Bibliographies thématiques / Bioinformatic, Computational Biology, GPCR

Littérature scientifique sur le sujet « Bioinformatic, Computational Biology, GPCR »

Auteur : Grafiati

Publié le 11 mars 2023

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Articles de revues sur le sujet "Bioinformatic, Computational Biology, GPCR"

García-Recio, Adrián, Gemma Navarro, Rafael Franco, Mireia Olivella, Ramon Guixà-González et Arnau Cordomí. « DIMERBOW : exploring possible GPCR dimer interfaces ». Bioinformatics 36, n^o 10 (25 février 2020) : 3271–72. http://dx.doi.org/10.1093/bioinformatics/btaa117.

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Abstract Motivation G protein-coupled receptors (GPCRs) can form homo-, heterodimers and larger order oligomers that exert different functions than monomers. The pharmacological potential of such complexes is hampered by the limited information available on the type of complex formed and its quaternary structure. Several GPCR structures in the Protein Data Bank display crystallographic interfaces potentially compatible with physiological interactions. Results Here, we present DIMERBOW, a database and web application aimed to visually browse the complete repertoire of potential GPCR dimers present in solved structures. The tool is suited to help finding the best possible structural template to model GPCR homomers. Availability and implementation DIMERBOW is available at http://lmc.uab.es/dimerbow/. Supplementary information Supplementary data are available at Bioinformatics online.

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Shigeta, R., M. Cline, G. Liu et M. A. Siani-Rose. « GPCR-GRAPA-LIB--a refined library of hidden Markov Models for annotating GPCRs ». Bioinformatics 19, n^o 5 (22 mars 2003) : 667–68. http://dx.doi.org/10.1093/bioinformatics/btg061.

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Davies, M. N., A. Secker, A. A. Freitas, E. Clark, J. Timmis et D. R. Flower. « Optimizing amino acid groupings for GPCR classification ». Bioinformatics 24, n^o 18 (1 août 2008) : 1980–86. http://dx.doi.org/10.1093/bioinformatics/btn382.

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Sreekumar, K. R., Y. Huang, M. H. Pausch et K. Gulukota. « Predicting GPCR-G-protein coupling using hidden Markov models ». Bioinformatics 20, n^o 18 (5 août 2004) : 3490–99. http://dx.doi.org/10.1093/bioinformatics/bth434.

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Townsend-Nicholson, Andrea, Nojood Altwaijry, Andrew Potterton, Inaki Morao et Alexander Heifetz. « Computational prediction of GPCR oligomerization ». Current Opinion in Structural Biology 55 (avril 2019) : 178–84. http://dx.doi.org/10.1016/j.sbi.2019.04.005.

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Zhu, Siyu, Meixian Wu, Ziwei Huang et Jing An. « Trends in application of advancing computational approaches in GPCR ligand discovery ». Experimental Biology and Medicine 246, n^o 9 (27 février 2021) : 1011–24. http://dx.doi.org/10.1177/1535370221993422.

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G protein-coupled receptors (GPCRs) comprise the most important superfamily of protein targets in current ligand discovery and drug development. GPCRs are integral membrane proteins that play key roles in various cellular signaling processes. Therefore, GPCR signaling pathways are closely associated with numerous diseases, including cancer and several neurological, immunological, and hematological disorders. Computer-aided drug design (CADD) can expedite the process of GPCR drug discovery and potentially reduce the actual cost of research and development. Increasing knowledge of biological structures, as well as improvements on computer power and algorithms, have led to unprecedented use of CADD for the discovery of novel GPCR modulators. Similarly, machine learning approaches are now widely applied in various fields of drug target research. This review briefly summarizes the application of rising CADD methodologies, as well as novel machine learning techniques, in GPCR structural studies and bioligand discovery in the past few years. Recent novel computational strategies and feasible workflows are updated, and representative cases addressing challenging issues on olfactory receptors, biased agonism, and drug-induced cardiotoxic effects are highlighted to provide insights into future GPCR drug discovery.

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Theodoropoulou, Margarita C., Pantelis G. Bagos, Ioannis C. Spyropoulos et Stavros J. Hamodrakas. « gpDB : a database of GPCRs, G-proteins, effectors and their interactions ». Bioinformatics 24, n^o 12 (25 avril 2008) : 1471–72. http://dx.doi.org/10.1093/bioinformatics/btn206.

