Subcellular Localization Data Research Articles

In Vivo Proximity Cross-Linking and Immunoprecipitation of Cell Wall Epitopes Identify Proteins Associated with the Biosynthesis of Matrix Polysaccharides.

Identification of proteins involved in cell wall matrix polysaccharide biosynthesis is crucial to understand plant cell wall biology. We utilized in vivo cross-linking and immunoprecipitation with cell wall antibodies that recognized xyloglucan, xylan, mannan, and homogalacturonan to capture proteins associated with matrix polysaccharides in Arabidopsis protoplasts. The use of cross-linkers allowed us to capture proteins actively associated with cell wall polymers, including those directly interacting with glycans via glycan-protein (GP) cross-linkers and those associated with proteins linked to glycans via a protein-protein (PP) cross-linker. Immunoprecipitations led to the identification of 65 Arabidopsis protein IDs localized in the Golgi, ER, plasma membrane, and others without subcellular localization data. Among these, we found several glycosyltransferases directly involved in polysaccharide synthesis, along with proteins related to cell wall modification and vesicle trafficking. Protein interaction networks from DeepAraPPI and AtMAD databases showed interactions between various IDs, including those related to cell-wall-associated proteins and membrane/vesicle trafficking proteins. Gene expression and coexpression analyses supported the presence and relevance of the proteins to the cell wall processes. Reverse genetic studies using T-DNA insertion mutants of selected proteins revealed changes in cell wall composition and saccharification, further supporting their potential roles in cell wall biosynthesis. Overall, our approach represents a novel approach for studying cell wall polysaccharide biosynthesis and associated proteins, providing advantages over traditional immunoprecipitation techniques. This study provides a list of putative proteins associated with different matrix polysaccharides for further investigation and highlights the complexity of cell wall biosynthesis and trafficking within plant cells.

ACDMBI: A deep learning model based on community division and multi-source biological information fusion predicts essential proteins

Accurately identifying essential proteins is vital for drug research and disease diagnosis. Traditional centrality methods and machine learning approaches often face challenges in accurately discerning essential proteins, primarily relying on information derived from protein-protein interaction (PPI) networks. Despite attempts by some researchers to integrate biological data and PPI networks for predicting essential proteins, designing effective integration methods remains a challenge. In response to these challenges, this paper presents the ACDMBI model, specifically designed to overcome the aforementioned issues. ACDMBI is comprised of two key modules: feature extraction and classification. In terms of capturing relevant information, we draw insights from three distinct data sources. Initially, structural features of proteins are extracted from the PPI network through community division. Subsequently, these features are further optimized using Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT). Moving forward, protein features are extracted from gene expression data utilizing Bidirectional Long Short-Term Memory networks (BiLSTM) and a multi-head self-attention mechanism. Finally, protein features are derived by mapping subcellular localization data to a one-dimensional vector and processing it through fully connected layers. In the classification phase, we integrate features extracted from three different data sources, crafting a multi-layer deep neural network (DNN) for protein classification prediction. Experimental results on brewing yeast data showcase the ACDMBI model’s superior performance, with AUC reaching 0.9533 and AUPR reaching 0.9153. Ablation experiments further reveal that the effective integration of features from diverse biological information significantly boosts the model’s performance.

Classifying Virus Strain Using a Machine Learning Model Based on Subcellular Localization Data

Classifying Virus Strain Using a Machine Learning Model Based on Subcellular Localization Data

ECDEP: identifying essential proteins based on evolutionary community discovery and subcellular localization

