Are Long-Range Structural Correlations Behind the Aggregration Phenomena of Polyglutamine Diseases?

We have characterized the conformational ensembles of polyglutamine peptides of various lengths (ranging from to ), both with and without the presence of a C-terminal polyproline hexapeptide. For this, we used state-of-the-art molecular dynamics simulations combined with a novel statistical analysis to characterize the various properties of the backbone dihedral angles and secondary structural motifs of the glutamine residues. For (i.e., just above the pathological length for Huntington's disease), the equilibrium conformations of the monomer consist primarily of disordered, compact structures with non-negligible -helical and turn content. We also observed a relatively small population of extended structures suitable for forming aggregates including - and -strands, and - and -hairpins. Most importantly, for we find that there exists a long-range correlation (ranging for at least residues) among the backbone dihedral angles of the Q residues. For polyglutamine peptides below the pathological length, the population of the extended strands and hairpins is considerably smaller, and the correlations are short-range (at most residues apart). Adding a C-terminal hexaproline to suppresses both the population of these rare motifs and the long-range correlation of the dihedral angles. We argue that the long-range correlation of the polyglutamine homopeptide, along with the presence of these rare motifs, could be responsible for its aggregation phenomena

EGCG Enhances the Therapeutic Potential of Gemcitabine and CP690550 by Inhibiting STAT3 Signaling Pathway in Human Pancreatic Cancer

Background: Signal Transducer and Activator of Transcription 3 (STAT3) is an oncogene, which promotes cell survival, proliferation, motility and progression in cancer cells. Targeting STAT3 signaling may lead to the development of novel therapeutic approaches for human cancers. Here, we examined the effects of epigallocathechin gallate (EGCG) on STAT3 signaling in pancreatic cancer cells, and assessed the therapeutic potential of EGCG with gemcitabine or JAK3 inhibitor CP690550 (Tasocitinib) for the treatment and/or prevention of pancreatic cancer. Methodology/Principal Findings: Cell viability and apoptosis were measured by XTT assay and TUNEL staining, respectively. Gene and protein expressions were measured by qRT-PCR and Western blot analysis, respectively. The results revealed that EGCG inhibited the expression of phospho and total JAK3 and STAT3, STAT3 transcription and activation, and the expression of STAT3-regulated genes, resulting in the inhibition of cell motility, migration and invasion, and the induction of caspase-3 and PARP cleavage. The inhibition of STAT3 enhanced the inhibitory effects of EGCG on cell motility and viability. Additionally, gemcitabine and CP690550 alone inhibited STAT3 target genes and synergized with EGCG to inhibit cell viability and induce apoptosis in pancreatic cancer cells. Conclusions/Significance: Overall, these results suggest that EGCG suppresses the growth, invasion and migration of pancreatic cancer cells, and induces apoptosis by interfering with the STAT3 signaling pathway. Moreover, EGCG furthe

Occupancy Classification of Position Weight Matrix-Inferred Transcription Factor Binding Sites

BACKGROUND: Computational prediction of Transcription Factor Binding Sites (TFBS) from sequence data alone is difficult and error-prone. Machine learning techniques utilizing additional environmental information about a predicted binding site (such as distances from the site to particular chromatin features) to determine its occupancy/functionality class show promise as methods to achieve more accurate prediction of true TFBS in silico. We evaluate the Bayesian Network (BN) and Support Vector Machine (SVM) machine learning techniques on four distinct TFBS data sets and analyze their performance. We describe the features that are most useful for classification and contrast and compare these feature sets between the factors. RESULTS: Our results demonstrate good performance of classifiers both on TFBS for transcription factors used for initial training and for TFBS for other factors in cross-classification experiments. We find that distances to chromatin modifications (specifically, histone modification islands) as well as distances between such modifications to be effective predictors of TFBS occupancy, though the impact of individual predictors is largely TF specific. In our experiments, Bayesian network classifiers outperform SVM classifiers. CONCLUSIONS: Our results demonstrate good performance of machine learning techniques on the problem of occupancy classification, and demonstrate that effective classification can be achieved using distances to chromatin features. We additionally demonstrate that cross-classification of TFBS is possible, suggesting the possibility of constructing a generalizable occupancy classifier capable of handling TFBS for many different transcription factors

