Integrated network analysis of transcriptomic and protein-protein interaction data in taurine-treated hepatic stellate cells

BACKGROUND Studies show that the antifibrotic mechanism of taurine may involve its inhibition of the activation and proliferation of hepatic stellate cells(HSCs). Since the molecular mechanism of taurine-mediated antifibrotic activity has not been fully unveiled and is little studied, it is imperative to use "omics" methods to systematically investigate the molecular mechanism by which taurine inhibits liver fibrosis.AIM To establish a network including transcriptomic and protein-protein interaction data to elucidate the molecular mechanism of taurine-induced HSC apoptosis.METHODS We used microarrays, bioinformatics, protein-protein interaction(PPI) network,and sub-modules to investigate taurine-induced changes in gene expression in human HSCs(LX-2). Subsequently, all of the differentially expressed genes(DEGs) were subjected to gene ontology function and Kyoto encyclopedia of genes and genomes pathway enrichment analysis. Furthermore, the interactions of DEGs were explored in a human PPI network, and sub-modules of the DEGs interaction network were analyzed using Cytoscape software.RESULTS A total of 635 DEGs were identified in taurine-treated HSCs when compared with the controls. Of these, 304 genes were statistically significantly up-regulated, and 331 down-regulated. Most of these DEGs were mainly located on the membrane and extracellular region, and are involved in the biological processes of signal transduction, cell proliferation, positive regulation of extracellular regulated protein kinases 1(ERK1) and ERK2 cascade, extrinsic apoptotic signaling pathway and so on. Fifteen significantly enriched pathways with DEGs were identified, including mitogen-activated protein kinase(MAPK) signaling pathway, peroxisome proliferators-activated receptor signaling pathway,estrogen signaling pathway, Th1 and Th2 cell differentiation, cyclic adenosine monophosphate signaling pathway and so on. By integrating the transcriptomics and human PPI data, nine critical genes, including MMP2, MMP9, MMP21,TIMP3, KLF10, CX3CR1, TGFB1, VEGFB, and EGF, were identified in the PPI network analysis.CONCLUSION Taurine promotes the apoptosis of HSCs via up-regulating TGFB1 and then activating the p38 MAPK-JNK-Caspase9/8/3 pathway. These findings enhance the understanding of the molecular mechanism of taurine-induced HSC apoptosis and provide references for liver disorder therapy.

Protein-protein interaction map is a key gateway into liver regeneration

Recent studies indicate that the process of liver regeneration involves multiple signaling pathways and a variety of genes,cytokines and growth factors. Protein-protein interactions(PPIs)play a role in nearly all events that take place within the cell and PPI maps should be helpful in further understanding the process of liver regeneration.In this review,we discuss recent progress in understanding the PPIs that occur during liver regeneration especially those in the transforming growth factorβsignaling pathways.We believe the use of large-scale PPI maps for integrating the information already known about the liver regeneration is a useful approach in understanding liver regeneration from the standpoint of systems biology.

nNOS-mediated protein-protein interactions:promising targets for treating neurological and neuropsychiatric disorders

Neurological and neuropsychiatric disorders are one of the leading causes of disability worldwide and affect the health of billions of people.Nitric oxide(NO),a free gas with multitudinous bioactivities,is mainly produced from the oxidation of L-arginine by neuronal nitric oxide synthase(nNOS)in the brain.Inhibiting nNOS benefits a variety of neurological and neuropsychiatric disorders,including stroke,depression and anxiety disorders,posttraumatic stress disorder,Parkinson’s disease,Alzheimer’s disease,chronic pain,and drug addiction.Due to critical roles of nNOS in learning and memory and synaptic plasticity,direct inhibition of nNOS may cause severe side effects.Importantly,interactions of several proteins,including post-synaptic density 95(PSD-95),carboxyterminal PDZ ligand of nNOS(CAPON)and serotonin transporter(SERT),with the PSD/Disc-large/ZO-1 homologous(PDZ)domain of nNOS have been demonstrated to influence the subcellular distribution and activity of the enzyme in the brain.Therefore,it will be a preferable means to interfere with nNOS-mediated proteinprotein interactions(PPIs),which do not lead to undesirable effects.Herein,we summarize the current literatures on nNOS-mediated PPIs involved in neurological and neuropsychiatric disorders,and the discovery of drugs targeting the PPIs,which is expected to provide potential targets for developing novel drugs and new strategy for the treatment of neurological and neuropsychiatric disorders.

