ResearchPad - protein-protein-interactions https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Neural networks for open and closed Literature-based Discovery]]> https://www.researchpad.co/article/elastic_article_14696 Literature-based Discovery (LBD) aims to discover new knowledge automatically from large collections of literature. Scientific literature is growing at an exponential rate, making it difficult for researchers to stay current in their discipline and easy to miss knowledge necessary to advance their research. LBD can facilitate hypothesis testing and generation and thus accelerate scientific progress. Neural networks have demonstrated improved performance on LBD-related tasks but are yet to be applied to it. We propose four graph-based, neural network methods to perform open and closed LBD. We compared our methods with those used by the state-of-the-art LION LBD system on the same evaluations to replicate recently published findings in cancer biology. We also applied them to a time-sliced dataset of human-curated peer-reviewed biological interactions. These evaluations and the metrics they employ represent performance on real-world knowledge advances and are thus robust indicators of approach efficacy. In the first experiments, our best methods performed 2-4 times better than the baselines in closed discovery and 2-3 times better in open discovery. In the second, our best methods performed almost 2 times better than the baselines in open discovery. These results are strong indications that neural LBD is potentially a very effective approach for generating new scientific discoveries from existing literature. The code for our models and other information can be found at: https://github.com/cambridgeltl/nn_for_LBD.

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<![CDATA[Deregulation of LRSAM1 expression impairs the levels of TSG101, UBE2N, VPS28, MDM2 and EGFR]]> https://www.researchpad.co/article/5c648d54d5eed0c484c825ab

CMT is the most common hereditary neuromuscular disorder of the peripheral nervous system with a prevalence of 1/2500 individuals and it is caused by mutations in more than 80 genes. LRSAM1, a RING finger ubiquitin ligase also known as TSG101-associated ligase (TAL), has been associated with Charcot-Marie-Tooth disease type 2P (CMT2P) and to date eight causative mutations have been identified. Little is currently known on the pathogenetic mechanisms that lead to the disease. We investigated the effect of LRSAM1 deregulation on possible LRSAM1 interacting molecules in cell based models. Possible LRSAM1 interacting molecules were identified using protein-protein interaction databases and literature data. Expression analysis of these molecules was performed in both CMT2P patient and control lymphoblastoid cell lines as well as in LRSAM1 and TSG101 downregulated SH-SY5Y cells.TSG101, UBE2N, VPS28, EGFR and MDM2 levels were significantly decreased in the CMT2P patient lymphoblastoid cell line as well as in LRSAM1 downregulated cells. TSG101 downregulation had a significant effect only on the expression of VPS28 and MDM2 and it did not affect the levels of LRSAM1. This study confirms that LRSAM1 is a regulator of TSG101 expression. Furthermore, deregulation of LRSAM1 significantly affects the levels of UBE2N, VPS28, EGFR and MDM2.

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<![CDATA[Single-molecule dynamics and genome-wide transcriptomics reveal that NF-kB (p65)-DNA binding times can be decoupled from transcriptional activation]]> https://www.researchpad.co/article/5c4a3091d5eed0c4844c0568

Transcription factors (TFs) regulate gene expression in both prokaryotes and eukaryotes by recognizing and binding to specific DNA promoter sequences. In higher eukaryotes, it remains unclear how the duration of TF binding to DNA relates to downstream transcriptional output. Here, we address this question for the transcriptional activator NF-κB (p65), by live-cell single molecule imaging of TF-DNA binding kinetics and genome-wide quantification of p65-mediated transcription. We used mutants of p65, perturbing either the DNA binding domain (DBD) or the protein-protein transactivation domain (TAD). We found that p65-DNA binding time was predominantly determined by its DBD and directly correlated with its transcriptional output as long as the TAD is intact. Surprisingly, mutation or deletion of the TAD did not modify p65-DNA binding stability, suggesting that the p65 TAD generally contributes neither to the assembly of an “enhanceosome,” nor to the active removal of p65 from putative specific binding sites. However, TAD removal did reduce p65-mediated transcriptional activation, indicating that protein-protein interactions act to translate the long-lived p65-DNA binding into productive transcription.

