Marcia Roy

CIF-1, a Shared Subunit of the COP9/Signalosome and Eukaryotic Initiation Factor 3 Complexes, Regulates MEL-26 Levels in the Caenorhabditis elegans Embryo

ABSTRACT The COP9/signalosome (CSN) is an evolutionarily conserved macromolecular complex that regulates the cullin-RING ligase (CRL) class of E3 ubiquitin ligases, primarily by removing the ubiquitin-like protein Nedd8 from the cullin subunit. In the Caenorhabditis elegans embryo, the CSN controls the degradation of the microtubule-severing protein MEI-1 through CUL-3 deneddylation. However, the molecular mechanisms of CSN function and its subunit composition remain to be elucidated. Here, using a proteomic approach, we have characterized the CSN and CUL-3 complexes from C. elegans embryos. We show that the CSN physically interacts with the CUL-3-based CRL and regulates its activity by counteracting the autocatalytic instability of the substrate-specific adaptor MEL-26. Importantly, we identified the uncharacterized protein K08F11.3/CIF-1 (for CSN-eukaryotic initiation factor 3 [eIF3]) as a stoichiometric and functionally important subunit of the CSN complex. CIF-1 appears to be the only ortholog of Csn7 encoded by the C. elegans genome, but it also exhibits extensive sequence similarity to eIF3m family members, which are required for the initiation of protein translation. Indeed, CIF-1 binds eIF-3.F and inactivation of cif-1 impairs translation in vivo. Taken together, our results indicate that CIF-1 is a shared subunit of the CSN and eIF3 complexes and may therefore link protein translation and degradation.

An interaction network of the mammalian COP9 signalosome identifies Dda1 as a core subunit of multiple Cul4-based E3 ligases

The COP9 signalosome (CSN) is an evolutionarily conserved macromolecular complex that interacts with cullin-RING E3 ligases (CRLs) and regulates their activity by hydrolyzing cullin-Nedd8 conjugates. The CSN sequesters inactive CRL4Ddb2, which rapidly dissociates from the CSN upon DNA damage. Here we systematically define the protein interaction network of the mammalian CSN through mass spectrometric interrogation of the CSN subunits Csn1, Csn3, Csn4, Csn5, Csn6 and Csn7a. Notably, we identified a subset of CRL complexes that stably interact with the CSN and thus might similarly be activated by dissociation from the CSN in response to specific cues. In addition, we detected several new proteins in the CRL-CSN interactome, including Dda1, which we characterized as a chromatin-associated core subunit of multiple CRL4 proteins. Cells depleted of Dda1 spontaneously accumulated double-stranded DNA breaks in a similar way to Cul4A-, Cul4B- or Wdr23-depleted cells, indicating that Dda1 interacts physically and functionally with CRL4 complexes. This analysis identifies new components of the CRL family of E3 ligases and elaborates new connections between the CRL and CSN complexes.

Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning

The structure of genetic interaction networks predicts that, analogous to synthetic lethal interactions between non-essential genes, combinations of compounds with latent activities may exhibit potent synergism. To test this hypothesis, we generated a chemical-genetic matrix of 195 diverse yeast deletion strains treated with 4,915 compounds. This approach uncovered 1,221 genotype-specific inhibitors, which we termed cryptagens. Synergism between 8,128 structurally disparate cryptagen pairs was assessed experimentally and used to benchmark predictive algorithms. A model based on the chemical-genetic matrix and the genetic interaction network failed to accurately predict synergism. However, a combined random forest and Naive Bayesian learner that associated chemical structural features with genotype-specific growth inhibition had strong predictive power. This approach identified previously unknown compound combinations that exhibited species-selective toxicity toward human fungal pathogens. This work demonstrates that machine learning methods trained on unbiased chemical-genetic interaction data may be widely applicable for the discovery of synergistic combinations in different species.