Protein-protein Interaction Between Domains of PDZ and BAR from PICK1

Two DNA fragments encoding PDZ domain （21-110 residues） and BAR domain （ 150-360 residues） from PICK1 （1-416 residues） were amplified by PCR and then introduced into vectors, pET-32M and pMAL-e2X respectively to generate recombinant plasmids, pE-pdz and pM-bar. Having been separately transferred into the hosts E. coli BL21 and E. coli JM109, these two strains can express fusion proteins： His-tagged PDZ（PDZ domain） and maltose binding protein-BAR（ MBP-BAR domain） respectively, as confirmed by both SDS-PAGE and Wostem blotting. The interaction between these two domains is dose-dependence, as identified by a pull-down test. Moreover, it has been shown from the ELISA analysis that the actual amount of PDZ bound to MBP-BAR-amylose beads reaches （ 16 ± 0. 5）%, as calculated by the molar ratio of PDZ to MBP-BAR. In addition, the interaction between BAR（bait） and PDZ（prey） in vivo was also examined with a yeast two-hybrid system.

A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

Protein-protein interactions are of great significance for human to understand the functional mechanisms of proteins.With the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them efficiently.To address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using MapReduce.To do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and prediction.Respective solutions are then devised to overcome these limitations.In particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of proteins.After that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and accuracy.Experimental results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

Protein-protein interactions: Methods, databases, and applications in virus-host study

Almost all the cellular processes in a living system are controlled by proteins:They regulate gene expression,catalyze chemical reactions,transport small molecules across membranes,and transmit signal across membranes.Even,a viral infection is often initiated through virus-host protein interactions.Protein-protein interactions(PPIs)are the physical contacts between two or more proteins and they represent complex biological functions.Nowadays,PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins.Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets.In this review,we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies.Here,we present a short but comprehensive review on PPIs,including the experimental and computational methods of finding PPIs,the databases dedicated to virus-host PPIs,and the associated various applications in protein interaction networks of some lethal viruses with their hosts.

A novel computational method for protein-protein interaction networks prediction of alpha-synuclein

Alpha-synuclein plays an important role in Parkinson's disease(PD).The current study of alpha-synuclein mainly concentrates at the gene level.However, it is found that the study at the protein level has special significance.Meanwhile, there is free information on the Internet, such as databases and algorithms of protein-protein interactions(PPIs).In this paper, a novel method which integrates distributed heterogeneous data sources and algorithms to predict PPIs for alpha-synuclein in silico is proposed.The PPIs generated by the method take advantage of various experimental data, and indicate new information about PPIs for alpha-synuclein.In the end of this paper, the result illustrates that the method is practical.It is hoped that the prediction result obtained by this method can provide guidance for biological experiments of PPIs for alpha-synuclein to reveal possible mechanisms of PD.

Protein-Protein Interaction Extraction Based on Convex Combination Kernel Function

Owing to the effect of classified models was different in Protein-Protein Interaction(PPI) extraction, which was made by different single kernel functions, and only using single kernel function hardly trained the optimal classified model to extract PPI, this paper presents a strategy to find the optimal kernel function from a kernel function set. The strategy is that in the kernel function set which consists of different single kernel functions, endlessly finding the last two kernel functions on the performance in PPI extraction, using their optimal kernel function to replace them, until there is only one kernel function and it’s the final optimal kernel function. Finally, extracting PPI using the classified model made by this kernel function. This paper conducted the PPI extraction experiment on AIMed corpus, the experimental result shows that the optimal convex combination kernel function this paper presents can effectively improve the extraction performance than single kernel function, and it gets the best precision which reaches 65.0 among the similar PPI extraction systems.

The DUF579 proteins GhIRX15s regulate cotton fiber development by interacting with proteins involved in xylan synthesis

Cotton provides the most abundant natural fiber for the textile industry.The mature cotton fiber largely consists of secondary cell walls with the highest proportion of cellulose and a small amount of hemicellulose and lignin.To dissect the roles of hemicellulosic polysaccharides during fiber development,four IRREGULAR XYLEM 15(IRX15)genes,GhIRX15-1/-2/-3/-4,were functionally characterized in cotton.These genes encode DUF579 domain-containing proteins,which are homologs of AtIRX15 involved in xylan biosynthesis.The four GhIRX15 genes were predominantly expressed during fiber secondary wall thickening,and the encoded proteins were localized to the Golgi apparatus.Each GhIRX15 gene could restore the xylan deficient phenotype in the Arabidopsis irx15irx15l double mutant.Silencing of GhIRX15s in cotton resulted in shorter mature fibers with a thinner cell wall and reduced cellulose content as compared to the wild type.Intriguingly,GhIRX15-2 and GhIRX15-4 formed homodimers and heterodimers.In addition,the GhIRX15s showed physical interaction with glycosyltransferases GhGT43C,GhGT47A and GhGT47B,which are responsible for synthesis of the xylan backbone and reducing end sequence.Moreover,the GhIRX15s can form heterocomplexes with enzymes involved in xylan modification and side chain synthesis,such as GhGUX1/2,GhGXM1/2 and GhTBL1.These findings suggest that GhIRX15s participate in fiber xylan biosynthesis and modulate fiber development via forming large multiprotein complexes.