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Lahvic, Jamie L., Michelle B. Ammerman, Pulin Li, Song Yang, Nan Chiang, Michael Chase, Olivia Weis, Yi Zhou, Charles Serhan et Leonard I. Zon. « Eicosanoid-GPCR Signaling Enhances Hematopoiesis and Marrow Transplant ». Blood 128, n^o 22 (2 décembre 2016) : 495. http://dx.doi.org/10.1182/blood.v128.22.495.495.

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Abstract Small molecule treatment of hematopoietic stem cells ex vivo has the potential to expand these cells or increase their engraftability. Previously, we discovered that ex vivo treatment of marrow with 11,12-epoxyeicosatrienoic acid (EET) enhances the engraftment of hematopoietic stem and progenitor cells in both zebrafish and mammals. Additionally, EET treatment promotes specification of HSPC from the hemogenic endothelium, suggesting a broad pro-hematopoietic role of this molecule. Indeed, bioactive lipids play an important role as signaling molecules both during embryo development and adult tissue homeostasis. However, due to their small-molecule nature, identifying their receptors biochemically has been a long-standing challenge which impedes the understanding of the biological processes they regulate. The identity of the EET receptor remains unknown despite more than a decade of research. Here, we utilized a novel bioinformatic approach to identify candidate EET receptors and identified a candidate functional in cell culture, zebrafish and mouse assays. EET signaling is known to be G-protein dependent, suggesting its receptor is a G-protein coupled receptor (GPCR). We performed RNAseq on U937 monocytes, EaHy endothelial cells, and PC3M-LN4 prostate cancer cells, three human cell lines with clear EET-responsive phenotypes. These three cell lines expressed 37 GPCR in common at a basal level of greater than or equal to 0.3 fragments per kilobase per million reads (FPKM). 27 of these GPCR were also expressed in a non-EET-responsive cell line, HEK293, leaving only 10 candidate EET receptors. We screened 7 of these candidates for EET-responsiveness using a cell-culture based β-arrestin recruitment assay. Of these, only GPR132 exhibited EET-dependent recruitment of β-arrestin to the cell membrane, indicating GPCR activation. GPR132 was previously identified as a receptor for a variety of small oxygenated fatty acids, and we confirmed that these related molecules induce GPR132-dependent β-arrestin recruitment. We additionally treated developing zebrafish embryos with these molecules. Like EET, these GPR132 ligands increased HSPC numbers in the zebrafish aorta-gonad-mesonephros (AGM) and caused ectopic expression of the HSPC marker runx1 in the zebrafish tail, a phenotype that was previously seen only with EET treatment. To test the requirement of GPR132 for EET signaling, we knocked down the zebrafish ortholog of GPR132 by morpholino injection, which prevented the EET-induced increase of runx1in both the AGM and tail. Finally, we performed competitive whole bone marrow transplant using wildtype and GPR132-/- mice as donors and found that while treatment with EET increases engraftment of WT donor cells, no such improvement is seen in GPR132-/- cells. GPR132 is thus required in both zebrafish and mice for EET phenotypes. Combining bioinformatic, biochemical, and genetic approaches, we identified GPR132 as a receptor for EET involved in regulating hematopoiesis and marrow transplant. GPR132 thus represents a therapeutic target for the enhancement of hematopoietic stem cell transplant, and genetic manipulation of GPR132 could help illuminate the endogenous roles of its fatty acid ligands. Disclosures Zon: Fate, Inc.: Equity Ownership, Membership on an entity's Board of Directors or advisory committees, Other: Founder; Marauder Therapeutics: Equity Ownership, Other: Founder; Scholar Rock: Equity Ownership, Membership on an entity's Board of Directors or advisory committees, Other: Founder.

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Yang, Zi, et George Michailidis. « Quantifying heterogeneity of expression data based on principal components ». Bioinformatics 35, n^o 4 (28 juillet 2018) : 553–59. http://dx.doi.org/10.1093/bioinformatics/bty671.

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Abstract Motivation The diversity of biological omics data provides richness of information, but also presents an analytic challenge. While there has been much methodological and theoretical development on the statistical handling of large volumes of biological data, far less attention has been devoted to characterizing their veracity and variability. Results We propose a method of statistically quantifying heterogeneity among multiple groups of datasets, derived from different omics modalities over various experimental and/or disease conditions. It draws upon strategies from analysis of variance and principal component analysis in order to reduce dimensionality of the variability across multiple data groups. The resulting hypothesis-based inference procedure is demonstrated with synthetic and real data from a cell line study of growth factor responsiveness based on a factorial experimental design. Availability and implementation Source code and datasets are freely available at https://github.com/yangzi4/gPCA. Supplementary information Supplementary data are available at Bioinformatics online.