A Linear Model for Transcription Factor Binding Affinity Prediction in Protein Binding Microarrays

Protein binding microarrays (PBM) are a high throughput technology used to characterize protein-DNA binding. The arrays measure a protein's affinity toward thousands of double-stranded DNA sequences at once, producing a comprehensive binding specificity catalog. We present a linear model for predicting the binding affinity of a protein toward DNA sequences based on PBM data. Our model represents the measured intensity of an individual probe as a sum of the binding affinity contributions of the probe's subsequences. These subsequences characterize a DNA binding motif and can be used to predict the intensity of protein binding against arbitrary DNA sequences. Our method was the best performer in the Dialogue for Reverse Engineering Assessments and Methods 5 (DREAM5) transcription factor/DNA motif recognition challenge. For the DREAM5 bonus challenge, we also developed an approach for the identification of transcription factors based on their PBM binding profiles. Our approach for TF identification achieved the best performance in the bonus challenge

MER41 Repeat Sequences Contain Inducible STAT1 Binding Sites

Chromatin immunoprecipitation combined with massively parallel sequencing methods (ChIP-seq) is becoming the standard approach to study interactions of transcription factors (TF) with genomic sequences. At the example of public STAT1 ChIP-seq data sets, we present novel approaches for the interpretation of ChIP-seq data

Identification of a novel site of interaction between ataxin-3 and the amyloid aggregation inhibitor polyglutamine binding peptide 1

Amyloid diseases represent a growing social and economic burden in the developed world. Understanding the assembly pathway and the inhibition of amyloid formation is key to developing therapies to treat these diseases. The neurodegenerative condition Machado–Joseph disease is characterised by the self-aggregation of the protein ataxin-3. Ataxin-3 consists of a globular N-terminal Josephin domain, which can aggregate into curvilinear protofibrils, and an unstructured, dynamically disordered C-terminal domain containing three ubiquitin interacting motifs separated by a polyglutamine stretch. Upon expansion of the polyglutamine region above 50 residues, ataxin-3 undergoes a second stage of aggregation in which long, straight amyloid fibrils form. A peptide inhibitor of polyglutamine aggregation, known as polyQ binding peptide 1, has been shown previously to prevent the maturation of ataxin-3 fibrils. However, the mechanism of this inhibition remains unclear. Using nanoelectrospray ionisation-mass spectrometry, we demonstrate that polyQ binding peptide 1 binds to monomeric ataxin-3. By investigating the ability of polyQ binding peptide 1 to bind to truncated ataxin-3 constructs lacking one or more domains, we localise the site of this interaction to a 39-residue sequence immediately C-terminal to the Josephin domain. The results suggest a new mechanism for the inhibition of polyglutamine aggregation by polyQ binding peptide 1 in which binding to a region outside of the polyglutamine tract can prevent fibril formation, highlighting the importance of polyglutamine flanking regions in controlling aggregation and disease

An Integrated Pipeline for the Genome-Wide Analysis of Transcription Factor Binding Sites from ChIP-Seq

ChIP-Seq has become the standard method for genome-wide profiling DNA association of transcription factors. To simplify analyzing and interpreting ChIP-Seq data, which typically involves using multiple applications, we describe an integrated, open source, R-based analysis pipeline. The pipeline addresses data input, peak detection, sequence and motif analysis, visualization, and data export, and can readily be extended via other R and Bioconductor packages. Using a standard multicore computer, it can be used with datasets consisting of tens of thousands of enriched regions. We demonstrate its effectiveness on published human ChIP-Seq datasets for FOXA1, ER, CTCF and STAT1, where it detected co-occurring motifs that were consistent with the literature but not detected by other methods. Our pipeline provides the first complete set of Bioconductor tools for sequence and motif analysis of ChIP-Seq and ChIP-chip data

15-PGJ2, but not thiazolidinediones, inhibits cell growth, induces apoptosis, and causes downregulation of Stat3 in human oral SCCa cells

