Unbiased kidney-centric molecular categorization of chronic kidney disease as a step towards precision medicine.

Current classification of chronic kidney disease (CKD) into stages using indirect systemic measures (estimated glomerular filtration rate (eGFR) and albuminuria) is agnostic to the heterogeneity of underlying molecular processes in the kidney thereby limiting precision medicine approaches. To generate a novel CKD categorization that directly reflects within kidney disease drivers we analyzed publicly available transcriptomic data from kidney biopsy tissue. A Self-Organizing Maps unsupervised artificial neural network machine-learning algorithm was used to stratify a total of 369 patients with CKD and 46 living kidney donors as healthy controls. Unbiased stratification of the discovery cohort resulted in identification of four novel molecular categories of disease termed CKD-Blue, CKD-Gold, CKD-Olive, CKD-Plum that were replicated in independent CKD and diabetic kidney disease datasets and can be further tested on any external data at kidneyclass.org. Each molecular category spanned across CKD stages and histopathological diagnoses and represented transcriptional activation of distinct biological pathways. Disease progression rates were highly significantly different between the molecular categories. CKD-Gold displayed rapid progression, with significant eGFR-adjusted Cox regression hazard ratio of 5.6 [1.01-31.3] for kidney failure and hazard ratio of 4.7 [1.3-16.5] for composite of kidney failure or a 40% or more eGFR decline. Urine proteomics revealed distinct patterns between the molecular categories, and a 25-protein signature was identified to distinguish CKD-Gold from other molecular categories. Thus, patient stratification based on kidney tissue omics offers a gateway to non-invasive biomarker-driven categorization and the potential for future clinical implementation, as a key step towards precision medicine in CKD.

Discovery of Autoantibodies Targeting Nephrin in Minimal Change Disease Supports a Novel Autoimmune Etiology.

BACKGROUND: Failure of the glomerular filtration barrier, primarily by loss of slit diaphragm architecture, underlies nephrotic syndrome in minimal change disease. The etiology remains unknown. The efficacy of B cell-targeted therapies in some patients, together with the known proteinuric effect of anti-nephrin antibodies in rodent models, prompted us to hypothesize that nephrin autoantibodies may be present in patients with minimal change disease. METHODS: We evaluated sera from patients with minimal change disease, enrolled in the Nephrotic Syndrome Study Network (NEPTUNE) cohort and from our own institutions, for circulating nephrin autoantibodies by indirect ELISA and by immunoprecipitation of full-length nephrin from human glomerular extract or a recombinant purified extracellular domain of human nephrin. We also evaluated renal biopsies from our institutions for podocyte-associated punctate IgG colocalizing with nephrin by immunofluorescence. RESULTS: In two independent patient cohorts, we identified circulating nephrin autoantibodies during active disease that were significantly reduced or absent during treatment response in a subset of patients with minimal change disease. We correlated the presence of these autoantibodies with podocyte-associated punctate IgG in renal biopsies from our institutions. We also identified a patient with steroid-dependent childhood minimal change disease that progressed to end stage kidney disease; she developed a massive post-transplant recurrence of proteinuria that was associated with high pretransplant circulating nephrin autoantibodies. CONCLUSIONS: Our discovery of nephrin autoantibodies in a subset of adults and children with minimal change disease aligns with published animal studies and provides further support for an autoimmune etiology. We propose a new molecular classification of nephrin autoantibody minimal change disease to serve as a framework for instigation of precision therapeutics for these patients.

Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease.