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Lazim, Raudah, Donghyuk Suh, Jai Woo Lee, Thi Ngoc Lan Vu, Sanghee Yoon et Sun Choi. « Structural Characterization of Receptor–Receptor Interactions in the Allosteric Modulation of G Protein-Coupled Receptor (GPCR) Dimers ». International Journal of Molecular Sciences 22, n^o 6 (22 mars 2021) : 3241. http://dx.doi.org/10.3390/ijms22063241.

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G protein-coupled receptor (GPCR) oligomerization, while contentious, continues to attract the attention of researchers. Numerous experimental investigations have validated the presence of GPCR dimers, and the relevance of dimerization in the effectuation of physiological functions intensifies the attractiveness of this concept as a potential therapeutic target. GPCRs, as a single entity, have been the main source of scrutiny for drug design objectives for multiple diseases such as cancer, inflammation, cardiac, and respiratory diseases. The existence of dimers broadens the research scope of GPCR functions, revealing new signaling pathways that can be targeted for disease pathogenesis that have not previously been reported when GPCRs were only viewed in their monomeric form. This review will highlight several aspects of GPCR dimerization, which include a summary of the structural elucidation of the allosteric modulation of class C GPCR activation offered through recent solutions to the three-dimensional, full-length structures of metabotropic glutamate receptor and γ-aminobutyric acid B receptor as well as the role of dimerization in the modification of GPCR function and allostery. With the growing influence of computational methods in the study of GPCRs, we will also be reviewing recent computational tools that have been utilized to map protein–protein interactions (PPI).

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Thèses sur le sujet "Bioinformatic, Computational Biology, GPCR"

Poudel, Sagar. « GPCR-Directed Libraries for High Throughput Screening ». Thesis, University of Skövde, School of Humanities and Informatics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-29.

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Guanine nucleotide binding protein (G-protein) coupled receptors (GPCRs), the largest receptor family, is enormously important for the pharmaceutical industry as they are the target of 50-60% of all existing medicines. Discovery of many new GPCR receptors by the “human genome project”, open up new opportunities for developing novel therapeutics. High throughput screening (HTS) of chemical libraries is a well established method for finding new lead compounds in drug discovery. Despite some success this approach has suffered from the near absence of more focused and specific targeted libraries. To improve the hit rates and to maximally exploit the full potential of current corporate screening collections, in this thesis work, identification and analysis of the critical drug-binding positions within the GPCRs were done, based on their overall sequence, their transmembrane regions and their drug binding fingerprints. A proper classification based on drug binding fingerprints on the basis for a successful pharmacophore modelling and virtual screening were done, which facilities in the development of more specific and focused targeted libraries for HTS.

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Bahena, Silvia. « Computational Methods for the structural and dynamical understanding of GPCR-RAMP interactions ». Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-416790.

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Protein-protein interaction dominates all major biology processes in living cells. Recent studies suggestthat the surface expression and activity of G protein-coupled receptors (GPCRs), which are the largestfamily of receptors in human cells, can be modulated by receptor activity–modifying proteins (RAMPs). Computational tools are essential to complement experimental approaches for the understanding ofmolecular activity of living cells and molecular dynamics simulations are well suited to providemolecular details of proteins function and structure. The classical atom-level molecular modeling ofbiological systems is limited to small systems and short time scales. Therefore, its application iscomplicated for systems such as protein-protein interaction in cell-surface membrane. For this reason, coarse-grained (CG) models have become widely used and they represent an importantstep in the study of large biomolecular systems. CG models are computationally more effective becausethey simplify the complexity of the protein structure allowing simulations to have longer timescales. The aim of this degree project was to determine if the applications of coarse-grained molecularsimulations were suitable for the understanding of the dynamics and structural basis of the GPCRRAMP interactions in a membrane environment. Results indicate that the study of protein-proteininteractions using CG needs further improvement with a more accurate parameterization that will allowthe study of complex systems.

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Kallberg, Yvonne. « Bioinformatic methods in protein characterization / ». Stockholm, 2002. http://diss.kib.ki.se/2002/91-7349-370-8/.

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Brandström, Mikael. « Bioinformatic analysis of mutation and selection in the vertebrate non-coding genome / ». Uppsala : Acta Universitatis Upsaliensis Acta Universitatis Upsaliensis, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8240.

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Lang, Tiange. « Evolution of transmembrane and gel-forming mucins studied with bioinformatic methods / ». Göteborg : The Sahlgrenska Academy at Göteborg University, Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, 2007. http://hdl.handle.net/2077/7502.

