Explore the vast range of titles available.

Protein misfolding and formation of β-sheet-rich amyloid fibrils or aggregates is related to cellular toxicity and decay in various human disorders including Alzheimer's and Parkinson's disease. Recently, we demonstrated that the polyphenol (-)-epi-gallocatechine gallate (EGCG) inhibits α-synuclein and amyloid-β fibrillogenesis. It associates with natively unfolded polypeptides and promotes the self-assembly of unstructured oligomers of a new type. Whether EGCG disassembles preformed amyloid fibrils, however, remained unclear. Here, we show that EGCG has the ability to convert large, mature α-synuclein and amyloid-β fibrils into smaller, amorphous protein aggregates that are nontoxic to mammalian cells. Mechanistic studies revealed that the compound directly binds to β-sheet-rich aggregates and mediates the conformational change without their disassembly into monomers or small diffusible oligomers. These findings suggest that EGCG is a potent remodeling agent of mature amyloid fibrils.

Protein function is often modulated by protein-protein interactions (PPIs) and therefore defining the partners of a protein helps to understand its activity. PPIs can be detected through different experimental approaches and are collected in several expert curated databases. These databases are used by researchers interested in examining detailed information on particular proteins. In many analyses the reliability of the characterization of the interactions becomes important and it might be necessary to select sets of PPIs of different confidence levels. To this goal, we generated HIPPIE (Human Integrated Protein-Protein Interaction rEference), a human PPI dataset with a normalized scoring scheme that integrates multiple experimental PPI datasets. HIPPIE's scoring scheme has been optimized by human experts and a computer algorithm to reflect the amount and quality of evidence for a given PPI and we show that these scores correlate to the quality of the experimental characterization. The HIPPIE web tool (available at http://cbdm.mdc-berlin.de/tools/hippie) allows researchers to do network analyses focused on likely true PPI sets by generating subnetworks around proteins of interest at a specified confidence level.

Histone deacetylase (HDAC) 4 is a transcriptional repressor that contains a glutamine-rich domain. We hypothesised that it may be involved in the molecular pathogenesis of Huntington's disease (HD), a protein-folding neurodegenerative disorder caused by an aggregation-prone polyglutamine expansion in the huntingtin protein. We found that HDAC4 associates with huntingtin in a polyglutamine-length-dependent manner and co-localises with cytoplasmic inclusions. We show that HDAC4 reduction delayed cytoplasmic aggregate formation, restored Bdnf transcript levels, and rescued neuronal and cortico-striatal synaptic function in HD mouse models. This was accompanied by an improvement in motor coordination, neurological phenotypes, and increased lifespan. Surprisingly, HDAC4 reduction had no effect on global transcriptional dysfunction and did not modulate nuclear huntingtin aggregation. Our results define a crucial role for the cytoplasmic aggregation process in the molecular pathology of HD. HDAC4 reduction presents a novel strategy for targeting huntingtin aggregation, which may be amenable to small-molecule therapeutics.

Background Alzheimer’s disease (AD) is characterized by the intra- and extracellular accumulation of amyloid-β (Aβ) peptides. How Aβ aggregates perturb the proteome in brains of patients and AD transgenic mouse models, remains largely unclear. State-of-the-art mass spectrometry (MS) methods can comprehensively detect proteomic alterations, providing relevant insights unobtainable with transcriptomics investigations. Analyses of the relationship between progressive Aβ aggregation and protein abundance changes in brains of 5xFAD transgenic mice have not been reported previously. Methods We quantified progressive Aβ aggregation in hippocampus and cortex of 5xFAD mice and controls with immunohistochemistry and membrane filter assays. Protein changes in different mouse tissues were analyzed by MS-based proteomics using label-free quantification; resulting MS data were processed using an established pipeline. Results were contrasted with existing proteomic data sets from postmortem AD patient brains. Finally, abundance changes in the candidate marker Arl8b were validated in cerebrospinal fluid (CSF) from AD patients and controls using ELISAs. Results Experiments revealed faster accumulation of Aβ42 peptides in hippocampus than in cortex of 5xFAD mice, with more protein abundance changes in hippocampus, indicating that Aβ42 aggregate deposition is associated with brain region-specific proteome perturbations. Generating time-resolved data sets, we defined Aβ aggregate-correlated and anticorrelated proteome changes, a fraction of which was conserved in postmortem AD patient brain tissue, suggesting that proteome changes in 5xFAD mice mimic disease-relevant changes in human AD. We detected a positive correlation between Aβ42 aggregate deposition in the hippocampus of 5xFAD mice and the abundance of the lysosome-associated small GTPase Arl8b, which accumulated together with axonal lysosomal membranes in close proximity of extracellular Aβ plaques in 5xFAD brains. Abnormal aggregation of Arl8b was observed in human AD brain tissue. Arl8b protein levels were significantly increased in CSF of AD patients. Conclusions We report a comprehensive biochemical and proteomic investigation of hippocampal and cortical brain tissue derived from 5xFAD transgenic mice, providing a valuable resource to the neuroscientific community. We identified Arl8b, with significant abundance changes in 5xFAD and AD patient brains. Arl8b might enable the measurement of progressive lysosome accumulation in AD patients and have clinical utility as a candidate biomarker.