AIMS/HYPOTHESIS: Diabetic kidney disease (DKD) is the leading cause of kidney failure and has a substantial genetic component. Our aim was to identify novel genetic factors and genes contributing to DKD by performing meta-analysis of previous genome-wide association studies (GWAS) on DKD and by integrating the results with renal transcriptomics datasets. METHODS: We performed GWAS meta-analyses using ten phenotypic definitions of DKD, including nearly 27,000 individuals with diabetes. Meta-analysis results were integrated with estimated quantitative trait locus data from human glomerular (N=119) and tubular (N=121) samples to perform transcriptome-wide association study. We also performed gene aggregate tests to jointly test all available common genetic markers within a gene, and combined the results with various kidney omics datasets. RESULTS: The meta-analysis identified a novel intronic variant (rs72831309) in the TENM2 gene associated with a lower risk of the combined chronic kidney disease (eGFR<60 ml/min per 1.73 m2) and DKD (microalbuminuria or worse) phenotype (p=9.8×10-9; although not withstanding correction for multiple testing, p>9.3×10-9). Gene-level analysis identified ten genes associated with DKD (COL20A1, DCLK1, EIF4E, PTPRN-RESP18, GPR158, INIP-SNX30, LSM14A and MFF; p<2.7×10-6). Integration of GWAS with human glomerular and tubular expression data demonstrated higher tubular AKIRIN2 gene expression in individuals with vs without DKD (p=1.1×10-6). The lead SNPs within six loci significantly altered DNA methylation of a nearby CpG site in kidneys (p<1.5×10-11). Expression of lead genes in kidney tubules or glomeruli correlated with relevant pathological phenotypes (e.g. TENM2 expression correlated positively with eGFR [p=1.6×10-8] and negatively with tubulointerstitial fibrosis [p=2.0×10-9], tubular DCLK1 expression correlated positively with fibrosis [p=7.4×10-16], and SNX30 expression correlated positively with eGFR [p=5.8×10-14] and negatively with fibrosis [p<2.0×10-16]). CONCLUSIONS/INTERPRETATION: Altogether, the results point to novel genes contributing to the pathogenesis of DKD. DATA AVAILABILITY: The GWAS meta-analysis results can be accessed via the type 1 and type 2 diabetes (T1D and T2D, respectively) and Common Metabolic Diseases (CMD) Knowledge Portals, and downloaded on their respective download pages ( https://t1d.hugeamp.org/downloads.html ; https://t2d.hugeamp.org/downloads.html ; https://hugeamp.org/downloads.html ).

A glomerular transcriptomic landscape of apolipoprotein L1 in Black patients with focal segmental glomerulosclerosis.

Apolipoprotein L1 (APOL1)-associated focal segmental glomerulosclerosis (FSGS) is the dominant form of FSGS in Black individuals. There are no targeted therapies for this condition, in part because the molecular mechanisms underlying APOL1's pathogenic contribution to FSGS are incompletely understood. Studying the transcriptomic landscape of APOL1 FSGS in patient kidneys is an important way to discover genes and molecular behaviors that are unique or most relevant to the human disease. With the hypothesis that the pathology driven by the high-risk APOL1 genotype is reflected in alteration of gene expression across the glomerular transcriptome, we compared expression and co-expression profiles of 15,703 genes in 16 Black patients with FSGS at high-risk vs 14 Black patients with a low-risk APOL1 genotype. Expression data from APOL1-inducible HEK293 cells and normal human glomeruli were used to pursue genes and molecular pathways uncovered in these studies. We discovered increased expression of APOL1 and nine other significant differentially expressed genes in high-risk patients. This included stanniocalcin, which has a role in mitochondrial and calcium-related processes along with differential correlations between high- and low-risk APOL1 and metabolism pathway genes. There were similar correlations with extracellular matrix- and immune-related genes, but significant loss of co-expression of mitochondrial genes in high-risk FSGS, and an NF-κB-down regulating gene, NKIRAS1, as the most significant hub gene with strong differential correlations with NDUF family (mitochondrial respiratory genes) and immune-related (JAK-STAT) genes. Thus, differences in mitochondrial gene regulation appear to underlie many differences observed between high- and low-risk Black patients with FSGS.

Molecular programs associated with glomerular hyperfiltration in early diabetic kidney disease.

Hyperfiltration is a state of high glomerular filtration rate (GFR) observed in early diabetes that damages glomeruli, resulting in an iterative process of increasing filtration load on fewer and fewer remaining functional glomeruli. To delineate underlying cellular mechanisms of damage associated with hyperfiltration, transcriptional profiles of kidney biopsies from Pima Indians with type 2 diabetes with or without early-stage diabetic kidney disease were grouped into two hyperfiltration categories based on annual iothalamate GFR measurements. Twenty-six participants with a peak GFR measurement within two years of biopsy were categorized as the hyperfiltration group, and 26 in whom biopsy preceded peak GFR by over two years were considered pre-hyperfiltration. The hyperfiltration group had higher hemoglobin A1c, higher urine albumin-to-creatinine ratio, increased glomerular basement membrane width and lower podocyte density compared to the pre-hyperfiltration group. A glomerular 1240-gene transcriptional signature identified in the hyperfiltration group was enriched for endothelial stress response signaling genes, including endothelin-1, tec-kinase and transforming growth factor-ß1 pathways, with the majority of the transcripts mapped to endothelial and inflammatory cell clusters in kidney single cell transcriptional data. Thus, our analysis reveals molecular pathomechanisms associated with hyperfiltration in early diabetic kidney disease involving putative ligand-receptor pairs with downstream intracellular targets linked to cellular crosstalk between endothelial and mesangial cells.