Human post-mortem synapse proteome integrity screening for proteomic studies of postsynaptic complexes

BackgroundSynapses are fundamental components of brain circuits and are disrupted in over 100 neurological and psychiatric diseases. The synapse proteome is physically organized into multiprotein complexes and polygenic mutations converge on postsynaptic complexes in schizophrenia, autism and intellectual disability. Directly characterising human synapses and their multiprotein complexes from post-mortem tissue is essential to understanding disease mechanisms. However, multiprotein complexes have not been directly isolated from human synapses and the feasibility of their isolation from post-mortem tissue is unknown.ResultsHere we establish a screening assay and criteria to identify post-mortem brain samples containing well-preserved synapse proteomes, revealing that neocortex samples are best preserved. We also develop a rapid method for the isolation of synapse proteomes from human brain, allowing large numbers of post-mortem samples to be processed in a short time frame. We perform the first purification and proteomic mass spectrometry analysis of MAGUK Associated Signalling Complexes (MASC) from neurosurgical and post-mortem tissue and find genetic evidence for their involvement in over seventy human brain diseases.ConclusionsWe have demonstrated that synaptic proteome integrity can be rapidly assessed from human post-mortem brain samples prior to its analysis with sophisticated proteomic methods. We have also shown that proteomics of synapse multiprotein complexes from well preserved post-mortem tissue is possible, obtaining structures highly similar to those isolated from biopsy tissue. Finally we have shown that MASC from human synapses are involved with over seventy brain disorders. These findings should have wide application in understanding the synaptic basis of psychiatric and other mental disorders.

Photobacterium profundum under Pressure: A MS-Based Label-Free Quantitative Proteomics Study

Photobacterium profundum SS9 is a Gram-negative bacterium, originally collected from the Sulu Sea. Its genome consists of two chromosomes and a 80 kb plasmid. Although it can grow under a wide range of pressures, P. profundum grows optimally at 28 MPa and 15°C. Its ability to grow at atmospheric pressure allows for both easy genetic manipulation and culture, making it a model organism to study piezophily. Here, we report a shotgun proteomic analysis of P. profundum grown at atmospheric compared to high pressure using label-free quantitation and mass spectrometry analysis. We have identified differentially expressed proteins involved in high pressure adaptation, which have been previously reported using other methods. Proteins involved in key metabolic pathways were also identified as being differentially expressed. Proteins involved in the glycolysis/gluconeogenesis pathway were up-regulated at high pressure. Conversely, several proteins involved in the oxidative phosphorylation pathway were up-regulated at atmospheric pressure. Some of the proteins that were differentially identified are regulated directly in response to the physical impact of pressure. The expression of some proteins involved in nutrient transport or assimilation, are likely to be directly regulated by pressure. In a natural environment, different hydrostatic pressures represent distinct ecosystems with their own particular nutrient limitations and abundances. However, the only variable considered in this study was atmospheric pressure.

Proteomic analysis of postsynaptic proteins in regions of the human neocortex

The postsynaptic proteome of excitatory synapses comprises ~1,000 highly conserved proteins that control the behavioral repertoire, and mutations disrupting their function cause >130 brain diseases. Here, we document the composition of postsynaptic proteomes in human neocortical regions and integrate it with genetic, functional and structural magnetic resonance imaging, positron emission tomography imaging, and behavioral data. Neocortical regions show signatures of expression of individual proteins, protein complexes, biochemical and metabolic pathways. We characterized the compositional signatures in brain regions involved with language, emotion and memory functions. Integrating large-scale GWAS with regional proteome data identifies the same cortical region for smoking behavior as found with fMRI data. The neocortical postsynaptic proteome data resource can be used to link genetics to brain imaging and behavior, and to study the role of postsynaptic proteins in localization of brain functions.The protein composition of excitatory synapses differs in the areas of the human neocortex controlling language, emotion and other behaviors. This neocortical postsynaptic proteome data resource can be used to link genetics to brain imaging and behavior.