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<![CDATA[SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions]]> https://www.researchpad.co/article/5c196699d5eed0c484b52590

LncRNA-protein interactions play important roles in post-transcriptional gene regulation, poly-adenylation, splicing and translation. Identification of lncRNA-protein interactions helps to understand lncRNA-related activities. Existing computational methods utilize multiple lncRNA features or multiple protein features to predict lncRNA-protein interactions, but features are not available for all lncRNAs or proteins; most of existing methods are not capable of predicting interacting proteins (or lncRNAs) for new lncRNAs (or proteins), which don’t have known interactions. In this paper, we propose the sequence-based feature projection ensemble learning method, “SFPEL-LPI”, to predict lncRNA-protein interactions. First, SFPEL-LPI extracts lncRNA sequence-based features and protein sequence-based features. Second, SFPEL-LPI calculates multiple lncRNA-lncRNA similarities and protein-protein similarities by using lncRNA sequences, protein sequences and known lncRNA-protein interactions. Then, SFPEL-LPI combines multiple similarities and multiple features with a feature projection ensemble learning frame. In computational experiments, SFPEL-LPI accurately predicts lncRNA-protein associations and outperforms other state-of-the-art methods. More importantly, SFPEL-LPI can be applied to new lncRNAs (or proteins). The case studies demonstrate that our method can find out novel lncRNA-protein interactions, which are confirmed by literature. Finally, we construct a user-friendly web server, available at http://www.bioinfotech.cn/SFPEL-LPI/.

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<![CDATA[Protein—protein binding supersites]]> https://www.researchpad.co/article/5c3d00e9d5eed0c4840369fc

The lack of a deep understanding of how proteins interact remains an important roadblock in advancing efforts to identify binding partners and uncover the corresponding regulatory mechanisms of the functions they mediate. Understanding protein-protein interactions is also essential for designing specific chemical modifications to develop new reagents and therapeutics. We explored the hypothesis of whether protein interaction sites serve as generic biding sites for non-cognate protein ligands, just as it has been observed for small-molecule-binding sites in the past. Using extensive computational docking experiments on a test set of 241 protein complexes, we found that indeed there is a strong preference for non-cognate ligands to bind to the cognate binding site of a receptor. This observation appears to be robust to variations in docking programs, types of non-cognate protein probes, sizes of binding patches, relative sizes of binding patches and full-length proteins, and the exploration of obligate and non-obligate complexes. The accuracy of the docking scoring function appears to play a role in defining the correct site. The frequency of interaction of unrelated probes recognizing the binding interface was utilized in a simple prediction algorithm that showed accuracy competitive with other state of the art methods.

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<![CDATA[The Prediction of Key Cytoskeleton Components Involved in Glomerular Diseases Based on a Protein-Protein Interaction Network]]> https://www.researchpad.co/article/5989dad7ab0ee8fa60bb8508

Maintenance of the physiological morphologies of different types of cells and tissues is essential for the normal functioning of each system in the human body. Dynamic variations in cell and tissue morphologies depend on accurate adjustments of the cytoskeletal system. The cytoskeletal system in the glomerulus plays a key role in the normal process of kidney filtration. To enhance the understanding of the possible roles of the cytoskeleton in glomerular diseases, we constructed the Glomerular Cytoskeleton Network (GCNet), which shows the protein-protein interaction network in the glomerulus, and identified several possible key cytoskeletal components involved in glomerular diseases. In this study, genes/proteins annotated to the cytoskeleton were detected by Gene Ontology analysis, and glomerulus-enriched genes were selected from nine available glomerular expression datasets. Then, the GCNet was generated by combining these two sets of information. To predict the possible key cytoskeleton components in glomerular diseases, we then examined the common regulation of the genes in GCNet in the context of five glomerular diseases based on their transcriptomic data. As a result, twenty-one cytoskeleton components as potential candidate were highlighted for consistently down- or up-regulating in all five glomerular diseases. And then, these candidates were examined in relation to existing known glomerular diseases and genes to determine their possible functions and interactions. In addition, the mRNA levels of these candidates were also validated in a puromycin aminonucleoside(PAN) induced rat nephropathy model and were also matched with existing Diabetic Nephropathy (DN) transcriptomic data. As a result, there are 15 of 21 candidates in PAN induced nephropathy model were consistent with our predication and also 12 of 21 candidates were matched with differentially expressed genes in the DN transcriptomic data. By providing a novel interaction network and prediction, GCNet contributes to improving the understanding of normal glomerular function and will be useful for detecting target cytoskeleton molecules of interest that may be involved in glomerular diseases in future studies.