Complementary assays are required to comprehensively map complex biological entities such as genomes, proteomes and interactome networks. However, how various assays can be optimally combined to approach completeness while maintaining high precision often remains unclear. Here, we propose a framework for binary protein-protein interaction (PPI) mapping based on optimally combining assays and/or assay versions to maximize detection of true positive interactions, while avoiding detection of random protein pairs. We have engineered a novel NanoLuc two-hybrid (N2H) system that integrates 12 different versions, differing by protein expression systems and tagging configurations. The resulting union of N2H versions recovers as many PPIs as 10 distinct assays combined. Thus, to further improve PPI mapping, developing alternative versions of existing assays might be as productive as designing completely new assays. Our findings should be applicable to systematic mapping of other biological landscapes. Comprehensive mapping of binary protein-protein interactions requires to combine several complementary assays. Here, the authors show that complete coverage could be reached with a minimal number of assays as long as they explore various experimental conditions.

A framework based on numerous empirical data, including protein-protein interaction reference sets, provides parameters for assessing the quality and coverage of protein-protein interaction datasets and estimation of the size of the human interactome. Braun et al ., also in this issue, use the reference sets to help derive confidence scores for individual protein-protein interactions. Several attempts have been made to systematically map protein-protein interaction, or 'interactome', networks. However, it remains difficult to assess the quality and coverage of existing data sets. Here we describe a framework that uses an empirically-based approach to rigorously dissect quality parameters of currently available human interactome maps. Our results indicate that high-throughput yeast two-hybrid (HT-Y2H) interactions for human proteins are more precise than literature-curated interactions supported by a single publication, suggesting that HT-Y2H is suitable to map a significant portion of the human interactome. We estimate that the human interactome contains ∼130,000 binary interactions, most of which remain to be mapped. Similar to estimates of DNA sequence data quality and genome size early in the Human Genome Project, estimates of protein interaction data quality and interactome size are crucial to establish the magnitude of the task of comprehensive human interactome mapping and to elucidate a path toward this goal.

Type II diabetes (T2D) is characterized by diminished insulin production and resistance of cells to insulin. Among others, endoplasmic reticulum (ER) stress is a principal factor contributing to T2D and induces a shift towards a more reducing cellular environment. At the same time, peripheral insulin resistance triggers the over-production of regulatory hormones such as insulin and human islet amyloid polypeptide (hIAPP). We show that the differential aggregation of reduced and oxidized hIAPP assists to maintain the redox equilibrium by restoring redox equivalents. Aggregation thus induces redox balancing which can assist initially to counteract ER stress. Failure of the protein degradation machinery might finally result in β-cell disruption and cell death. We further present a structural characterization of hIAPP in solution, demonstrating that the N-terminus of the oxidized peptide has a high propensity to form an α-helical structure which is lacking in the reduced state of hIAPP. In healthy cells, this residual structure prevents the conversion into amyloidogenic aggregates.

A high-sensitivity, high-quality yeast two-hybrid screen using short-read sequencing as its readout is used to map the interactome of human methyltransferases. To accelerate high-density interactome mapping, we developed a yeast two-hybrid interaction screening approach involving short-read second-generation sequencing (Y2H-seq) with improved sensitivity and a quantitative scoring readout allowing rapid interaction validation. We applied Y2H-seq to investigate enzymes involved in protein methylation, a largely unexplored post-translational modification. The reported network of 523 interactions involving 22 methyltransferases or demethylases is comprehensively annotated and validated through coimmunoprecipitation experiments and defines previously undiscovered cellular roles of nonhistone protein methylation.

The formation of amyloid fibrils by human islet amyloid polypeptide protein (hIAPP) has been implicated in pancreas dysfunction and diabetes. However, efficient treatment options to reduce amyloid fibrils in vivo are still lacking. Therefore, we tested the effect of epigallocatechin gallate (EGCG) on fibril formation in vitro and in vivo . To determine the binding of hIAPP and EGCG, in vitro interaction studies were performed. To inhibit amyloid plaque formation in vivo , homozygous (tg/tg), hemizygous (wt/tg), and control mice (wt/wt) were treated with EGCG. EGCG bound to hIAPP in vitro and induced formation of amorphous aggregates instead of amyloid fibrils. Amyloid fibrils were detected in the pancreatic islets of tg/tg mice, which was associated with disrupted islet structure and diabetes. Although pancreatic amyloid fibrils could be detected in wt/tg mice, these animals were non-diabetic. EGCG application decreased amyloid fibril intensity in wt/tg mice, however it was ineffective in tg/tg animals. Our data indicate that EGCG inhibits amyloid fibril formation in vitro and reduces fibril intensity in non-diabetic wt/tg mice. These results demonstrate a possible in vivo effectiveness of EGCG on amyloid formation and suggest an early therapeutical application.

Essentially all biological processes depend on protein-protein interactions (PPIs). Timing of such interactions is crucial for regulatory function. Although circadian (similar to 24-hour) clocks constitute fundamental cellular timing mechanisms regulating important physiological processes, PPI dynamics on this timescale are largely unknown. Here, we identified 109 novel PPIs among circadian clock proteins via a yeast-two-hybrid approach. Among them, the interaction of protein phosphatase 1 and CLOCK/BMAL1 was found to result in BMAL1 destabilization. We constructed a dynamic circadian PPI network predicting the PPI timing using circadian expression data. Systematic circadian phenotyping (RNAi and overexpression) suggests a crucial role for components involved in dynamic interactions. Systems analysis of a global dynamic network in liver revealed that interacting proteins are expressed at similar times likely to restrict regulatory interactions to specific phases. Moreover, we predict that circadian PPIs dynamically connect many important cellular processes (signal transduction, cell cycle, etc.) contributing to temporal organization of cellular physiology in an unprecedented manner.