MS were confirmed by Western blotting and reciprocal immunoprecipitation. This study demonstrates the utility of antibody immunoprecipitation and subsequent peptide identification by tandem mass spectrometry for the elucidation of ALK-binding proteins, and its potential signal transduction pathways.

Annotated proteome of a human T-cell lymphoma.

As the reliable identification of proteins by tandem mass spectrometry becomes increasingly common, the full characterization of large data sets of proteins remains a difficult challenge. Our goal was to survey the proteome of a human T-cell lymphoma-derived cell line in a single set of experiments and present an automated method for the annotation of lists of proteins. A downstream application of these data includes the identification of novel pathogenetic and candidate diagnostic markers of T-cell lymphoma. Total protein isolated from cytoplasmic, membrane, and nuclear fractions of the SUDHL-1 T-cell lymphoma cell line was resolved by SDS-PAGE, and the entire gel lanes digested and analyzed by tandem mass spectrometry. Acquired data files were searched against the UniProt protein database using the SEQUEST algorithm. Search results for each subcellular fraction were analyzed using INTERACT and ProteinProphet. All protein identifications with an error rate of less than 10% were directly exported into excel and analyzed using GOMiner (NIH/NCI). The Gene ontology molecular function and cell location data were summarized for the identified proteins and results exported as user-interactive directed acyclic graphs. A total of 1105 unique proteins were identified and fully annotated, including numerous proteins that had not been previously characterized in lymphoma, in functional categories such as cell adhesion, migration, signaling, and stress response. This study demonstrates the utility of currently available bioinformatics tools for the robust identification and annotation of large numbers of proteins in a batchwise fashion.