Secreted retrovirus-like GAG-domain-containing protein PEG10 is regulated by UBE3A and is involved in Angelman syndrome pathophysiology.

Angelman syndrome (AS) is a neurodevelopmental disorder caused by the loss of maternal UBE3A, a ubiquitin protein ligase E3A. Here, we study neurons derived from patients with AS and neurotypical individuals, and reciprocally modulate UBE3A using antisense oligonucleotides. Unbiased proteomics reveal proteins that are regulated by UBE3A in a disease-specific manner, including PEG10, a retrotransposon-derived GAG protein. PEG10 protein increase, but not RNA, is dependent on UBE3A and proteasome function. PEG10 binds to both RNA and ataxia-associated proteins (ATXN2 and ATXN10), localizes to stress granules, and is secreted in extracellular vesicles, modulating vesicle content. Rescue of AS patient-derived neurons by UBE3A reinstatement or PEG10 reduction reveals similarity in transcriptome changes. Overexpression of PEG10 during mouse brain development alters neuronal migration, suggesting that it can affect brain development. These findings imply that PEG10 is a secreted human UBE3A target involved in AS pathophysiology.

MassIVE.quant: a community resource of quantitative mass spectrometry-based proteomics datasets.

MassIVE.quant is a repository infrastructure and data resource for reproducible quantitative mass spectrometry-based proteomics, which is compatible with all mass spectrometry data acquisition types and computational analysis tools. A branch structure enables MassIVE.quant to systematically store raw experimental data, metadata of the experimental design, scripts of the quantitative analysis workflow, intermediate input and output files, as well as alternative reanalyses of the same dataset.

MSstatsTMT: Statistical Detection of Differentially Abundant Proteins in Experiments with Isobaric Labeling and Multiple Mixtures.

Tandem mass tag (TMT) is a multiplexing technology widely-used in proteomic research. It enables relative quantification of proteins from multiple biological samples in a single MS run with high efficiency and high throughput. However, experiments often require more biological replicates or conditions than can be accommodated by a single run, and involve multiple TMT mixtures and multiple runs. Such larger-scale experiments combine sources of biological and technical variation in patterns that are complex, unique to TMT-based workflows, and challenging for the downstream statistical analysis. These patterns cannot be adequately characterized by statistical methods designed for other technologies, such as label-free proteomics or transcriptomics. This manuscript proposes a general statistical approach for relative protein quantification in MS- based experiments with TMT labeling. It is applicable to experiments with multiple conditions, multiple biological replicate runs and multiple technical replicate runs, and unbalanced designs. It is based on a flexible family of linear mixed-effects models that handle complex patterns of technical artifacts and missing values. The approach is implemented in MSstatsTMT, a freely available open-source R/Bioconductor package compatible with data processing tools such as Proteome Discoverer, MaxQuant, OpenMS, and SpectroMine. Evaluation on a controlled mixture, simulated datasets, and three biological investigations with diverse designs demonstrated that MSstatsTMT balanced the sensitivity and the specificity of detecting differentially abundant proteins, in large-scale experiments with multiple biological mixtures.

Selection of Features with Consistent Profiles Improves Relative Protein Quantification in Mass Spectrometry Experiments.

In bottom-up mass spectrometry-based proteomics, relative protein quantification is often achieved with data-dependent acquisition (DDA), data-independent acquisition (DIA), or selected reaction monitoring (SRM). These workflows quantify proteins by summarizing the abundances of all the spectral features of the protein (e.g. precursor ions, transitions or fragments) in a single value per protein per run. When abundances of some features are inconsistent with the overall protein profile (for technological reasons such as interferences, or for biological reasons such as post-translational modifications), the protein-level summaries and the downstream conclusions are undermined. We propose a statistical approach that automatically detects spectral features with such inconsistent patterns. The detected features can be separately investigated, and if necessary, removed from the data set. We evaluated the proposed approach on a series of benchmark-controlled mixtures and biological investigations with DDA, DIA and SRM data acquisitions. The results demonstrated that it could facilitate and complement manual curation of the data. Moreover, it can improve the estimation accuracy, sensitivity and specificity of detecting differentially abundant proteins, and reproducibility of conclusions across different data processing tools. The approach is implemented as an option in the open-source R-based software MSstats.