A Rare Autosomal Dominant Variant in Regulator of Calcineurin Type 1 (RCAN1) Gene Confers Enhanced Calcineurin Activity and May Cause FSGS.

BACKGROUND: Podocyte dysfunction is the main pathologic mechanism driving the development of FSGS and other morphologic types of steroid-resistant nephrotic syndrome (SRNS). Despite significant progress, the genetic causes of most cases of SRNS have yet to be identified. METHODS: Whole-genome sequencing was performed on 320 individuals from 201 families with familial and sporadic NS/FSGS with no pathogenic mutations in any known NS/FSGS genes. RESULTS: Two variants in the gene encoding regulator of calcineurin type 1 (RCAN1) segregate with disease in two families with autosomal dominant FSGS/SRNS. In vitro, loss of RCAN1 reduced human podocyte viability due to increased calcineurin activity. Cells expressing mutant RCAN1 displayed increased calcineurin activity and NFAT activation that resulted in increased susceptibility to apoptosis compared with wild-type RCAN1. Treatment with GSK-3 inhibitors ameliorated this elevated calcineurin activity, suggesting the mutation alters the balance of RCAN1 regulation by GSK-3ß, resulting in dysregulated calcineurin activity and apoptosis. CONCLUSIONS: These data suggest mutations in RCAN1 can cause autosomal dominant FSGS. Despite the widespread use of calcineurin inhibitors in the treatment of NS, genetic mutations in a direct regulator of calcineurin have not been implicated in the etiology of NS/FSGS before this report. The findings highlight the therapeutic potential of targeting RCAN1 regulatory molecules, such as GSK-3ß, in the treatment of FSGS.

An eQTL Landscape of Kidney Tissue in Human Nephrotic Syndrome.

Expression quantitative trait loci (eQTL) studies illuminate the genetics of gene expression and, in disease research, can be particularly illuminating when using the tissues directly impacted by the condition. In nephrology, there is a paucity of eQTL studies of human kidney. Here, we used whole-genome sequencing (WGS) and microdissected glomerular (GLOM) and tubulointerstitial (TI) transcriptomes from 187 individuals with nephrotic syndrome (NS) to describe the eQTL landscape in these functionally distinct kidney structures. Using MatrixEQTL, we performed cis-eQTL analysis on GLOM (n = 136) and TI (n = 166). We used the Bayesian "Deterministic Approximation of Posteriors" (DAP) to fine-map these signals, eQTLBMA to discover GLOM- or TI-specific eQTLs, and single-cell RNA-seq data of control kidney tissue to identify the cell type specificity of significant eQTLs. We integrated eQTL data with an IgA Nephropathy (IgAN) GWAS to perform a transcriptome-wide association study (TWAS). We discovered 894 GLOM eQTLs and 1,767 TI eQTLs at FDR < 0.05. 14% and 19% of GLOM and TI eQTLs, respectively, had >1 independent signal associated with its expression. 12% and 26% of eQTLs were GLOM specific and TI specific, respectively. GLOM eQTLs were most significantly enriched in podocyte transcripts and TI eQTLs in proximal tubules. The IgAN TWAS identified significant GLOM and TI genes, primarily at the HLA region. In this study, we discovered GLOM and TI eQTLs, identified those that were tissue specific, deconvoluted them into cell-specific signals, and used them to characterize known GWAS alleles. These data are available for browsing and download via our eQTL browser, "nephQTL."

A protein-centric approach for exome variant aggregation enables sensitive association analysis with clinical outcomes.