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<![CDATA[ICln: A New Regulator of Non-Erythroid 4.1R Localisation and Function]]> https://www.researchpad.co/article/5989da3aab0ee8fa60b87bc3

To optimise the efficiency of cell machinery, cells can use the same protein (often called a hub protein) to participate in different cell functions by simply changing its target molecules. There are large data sets describing protein-protein interactions (“interactome”) but they frequently fail to consider the functional significance of the interactions themselves. We studied the interaction between two potential hub proteins, ICln and 4.1R (in the form of its two splicing variants 4.1R80 and 4.1R135), which are involved in such crucial cell functions as proliferation, RNA processing, cytoskeleton organisation and volume regulation. The sub-cellular localisation and role of native and chimeric 4.1R over-expressed proteins in human embryonic kidney (HEK) 293 cells were examined. ICln interacts with both 4.1R80 and 4.1R135 and its over-expression displaces 4.1R from the membrane regions, thus affecting 4.1R interaction with ß-actin. It was found that 4.1R80 and 4.1R135 are differently involved in regulating the swelling activated anion current (ICl,swell) upon hypotonic shock, a condition under which both isoforms are dislocated from the membrane region and thus contribute to ICl,swell current regulation. Both 4.1R isoforms are also differently involved in regulating cell morphology, and ICln counteracts their effects. The findings of this study confirm that 4.1R plays a role in cell volume regulation and cell morphology and indicate that ICln is a new negative regulator of 4.1R functions.

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<![CDATA[Evolution of protein-protein interaction networks in yeast]]> https://www.researchpad.co/article/5989db50ab0ee8fa60bdbdb9

Interest in the evolution of protein-protein and genetic interaction networks has been rising in recent years, but the lack of large-scale high quality comparative datasets has acted as a barrier. Here, we carried out a comparative analysis of computationally predicted protein-protein interaction (PPI) networks from five closely related yeast species. We used the Protein-protein Interaction Prediction Engine (PIPE), which uses a database of known interactions to make sequence-based PPI predictions, to generate high quality predicted interactomes. Simulated proteomes and corresponding PPI networks were used to provide null expectations for the extent and nature of PPI network evolution. We found strong evidence for conservation of PPIs, with lower than expected levels of change in PPIs for about a quarter of the proteome. Furthermore, we found that changes in predicted PPI networks are poorly predicted by sequence divergence. Our analyses identified a number of functional classes experiencing fewer PPI changes than expected, suggestive of purifying selection on PPIs. Our results demonstrate the added benefit of considering predicted PPI networks when studying the evolution of closely related organisms.

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<![CDATA[Network Analysis of Genome-Wide Selective Constraint Reveals a Gene Network Active in Early Fetal Brain Intolerant of Mutation]]> https://www.researchpad.co/article/5989db49ab0ee8fa60bd9a8e

Using robust, integrated analysis of multiple genomic datasets, we show that genes depleted for non-synonymous de novo mutations form a subnetwork of 72 members under strong selective constraint. We further show this subnetwork is preferentially expressed in the early development of the human hippocampus and is enriched for genes mutated in neurological Mendelian disorders. We thus conclude that carefully orchestrated developmental processes are under strong constraint in early brain development, and perturbations caused by mutation have adverse outcomes subject to strong purifying selection. Our findings demonstrate that selective forces can act on groups of genes involved in the same process, supporting the notion that purifying selection can act coordinately on multiple genes. Our approach provides a statistically robust, interpretable way to identify the tissues and developmental times where groups of disease genes are active.