N-Terminomics identifies HtrA1 cleavage of thrombospondin-1 with generation of a proangiogenic fragment in the polarized retinal pigment epithelial cell model of age-related macular degeneration.

Age-related macular degeneration (AMD) is the leading cause of irreversible blindness in the elderly population. Variants in the HTRA1-ARMS2 locus have been linked to increased AMD risk. In the present study we investigated the impact of elevated HtrA1 levels on the retina pigment epithelial (RPE) secretome using a polarized culture system. Upregulation of HtrA1 alters the abundance of key proteins involved in angiogenesis and extracellular matrix remodeling. Thrombospondin-1, an angiogenesis modulator, was identified as a substrate for HtrA1 using terminal amine isotope labeling of substrates in conjunction with HtrA1 specificity profiling. HtrA1 cleavage of thrombospondin-1 was further corroborated by in vitro cleavage assays and targeted proteomics together with small molecule inhibition of HtrA1. While thrombospondin-1 is anti-angiogenic, the proteolytically released N-terminal fragment promotes the formation of tube-like structure by endothelial cells. Taken together, our findings suggest a mechanism by which increased levels of HtrA1 may contribute to AMD pathogenesis. The proteomic data has been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier. For quantitative secretome analysis, project accession: PXD007691, username: reviewer45093@ebi.ac.uk, password: 1FUpS6Yq. For TAILS analysis, project accession: PXD007139, username: reviewer76731@ebi.ac.uk, password: sNbMp7xK.

HtrA1 Mediated Intracellular Effects on Tubulin Using a Polarized RPE Disease Model.

Age-related macular degeneration (AMD) is the leading cause of irreversible vision loss. The protein HtrA1 is enriched in retinal pigment epithelial (RPE) cells isolated from AMD patients and in drusen deposits. However, it is poorly understood how increased levels of HtrA1 affect the physiological function of the RPE at the intracellular level. Here, we developed hfRPE (human fetal retinal pigment epithelial) cell culture model where cells fully differentiated into a polarized functional monolayer. In this model, we fine-tuned the cellular levels of HtrA1 by targeted overexpression. Our data show that HtrA1 enzymatic activity leads to intracellular degradation of tubulin with a corresponding reduction in the number of microtubules, and consequently to an altered mechanical cell phenotype. HtrA1 overexpression further leads to impaired apical processes and decreased phagocytosis, an essential function for photoreceptor survival. These cellular alterations correlate with the AMD phenotype and thus highlight HtrA1 as an intracellular target for therapeutic interventions towards AMD treatment.

HtrA1 activation is driven by an allosteric mechanism of inter-monomer communication.

The human protease family HtrA is responsible for preventing protein misfolding and mislocalization, and a key player in several cellular processes. Among these, HtrA1 is implicated in several cancers, cerebrovascular disease and age-related macular degeneration. Currently, HtrA1 activation is not fully characterized and relevant for drug-targeting this protease. Our work provides a mechanistic step-by-step description of HtrA1 activation and regulation. We report that the HtrA1 trimer is regulated by an allosteric mechanism by which monomers relay the activation signal to each other, in a PDZ-domain independent fashion. Notably, we show that inhibitor binding is precluded if HtrA1 monomers cannot communicate with each other. Our study establishes how HtrA1 trimerization plays a fundamental role in proteolytic activity. Moreover, it offers a structural explanation for HtrA1-defective pathologies as well as mechanistic insights into the degradation of complex extracellular fibrils such as tubulin, amyloid beta and tau that belong to the repertoire of HtrA1.

mTORC1 Inhibition Corrects Neurodevelopmental and Synaptic Alterations in a Human Stem Cell Model of Tuberous Sclerosis.