Somatic mutations are early drivers of tumorigenesis and tumor progression. However, the mutations typically occur at variable positions across different individuals, resulting in the data being too sparse to test meaningful associations between variants and phenotypes. To overcome this challenge, we devised a novel approach called Gene-to-Protein-to-Disease (GPD) which accumulates variants into new sequence units as the degree of genetic assault on structural or functional units of each protein. The variant frequencies in the sequence units were highly reproducible between two large cancer cohorts. Survival analysis identified 232 sequence units in which somatic mutations had deleterious effects on overall survival, including consensus driver mutations obtained from multiple calling algorithms. By contrast, around 76% of the survival predictive units had been undetected by conventional gene-level analysis. We demonstrate the ability of these signatures to separate patient groups according to overall survival, therefore, providing novel prognostic tools for various cancers. GPD also identified sequence units with somatic mutations whose impact on survival was modified by the occupancy of germline variants in the surrounding regions. The findings indicate that a patient's genetic predisposition interacts with the effect of somatic mutations on survival outcomes in some cancers.

Disruption of the exocyst induces podocyte loss and dysfunction.

Although the slit diaphragm proteins in podocytes are uniquely organized to maintain glomerular filtration assembly and function, little is known about the underlying mechanisms that participate in trafficking these proteins to the correct location for development and homeostasis. Identifying these mechanisms will likely provide novel targets for therapeutic intervention to preserve podocyte function following glomerular injury. Analysis of structural variation in cases of human nephrotic syndrome identified rare heterozygous deletions of EXOC4 in two patients. This suggested that disruption of the highly-conserved eight-protein exocyst trafficking complex could have a role in podocyte dysfunction. Indeed, mRNA profiling of injured podocytes identified significant exocyst down-regulation. To test the hypothesis that the exocyst is centrally involved in podocyte development/function, we generated homozygous podocyte-specific Exoc5 (a central exocyst component that interacts with Exoc4) knockout mice that showed massive proteinuria and died within 4 weeks of birth. Histological and ultrastructural analysis of these mice showed severe glomerular defects with increased fibrosis, proteinaceous casts, effaced podocytes, and loss of the slit diaphragm. Immunofluorescence analysis revealed that Neph1 and Nephrin, major slit diaphragm constituents, were mislocalized and/or lost. mRNA profiling of Exoc5 knockdown podocytes showed that vesicular trafficking was the most affected cellular event. Mapping of signaling pathways and Western blot analysis revealed significant up-regulation of the mitogen-activated protein kinase and transforming growth factor-ß pathways in Exoc5 knockdown podocytes and in the glomeruli of podocyte-specific Exoc5 KO mice. Based on these data, we propose that exocyst-based mechanisms regulate Neph1 and Nephrin signaling and trafficking, and thus podocyte development and function.

SARS-CoV-2 receptor networks in diabetic and COVID-19-associated kidney disease.

COVID-19 morbidity and mortality are increased via unknown mechanisms in patients with diabetes and kidney disease. SARS-CoV-2 uses angiotensin-converting enzyme 2 (ACE2) for entry into host cells. Because ACE2 is a susceptibility factor for infection, we investigated how diabetic kidney disease and medications alter ACE2 receptor expression in kidneys. Single cell RNA profiling of kidney biopsies from healthy living donors and patients with diabetic kidney disease revealed ACE2 expression primarily in proximal tubular epithelial cells. This cell-specific localization was confirmed by in situ hybridization. ACE2 expression levels were unaltered by exposures to renin-angiotensin-aldosterone system inhibitors in diabetic kidney disease. Bayesian integrative analysis of a large compendium of public -omics datasets identified molecular network modules induced in ACE2-expressing proximal tubular epithelial cells in diabetic kidney disease (searchable at hb.flatironinstitute.org/covid-kidney) that were linked to viral entry, immune activation, endomembrane reorganization, and RNA processing. The diabetic kidney disease ACE2-positive proximal tubular epithelial cell module overlapped with expression patterns seen in SARS-CoV-2-infected cells. Similar cellular programs were seen in ACE2-positive proximal tubular epithelial cells obtained from urine samples of 13 hospitalized patients with COVID-19, suggesting a consistent ACE2-coregulated proximal tubular epithelial cell expression program that may interact with the SARS-CoV-2 infection processes. Thus SARS-CoV-2 receptor networks can seed further research into risk stratification and therapeutic strategies for COVID-19-related kidney damage.

