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Research has been conductsd on nicotinic acetylcholine receptors which contain alpha7 subunits. The hypothesis that functional expression of these receptors may be inhibited by the additional cysteine forming alternative disulfide bonds with the concerved cysteine of the disulfide loop has been tested, and the results are reported.

Abstract 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.

Abstract 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.

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., Competing Interests: N.J.P., V.C., C.W., P.L., S.M., P.G., T.D., M.T., Y.M., B.B., C.P., S.R., M.H., M.B., T.K., T.D., M.E., and R.J. are employed by F. Hoffmann-La Roche. Parts of the work in this study have been filed in the patent WO2020/148310. The remaining authors declare no competing financial interests., (© 2021 The Authors.)

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., Competing Interests: Conflict of interest—Authors declare no competing interests., (© 2020 Huang et al.)

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.

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., (© 2020 Tsai et al.)

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., (Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.)

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., (Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.)

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.

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., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

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., (© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

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.

The cause of sporadic amyotrophic lateral sclerosis (ALS) is largely unknown, but genetic factors are thought to play a significant role in determining susceptibility to motor neuron degeneration. To identify genetic variants altering risk of ALS, we undertook a two-stage genome-wide association study (GWAS): we followed our initial GWAS of 545 066 SNPs in 553 individuals with ALS and 2338 controls by testing the 7600 most associated SNPs from the first stage in three independent cohorts consisting of 2160 cases and 3008 controls. None of the SNPs selected for replication exceeded the Bonferroni threshold for significance. The two most significantly associated SNPs, rs2708909 and rs2708851 [odds ratio (OR) = 1.17 and 1.18, and P-values = 6.98 x 10(-7) and 1.16 x 10(-6)], were located on chromosome 7p13.3 within a 175 kb linkage disequilibrium block containing the SUNC1, HUS1 and C7orf57 genes. These associations did not achieve genome-wide significance in the original cohort and failed to replicate in an additional independent cohort of 989 US cases and 327 controls (OR = 1.18 and 1.19, P-values = 0.08 and 0.06, respectively). Thus, we chose to cautiously interpret our data as hypothesis-generating requiring additional confirmation, especially as all previously reported loci for ALS have failed to replicate successfully. Indeed, the three loci (FGGY, ITPR2 and DPP6) identified in previous GWAS of sporadic ALS were not significantly associated with disease in our study. Our findings suggest that ALS is more genetically and clinically heterogeneous than previously recognized. Genotype data from our study have been made available online to facilitate such future endeavors.

Perhaps the greatest difficulty in interpreting large sets of protein identifications derived from mass spectrometric methods is whether or not to trust the results. For such experiments, the level of confidence in each protein identification made needs to be far greater than the often used 95% significance threshold to avoid the identification of many false-positives. To provide higher confidence results, we have developed an innovative scoring strategy coupling the recently published Average Peptide Score (APS) method with pre-filtering of peptide identifications, using a simple peptide quality filter. Iterative generation of these filters in conjunction with reversed database searching is used to determine the correct levels at which the APS and peptide quality thresholds should be set to return virtually zero false-positive reports. This proceeds without the need to reference a known dataset., (Copyright (c) 2005 John Wiley & Sons, Ltd.)

We describe a proteomics method for determining the subcellular localization of membrane proteins. Organelles are partially separated using centrifugation through self-generating density gradients. Proteins from each organelle co-fractionate and therefore exhibit similar distributions in the gradient. Protein distributions can be determined through a series of pair-wise comparisons of gradient fractions, using cleavable ICAT to enable relative quantitation of protein levels by MS. The localization of novel proteins is determined using multivariate data analysis techniques to match their distributions to those of proteins that are known to reside in specific organelles. Using this approach, we have simultaneously demonstrated the localization of membrane proteins in both the endoplasmic reticulum and the Golgi apparatus in Arabidopsis. Localization of organelle proteins by isotope tagging is a new tool for high-throughput protein localization, which is applicable to a wide range of research areas such as the study of organelle function and protein trafficking.

Organelle proteomics is the analysis of the protein contents of a subcellular compartment. Proteins identified in subcellular proteomic studies can only be assigned to an organelle if there are no contaminants present in the sample preparation. As a result, the majority of plant organelle proteomic studies have focused on the chloroplast and mitochondria, which can be isolated relatively easily. However, the isolation of components of the endomembrane system is far more difficult due to their similar sizes and densities. For this reason, quantitative proteomics methods are being developed to enable the assignment of proteins to a specific component of the endomembrane system without the need to obtain pure organelles., (Copyright 2004 Biochemical Society)

Both the generation and the analysis of proteome data are becoming increasingly widespread, and the field of proteomics is moving incrementally toward high-throughput approaches. Techniques are also increasing in complexity as the relevant technologies evolve. A standard representation of both the methods used and the data generated in proteomics experiments, analogous to that of the MIAME (minimum information about a microarray experiment) guidelines for transcriptomics, and the associated MAGE (microarray gene expression) object model and XML (extensible markup language) implementation, has yet to emerge. This hinders the handling, exchange, and dissemination of proteomics data. Here, we present a UML (unified modeling language) approach to proteomics experimental data, describe XML and SQL (structured query language) implementations of that model, and discuss capture, storage, and dissemination strategies. These make explicit what data might be most usefully captured about proteomics experiments and provide complementary routes toward the implementation of a proteome repository.

This study examined the perceived role of three types of sociocultural agents (peers, parents, and media) in influencing body dissatisfaction and dietary restraint in adolescent girls. Participants were 577 grade 10 girls from six schools who completed questionnaires in class and had height and weight measured. Two path analyses resulted in a similar pattern. While current body size strongly predicted ideal body size and body dissatisfaction, perceived influence of multiple sociocultural agents regarding thinness also had a direct relationship with body ideal and dissatisfaction. Dietary restraint was predicted directly from body dissatisfaction and sociocultural influences. Peers, parents, and media varied in their perceived influence. The findings support the idea that those girls who show the most body dissatisfaction and dietary restraint live in a subculture supporting a thin ideal and encouraging dieting.