BackgroundIn cellular activities, essential proteins play a vital role and are instrumental in comprehending fundamental biological necessities and identifying pathogenic genes. Current deep learning approaches for predicting essential proteins underutilize the potential of gene expression data and are inadequate for the exploration of dynamic networks with limited evaluation across diverse species.ResultsWe introduce ECDEP, an essential protein identification model based on evolutionary community discovery. ECDEP integrates temporal gene expression data with a protein–protein interaction (PPI) network and employs the 3-Sigma rule to eliminate outliers at each time point, constructing a dynamic network. Next, we utilize edge birth and death information to establish an interaction streaming source to feed into the evolutionary community discovery algorithm and then identify overlapping communities during the evolution of the dynamic network. SVM recursive feature elimination (RFE) is applied to extract the most informative communities, which are combined with subcellular localization data for classification predictions.We assess the performance of ECDEP by comparing it against ten centrality methods, four shallow machine learning methods with RFE, and two deep learning methods that incorporate multiple biological data sources on Saccharomyces. Cerevisiae (S. cerevisiae), Homo sapiens (H. sapiens), Mus musculus, and Caenorhabditis elegans. ECDEP achieves an AP value of 0.86 on the H. sapiens dataset and the contribution ratio of community features in classification reaches 0.54 on the S. cerevisiae (Krogan) dataset.ConclusionsOur proposed method adeptly integrates network dynamics and yields outstanding results across various datasets. Furthermore, the incorporation of evolutionary community discovery algorithms amplifies the capacity of gene expression data in classification.

VEuPathDB Resources: A Platform for Free Online Data Exploration, Integration, and Analysis.

The Eukaryotic Pathogen, Vector and Host Informatics Resources ( VEuPathDB.org ) provide free online access to omic data from eukaryotic protozoan and fungal pathogens, arthropod vectors of disease, and host responses to pathogen infection. The goal of VEuPathDB is to make data easily accessible, findable, and importantly, re-usable by laboratory scientists. All integrated data and analyses follow standard workflows and methods to ensure data accuracy and enable data interoperability. Integrated data include genomes and annotation, transcriptomic (e.g., single-cell/bulk RNA-sequence and microarray data), proteomic (e.g., mass spectrometry evidence and quantitative data), isolate sequencing data used for variant calling and copy number variation determination, epigenomics, whole-genome phenotyping data (e.g., CRISPR screens and large-scale imaging and subcellular localization data), etc. Standard analyses provide additional data such as InterPro domains, signal peptide and transmembrane domain predictions, and metabolic pathways. Comparative genomic analysis in VEuPathDB is facilitated by leveraging orthology to enable the transformation of results between organisms and identifying genes with specific phyletic patterns. In addition, synteny between genomes is facilitated by shading orthologs across species and strains. Accessibility to and re-usability of the data is made possible through specialized searches and a graphical search strategy system that enables scientists to build in silico experiments combining results from multiple experiments with diverse data types.

Essential proteins discovery based on dominance relationship and neighborhood similarity centrality.

Essential proteins play a vital role in development and reproduction of cells. The identification of essential proteins helps to understand the basic survival of cells. Due to time-consuming, costly and inefficient with biological experimental methods for discovering essential proteins, computational methods have gained increasing attention. In the initial stage, essential proteins are mainly identified by the centralities based on protein-protein interaction (PPI) networks, which limit their identification rate due to many false positives in PPI networks. In this study, a purified PPI network is firstly introduced to reduce the impact of false positives in the PPI network. Secondly, by analyzing the similarity relationship between a protein and its neighbors in the PPI network, a new centrality called neighborhood similarity centrality (NSC) is proposed. Thirdly, based on the subcellular localization and orthologous data, the protein subcellular localization score and ortholog score are calculated, respectively. Fourthly, by analyzing a large number of methods based on multi-feature fusion, it is found that there is a special relationship among features, which is called dominance relationship, then, a novel model based on dominance relationship is proposed. Finally, NSC, subcellular localization score, and ortholog score are fused by the dominance relationship model, and a new method called NSO is proposed. In order to verify the performance of NSO, the seven representative methods (ION, NCCO, E_POC, SON, JDC, PeC, WDC) are compared on yeast datasets. The experimental results show that the NSO method has higher identification rate than other methods.

CiliaMiner: an integrated database forciliopathy genes andciliopathies.