Functional proteogenomics reveals biomarkers and therapeutic targets in lymphomas.

Identification of biomarkers and therapeutic targets is a critical goal of precision medicine. N-glycoproteins are a particularly attractive class of proteins that constitute potential cancer biomarkers and therapeutic targets for small molecules, antibodies, and cellular therapies. Using mass spectrometry (MS), we generated a compendium of 1,091 N-glycoproteins (from 40 human primary lymphomas and cell lines). Hierarchical clustering revealed distinct subtype signatures that included several subtype-specific biomarkers. Orthogonal immunological studies in 671 primary lymphoma tissue biopsies and 32 lymphoma-derived cell lines corroborated MS data. In anaplastic lymphoma kinase-positive (ALK+) anaplastic large cell lymphoma (ALCL), integration of N-glycoproteomics and transcriptome sequencing revealed an ALK-regulated cytokine/receptor signaling network, including vulnerabilities corroborated by a genome-wide clustered regularly interspaced short palindromic screen. Functional targeting of IL-31 receptor ß, an ALCL-enriched and ALK-regulated N-glycoprotein in this network, abrogated ALK+ALCL growth in vitro and in vivo. Our results highlight the utility of functional proteogenomic approaches for discovery of cancer biomarkers and therapeutic targets.

SAINTq: Scoring protein-protein interactions in affinity purification - mass spectrometry experiments with fragment or peptide intensity data.

SAINT (Significance Analysis of INTeractome) is a probabilistic method for scoring bait-prey interactions against negative controls in affinity purification - mass spectrometry (AP-MS) experiments. Our published SAINT algorithms use spectral counts or protein intensities as the input for calculating the probability of true interaction, which enables objective selection of high-confidence interactions with false discovery control. With the advent of new protein quantification methods such as Data Independent Acquisition (DIA), we redeveloped the scoring method to utilize the reproducibility information embedded in the peptide or fragment intensity data as a key scoring criterion, bypassing protein intensity summarization required in the previous SAINT workflow. The new software package, SAINTq, addresses key issues in the interaction scoring based on intensity data, including treatment of missing values and selection of peptides and fragments for scoring each prey protein. We applied SAINTq to two independent DIA AP-MS data sets profiling the interactome of MEPCE and EIF4A2 and that of 14-3-3ß, and benchmarked the performance in terms of recovering previously reported literature interactions in the iRefIndex database. In both data sets, the SAINTq analysis using the fragment-level intensity data led to the most sensitive detection of literature interactions at the same level of specificity. This analysis outperforms the analysis using protein intensity data summed from fragment intensity data that is equivalent to the model in SAINTexpress.

The CRAPome: a contaminant repository for affinity purification-mass spectrometry data.

Affinity purification coupled with mass spectrometry (AP-MS) is a widely used approach for the identification of protein-protein interactions. However, for any given protein of interest, determining which of the identified polypeptides represent bona fide interactors versus those that are background contaminants (for example, proteins that interact with the solid-phase support, affinity reagent or epitope tag) is a challenging task. The standard approach is to identify nonspecific interactions using one or more negative-control purifications, but many small-scale AP-MS studies do not capture a complete, accurate background protein set when available controls are limited. Fortunately, negative controls are largely bait independent. Hence, aggregating negative controls from multiple AP-MS studies can increase coverage and improve the characterization of background associated with a given experimental protocol. Here we present the contaminant repository for affinity purification (the CRAPome) and describe its use for scoring protein-protein interactions. The repository (currently available for Homo sapiens and Saccharomyces cerevisiae) and computational tools are freely accessible at http://www.crapome.org/.

LuciPHOr2: site localization of generic post-translational modifications from tandem mass spectrometry data.

UNLABELLED: We present LuciPHOr2, a site localization tool for generic post-translational modifications (PTMs) using tandem mass spectrometry data. As an extension of the original LuciPHOr (version 1) for phosphorylation site localization, the new software provides a site-level localization score for generic PTMs and associated false discovery rate called the false localization rate. We describe several novel features such as operating system independence and reduced computation time through multiple threading. We also discuss optimal parameters for different types of data and illustrate the new tool on a human skeletal muscle dataset for lysine-acetylation. AVAILABILITY AND IMPLEMENTATION: The software is freely available on the SourceForge website http://luciphor2.sourceforge.net. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

EBprot: Statistical analysis of labeling-based quantitative proteomics data.