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<![CDATA[Characterisation of Translation Elongation Factor eEF1B Subunit Expression in Mammalian Cells and Tissues and Co-Localisation with eEF1A2]]> https://www.researchpad.co/article/5989da49ab0ee8fa60b8c7a8

Translation elongation is the stage of protein synthesis in which the translation factor eEF1A plays a pivotal role that is dependent on GTP exchange. In vertebrates, eEF1A can exist as two separately encoded tissue-specific isoforms, eEF1A1, which is almost ubiquitously expressed, and eEF1A2, which is confined to neurons and muscle. The GTP exchange factor for eEF1A1 is a complex called eEF1B made up of subunits eEF1Bα, eEF1Bδ and eEF1Bγ. Previous studies have cast doubt on the ability of eEF1B to interact with eEF1A2, suggesting that this isoform might use a different GTP exchange factor. We show that eEF1B subunits are all widely expressed to varying degrees in different cell lines and tissues, and at different stages of development. We show that ablation of any of the subunits in human cell lines has a small but significant impact on cell viability and cycling. Finally, we show that both eEF1A1 and eEF1A2 colocalise with all eEF1B subunits, in such close proximity that they are highly likely to be in a complex.

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<![CDATA[Pathway Analysis Incorporating Protein-Protein Interaction Networks Identified Candidate Pathways for the Seven Common Diseases]]> https://www.researchpad.co/article/5989db26ab0ee8fa60bd0607

Pathway analysis has become popular as a secondary analysis strategy for genome-wide association studies (GWAS). Most of the current pathway analysis methods aggregate signals from the main effects of single nucleotide polymorphisms (SNPs) in genes within a pathway without considering the effects of gene-gene interactions. However, gene-gene interactions can also have critical effects on complex diseases. Protein-protein interaction (PPI) networks have been used to define gene pairs for the gene-gene interaction tests. Incorporating the PPI information to define gene pairs for interaction tests within pathways can increase the power for pathway-based association tests. We propose a pathway association test, which aggregates the interaction signals in PPI networks within a pathway, for GWAS with case-control samples. Gene size is properly considered in the test so that genes do not contribute more to the test statistic simply due to their size. Simulation studies were performed to verify that the method is a valid test and can have more power than other pathway association tests in the presence of gene-gene interactions within a pathway under different scenarios. We applied the test to the Wellcome Trust Case Control Consortium GWAS datasets for seven common diseases. The most significant pathway is the chaperones modulate interferon signaling pathway for Crohn’s disease (p-value = 0.0003). The pathway modulates interferon gamma, which induces the JAK/STAT pathway that is involved in Crohn’s disease. Several other pathways that have functional implications for the seven diseases were also identified. The proposed test based on gene-gene interaction signals in PPI networks can be used as a complementary tool to the current existing pathway analysis methods focusing on main effects of genes. An efficient software implementing the method is freely available at http://puppi.sourceforge.net.

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<![CDATA[Finding Candidate Drugs for Hepatitis C Based on Chemical-Chemical and Chemical-Protein Interactions]]> https://www.researchpad.co/article/5989dab3ab0ee8fa60bac29b

Hepatitis C virus (HCV) is an infectious virus that can cause serious illnesses. Only a few drugs have been reported to effectively treat hepatitis C. To have greater diversity in drug choice and better treatment options, it is necessary to develop more drugs to treat the infection. However, it is time-consuming and expensive to discover candidate drugs using experimental methods, and computational methods may complement experimental approaches as a preliminary filtering process. This type of approach was proposed by using known chemical-chemical interactions to extract interactive compounds with three known drug compounds of HCV, and the probabilities of these drug compounds being able to treat hepatitis C were calculated using chemical-protein interactions between the interactive compounds and HCV target genes. Moreover, the randomization test and expectation-maximization (EM) algorithm were both employed to exclude false discoveries. Analysis of the selected compounds, including acyclovir and ganciclovir, indicated that some of these compounds had potential to treat the HCV. Hopefully, this proposed method could provide new insights into the discovery of candidate drugs for the treatment of HCV and other diseases.