Cilia are found in eukaryotic species ranging from single-celled organisms, such as Chlamydomonas reinhardtii, to humans, but not in plants. The ability to respond to repellents and/or attractants, regulate cell proliferation and differentiation and provide cellular mobility are just a few examples of how crucial cilia are to cells and organisms. Over 30 distinct rare disorders generally known as ciliopathy are caused by abnormalities or functional impairments in cilia and cilia-related compartments. Because of the complexity of ciliopathies and the rising number of ciliopathies and ciliopathy genes, a ciliopathy-oriented and up-to-date database is required. Here, we present CiliaMiner, a manually curated ciliopathy database that includes ciliopathy lists collected from articles and databases. Analysis reveals that there are 55 distinct disorders likely related to ciliopathy, with over 4000 clinical manifestations. Based on comparative symptom analysis and subcellular localization data, diseases are classified as primary, secondary or atypical ciliopathies. CiliaMiner provides easy access to all of these diseases and disease genes, as well as clinical features and gene-specific clinical features, as well as subcellular localization of each protein. Additionally, the orthologs of disease genes are also provided for mice, zebrafish, Xenopus, Drosophila, Caenorhabditis elegans and Chlamydomonas reinhardtii. CiliaMiner (https://kaplanlab.shinyapps.io/ciliaminer) aims to serve the cilia community with its comprehensive content and highly enriched interactive heatmaps, and will be continually updated. Database URL: https://kaplanlab.shinyapps.io/ciliaminer/.

Refining Protein Interaction Network for Identifying Essential Proteins

Aim: The study aimed to reconstruct the protein-protein interaction network for the identification of essential proteins. Background: In a living organism, essential proteins play an indispensable role in its survival and development. Hence, how to identify essential proteins from the protein interaction network (PIN) has become a hot topic in the field of bioinformatics. However, existing methods’ accuracies for identifying essential proteins are still limited due to the false positives of the protein-protein interaction data. Objective: The objective of the study was to propose an efficient algorithm for the reconstruction of a protein-protein interaction network. Methods: In this paper, a method for the refinement of PIN based on three kinds of biological data (subcellular localization data, protein complex data, and gene expression data) is proposed. Through evaluating each interaction within the original PIN, a refined clean PIN could be obtained. To verify the effectiveness of the refined PIN for the identification of essential proteins, we applied eight networkbased essential protein discovery methods (DC, BC, CC, LC, HC, SC, LAC, and NC) to it. Result: Based on the obtained experimental results, we demonstrated that the precision for identifying essential proteins could be greatly improved by refining the original PIN using our method. Conclusion: Our method could effectively enhance the protein-protein interaction network and improve the accuracy of identifying essential proteins. In the future, we plan to integrate more biological information to enhance our refinement method and apply it to more species and more PIN-based discovery tasks, like the identification of protein complexes or functional modules.

Hypoxia Affects the Antioxidant Activity of Glutaredoxin 3 in Scylla paramamosain through Hypoxia Response Elements.

Hypoxia is a major environmental stressor that can damage the oxidation metabolism of crustaceans. Glutaredoxin (Grx) is a key member of the thioredoxin superfamily and plays an important role in the host's defense against oxidative stress. At present, the role of Grx in response to hypoxia in crustaceans remains unclear. In this study, the full-length cDNA of Grx3 (SpGrx3) was obtained from the mud crab Scylla paramamosain, which contains a 129-bp 5' untranslated region, a 981-bp open reading frame, and a 1,183-bp 3' untranslated region. The putative SpGrx3 protein contains an N-terminal thioredoxin domain and two C-terminal Grx domains. SpGrx3 was expressed in all tissues examined, with the highest expression in the anterior gills. After hypoxia, SpGrx3 expression was significantly up-regulated in the anterior gills of mud crabs. The expression of Grx2 and glutathione S-transferases was decreased, while the expression of glutathione peroxidases was increased following hypoxia when SpGrx3 was silenced in vivo. In addition, the total antioxidant capacity of SpGrx3-interfered mud crabs was significantly decreased, and the malondialdehyde content was significantly increased during hypoxia. The subcellular localization data indicated that SpGrx3 was predominantly localized in the nucleus when expressed in Drosophila Schneider 2 (S2) cells. Moreover, overexpression of SpGrx3 reduced the content of reactive oxygen species in S2 cells during hypoxia. To further investigate the transactivation mechanism of SpGrx3 during hypoxia, the promoter region of the SpGrx3 was obtained by Genome Walking and three hypoxia response elements (HREs) were predicted. Dual-luciferase reporter assay results demonstrated that SpGrx3 was likely involved in the hypoxia-inducible factor-1 (HIF-1) pathway during hypoxia, which could be mediated through HREs. The results indicated that SpGrx3 is involved in regulating the antioxidant system of mud crabs and plays a critical role in the response to hypoxia.