Labeling-based proteomics is a powerful method for detection of differentially expressed proteins (DEPs). The current data analysis platform typically relies on protein-level ratios, which is obtained by summarizing peptide-level ratios for each protein. In shotgun proteomics, however, some proteins are quantified with more peptides than others, and this reproducibility information is not incorporated into the differential expression (DE) analysis. Here, we propose a novel probabilistic framework EBprot that directly models the peptide-protein hierarchy and rewards the proteins with reproducible evidence of DE over multiple peptides. To evaluate its performance with known DE states, we conducted a simulation study to show that the peptide-level analysis of EBprot provides better receiver-operating characteristic and more accurate estimation of the false discovery rates than the methods based on protein-level ratios. We also demonstrate superior classification performance of peptide-level EBprot analysis in a spike-in dataset. To illustrate the wide applicability of EBprot in different experimental designs, we applied EBprot to a dataset for lung cancer subtype analysis with biological replicates and another dataset for time course phosphoproteome analysis of EGF-stimulated HeLa cells with multiplexed labeling. Through these examples, we show that the peptide-level analysis of EBprot is a robust alternative to the existing statistical methods for the DE analysis of labeling-based quantitative datasets. The software suite is freely available on the Sourceforge website http://ebprot.sourceforge.net/. All MS data have been deposited in the ProteomeXchange with identifier PXD001426 (http://proteomecentral.proteomexchange.org/dataset/PXD001426/).

Global phosphoproteomic profiling reveals distinct signatures in B-cell non-Hodgkin lymphomas.

Deregulation of signaling pathways controlled by protein phosphorylation underlies the pathogenesis of hematological malignancies; however, the extent to which deregulated phosphorylation may be involved in B-cell non-Hodgkin lymphoma (B-NHL) pathogenesis is largely unknown. To identify phosphorylation events important in B-NHLs, we performed mass spectrometry-based, label-free, semiquantitative phosphoproteomic profiling of 11 cell lines derived from three B-NHL categories: Burkitt lymphoma, follicular lymphoma, and mantle-cell lymphoma. In all, 6579 unique phosphopeptides, corresponding to 1701 unique phosphorylated proteins, were identified and quantified. The data are available via ProteomeXchange with identifier PXD000658. Hierarchical clustering highlighted distinct phosphoproteomic signatures associated with each lymphoma subtype. Interestingly, germinal center-derived B-NHL cell lines were characterized by phosphorylation of proteins involved in the B-cell receptor signaling. Of these proteins, phosphoprotein associated with glycosphingolipid-enriched microdomains 1 (PAG1) was identified with the most phosphorylated tyrosine peptides in Burkitt lymphoma and follicular lymphoma. PAG1 knockdown resulted in perturbation of the tyrosine phosphosignature of B-cell receptor signaling components. Significantly, PAG1 knockdown increased cell proliferation and response to antigen stimulation of these germinal center-derived B-NHLs. These data provide a detailed annotation of phosphorylated proteins in human lymphoid cancer. Overall, our study revealed the utility of unbiased phosphoproteome interrogation in characterizing signaling networks that may provide insights into pathogenesis mechanisms in B-cell lymphomas.

Integrated phosphoproteomic and metabolomic profiling reveals NPM-ALK-mediated phosphorylation of PKM2 and metabolic reprogramming in anaplastic large cell lymphoma.

The mechanisms underlying the pathogenesis of the constitutively active tyrosine kinase nucleophosmin-anaplastic lymphoma kinase (NPM-ALK) expressing anaplastic large cell lymphoma are not completely understood. Here we show using an integrated phosphoproteomic and metabolomic strategy that NPM-ALK induces a metabolic shift toward aerobic glycolysis, increased lactate production, and biomass production. The metabolic shift is mediated through the anaplastic lymphoma kinase (ALK) phosphorylation of the tumor-specific isoform of pyruvate kinase (PKM2) at Y105, resulting in decreased enzymatic activity. Small molecule activation of PKM2 or expression of Y105F PKM2 mutant leads to reversal of the metabolic switch with increased oxidative phosphorylation and reduced lactate production coincident with increased cell death, decreased colony formation, and reduced tumor growth in an in vivo xenograft model. This study provides comprehensive profiling of the phosphoproteomic and metabolomic consequences of NPM-ALK expression and reveals a novel role of ALK in the regulation of multiple components of cellular metabolism. Our studies show that PKM2 is a novel substrate of ALK and plays a critical role in mediating the metabolic shift toward biomass production and tumorigenesis.