Hyperfunction of the mTORC1 pathway has been associated with idiopathic and syndromic forms of autism spectrum disorder (ASD), including tuberous sclerosis, caused by loss of either TSC1 or TSC2. It remains largely unknown how developmental processes and biochemical signaling affected by mTORC1 dysregulation contribute to human neuronal dysfunction. Here, we have characterized multiple stages of neurogenesis and synapse formation in human neurons derived from TSC2-deleted pluripotent stem cells. Homozygous TSC2 deletion causes severe developmental abnormalities that recapitulate pathological hallmarks of cortical malformations in patients. Both TSC2(+/-) and TSC2(-/-) neurons display altered synaptic transmission paralleled by molecular changes in pathways associated with autism, suggesting the convergence of pathological mechanisms in ASD. Pharmacological inhibition of mTORC1 corrects developmental abnormalities and synaptic dysfunction during independent developmental stages. Our results uncouple stage-specific roles of mTORC1 in human neuronal development and contribute to a better understanding of the onset of neuronal pathophysiology in tuberous sclerosis.

Identification of DYRK1B as a substrate of ERK1/2 and characterisation of the kinase activity of DYRK1B mutants from cancer and metabolic syndrome.

The dual-specificity tyrosine-phosphorylation-regulated kinase, DYRK1B, is expressed de novo during myogenesis, amplified or mutated in certain cancers and mutated in familial cases of metabolic syndrome. DYRK1B is activated by cis auto-phosphorylation on tyrosine-273 (Y273) within the activation loop during translation but few other DYRK1B phosphorylation sites have been characterised to date. Here, we demonstrate that DYRK1B also undergoes trans-autophosphorylation on serine-421 (S421) in vitro and in cells and that this site contributes to DYRK1B kinase activity. Whilst a DYRK1B(S421A) mutant was completely defective for p-S421 in cells, DYRK1B inhibitors caused only a partial loss of p-S421 suggesting the existence of an additional kinase that could also phosphorylate DYRK1B S421. Indeed, a catalytically inactive DYRK1B(D239A) mutant exhibited very low levels of p-S421 in cells but this was increased by KRAS(G12V). In addition, selective activation of the RAF-MEK1/2-ERK1/2 signalling pathway rapidly increased p-S421 in cells whereas activation of the stress kinases JNK or p38 could not. S421 resides within a Ser-Pro phosphoacceptor motif that is typical for ERK1/2 and recombinant ERK2 phosphorylated DYRK1B at S421 in vitro. Our results show that DYRK1B is a novel ERK2 substrate, uncovering new links between two kinases involved in cell fate decisions. Finally, we show that DYRK1B mutants that have recently been described in cancer and metabolic syndrome exhibit normal or reduced intrinsic kinase activity.

Characterization of a human pluripotent stem cell-derived model of neuronal development using multiplexed targeted proteomics.

PURPOSE: Human pluripotent stem cell (hPSC)-derived cellular models have great potential to enable drug discovery and improve translation of preclinical insights to the clinic. We have developed a hPSC-derived neural precursor cell model for studying early events in human brain development. We present protein-level characterization of this model, using a multiplexed SRM approach, to establish reproducibility and physiological relevance; essential prerequisites for utilization of the neuronal development model in phenotypic screening-based drug discovery. EXPERIMENTAL DESIGN: Profiles of 246 proteins across three key stages of in vitro neuron differentiation were analyzed by SRM. Three independently hPSC-derived isogenic neural stem cell (NSC) lines were analyzed across five to nine independent neuronal differentiations. RESULTS: One hundred seventy-five proteins were reliably quantified revealing a time-dependent pattern of protein regulation that reflected protein dynamics during in vivo brain development and that was conserved across replicate differentiations and multiple cell lines. CONCLUSIONS AND CLINICAL RELEVANCE: SRM-based protein profiling enabled establishment of the reproducibility and physiological relevance of the hPSC-derived neuronal model. Combined with the successful quantification of proteins relevant to neurodevelopmental diseases, this validates the platform for use as a model to enable neuroscience drug discovery.

Quantitative ADME proteomics - CYP and UGT enzymes in the Beagle dog liver and intestine.