CNKSR2 interactome analysis indicates its association with the centrosome/microtubule system

The protein connector enhancer of kinase suppressor of Ras 2(CNKSR2),present in both the postsynaptic density and cytoplasm of neurons,is a scaffolding protein with several protein-binding domains.Variants of the CNKSR2 gene have been implicated in neurodevelopmental disorders,particularly intellectual disability,although the precise mechanism involved has not yet been fully understood.Research has demonstrated that CNKSR2 plays a role in facilitating the localization of postsynaptic density protein complexes to the membrane,thereby influencing synaptic signaling and the morphogenesis of dendritic spines.However,the function of CNKSR2 in the cytoplasm remains to be elucidated.In this study,we used immunoprecipitation and high-resolution liquid chromatography-mass spectrometry to identify the interactors of CNKSR2.Through a combination of bioinformatic analysis and cytological experiments,we found that the CNKSR2 interactors were significantly enriched in the proteome of the centrosome.We also showed that CNKSR2 interacted with the microtubule protein DYNC1H1 and with the centrosome marker CEP290.Subsequent colocalization analysis confirmed the centrosomal localization of CNKSR2.When we downregulated CNKSR2 expression in mouse neuroblastoma cells(Neuro 2A),we observed significant changes in the expression of numerous centrosomal genes.This manipulation also affected centrosome-related functions,including cell size and shape,cell proliferation,and motility.Furthermore,we found that CNKSR2 interactors were highly enriched in de novo variants associated with intellectual disability and autism spectrum disorder.Our findings establish a connection between CNKSR2 and the centrosome,and offer new insights into the underlying mechanisms of neurodevelopmental disorders.

Studies on the temporal,structural,and interacting features of the clubroot resistance gene Rcr1 using CRISPR/Cas9-based systems

Clubroot disease is a severe threat to Brassica crops globally,particularly in western Canada.Genetic resistance,achieved through pyramiding clubroot resistance(CR)genes with different modes of action,is the most important strategy for managing the disease.However,studies on the CR gene functions are quite limited.In this study,we have conducted investigations into the temporal,structural,and interacting features of a newly cloned CR gene,Rcr1,using CRISPR/Cas9 technology.For temporal functionality,we developed a novel CRISPR/Cas9-based binary vector,pHHIGR-Hsp18.2,to deliver Rcr1 into a susceptible canola line(DH12075)and observed that early expression of Rcr1 is critical for conferring resistance.For structural functionality,several independent mutations in specific domains of Rcr1 resulted in loss-offunction,highlighting their importance for CR phenotype.In the study of the interacting features of Rcr1,a cysteine protease gene and its homologous allele in canola were successfully disrupted via CRISPR/Cas9 as an interacting component with Rcr1 protein,resulting in the conversion from clubroot resistant to susceptible in plants carrying intact Rcr1.These results indicated an indispensable role of these two cysteine proteases in Rcr1-mediated resistance response.This study,the first of its kind,provides valuable insights into the functionality of Rcr1.Further,the new vector p HHIGR-Hsp18.2 demonstrated an inducible feature on the removal of add-on traits,which should be useful for functional genomics and other similar research in brassica crops.

Understanding Bridging Sites and Accelerating Quantum Efficiency for Photocatalytic CO_(2) Reduction

We report a novel double-shelled nanoboxes photocatalyst architecture with tailored interfaces that accelerate quantum efficiency for photocatalytic CO_(2) reduction reaction(CO_(2)RR)via Mo–S bridging bonds sites in S_(v)–In_(2)S_(3)@2H–MoTe_(2).The X-ray absorption near-edge structure shows that the formation of S_(v)–In_(2)S_(3)@2H–MoTe_(2) adjusts the coordination environment via interface engineering and forms Mo–S polarized sites at the interface.The interfacial dynamics and catalytic behavior are clearly revealed by ultrafast femtosecond transient absorption,time-resolved,and in situ diffuse reflectance–Infrared Fourier transform spectroscopy.A tunable electronic structure through steric interaction of Mo–S bridging bonds induces a 1.7-fold enhancement in S_(v)–In_(2)S_(3)@2H–MoTe_(2)(5)photogenerated carrier concentration relative to pristine S_(v)–In_(2)S_(3).Benefiting from lower carrier transport activation energy,an internal quantum efficiency of 94.01%at 380 nm was used for photocatalytic CO_(2)RR.This study proposes a new strategy to design photocatalyst through bridging sites to adjust the selectivity of photocatalytic CO_(2)RR.

