Ahmed M. Mehdi

Molecular basis for specificity of nuclear import and prediction of nuclear localization

Although proteins are translated on cytoplasmic ribosomes, many of these proteins play essential roles in the nucleus, mediating key cellular processes including but not limited to DNA replication and repair as well as transcription and RNA processing. Thus, understanding how these critical nuclear proteins are accurately targeted to the nucleus is of paramount importance in biology. Interaction and structural studies in the recent years have jointly revealed some general rules on the specificity determinants of the recognition of nuclear targeting signals by their specific receptors, at least for two nuclear import pathways: (i) the classical pathway, which involves the classical nuclear localization sequences (cNLSs) and the receptors importin-α/karyopherin-α and importin-β/karyopherin-β1; and (ii) the karyopherin-β2 pathway, which employs the proline-tyrosine (PY)-NLSs and the receptor transportin-1/karyopherin-β2. The understanding of specificity rules allows the prediction of protein nuclear localization. We review the current understanding of the molecular determinants of the specificity of nuclear import, focusing on the importin-α•cargo recognition, as well as the currently available databases and predictive tools relevant to nuclear localization. This article is part of a Special Issue entitled: Regulation of Signaling and Cellular Fate through Modulation of Nuclear Protein Import.

Citrullinated peptide dendritic cell immunotherapy in HLA risk genotype-positive rheumatoid arthritis patients.

Citrullinated peptide-exposed DCs induced immune regulatory effects in HLA risk genotype–positive RA patients. Immunotherapy out of joint Autoantibodies to anti–citrullinated peptides (ACPA) are found in most patients with rheumatoid arthritis (RA), especially those with HLA-DRB1 risk alleles. Benham et al. report a first-in-human phase 1 trial of a single injection of autologous dendritic cells modified with an NF-κB inhibitor that have been exposed to four citrullinated peptide antigens. They find that HLA risk genotype–positive RA patients had reduced numbers of effector T cells and decreased production of proinflammatory cytokines compared with untreated RA patient controls. The therapy was safe and did not induce disease flares. These data support larger studies of antigen-specific immunotherapy for RA. In animals, immunomodulatory dendritic cells (DCs) exposed to autoantigen can suppress experimental arthritis in an antigen-specific manner. In rheumatoid arthritis (RA), disease-specific anti–citrullinated peptide autoantibodies (ACPA or anti-CCP) are found in the serum of about 70% of RA patients and are strongly associated with HLA-DRB1 risk alleles. This study aimed to explore the safety and biological and clinical effects of autologous DCs modified with a nuclear factor κB (NF-κB) inhibitor exposed to four citrullinated peptide antigens, designated “Rheumavax,” in a single-center, open-labeled, first-in-human phase 1 trial. Rheumavax was administered once intradermally at two progressive dose levels to 18 human leukocyte antigen (HLA) risk genotype–positive RA patients with citrullinated peptide–specific autoimmunity. Sixteen RA patients served as controls. Rheumavax was well tolerated: adverse events were grade 1 (of 4) severity. At 1 month after treatment, we observed a reduction in effector T cells and an increased ratio of regulatory to effector T cells; a reduction in serum interleukin-15 (IL-15), IL-29, CX3CL1, and CXCL11; and reduced T cell IL-6 responses to vimentin447–455–Cit450 relative to controls. Rheumavax did not induce disease flares in patients recruited with minimal disease activity, and DAS28 decreased within 1 month in Rheumavax-treated patients with active disease. This exploratory study demonstrates safety and biological activity of a single intradermal injection of autologous modified DCs exposed to citrullinated peptides, and provides rationale for further studies to assess clinical efficacy and antigen-specific effects of autoantigen immunomodulatory therapy in RA.

Dynamics of re-constitution of the human nuclear proteome after cell division is regulated by NLS-adjacent phosphorylation.

Phosphorylation by the cyclin-dependent kinase 1 (Cdk1) adjacent to nuclear localization signals (NLSs) is an important mechanism of regulation of nucleocytoplasmic transport. However, no systematic survey has yet been performed in human cells to analyze this regulatory process, and the corresponding cell-cycle dynamics have not yet been investigated. Here, we focused on the human proteome and found that numerous proteins, previously not identified in this context, are associated with Cdk1-dependent phosphorylation sites adjacent to their NLSs. Interestingly, these proteins are involved in key regulatory events of DNA repair, epigenetics, or RNA editing and splicing. This finding indicates that cell-cycle dependent events of genome editing and gene expression profiling may be controlled by nucleocytoplasmic trafficking. For in-depth investigations, we selected a number of these proteins and analyzed how point mutations, expected to modify the phosphorylation ability of the NLS segments, perturb nucleocytoplasmic localization. In each case, we found that mutations mimicking hyper-phosphorylation abolish nuclear import processes. To understand the mechanism underlying these phenomena, we performed a video microscopy-based kinetic analysis to obtain information on cell-cycle dynamics on a model protein, dUTPase. We show that the NLS-adjacent phosphorylation by Cdk1 of human dUTPase, an enzyme essential for genomic integrity, results in dynamic cell cycle-dependent distribution of the protein. Non-phosphorylatable mutants have drastically altered protein re-import characteristics into the nucleus during the G1 phase. Our results suggest a dynamic Cdk1-driven mechanism of regulation of the nuclear proteome composition during the cell cycle.