PURPOSE: Beagle dogs are used to study oral pharmacokinetics and guide development of drug formulations for human use. Since mechanistic insight into species differences is needed to translate findings in this species to human, abundances of cytochrome P450 (CYP) and uridine diphosphate glucuronosyltransferase (UGT) drug metabolizing enzymes have been quantified in dog liver and intestine. METHODS: Abundances of enzymes were measured in Beagle dog intestine and liver using selected reaction monitoring mass spectrometry. RESULTS: Seven and two CYPs were present in the liver and intestine, respectively. CYP3A12 was the most abundant CYP in both tissues. Seven UGT enzymes were quantified in the liver and seven in the intestine although UGT1A11 and UGT1A9 were present only in the intestine and UGT1A7 and UGT2B31 were found only in the liver. UGT1A11 and UGT1A2 were the most abundant UGTs in the intestine and UGT2B31 was the most abundant UGT in the liver. Summed abundance of UGT enzymes was similar to the sum of CYP enzymes in the liver whereas intestinal UGTs were up to four times more abundant than CYPs. The estimated coefficients of variation of abundance estimates in the livers of 14 donors were separated into biological and technical components which ranged from 14 to 49% and 20 to 39%, respectively. CONCLUSIONS: Abundances of canine CYP enzymes in liver and intestine have been confirmed in a larger number of dogs and UGT abundances have been quantified for the first time. The biological variability in hepatic CYPs and UGTs has also been estimated.

Targeted profiling of polar intracellular metabolites using ion-pair-high performance liquid chromatography and -ultra high performance liquid chromatography coupled to tandem mass spectrometry: applications to serum, urine and tissue extracts.

The effective analysis of polar ionic metabolites by LC-MS, such as those encountered in central carbon metabolism, represents a major problem for metabolic profiling that is not adequately addressed using strategies based on either reversed-phase or HILIC methods. Here we have compared analysis of central carbon metabolites on optimized methods using HILIC, porous graphitic carbon or ion pair chromatography (IPC) using tributyl ammonium as IP reagent. Of the 3 chromatographic approaches examined only IPC enabled us to obtain a robust analytical methodology. This system was used to profile more than a hundred endogenous metabolic intermediates in urine, serum and tissue samples. However, whilst we found IPC to be the best of the approaches examined considerable care was still needed to obtain robust data. Thus, in excess of 40 of representative biological samples were needed to "condition" a new analytical column and further 10 matrix injections were then required at the beginning of each analytical batch in order to obtain robust and reproducible chromatographic separations. An additional limitation that we have found was that, for a small number of phosphorylated and poly carboxylic acid metabolites, measurement was only possible if the analytes were present in relatively high concentrations. We also found that, whilst this methodology could be used for the analysis of both in vitro cell culture media, cell extracts, tissue, and biological fluids (blood, urine), for the best results columns should only be used to analyze a single matrix. However, despite the need for extensive column conditioning, and the manifold disadvantages resulting from the contamination of the separation system and mass spectrometer with the ion pair reagent, IPC-MS currently provides the best means of analyzing these polar, ionic and problematic metabolites.

AZD3514: a small molecule that modulates androgen receptor signaling and function in vitro and in vivo.

Continued androgen receptor (AR) expression and signaling is a key driver in castration-resistant prostate cancer (CRPC) after classical androgen ablation therapies have failed, and therefore remains a target for the treatment of progressive disease. Here, we describe the biological characterization of AZD3514, an orally bioavailable drug that inhibits androgen-dependent and -independent AR signaling. AZD3514 modulates AR signaling through two distinct mechanisms, an inhibition of ligand-driven nuclear translocation of AR and a downregulation of receptor levels, both of which were observed in vitro and in vivo. AZD3514 inhibited testosterone-driven seminal vesicle development in juvenile male rats and the growth of androgen-dependent Dunning R3327H prostate tumors in adult rats. Furthermore, this class of compound showed antitumor activity in the HID28 mouse model of CRPC in vivo. AZD3514 is currently in phase I clinical evaluation.

Putative glycosyltransferases and other plant Golgi apparatus proteins are revealed by LOPIT proteomics.