Spatial Proteomics Reveals Differences in the Cellular Architecture of Antibody-Producing CHO and Plasma Cell–Derived Cells

Most of the recombinant biotherapeutics employed today to combat severe illnesses, for example, various types of cancer or autoimmune diseases, are produced by Chinese hamster ovary (CHO) cells. To meet the growing demand of these pharmaceuticals, CHO cells are under constant development in order to enhance their stability and productivity. The last decades saw a shift from empirical cell line optimization toward rational cell engineering using a growing number of large omics datasets to alter cell physiology on various levels. Especially proteomics workflows reached new levels in proteome coverage and data quality because of advances in high-resolution mass spectrometry instrumentation. One type of workflow concentrates on spatial proteomics by usage of subcellular fractionation of organelles with subsequent shotgun mass spectrometry proteomics and machine learning algorithms to determine the subcellular localization of large portions of the cellular proteome at a certain time point. Here, we present the first subcellular spatial proteome of a CHO-K1 cell line producing high titers of recombinant antibody in comparison to the spatial proteome of an antibody-producing plasma cell–derived myeloma cell line. Both cell lines show colocalization of immunoglobulin G chains with chaperones and proteins associated in protein glycosylation within the endoplasmic reticulum compartment. However, we report differences in the localization of proteins associated to vesicle-mediated transport, transcription, and translation, which may affect antibody production in both cell lines. Furthermore, pairing subcellular localization data with protein expression data revealed elevated protein masses for organelles in the secretory pathway in plasma cell–derived MPC-11 (Merwin plasma cell tumor-11) cells. Our study highlights the potential of subcellular spatial proteomics combined with protein expression as potent workflow to identify characteristics of highly efficient recombinant protein–expressing cell lines. Data are available via ProteomeXchange with identifier PXD029115.

CFTR Rescue by Lumacaftor (VX-809) Induces an Extensive Reorganization of Mitochondria in the Cystic Fibrosis Bronchial Epithelium.

Background: Cystic Fibrosis (CF) is a genetic disorder affecting around 1 in every 3000 newborns. In the most common mutation, F508del, the defective anion channel, CFTR, is prevented from reaching the plasma membrane (PM) by the quality check control of the cell. Little is known about how CFTR pharmacological rescue impacts the cell proteome. Methods: We used high-resolution mass spectrometry, differential ultracentrifugation, machine learning and bioinformatics to investigate both changes in the expression and localization of the human bronchial epithelium CF model (F508del-CFTR CFBE41o-) proteome following treatment with VX-809 (Lumacaftor), a drug able to improve the trafficking of CFTR. Results: The data suggested no stark changes in protein expression, yet subtle localization changes of proteins of the mitochondria and peroxisomes were detected. We then used high-content confocal microscopy to further investigate the morphological and compositional changes of peroxisomes and mitochondria under these conditions, as well as in patient-derived primary cells. We profiled several thousand proteins and we determined the subcellular localization data for around 5000 of them using the LOPIT-DC spatial proteomics protocol. Conclusions: We observed that treatment with VX-809 induces extensive structural and functional remodelling of mitochondria and peroxisomes that resemble the phenotype of healthy cells. Our data suggest additional rescue mechanisms of VX-809 beyond the correction of aberrant folding of F508del-CFTR and subsequent trafficking to the PM.

IDM-SPS: Identifying driver module with somatic mutation, PPI network and subcellular localization

Mutation profiles together with prior knowledge such as interactions between genes/proteins provide abundant critical information for the identification of driver modules, which is very important for analyzing mutational heterogeneity in human cancers. Due to the negative effects of inevitable false positive interactions in the PPI network, subcellular localization data are exerted to filter out them firstly, and somatic mutation profiles are used to weight the retained interactions. Five novel recombination operators are introduced basing on the vertex degrees and the edge weights in the PPI network, and a parthenogenetic algorithm is devised for solving the presented identification model which takes into account network connectivity, mutual exclusivity, coverage, and hops between genes within a module. Extensive experimental results indicate that compared with two state-of-the-art computational methods Hotnet2 and MEXCOwalk, the proposed method exhibits competitive performance in most cases in terms of recovering known cancer genes, providing modules that have satisfied coverage and mutual exclusivity, and are enriched for mutations in specific cancer types. Many identified gene sets are involved in known signaling pathways, most of the implicated genes are oncogenes or tumor suppressors previously reported in the literature. In addition, the proposed method does identify many cancer related genes missed by methods Hotnet2 and MEXCOwalk, including some recognized genes covering many types of cancers but having low mutation frequency.