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PALOMBO, VALENTINO. « Genomics, Transcriptomics and Computational Biology : new insights into bovine and swine breeding and genetics ». Doctoral thesis, Università degli studi del Molise, 2019. http://hdl.handle.net/11695/91489.

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Enormi progressi sono stati fatti nella selezione degli animali per specifici caratteri di interesse zootecnico avvalendosi dei tradizionali approcci di genetica quantitativa. Tuttavia, una considerevole quantità di variabilità fenotipica resta ancora non completamente spiegata; in tal senso una migliore conoscenza delle sue basi molecolari e genetiche rappresenterebbe un ulteriore vantaggio. A tal proposito, il recente sviluppo di tecnologie high-throughput (HT), basate su metodi ad alta specificità di ibridazione e sulle ultime tecniche di sequenziamento (NGS), rappresenta una nuova opportunità per esplorare i più complessi meccanismi biologici. La rapida diffusione di queste tecnologie ha segnato l’inizio dell’era ‘omica’. Gli approcci ‘omici’ si basano sull’analisi complessiva di una specifica classe di molecole contenute in una cellula, un tessuto o un organismo; ovvero sono primariamente indirizzati all’analisi di tutti i geni (genomica), di tutti i trascritti (trascrittomica), di tutte le proteine (proteomica) o di tutti i metaboliti (metabolomica) presenti in un campione biologico. La convizione è che un sistema complesso può essere compreso più a fondo, e più fedelmente, se considerato nella sua globalità. La grandissima mole di dati generata, tuttavia, ha senso soltanto se si è equipaggiati con opportuni strumenti per esplorala. Per questo motivo, di pari passo con tali progressi tecnologici, la bioinformatica, conosciuta anche come biologia computazionale, sta acquisendo progressiva importanza. Anche la zootecnia e il miglioramento genetico si stanno avvalendo delle opportunità offerte da questo nuovo scenario. In particolare, ci si sta spostando dagli approcci tradizionali a quelli che prevedono l’uso integrato di analisi omiche. Ciò permette di meglio investigare e decifrate l’architettura genetica alla base dei caratteri di interesse zootecnico ed utilizzare questa informazione per la selezione dei candidati destinati alla riproduzione. L’obiettivo di questa tesi è stato quello di utilizzare le più innovative analisi genomiche e trascrittomiche per (1) investigare le differenze genetiche alla base del profilo acidico del latte in due razze bovine italiane; (2) individuare i geni e i fattori di trascrizione coinvolti nel controllo della colostrogenesi/lattogenesi suina. A tal fine, sono stati effettuati rispettivamente uno studio di associazione lungo tutto il genoma (GWAS) considerando gli acidi grassi del latte in Frisona e Pezzata Rossa Italiana ed è stato sequenziato il trascrittoma (RNA-Sequencing) di ghiandola mammaria suina. In aggiunta (3) è stato sviluppato un nuovo strumento bioinformatico interamente in R, chiamato PIA (Pathways Interaction Analysis), che consente un’originale analisi delle pathway metaboliche utile ad agevolare l’interpretazione dei risultati genomici e trascrittomici.
Enormous progress has been made in the selection of animals for specific traits using traditional quantitative genetic approaches. Nevertheless, a considerable amount of variation in phenotypes remains unexplained therefore a better knowledge of its genetic basis represents a potential additional gain for animal production. In this regard, the recently developed high-throughput (HT) technologies based on microarray and next-generation sequencing (NGS) methods are a powerful opportunity to prise open the ‘black box’ underlying complex biological processes. These technological advancements have marked the beginning of the ‘omic era’. Broadly, ‘omic’ approaches adopt a holistic view of the molecules that make up a cell, tissue or organism. They are aimed primarily at the universal detection of genes (genomics), RNA (transcriptomics), proteins (proteomics) and metabolites (metabolomics) in a specific biological sample. The basic aspect of these approaches is that a complex system can be understood more thoroughly if considered as a whole. At the same time, the large amount of data generated by these revolutionary approaches makes sense only if one is equipped with the necessary resources and tools to manage and explore it. For this reason, along with HT technical progresses, bioinformatics, often known as computational biology, is gaining immense importance. Animal breeding is gaining new momentum from this renewed scenario. Particularly it pushed to move away from traditional approaches toward systems approaches using integrative analysis of ‘omic’ data to better elucidate the genetic architecture controlling the traits of interest and ultimately use this knowledge for selection of candidates. The aim of this thesis is to (1) investigate the differences of genetic basis related to the milk fatty acids profiles in two Italian dairy cattle breeds and (2) delineate the genes and transcription regulators implicated in the control of the transition from colostrogenesis to lactogenesis in swine, using the state-of-art genomic and transcriptomic analyses. For these reasons, a genome-wide association study (GWAS) on milk fatty acids of Italian Holstein and Italian Simmental cattle breads and an RNASeq study on transcriptional profiles of swine mammary gland are conducted, respectively. In addition, (3) an in-house bioinformatics tool performing an original pathway analysis is presented. The tool, entirely built in R and named PIA (Pathways Interaction Analysis), is designed for post-genomic and transcriptomic data mining.