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<![CDATA[Linear Motif-Mediated Interactions Have Contributed to the Evolution of Modularity in Complex Protein Interaction Networks]]> https://www.researchpad.co/article/5989db0dab0ee8fa60bcada0

The modular architecture of protein-protein interaction (PPI) networks is evident in diverse species with a wide range of complexity. However, the molecular components that lead to the evolution of modularity in PPI networks have not been clearly identified. Here, we show that weak domain-linear motif interactions (DLIs) are more likely to connect different biological modules than strong domain-domain interactions (DDIs). This molecular division of labor is essential for the evolution of modularity in the complex PPI networks of diverse eukaryotic species. In particular, DLIs may compensate for the reduction in module boundaries that originate from increased connections between different modules in complex PPI networks. In addition, we show that the identification of biological modules can be greatly improved by including molecular characteristics of protein interactions. Our findings suggest that transient interactions have played a unique role in shaping the architecture and modularity of biological networks over the course of evolution.

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<![CDATA[A Novel Approach of Dynamic Cross Correlation Analysis on Molecular Dynamics Simulations and Its Application to Ets1 Dimer–DNA Complex]]> https://www.researchpad.co/article/5989d9feab0ee8fa60b730b9

The dynamic cross correlation (DCC) analysis is a popular method for analyzing the trajectories of molecular dynamics (MD) simulations. However, it is difficult to detect correlative motions that appear transiently in only a part of the trajectory, such as atomic contacts between the side-chains of amino acids, which may rapidly flip. In order to capture these multi-modal behaviors of atoms, which often play essential roles, particularly at the interfaces of macromolecules, we have developed the “multi-modal DCC (mDCC)” analysis. The mDCC is an extension of the DCC and it takes advantage of a Bayesian-based pattern recognition technique. We performed MD simulations for molecular systems modeled from the (Ets1)2–DNA complex and analyzed their results with the mDCC method. Ets1 is an essential transcription factor for a variety of physiological processes, such as immunity and cancer development. Although many structural and biochemical studies have so far been performed, its DNA binding properties are still not well characterized. In particular, it is not straightforward to understand the molecular mechanisms how the cooperative binding of two Ets1 molecules facilitates their recognition of Stromelysin-1 gene regulatory elements. A correlation network was constructed among the essential atomic contacts, and the two major pathways by which the two Ets1 molecules communicate were identified. One is a pathway via direct protein-protein interactions and the other is that via the bound DNA intervening two recognition helices. These two pathways intersected at the particular cytosine bases (C110/C11), interacting with the H1, H2, and H3 helices. Furthermore, the mDCC analysis showed that both pathways included the transient interactions at their intermolecular interfaces of Tyr396–C11 and Ala327–Asn380 in multi-modal motions of the amino acid side chains and the nucleotide backbone. Thus, the current mDCC approach is a powerful tool to reveal these complicated behaviors and scrutinize intermolecular communications in a molecular system.

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<![CDATA[Phage Orf Family Recombinases: Conservation of Activities and Involvement of the Central Channel in DNA Binding]]> https://www.researchpad.co/article/5989da4fab0ee8fa60b8d9e1