A probabilistic model of nuclear import of proteins

MOTIVATION Nucleo-cytoplasmic trafficking of proteins is a core regulatory process that sustains the integrity of the nuclear space of eukaryotic cells via an interplay between numerous factors. Despite progress on experimentally characterizing a number of nuclear localization signals, their presence alone remains an unreliable indicator of actual translocation. RESULTS This article introduces a probabilistic model that explicitly recognizes a variety of nuclear localization signals, and integrates relevant amino acid sequence and interaction data for any candidate nuclear protein. In particular, we develop and incorporate scoring functions based on distinct classes of classical nuclear localization signals. Our empirical results show that the model accurately predicts whether a protein is imported into the nucleus, surpassing the classification accuracy of similar predictors when evaluated on the mouse and yeast proteomes (area under the receiver operator characteristic curve of 0.84 and 0.80, respectively). The model also predicts the sequence position of a nuclear localization signal and whether it interacts with importin-α. AVAILABILITY http://pprowler.itee.uq.edu.au/NucImport

DLocalMotif: A discriminative approach for discovering local motifs in protein sequences

MOTIVATION Local motifs are patterns of DNA or protein sequences that occur within a sequence interval relative to a biologically defined anchor or landmark. Current protein motif discovery methods do not adequately consider such constraints to identify biologically significant motifs that are only weakly over-represented but spatially confined. Using negatives, i.e. sequences known to not contain a local motif, can further increase the specificity of their discovery. RESULTS This article introduces the method DLocalMotif that makes use of positional information and negative data for local motif discovery in protein sequences. DLocalMotif combines three scoring functions, measuring degrees of motif over-representation, entropy and spatial confinement, specifically designed to discriminatively exploit the availability of negative data. The method is shown to outperform current methods that use only a subset of these motif characteristics. We apply the method to several biological datasets. The analysis of peroxisomal targeting signals uncovers several novel motifs that occur immediately upstream of the dominant peroxisomal targeting signal-1 signal. The analysis of proline-tyrosine nuclear localization signals uncovers multiple novel motifs that overlap with C2H2 zinc finger domains. We also evaluate the method on classical nuclear localization signals and endoplasmic reticulum retention signals and find that DLocalMotif successfully recovers biologically relevant sequence properties. AVAILABILITY http://bioinf.scmb.uq.edu.au/dlocalmotif/

Predicting the Dynamics of Protein Abundance

Protein synthesis is finely regulated across all organisms, from bacteria to humans, and its integrity underpins many important processes. Emerging evidence suggests that the dynamic range of protein abundance is greater than that observed at the transcript level. Technological breakthroughs now mean that sequencing-based measurement of mRNA levels is routine, but protocols for measuring protein abundance remain both complex and expensive. This paper introduces a Bayesian network that integrates transcriptomic and proteomic data to predict protein abundance and to model the effects of its determinants. We aim to use this model to follow a molecular response over time, from condition-specific data, in order to understand adaptation during processes such as the cell cycle. With microarray data now available for many conditions, the general utility of a protein abundance predictor is broad. Whereas most quantitative proteomics studies have focused on higher organisms, we developed a predictive model of protein abundance for both Saccharomyces cerevisiae and Schizosaccharomyces pombe to explore the latitude at the protein level. Our predictor primarily relies on mRNA level, mRNA–protein interaction, mRNA folding energy and half-life, and tRNA adaptation. The combination of key features, allowing for the low certainty and uneven coverage of experimental observations, gives comparatively minor but robust prediction accuracy. The model substantially improved the analysis of protein regulation during the cell cycle: predicted protein abundance identified twice as many cell-cycle-associated proteins as experimental mRNA levels. Predicted protein abundance was more dynamic than observed mRNA expression, agreeing with experimental protein abundance from a human cell line. We illustrate how the same model can be used to predict the folding energy of mRNA when protein abundance is available, lending credence to the emerging view that mRNA folding affects translation efficiency. The software and data used in this research are available at http://bioinf.scmb.uq.edu.au/proteinabundance/.

Estimation of power factor by the analysis of Power Quality data for voltage unbalance

Power Quality (PQ) has been identified as a complex and diversified problem across the board in electrical power industry. It has not only confused the power utilities but also has attracted the attention of its customers and equipment manufacturers. Although extensive research work is being done in this field but one of the main problem encountered by its stake holders is the voltage unbalance in electrical power system. The problem of voltage unbalance has affected the safety, reliability and economic efficiency at all levels in power industry. In this research Computational Intelligence Techniques have been used for efficiently predicting the power factor of unbalanced load of a power distribution system. The Principal Component Analysis Technique was used to find the optimized number of new dimensions of PQ data. Finally Feed Forward Back Propagation (FFBP) algorithm was used to estimate the power factor of a distribution network by analyzing the real power system parameters. This research highlights the importance of maintaining power factor close to unity for power utilities to achieve sustainable availability of quality supply of electrical power for its customers. The outcomes of the proposed techniques were compared and tested with the field results of a power utility in Victoria, Australia.