An iteration model for identifying essential proteins by combining comprehensive PPI network with biological information

BackgroundEssential proteins have great impacts on cell survival and development, and played important roles in disease analysis and new drug design. However, since it is inefficient and costly to identify essential proteins by using biological experiments, then there is an urgent need for automated and accurate detection methods. In recent years, the recognition of essential proteins in protein interaction networks (PPI) has become a research hotspot, and many computational models for predicting essential proteins have been proposed successively.ResultsIn order to achieve higher prediction performance, in this paper, a new prediction model called TGSO is proposed. In TGSO, a protein aggregation degree network is constructed first by adopting the node density measurement method for complex networks. And simultaneously, a protein co-expression interactive network is constructed by combining the gene expression information with the network connectivity, and a protein co-localization interaction network is constructed based on the subcellular localization data. And then, through integrating these three kinds of newly constructed networks, a comprehensive protein–protein interaction network will be obtained. Finally, based on the homology information, scores can be calculated out iteratively for different proteins, which can be utilized to estimate the importance of proteins effectively. Moreover, in order to evaluate the identification performance of TGSO, we have compared TGSO with 13 different latest competitive methods based on three kinds of yeast databases. And experimental results show that TGSO can achieve identification accuracies of 94%, 82% and 72% out of the top 1%, 5% and 10% candidate proteins respectively, which are to some degree superior to these state-of-the-art competitive models.ConclusionsWe constructed a comprehensive interactive network based on multi-source data to reduce the noise and errors in the initial PPI, and combined with iterative methods to improve the accuracy of necessary protein prediction, and means that TGSO may be conducive to the future development of essential protein recognition as well.

Identification and Characterization of PLATZ Transcription Factors in Wheat.

The PLATZ (plant AT-rich protein and zinc-binding protein) transcription factor family is a class of plant-specific zinc-dependent DNA-binding proteins. PLATZ has essential roles in seed endosperm development, as well as promoting cell proliferation duration in the earlier stages of the crops. In the present study, 62 TaPLATZ genes were identified from the wheat genome, and they were unequally distributed on 15 chromosomes. According to the phylogenetic analysis, 62 TaPLATZ genes were classified into six groups, including two groups that were unique in wheat. Members in the same groups shared similar exon-intron structures. The polyploidization, together with genome duplication of wheat, plays a crucial role in the expansion of the TaPLATZs family. Transcriptome data indicated a distinct divergence expression pattern of TaPLATZ genes that could be clustered into four modules. The TaPLATZs in Module b possessed a seed-specific expression pattern and displayed obvious high expression in the earlier development stage of seeds. Subcellular localization data of TaPLATZs suggesting that they likely perform a function as a conventional transcription factor. This study provides insight into understanding the structure divergence, evolutionary features, expression profiles, and potential function of PLATZ in wheat.

Arabidopsis AZG2 transports cytokinins in vivo and regulates lateral root emergence.

Cytokinin and auxin are key regulators of plant growth and development. During the last decade transport mechanisms have turned out to be the key for the control of local and long-distance hormone distributions. In contrast with auxin, cytokinin transport is poorly understood. Here, we show that Arabidopsis thaliana AZG2, a member of the AZG purine transporter family, acts as cytokinin transporter involved in root system architecture determination. Even though purines are substrates for both AZG1 and AZG2, we found distinct transport mechanisms. The expression of AZG2 is restricted to a small group of cells surrounding the lateral root (LR) primordia and induced by auxins. Compared to the wild-type (WT), mutants carrying loss-of-function alleles of AZG2 have higher LR density, suggesting that AZG2 is part of a regulatory pathway in LR emergence. Moreover, azg2 is partially insensitive to exogenous cytokinin, which is consistent with the observation that the cytokinin reporter TCSnpro :GFP showed lower fluorescence signal in the roots of azg2 compared to the WT. These results indicate a defective cytokinin signalling pathway in the region of LR primordia. The integration of AZG2 subcellular localization and cytokinin transport capacity data allowed us to propose a local cytokinin:auxin signalling model for the regulation of LR emergence.