Genetic and biochemical evidence suggests that λ Orf is a recombination mediator, promoting nucleation of either bacterial RecA or phage Redβ recombinases onto single-stranded DNA (ssDNA) bound by SSB protein. We have identified a diverse family of Orf proteins that includes representatives implicated in DNA base flipping and those fused to an HNH endonuclease domain. To confirm a functional relationship with the Orf family, a distantly-related homolog, YbcN, from Escherichia coli cryptic prophage DLP12 was purified and characterized. As with its λ relative, YbcN showed a preference for binding ssDNA over duplex. Neither Orf nor YbcN displayed a significant preference for duplex DNA containing mismatches or 1-3 nucleotide bulges. YbcN also bound E. coli SSB, although unlike Orf, it failed to associate with an SSB mutant lacking the flexible C-terminal tail involved in coordinating heterologous protein-protein interactions. Residues conserved in the Orf family that flank the central cavity in the λ Orf crystal structure were targeted for mutagenesis to help determine the mode of DNA binding. Several of these mutant proteins showed significant defects in DNA binding consistent with the central aperture being important for substrate recognition. The widespread conservation of Orf-like proteins highlights the importance of targeting SSB coated ssDNA during lambdoid phage recombination.

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<![CDATA[Angiogenesis Interactome and Time Course Microarray Data Reveal the Distinct Activation Patterns in Endothelial Cells]]> https://www.researchpad.co/article/5989da38ab0ee8fa60b86f08

Angiogenesis involves stimulation of endothelial cells (EC) by various cytokines and growth factors, but the signaling mechanisms are not completely understood. Combining dynamic gene expression time-course data for stimulated EC with protein-protein interactions associated with angiogenesis (the “angiome”) could reveal how different stimuli result in different patterns of network activation and could implicate signaling intermediates as points for control or intervention. We constructed the protein-protein interaction networks of positive and negative regulation of angiogenesis comprising 367 and 245 proteins, respectively. We used five published gene expression datasets derived from in vitro assays using different types of blood endothelial cells stimulated by VEGFA (vascular endothelial growth factor A). We used the Short Time-series Expression Miner (STEM) to identify significant temporal gene expression profiles. The statistically significant patterns between 2D fibronectin and 3D type I collagen substrates for telomerase-immortalized EC (TIME) show that different substrates could influence the temporal gene activation patterns in the same cell line. We investigated the different activation patterns among 18 transmembrane tyrosine kinase receptors, and experimentally measured the protein level of the tyrosine-kinase receptors VEGFR1, VEGFR2 and VEGFR3 in human umbilical vein EC (HUVEC) and human microvascular EC (MEC). The results show that VEGFR1–VEGFR2 levels are more closely coupled than VEGFR1–VEGFR3 or VEGFR2–VEGFR3 in HUVEC and MEC. This computational methodology can be extended to investigate other molecules or biological processes such as cell cycle.

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<![CDATA[Structural Model of the hUbA1-UbcH10 Quaternary Complex: In Silico and Experimental Analysis of the Protein-Protein Interactions between E1, E2 and Ubiquitin]]> https://www.researchpad.co/article/5989da47ab0ee8fa60b8c0ae

UbcH10 is a component of the Ubiquitin Conjugation Enzymes (Ubc; E2) involved in the ubiquitination cascade controlling the cell cycle progression, whereby ubiquitin, activated by E1, is transferred through E2 to the target protein with the involvement of E3 enzymes. In this work we propose the first three dimensional model of the tetrameric complex formed by the human UbA1 (E1), two ubiquitin molecules and UbcH10 (E2), leading to the transthiolation reaction. The 3D model was built up by using an experimentally guided incremental docking strategy that combined homology modeling, protein-protein docking and refinement by means of molecular dynamics simulations. The structural features of the in silico model allowed us to identify the regions that mediate the recognition between the interacting proteins, revealing the active role of the ubiquitin crosslinked to E1 in the complex formation. Finally, the role of these regions involved in the E1–E2 binding was validated by designing short peptides that specifically interfere with the binding of UbcH10, thus supporting the reliability of the proposed model and representing valuable scaffolds for the design of peptidomimetic compounds that can bind selectively to Ubcs and inhibit the ubiquitylation process in pathological disorders.