Activation of peroxisome proliferator-activated receptor gamma (PPARγ) has been linked to induction of differentiation, cell growth inhibition and apoptosis in several types of human cancer. However, the possible effects of PPARγ agonists on human oral squamous cell carcinoma have not yet been reported. In this study, treatment with 15-deoxy-Δ12,14-PGJ2 (15-PGJ2), a natural PPARγ ligand, induced a significant reduction of oral squamous cell carcinoma cell growth, which was mainly attributed to upregulation of apoptosis. Interestingly, rosiglitazone and ciglitazone, two members of the thiazolidinedione family of PPARγ activators, did not exert a growth inhibitory effect. Given the critical role that the oncogene signal transducer and activator of transcription 3 (Stat3) plays in head and neck carcinogenesis, its potential regulation by PPARγ ligands was also examined. Treatment of oral squamous cell carcinoma cells with 15-PGJ2 induced an initial reduction and eventual elimination of both phosphorylated and unphosphorylated Stat3 protein levels. In contrast, other PPARγ did not induce similar effects. Our results provide the first evidence of significant antineoplastic effects of 15-PGJ2 on human oral squamous cell carcinoma cells, which may be related to downmodulation of Stat3 and are at least partly mediated through PPARγ-independent events

Transcriptome Analysis of Synaptoneurosomes Identifies Neuroplasticity Genes Overexpressed in Incipient Alzheimer's Disease

In Alzheimer's disease (AD), early deficits in learning and memory are a consequence of synaptic modification induced by toxic beta-amyloid oligomers (oAβ). To identify immediate molecular targets downstream of oAβ binding, we prepared synaptoneurosomes from prefrontal cortex of control and incipient AD (IAD) patients, and isolated mRNAs for comparison of gene expression. This novel approach concentrates synaptic mRNA, thereby increasing the ratio of synaptic to somal mRNA and allowing discrimination of expression changes in synaptically localized genes. In IAD patients, global measures of cognition declined with increasing levels of dimeric Aβ (dAβ). These patients also showed increased expression of neuroplasticity related genes, many encoding 3′UTR consensus sequences that regulate translation in the synapse. An increase in mRNA encoding the GluR2 subunit of the α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor (AMPAR) was paralleled by elevated expression of the corresponding protein in IAD. These results imply a functional impact on synaptic transmission as GluR2, if inserted, maintains the receptors in a low conductance state. Some overexpressed genes may induce early deficits in cognition and others compensatory mechanisms, providing targets for intervention to moderate the response to dAβ

Assignment of PolyProline II Conformation and Analysis of Sequence – Structure Relationship

International audienceBACKGROUND: Secondary structures are elements of great importance in structural biology, biochemistry and bioinformatics. They are broadly composed of two repetitive structures namely α-helices and β-sheets, apart from turns, and the rest is associated to coil. These repetitive secondary structures have specific and conserved biophysical and geometric properties. PolyProline II (PPII) helix is yet another interesting repetitive structure which is less frequent and not usually associated with stabilizing interactions. Recent studies have shown that PPII frequency is higher than expected, and they could have an important role in protein - protein interactions. METHODOLOGY/PRINCIPAL FINDINGS: A major factor that limits the study of PPII is that its assignment cannot be carried out with the most commonly used secondary structure assignment methods (SSAMs). The purpose of this work is to propose a PPII assignment methodology that can be defined in the frame of DSSP secondary structure assignment. Considering the ambiguity in PPII assignments by different methods, a consensus assignment strategy was utilized. To define the most consensual rule of PPII assignment, three SSAMs that can assign PPII, were compared and analyzed. The assignment rule was defined to have a maximum coverage of all assignments made by these SSAMs. Not many constraints were added to the assignment and only PPII helices of at least 2 residues length are defined. CONCLUSIONS/SIGNIFICANCE: The simple rules designed in this study for characterizing PPII conformation, lead to the assignment of 5% of all amino as PPII. Sequence - structure relationships associated with PPII, defined by the different SSAMs, underline few striking differences. A specific study of amino acid preferences in their N and C-cap regions was carried out as their solvent accessibility and contact patterns. Thus the assignment of PPII can be coupled with DSSP and thus opens a simple way for further analysis in this field