Fe-N_(x) sites coupled with core-shell FeS@C nanoparticles to boost the oxygen catalysis for rechargeable Zn-air batteries

The development of efficient single-atom catalysts(SACs) for the oxygen reduction reaction(ORR)remains a formidable challenge,primarily due to the symmetric charge distribution of metal singleatom sites(M-N_(4)).To address such issue,herein,Fe-N_(x) sites coupled synergistic catalysts fabrication strategy is presented to break the uniform electronic distribution,thus enhancing the intrinsic catalytic activity.Precisely,atomically dispersed Fe-N_(x) sites supported on N/S-doped mesoporous carbon(NSC)coupled with FeS@C core-shell nanoparticles(FAS-NSC@950) is synthesized by a facile hydrothermal reaction and subsequent pyrolysis.Due to the presence of an in situ-grown conductive graphitic layer(shell),the FeS nanoparticles(core) effectively adjust the electronic structure of single-atom Fe sites and facilitate the ORR kinetics via short/long-range coupling interactions.Consequently,FAS-NSC@950displays a more positive half-wave potential(E_(1/2)) of 0.871 V with a significantly boosted ORR kinetics(Tafel slope=52.2 mV dec^(-1)),outpacing the commercial Pt/C(E_(1/2)=0.84 V and Tafel slope=54.6 mV dec^(-1)).As a bifunctional electrocatalyst,it displays a smaller bifunctional activity parameter(ΔE) of 0.673 V,surpassing the Pt/C-RuO_(2) combination(ΔE=0.724 V).Besides,the FAS-NSC@950-based zincair battery(ZAB) displays superior power density,specific capacity,and long-term cycling performance to the Pt/C-Ir/C-based ZAB.This work significantly contributes to the field by offering a promising strategy to enhance the catalytic activity of SACs for ORR,with potential implications for energy conversion and storage technologies.

Spastin and alsin protein interactome analyses begin to reveal key canonical pathways and suggest novel druggable targets

Developing effective and long-term treatment strategies for rare and complex neurodegenerative diseases is challenging. One of the major roadblocks is the extensive heterogeneity among patients. This hinders understanding the underlying disease-causing mechanisms and building solutions that have implications for a broad spectrum of patients. One potential solution is to develop personalized medicine approaches based on strategies that target the most prevalent cellular events that are perturbed in patients. Especially in patients with a known genetic mutation, it may be possible to understand how these mutations contribute to problems that lead to neurodegeneration. Protein–protein interaction analyses offer great advantages for revealing how proteins interact, which cellular events are primarily involved in these interactions, and how they become affected when key genes are mutated in patients. This line of investigation also suggests novel druggable targets for patients with different mutations. Here, we focus on alsin and spastin, two proteins that are identified as “causative” for amyotrophic lateral sclerosis and hereditary spastic paraplegia, respectively, when mutated. Our review analyzes the protein interactome for alsin and spastin, the canonical pathways that are primarily important for each protein domain, as well as compounds that are either Food and Drug Administration–approved or are in active clinical trials concerning the affected cellular pathways. This line of research begins to pave the way for personalized medicine approaches that are desperately needed for rare neurodegenerative diseases that are complex and heterogeneous.

Tripartite states Bell-nonlocality sudden death in a spin environment with multisite interaction

Tis paper demonstrates that multipartite Bell-inequality violations can be fully destroyed in a finite time in three- qubit states coupled to a general XY spin-chain with a three-site interaction environment. The Mermin Ardehali- Belinksii-Klyshko inequality is used to detect the degree of nonlocality, as measured by the extent of their violations. The effects of system-environment couplings, the size of degrees of freedom of the environment and the strength of the three-site interaction on the Bell-inequality violations are given. The results indicate that the Bell-inequality violations of the tripartite states will be completely destroyed by decoherence under certain conditions for the GHZ state. The decoherence-free subspaces of our model are identified and the entanglement of quantum states is also discussed.