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<![CDATA[Plant Tandem CCCH Zinc Finger Proteins Interact with ABA, Drought, and Stress Response Regulators in Processing-Bodies and Stress Granules]]> https://www.researchpad.co/article/5989db1eab0ee8fa60bcea69

Although multiple lines of evidence have indicated that Arabidopsis thaliana Tandem CCCH Zinc Finger proteins, AtTZF4, 5 and 6 are involved in ABA, GA and phytochrome mediated seed germination responses, the interacting proteins involved in these processes are unknown. Using yeast two-hybrid screens, we have identified 35 putative AtTZF5 interacting protein partners. Among them, Mediator of ABA-Regulated Dormancy 1 (MARD1) is highly expressed in seeds and involved in ABA signal transduction, while Responsive to Dehydration 21A (RD21A) is a well-documented stress responsive protein. Co-immunoprecipitation (Co-IP) and bimolecular fluorescence complementation (BiFC) assays were used to confirm that AtTZF5 can interact with MARD1 and RD21A in plant cells, and the interaction is mediated through TZF motif. In addition, AtTZF4 and 6 could also interact with MARD1 and RD21A in Y-2-H and BiFC assay, respectively. The protein-protein interactions apparently take place in processing bodies (PBs) and stress granules (SGs), because AtTZF5, MARD1 and RD21A could interact and co-localize with each other and they all can co-localize with the same PB and SG markers in plant cells.

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<![CDATA[TRIpartite Motif 21 (TRIM21) Differentially Regulates the Stability of Interferon Regulatory Factor 5 (IRF5) Isoforms]]> https://www.researchpad.co/article/5989da04ab0ee8fa60b7549f

IRF5 is a member of the Interferon Regulatory Factor (IRF) family of transcription factors activated downstream of the Toll-Like receptors (TLRs). Polymorphisms in IRF5 have been shown to be associated with the autoimmune disease Systemic Lupus Erythematosus (SLE) and other autoimmune conditions, suggesting a central role for IRF5 in the regulation of the immune response. Four different IRF5 isoforms originate due to alternative splicing and to the presence or absence of a 30 nucleotide insertion in IRF5 exon 6. Since the polymorphic region disturbs a PEST domain, a region associated with protein degradation, we hypothesized that the isoforms bearing the insertion might have increased stability, thus explaining the association of individual IRF5 isoforms with SLE. As the E3 ubiquitin ligase TRIpartite Motif 21 (TRIM21) has been shown to regulate the stability and hence activity of members of the IRF family, we investigated whether IRF5 is subjected to regulation by TRIM21 and whether dysregulation of this mechanism could explain the association of IRF5 with SLE. Our results show that IRF5 is degraded following TLR7 activation and that TRIM21 is involved in this process. Comparison of the individual IRF5 variants demonstrates that isoforms generated by alternative splicing are resistant to TRIM21-mediated degradation following TLR7 stimulation, thus providing a functional link between isoforms expression and stability/activity which contributes to explain the association of IRF5 with SLE.

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<![CDATA[The amyloid interactome: Exploring protein aggregation]]> https://www.researchpad.co/article/5989db4fab0ee8fa60bdbc66

Protein-protein interactions are the quintessence of physiological activities, but also participate in pathological conditions. Amyloid formation, an abnormal protein-protein interaction process, is a widespread phenomenon in divergent proteins and peptides, resulting in a variety of aggregation disorders. The complexity of the mechanisms underlying amyloid formation/amyloidogenicity is a matter of great scientific interest, since their revelation will provide important insight on principles governing protein misfolding, self-assembly and aggregation. The implication of more than one protein in the progression of different aggregation disorders, together with the cited synergistic occurrence between amyloidogenic proteins, highlights the necessity for a more universal approach, during the study of these proteins. In an attempt to address this pivotal need we constructed and analyzed the human amyloid interactome, a protein-protein interaction network of amyloidogenic proteins and their experimentally verified interactors. This network assembled known interconnections between well-characterized amyloidogenic proteins and proteins related to amyloid fibril formation. The consecutive extended computational analysis revealed significant topological characteristics and unraveled the functional roles of all constituent elements. This study introduces a detailed protein map of amyloidogenicity that will aid immensely towards separate intervention strategies, specifically targeting sub-networks of significant nodes, in an attempt to design possible novel therapeutics for aggregation disorders.

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