Breast Cancer Candidate Gene Detection Through Integration of Subcellular Localization Data With Protein-Protein Interaction Networks.

Due to technological advances the quality and availability of biological data has increased dramatically in the last decade. Analysing protein-protein interaction networks (PPINs) in an integrated way, together with subcellular compartment data, provides such biological context, helps to fill in the gaps between a single type of biological data and genes causing diseases and can identify novel genes related to disease. In this study, we present BCCGD, a method for integrating subcellular localization data with PPINs that detects breast cancer candidate genes in protein complexes. We achieve this by defining the significance of the compartment, constructing edge-weighted PPINs, finding protein complexes with a non-negative matrix factorization approach, generating disease-specific networks based on the known disease genes, prioritizing disease candidate genes with a WDC method. As a case study, we investigate the breast cancer but the techniques described here are applicable to other disorders. For the top genes scored by BCCGD approach, we utilize the literature retrieving method to test the correlations of them with the breast cancer. The results show that BCCGD discover some novel breast cancer candidate genes which are valuable references for the biomedical scientists.

Rapid Affinity Purification of Tagged Plant Mitochondria (Mito-AP) for Metabolome and Proteome Analyses.

The isolation of organelles facilitates the focused analysis of subcellular protein and metabolite pools. Here we present a technique for the affinity purification of plant mitochondria (Mito-AP). The stable ectopic expression of a mitochondrial outer membrane protein fused to a GFP:Strep tag in Arabidopsis (Arabidopsis thaliana) exclusively decorates mitochondria, enabling their selective affinity purification using magnetic beads coated with Strep-Tactin. With Mito-AP, intact mitochondria from 0.5 g plant material were highly enriched in 30-60 min, considerably faster than with conventional gradient centrifugation. Combining gradient centrifugation and Mito-AP techniques resulted in high purity of >90% mitochondrial proteins in the lysate. Mito-AP supports mitochondrial proteome analysis by shotgun proteomics. The relative abundances of proteins from distinct mitochondrial isolation methods were correlated. A cluster of 619 proteins was consistently enriched by all methods. Among these were several proteins that lack subcellular localization data or that are currently assigned to other compartments. Mito-AP is also compatible with mitochondrial metabolome analysis by triple-quadrupole and orbitrap mass spectrometry. Mito-AP preparations showed a strong enrichment with typical mitochondrial lipids like cardiolipins and demonstrated the presence of several ubiquinones in Arabidopsis mitochondria. Affinity purification of organelles is a powerful tool for reaching higher spatial and temporal resolution for the analysis of metabolomic and proteomic dynamics within subcellular compartments. Mito-AP is small scale, rapid, economic, and potentially applicable to any organelle or to organelle subpopulations.

The in silico characterization of neutral alpha-glucosidase C (GANC) and its evolution from GANAB

In the Carbohydrate-Active enZymes database (CAZy) glycoside hydrolases (GHs) are classified presently into 156 GH families. In human, there are five known enzymes from the family GH31. Two (MGAM and SI) are intestinal glucosidases involved in saccharide digestion, the acidic glucosidase (GAA) is responsible for glycogen degradation in lysosomes and GANAB (glucosidase II) plays a role in the control of a proper protein folding in the endoplasmic reticulum. The fifth protein is called GANC. It is an α-glucosidase, which is able to release the terminal glucose from maltotriose and glycogen at neutral pH. Its subcellular localization and its physiological function have not been reported in scientific literature yet.Our phylogenetic analysis shows that GANC evolved in early vertebrates from the α-subunit of GANAB. We have thus used an in silico approach to identify changes leading from the α-subunit of GANAB to GANC. We have also searched for residues and regions, which are conserved and under influence of negative selection pressure and which could be important for the function of the enzymes. We have found three residues, which could be responsible for the difference in the substrate specificity reported between the α-subunit of GANAB and GANC. We have also retrieved expression and subcellular localization data, from the Human Protein Atlas database, which shows differences in the expression profiles between GANAB and GANC. Unlike GANAB, GANC seems to be expressed in the nucleoplasm and in the cytoplasm where it colocalizes with actin filaments. The signal sequence and the nuclear localization signal have also been analyzed.