LuciPHOr: algorithm for phosphorylation site localization with false localization rate estimation using modified target-decoy approach.

The localization of phosphorylation sites in peptide sequences is a challenging problem in large-scale phosphoproteomics analysis. The intense neutral loss peaks and the coexistence of multiple serine/threonine and/or tyrosine residues are limiting factors for objectively scoring site patterns across thousands of peptides. Various computational approaches for phosphorylation site localization have been proposed, including Ascore, Mascot Delta score, and ProteinProspector, yet few address direct estimation of the false localization rate (FLR) in each experiment. Here we propose LuciPHOr, a modified target-decoy-based approach that uses mass accuracy and peak intensities for site localization scoring and FLR estimation. Accurate estimation of the FLR is a difficult task at the individual-site level because the degree of uncertainty in localization varies significantly across different peptides. LuciPHOr carries out simultaneous localization on all candidate sites in each peptide and estimates the FLR based on the target-decoy framework, where decoy phosphopeptides generated by placing artificial phosphorylation(s) on non-candidate residues compete with the non-decoy phosphopeptides. LuciPHOr also reports approximate site-level confidence scores for all candidate sites as a means to localize additional sites from multiphosphorylated peptides in which localization can be partially achieved. Unlike the existing tools, LuciPHOr is compatible with any search engine output processed through the Trans-Proteomic Pipeline. We evaluated the performance of LuciPHOr in terms of the sensitivity and accuracy of FLR estimates using two synthetic phosphopeptide libraries and a phosphoproteomic dataset generated from complex mouse brain samples.

SAINT: probabilistic scoring of affinity purification-mass spectrometry data.

We present 'significance analysis of interactome' (SAINT), a computational tool that assigns confidence scores to protein-protein interaction data generated using affinity purification-mass spectrometry (AP-MS). The method uses label-free quantitative data and constructs separate distributions for true and false interactions to derive the probability of a bona fide protein-protein interaction. We show that SAINT is applicable to data of different scales and protein connectivity and allows transparent analysis of AP-MS data.

Sparsely correlated hidden Markov models with application to genome-wide location studies.

MOTIVATION: Multiply correlated datasets have become increasingly common in genome-wide location analysis of regulatory proteins and epigenetic modifications. Their correlation can be directly incorporated into a statistical model to capture underlying biological interactions, but such modeling quickly becomes computationally intractable. RESULTS: We present sparsely correlated hidden Markov models (scHMM), a novel method for performing simultaneous hidden Markov model (HMM) inference for multiple genomic datasets. In scHMM, a single HMM is assumed for each series, but the transition probability in each series depends on not only its own hidden states but also the hidden states of other related series. For each series, scHMM uses penalized regression to select a subset of the other data series and estimate their effects on the odds of each transition in the given series. Following this, hidden states are inferred using a standard forward-backward algorithm, with the transition probabilities adjusted by the model at each position, which helps retain the order of computation close to fitting independent HMMs (iHMM). Hence, scHMM is a collection of inter-dependent non-homogeneous HMMs, capable of giving a close approximation to a fully multivariate HMM fit. A simulation study shows that scHMM achieves comparable sensitivity to the multivariate HMM fit at a much lower computational cost. The method was demonstrated in the joint analysis of 39 histone modifications, CTCF and RNA polymerase II in human CD4+ T cells. scHMM reported fewer high-confidence regions than iHMM in this dataset, but scHMM could recover previously characterized histone modifications in relevant genomic regions better than iHMM. In addition, the resulting combinatorial patterns from scHMM could be better mapped to the 51 states reported by the multivariate HMM method of Ernst and Kellis. AVAILABILITY: The scHMM package can be freely downloaded from http://sourceforge.net/p/schmm/ and is recommended for use in a linux environment.