Dexamethasone and monophosphoryl lipid a induce a distinctive profile on monocyte-derived dendritic cells through transcriptional modulation of genes associated with essential processes of the immune response

There is growing interest in the use of tolerogenic dendritic cells (tolDCs) as a potential target for immunotherapy. However, the molecular bases that drive the differentiation of monocyte-derived DCs (moDCs) toward a tolerogenic state are still poorly understood. Here, we studied the transcriptional profile of moDCs from healthy subjects, modulated with dexamethasone (Dex) and activated with monophosphoryl lipid A (MPLA), referred to as Dex-modulated and MPLA-activated DCs (DM-DCs), as an approach to identify molecular regulators and pathways associated with the induction of tolerogenic properties in tolDCs. We found that DM-DCs exhibit a distinctive transcriptional profile compared to untreated (DCs) and MPLA-matured DCs. Differentially expressed genes downregulated by DM included MMP12, CD1c, IL-1B, and FCER1A involved in DC maturation/inflammation and genes upregulated by DM included JAG1, MERTK, IL-10, and IDO1 involved in tolerance. Genes related to chemotactic responses, cell-to-cell signaling and interaction, fatty acid oxidation, metal homeostasis, and free radical scavenging were strongly enriched, predicting the activation of alternative metabolic processes than those driven by counterpart DCs. Furthermore, we identified a set of genes that were regulated exclusively by the combined action of Dex and MPLA, which are mainly involved in the control of zinc homeostasis and reactive oxygen species production. These data further support the important role of metabolic processes on the control of the DC-driven regulatory immune response. Thus, Dex and MPLA treatments modify gene expression in moDCs by inducing a particular transcriptional profile characterized by the activation of tolerance-associated genes and suppression of the expression of inflammatory genes, conferring the potential to exert regulatory functions and immune response modulation.

Treatment with Dexamethasone and Monophosphoryl Lipid A Removes Disease-Associated Transcriptional Signatures in Monocyte-Derived Dendritic Cells from Rheumatoid Arthritis Patients and Confers Tolerogenic Features

Tolerogenic dendritic cells (TolDCs) are promising tools for therapy of autoimmune diseases, such as rheumatoid arthritis (RA). Here, we characterize monocyte-derived TolDCs from RA patients modulated with dexamethasone and activated with monophosphoryl lipid A (MPLA), referred to as MPLA-tDCs, in terms of gene expression, phenotype, cytokine profile, migratory properties, and T cell-stimulatory capacity in order to explore their suitability for cellular therapy. MPLA-tDCs derived from RA patients displayed an anti-inflammatory profile with reduced expression of co-stimulatory molecules and high IL-10/IL-12 ratio, but were capable of migrating toward the lymphoid chemokines CXCL12 and CCL19. These MPLA-tDCs induced hyporesponsiveness of autologous CD4+ T cells specific for synovial antigens in vitro. Global transcriptome analysis confirmed a unique transcriptional profile of MPLA-tDCs and revealed that RA-associated genes, which were upregulated in untreated DCs from RA patients, returned to expression levels of healthy donor-derived DCs after treatment with dexamethasone and MPLA. Thus, monocyte-derived DCs from RA patients have the capacity to develop tolerogenic features at transcriptional as well as at translational level, when modulated with dexamethasone and MPLA, overcoming disease-related effects. Furthermore, the ability of MPLA-tDCs to impair T cell responses to synovial antigens validates their potential as cellular treatment for RA.

Expression profiling pre-diabetic mice to uncover drugs with clinical application to type 1 diabetes.

In the NOD mouse model of type 1 diabetes (T1D), genetically identical mice in the same environment develop diabetes at different rates. Similar heterogeneity in the rate of progression to T1D exists in humans, but the underlying mechanisms are unclear. Here, we aimed to discover peripheral blood (PB) genes in NOD mice predicting insulitis severity and rate of progression to diabetes. We then wished to use these genes to mine existing databases to identify drugs effective in diabetes. In a longitudinal study, we analyzed gene expression in PB samples from NOD.CD45.2 mice at 10 weeks of age, then scored pancreatic insulitis at 14 weeks or determined age of diabetes onset. In a multilinear regression model, Tnf and Tgfb mRNA expression in PB predicted insulitis score (R2=0.56, P=0.01). Expression of these genes did not predict age of diabetes onset. However, by expression‐profiling PB genes in 10‐week‐old NOD.CD45.2 mice, we found a signature of upregulated genes that predicted delayed or no diabetes. Major associated pathways included chromatin organization, cellular protein location and regulation of nitrogen compounds and RNA. In a clinical cohort, three of these genes were differentially expressed between first‐degree relatives, T1D patients and controls. Bioinformatic analysis of differentially expressed genes in NOD.CD45.2 PB identified drugs that are predicted to delay or prevent diabetes. Of these drugs, 11 overlapped with drugs predicted to induce a human ‘non‐progressor’ expression profile. These data demonstrate that disease heterogeneity in diabetes‐prone mice can be exploited to mine novel clinical T1D biomarkers and drug targets.