The Golgi apparatus is the central organelle in the secretory pathway and plays key roles in glycosylation, protein sorting, and secretion in plants. Enzymes involved in the biosynthesis of complex polysaccharides, glycoproteins, and glycolipids are located in this organelle, but the majority of them remain uncharacterized. Here, we studied the Arabidopsis (Arabidopsis thaliana) membrane proteome with a focus on the Golgi apparatus using localization of organelle proteins by isotope tagging. By applying multivariate data analysis to a combined data set of two new and two previously published localization of organelle proteins by isotope tagging experiments, we identified the subcellular localization of 1,110 proteins with high confidence. These include 197 Golgi apparatus proteins, 79 of which have not been localized previously by a high-confidence method, as well as the localization of 304 endoplasmic reticulum and 208 plasma membrane proteins. Comparison of the hydrophobic domains of the localized proteins showed that the single-span transmembrane domains have unique properties in each organelle. Many of the novel Golgi-localized proteins belong to uncharacterized protein families. Structure-based homology analysis identified 12 putative Golgi glycosyltransferase (GT) families that have no functionally characterized members and, therefore, are not yet assigned to a Carbohydrate-Active Enzymes database GT family. The substantial numbers of these putative GTs lead us to estimate that the true number of plant Golgi GTs might be one-third above those currently annotated. Other newly identified proteins are likely to be involved in the transport and interconversion of nucleotide sugar substrates as well as polysaccharide and protein modification.

Improved sub-cellular resolution via simultaneous analysis of organelle proteomics data across varied experimental conditions.

Spatial organisation of proteins according to their function plays an important role in the specificity of their molecular interactions. Emerging proteomics methods seek to assign proteins to sub-cellular locations by partial separation of organelles and computational analysis of protein abundance distributions among partially separated fractions. Such methods permit simultaneous analysis of unpurified organelles and promise proteome-wide localisation in scenarios wherein perturbation may prompt dynamic re-distribution. Resolving organelles that display similar behavior during a protocol designed to provide partial enrichment represents a possible shortcoming. We employ the Localisation of Organelle Proteins by Isotope Tagging (LOPIT) organelle proteomics platform to demonstrate that combining information from distinct separations of the same material can improve organelle resolution and assignment of proteins to sub-cellular locations. Two previously published experiments, whose distinct gradients are alone unable to fully resolve six known protein-organelle groupings, are subjected to a rigorous analysis to assess protein-organelle association via a contemporary pattern recognition algorithm. Upon straightforward combination of single-gradient data, we observe significant improvement in protein-organelle association via both a non-linear support vector machine algorithm and partial least-squares discriminant analysis. The outcome yields suggestions for further improvements to present organelle proteomics platforms, and a robust analytical methodology via which to associate proteins with sub-cellular organelles.

Determination of genuine residents of plant endomembrane organelles using isotope tagging and multivariate statistics.

The knowledge of the localization of proteins to a particular subcellular structure or organelle is an important step towards assigning function to proteins predicted by genome-sequencing projects that have yet to be characterized. Moreover, the localization of novel proteins to organelles also enhances our understanding of the functions of organelles. Many organelles cannot be purified. In several cases where the degree of contamination by organelles with similar physical parameters to the organelle being studied has gone unchecked, this has lead to the mis-localization of proteins. Recently, several techniques have emerged, which depend on characterization of the distribution pattern of organelles partially separated using density centrifugation by quantitative proteomics approaches. Here, we discuss one of these approaches, the localization of organelle proteins by isotope tagging (LOPIT) where the distribution patterns of organelles are assessed by measuring the relative abundance of proteins between fractions along the length of density gradients using stable isotope-coded tags. The subcellular localizations of proteins can be determined by comparing their distributions to those of previously localized proteins by assuming that proteins that belong to the same organelle will cofractionate in density gradients. Analysis of distribution patterns can be achieved by employing multivariate statistical methods such as principal component analysis and partial least squares discriminate analysis. In this chapter, we focus on the use of the LOPIT technique in the assignment of membrane proteins to the plant Golgi apparatus and endoplasmic reticulum.

Growth control of the eukaryote cell: a systems biology study in yeast.