Transcriptome Profile Based on Protein-Protein Interaction Networks Provides a Set of Core Genes for Understanding the Metabolic Mechanisms of the Egg-Protecting Behavior in Amphioctopus fangsiao

Marine organisms cannot grow and reproduce without proper metabolic regulation.Within a metabolic network,problems with a given link will affect the normal life activities of the organism.Many metabolic mechanisms associated with behaviors of Am-phioctopus fangsiao are still unclear.Moreover,as a factor affecting the normal growth of A.fangsiao,egg protection has rarely been considered in previous behavioral studies.In this research,we analyzed the transcriptome profile of gene expression in A.fangsiao egg-unprotected larvae and egg-protected larvae,and identified 818 differentially expressed genes(DEGs).We used GO and KEGG enrichment analyses to search for metabolism-related DEGs.Protein-protein interaction networks were constructed to examine the interactions between metabolism-related genes.Twenty hub genes with multiple protein-protein interaction relationships or that were involved in multiple KEGG signaling pathways were obtained and verified by quantitative RT-PCR.We first studied the effects of egg protection on the metabolism of A.fangsiao larvae by means of protein-protein interaction networks,and the results provide va-luable gene resources for understanding the metabolism of invertebrate larvae.The data serve as a foundation for further research on the egg-protecting behavior of invertebrates.

Construct Protein-Protein Interaction Network by Mining Domain-Domain Interactions

Domain-domain interactions are important clues to inferring protein-protein interactions. Although about 8 000 domain-domain interactions are discovered so far,they are just the tip of the iceberg. Because domains are conservative and commonplace in proteins,domain-domain interactions are discovered based on pairs of domains which significantly co-exist in proteins. Meanwhile,it is realized that:( 1) domain-domain interactions may exist within the same proteins or across different proteins;( 2) only the domain-domain interactions across different proteins can mediate interactions between proteins;( 3) domains have biases to interact with other domains. And then,a novel method is put forward to construct protein-protein interaction network by using domain-domain interactions. The method is validated by experiments and compared with the state- of-art methods in the field. The experimental results suggest that the method is reasonable and effectiveness on constructing Protein-protein interactions network.

Optogenetic activation of intracellular signaling based on light-inducible protein-protein homo-interactions

Dynamic protein-protein interactions are essential for proper cell functioning.Homointeraction events—physical interactions between the same type of proteins—represent a pivotal subset of protein-protein interactions that are widely exploited in activating intracellular signaling pathways.Capacities of modulating protein-protein interactions with spatial and temporal resolution are greatly desired to decipher the dynamic nature of signal transduction mechanisms.The emerging optogenetic technology,based on genetically encoded light-sensitive proteins,provides promising opportunities to dissect the highly complex signaling networks with unmatched specificity and spatiotemporal precision.Here we review recent achievements in the development of optogenetic tools enabling light-inducible protein-protein homo-interactions and their applications in optical activation of signaling pathways.

Yeast protein-protein interaction network model based on biological experimental data

Duplication and divergence have been widely recognized as the two domi- nant evolutionary forces in shaping biological networks, e.g., gene regulatory networks and protein-protein interaction （PPI） networks. It has been shown that the network growth models constructed on the principle of duplication and divergence can recapture the topo- logical properties of real PPI networks. However, such network models only consider the evolution processes. How to select the model parameters with the real biological experi- mental data has not been presented. Therefore, based on the real PPI network statistical data, a yeast PPI network model is constructed. The simulation results indicate that the topological characteristics of the constructed network model are well consistent with those of real PPI networks, especially on sparseness, scale-free, small-world, hierarchical modularity, and disassortativity.

Prediction of Protein-Protein Interactions by a Novel Model Based on Domain Information

Domain-based protein-protein interactions( PPIs) is a problem that has drawn the attentions of many researchers in recent years and it has been studied using lots of computational approaches from many different perspectives. Existing domain-based methods to predict PPIs typically infer domain interactions from known interacting sets of proteins. However,these methods are costly and complex to implement. In this paper, a simple and effective prediction model is proposed. In this model,an improved multiinstance learning( MIL) algorithm( MilCaA) is designed that doesn't need to take the domain interactions into consideration to construct MIL bags. Then, the pseudo-amino acid composition( PseAAC) transformation method is used to encode the instances in a multi-instance bag and the principal components analysis( PCA) is also used to reduce the feature dimension. Finally, several traditional machine learning and MIL methods are used to verify the proposed model. Experimental results demonstrate that MilCaA performs better than state-of-the-art techniques including the traditional machine learning methods which are widely used in PPIs prediction.