P1.03-17 Function of Antisense LncRNA RP11-539E17.5 and FAM83A-AS1 Up-Regulating FAM83A in Lung Adenocarcinoma Tumorigenesis and Development

Chromosome 8q24 lies in a gene desert with only a few predicted coding genes, but its variation is associated with multiple tumors. FAM83A in this region activates both PI3K/AKT/TOR and RAS/MAPK signaling cascades downstream of EGFR. The contribution of long noncoding gene in the antisense chain of FAM83A (RP11-539E17.5 and FAM83A-AS1) to the risk of lung adenocarcinoma (LUAD) need to be studied. We use systematic informatics method to identify the differential expression genes of the Cancer Genome Atlas (TCGA) LUAD transcriptome sequencing data. Then use data mining method to analysis the lncATLAS, USCS, Cistrome, and ENCODE database to infer the biological role of these two lncRNAs. Significant analysis of gene ontology (GO) terms enrichment analyzed the co-expression genes of RP11-539E17.5 in the LUAD and adjacent tissues. At last we use cell experiment to verify the lncRNA function. FAM83A-AS1 and RP11-539E17.5 located in Chromosome 8q24 were significantly different expressed between cancer and adjacent tissues (log2foldchange>5, p<0.05). The two lncRNA expression levels had notable correlation with FAM83A expression (coR> 0.8, P<0.005) and were significantly associated with patient overall survival and clinical stage (p<0.05). RP11-539E17.5, located in the enhancer element GH08J123181，belong to the enhancer lncRNA group. lncRNA subcellular localization data (lncATLAS) results showed that RP11-539E17.5 transcripts in K562 cells is mainly distributed in the nucleus, and most around the nucleolus and chromosomes. USCS, Cistrome, and ENCODE data mining identified four transcription factors (FOSL2, CTBP2, CEBPB, and MAFK) have significant correlation with the expression of FAM83A-AS1 and RP11-539E17.5. In K562 cell line with high expression of FAM83A-AS1, RP11-539E17.5 and FAM83A showed that expanded regions of RP11-539E17.5 gene sequence were marked by enhancer-specific modifications (H3K27ac domains and H3K4me3 domains). This region also showed an excellent source of DNase sensitive site, and with several binding sites for CTCF, FOSL2, CTBP2, CEBPB and MAFK. In addition, RP11-539E17.5 gene also with enriched DNA methylation feature. RP11-539E17.5 and FAM83A-AS1 knocked down both inhibited the expression of FAM83A the proliferation of PC9, and RP11-539E17.5 gene owning a more significant effect. GO terms enrichment analysis of the co-expressed genes with RP11-539E17.5 showed that the most significantly activated biological pathways is involved with Cell cycle，and cell mitotic signaling pathway, while the molecular function involved with transcription factor binding, DNA binding, ATP binding, endonuclease activity, DNA topoisomerase activity. We found a LncRNA RP11-539E17.5 with enhancer character up regulate FAM83A expression in LUAD. These results can lay a theoretical foundation for LUAD corresponding countermeasures.

LncRRIsearch: A Web Server for lncRNA-RNA Interaction Prediction Integrated With Tissue-Specific Expression and Subcellular Localization Data.

Long non-coding RNAs (lncRNAs) play critical roles in various biological processes, but the function of the majority of lncRNAs is still unclear. One approach for estimating a function of a lncRNA is the identification of its interaction target because functions of lncRNAs are expressed through interaction with other biomolecules in quite a few cases. In this paper, we developed “LncRRIsearch,” which is a web server for comprehensive prediction of human and mouse lncRNA-lncRNA and lncRNA-mRNA interaction. The prediction was conducted using RIblast, which is a fast and accurate RNA-RNA interaction prediction tool. Users can investigate interaction target RNAs of a particular lncRNA through a web interface. In addition, we integrated tissue-specific expression and subcellular localization data for the lncRNAs with the web server. These data enable users to examine tissue-specific or subcellular localized lncRNA interactions. LncRRIsearch is publicly accessible at http://rtools.cbrc.jp/LncRRIsearch/.