BACKGROUND: Cell growth underlies many key cellular and developmental processes, yet a limited number of studies have been carried out on cell-growth regulation. Comprehensive studies at the transcriptional, proteomic and metabolic levels under defined controlled conditions are currently lacking. RESULTS: Metabolic control analysis is being exploited in a systems biology study of the eukaryotic cell. Using chemostat culture, we have measured the impact of changes in flux (growth rate) on the transcriptome, proteome, endometabolome and exometabolome of the yeast Saccharomyces cerevisiae. Each functional genomic level shows clear growth-rate-associated trends and discriminates between carbon-sufficient and carbon-limited conditions. Genes consistently and significantly upregulated with increasing growth rate are frequently essential and encode evolutionarily conserved proteins of known function that participate in many protein-protein interactions. In contrast, more unknown, and fewer essential, genes are downregulated with increasing growth rate; their protein products rarely interact with one another. A large proportion of yeast genes under positive growth-rate control share orthologs with other eukaryotes, including humans. Significantly, transcription of genes encoding components of the TOR complex (a major controller of eukaryotic cell growth) is not subject to growth-rate regulation. Moreover, integrative studies reveal the extent and importance of post-transcriptional control, patterns of control of metabolic fluxes at the level of enzyme synthesis, and the relevance of specific enzymatic reactions in the control of metabolic fluxes during cell growth. CONCLUSION: This work constitutes a first comprehensive systems biology study on growth-rate control in the eukaryotic cell. The results have direct implications for advanced studies on cell growth, in vivo regulation of metabolic fluxes for comprehensive metabolic engineering, and for the design of genome-scale systems biology models of the eukaryotic cell.

Mapping the Arabidopsis organelle proteome.

A challenging task in the study of the secretory pathway is the identification and localization of new proteins to increase our understanding of the functions of different organelles. Previous proteomic studies of the endomembrane system have been hindered by contaminating proteins, making it impossible to assign proteins to organelles. Here we have used the localization of organelle proteins by the isotope tagging technique in conjunction with isotope tags for relative and absolute quantitation and 2D liquid chromatography for the simultaneous assignment of proteins to multiple subcellular compartments. With this approach, the density gradient distributions of 689 proteins from Arabidopsis thaliana were determined, enabling confident and simultaneous localization of 527 proteins to the endoplasmic reticulum, Golgi apparatus, vacuolar membrane, plasma membrane, or mitochondria and plastids. This parallel analysis of endomembrane components has enabled protein steady-state distributions to be determined. Consequently, genuine organelle residents have been distinguished from contaminating proteins and proteins in transit through the secretory pathway.

Quantitative proteomic approach to study subcellular localization of membrane proteins.

As proteins within cells are spatially organized according to their role, knowledge about protein localization gives insight into protein function. Here, we describe the LOPIT technique (localization of organelle proteins by isotope tagging) developed for the simultaneous and confident determination of the steady-state distribution of hundreds of integral membrane proteins within organelles. The technique uses a partial membrane fractionation strategy in conjunction with quantitative proteomics. Localization of proteins is achieved by measuring their distribution pattern across the density gradient using amine-reactive isotope tagging and comparing these patterns with those of known organelle residents. LOPIT relies on the assumption that proteins belonging to the same organelle will co-fractionate. Multivariate statistical tools are then used to group proteins according to the similarities in their distributions, and hence localization without complete centrifugal separation is achieved. The protocol requires approximately 3 weeks to complete and can be applied in a high-throughput manner to material from many varied sources.

i-Tracker: for quantitative proteomics using iTRAQ.

BACKGROUND: iTRAQ technology for protein quantitation using mass spectrometry is a recent, powerful means of determining relative protein levels in up to four samples simultaneously. Although protein identification of samples generated using iTRAQ may be carried out using any current identification software, the quantitation calculations have been restricted to the ProQuant software supplied by Applied Biosciences. i-Tracker software has been developed to extract reporter ion peak ratios from non-centroided tandem MS peak lists in a format easily linked to the results of protein identification tools such as Mascot and Sequest. Such functionality is currently not provided by ProQuant, which is restricted to matching quantitative information to the peptide identifications from Applied Biosciences' Interrogator software. RESULTS: i-Tracker is shown to generate results that are consistent with those produced by ProQuant, thus validating both systems. CONCLUSION: i-Tracker allows quantitative information gained using the iTRAQ protocol to be linked with peptide identifications from popular tandem MS identification tools and hence is both a timely and useful tool for the proteomics community.