Systematic chemical-genetic and chemical-chemical interaction datasets for prediction of compound synergism

The network structure of biological systems suggests that effective therapeutic intervention may require combinations of agents that act synergistically. However, a dearth of systematic chemical combination datasets have limited the development of predictive algorithms for chemical synergism. Here, we report two large datasets of linked chemical-genetic and chemical-chemical interactions in the budding yeast Saccharomyces cerevisiae. We screened 5,518 unique compounds against 242 diverse yeast gene deletion strains to generate an extended chemical-genetic matrix (CGM) of 492,126 chemical-gene interaction measurements. This CGM dataset contained 1,434 genotype-specific inhibitors, termed cryptagens. We selected 128 structurally diverse cryptagens and tested all pairwise combinations to generate a benchmark dataset of 8,128 pairwise chemical-chemical interaction tests for synergy prediction, termed the cryptagen matrix (CM). An accompanying database resource called ChemGRID was developed to enable analysis, visualisation and downloads of all data. The CGM and CM datasets will facilitate the benchmarking of computational approaches for synergy prediction, as well as chemical structure-activity relationship models for anti-fungal drug discovery.

A genomic lifespan program that reorganises the young adult brain is targeted in schizophrenia

The genetic mechanisms regulating the brain and behaviour across the lifespan are poorly understood. We found that lifespan transcriptome trajectories describe a calendar of gene regulatory events in the brain of humans and mice. Transcriptome trajectories defined a sequence of gene expression changes in neuronal, glial and endothelial cell-types, which enabled prediction of age from tissue samples. A major lifespan landmark was the peak change in trajectories occurring in humans at 26 years and in mice at 5 months of age. This species-conserved peak was delayed in females and marked a reorganization of expression of synaptic and schizophrenia-susceptibility genes. The lifespan calendar predicted the characteristic age of onset in young adults and sex differences in schizophrenia. We propose a genomic program generates a lifespan calendar of gene regulation that times age-dependent molecular organization of the brain and mutations that interrupt the program in young adults cause schizophrenia.

Protein Co-Expression Analysis as a Strategy to Complement a Standard Quantitative Proteomics Approach: Case of a Glioblastoma Multiforme Study

Although correlation network studies from co-expression analysis are increasingly popular, they are rarely applied to proteomics datasets. Protein co-expression analysis provides a complementary view of underlying trends, which can be overlooked by conventional data analysis. The core of the present study is based on Weighted Gene Co-expression Network Analysis applied to a glioblastoma multiforme proteomic dataset. Using this method, we have identified three main modules which are associated with three different membrane associated groups; mitochondrial, endoplasmic reticulum, and a vesicle fraction. The three networks based on protein co-expression were assessed against a publicly available database (STRING) and show a statistically significant overlap. Each of the three main modules were de-clustered into smaller networks using different strategies based on the identification of highly connected networks, hierarchical clustering and enrichment of Gene Ontology functional terms. Most of the highly connected proteins found in the endoplasmic reticulum module were associated with redox activity while a core of the unfolded protein response was identified in addition to proteins involved in oxidative stress pathways. The proteins composing the electron transfer chain were found differently affected with proteins from mitochondrial Complex I being more down-regulated than proteins from Complex III. Finally, the two pyruvate kinases isoforms show major differences in their co-expressed protein networks suggesting roles in different cellular locations.

Regional Diversity in the Postsynaptic Proteome of the Mouse Brain

The proteome of the postsynaptic terminal of excitatory synapses comprises over one thousand proteins in vertebrate species and plays a central role in behavior and brain disease. The brain is organized into anatomically distinct regions and whether the synapse proteome differs across these regions is poorly understood. Postsynaptic proteomes were isolated from seven forebrain and hindbrain regions in mice and their composition determined using proteomic mass spectrometry. Seventy-four percent of proteins showed differential expression and each region displayed a unique compositional signature. These signatures correlated with the anatomical divisions of the brain and their embryological origins. Biochemical pathways controlling plasticity and disease, protein interaction networks and individual proteins involved with cognition all showed differential regional expression. Combining proteomic and connectomic data shows that interconnected regions have specific proteome signatures. Diversity in synapse proteome composition is key feature of mouse and human brain structure.