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Moss, Tiffanie. « CHARACTERIZATION OF STRUCTURAL VARIANTS AND ASSOCIATED MICRORNAS IN FLAX FIBER AND LINSEED GENOTYPES BY BIOINFORMATIC ANALYSIS AND HIGH-THROUGHPUT SEQUENCING ». Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1333648149.

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Santaniello, F. « CHANGES OF REPLICATION TIMING INDUCED BY PML-RARA ». Doctoral thesis, Università degli Studi di Milano, 2017. http://hdl.handle.net/2434/469739.

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DNA replication is a cellular process that, starting from precise genomic loci, ensures the loyal and faithful inheritance, from one parental cell to each daughter cell, of the genetic instructions contained in the double-strand DNA molecule. Due to the complexity and the crucial importance of the DNA replication, this process must be tightly regulated in both space and time. Up to now, however, the time-related features of DNA replication, together with the factors that might impact the temporal dimension of this system, are yet poorly studied and described. Given the lack of standard methods able to recognize differences in Replication Timing, we developed an innovative bioinformatic method (DART; Differential Analysis of Replication Timing) to accomplish this task. Moreover, the application of this procedure to our Repli-seq data was instrumental to investigate whether PML-RARα may fulfil its tumorigenic potential by eliciting an alteration of the normal replication timing pace in cells. As a result, we found that, after its expression, PML-RARα indeed exerts a deregulative effect on Replication Timing, inducing some regions to replicate earlier (LtoE-shifted) and some other later (EtoL-shifted), with respect to control cells. We observed a close association between these differentially replicated regions and both pre-existing, and PML-RARα-related, transcriptional status and chromatin structure. Regions presenting a EtoL-shifted replication coincide with ‘active’ chromatin foci enriched for direct down-regulated targets of PML-RARa; at the opposite, regions with a LtoE-shifted Replication Timing show moderate epigenetic ‘active’ features and are enriched for indirect up-regulated targets of PML-RARa.

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Coppe, Alessandro. « A bioinformatic and computational approach to regulation of genome function : integrated analysis of genome organization, promoter sequences and gene expression ». Doctoral thesis, Università degli studi di Padova, 2008. http://hdl.handle.net/11577/3426395.

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Although much is known about gene expression regulation in both Prokaryotes and Eukaryotes, this complex and fascinating mechanism still remains to be fully elucidated. The relatively recent advent of high-throughput techniques for studying transcription has made available an invaluable amount of data that can be used for genome-wide analysis using bioinformatics approaches. These computational methods have now become an integrative part of biological research. The different topics of this thesis are related to the development and application of computational methodologies to better understand the basis of genomic gene expression regulation at different levels. A first level of investigation regarded the relationships among chromosomal structure, expression profile and functional characteristics, focusing on genomic organization and structure. For this task, REEF (REgionally Enriched Features) software has been developed, designed to identify genomic regions enriched in specific features, such as a class or group of genes homogeneous for expression and/or functional characteristics. REEF can be used to detect density variations of specific features along the genome sequence, for example genomic regions with significant enrichment of genes which are co-expressed, differentially expressed, or related to particular molecular functions. Local feature enrichment is calculated using test statistic based on the hypergeometric distribution applied genome-wide by sliding windows and false discovery rate is used for controlling multiplicity. REEF has been applied to the study of genomic distribution of tissue-specific genes and to the analysis of gene differentially expressed when comparing different myeloid cell lines. These analyses identified clusters of tissue-specific genes in the human genome and positional enrichment of hemopoietic functional module-related genes. The second level of investigation regarded gene expression regulation at promoter level. Unknown transcription factor binding sites might be detected by searching for shared sequence elements in upstream regulatory regions of genes with common biological function and/or similar expression profile. In fact, genes with similar expression are frequently co-regulated and genes with related function are often similarly expressed. New methodologies for the identification of regulatory motifs in human promoters were developed and tested. Since a drawback of this approach is the exceedingly high number of results, the use of biological knowledge both before and after application of automated pattern discovery allowed the definition of a “sheltered environment” enhancing the specificity of the computational analysis. COOP (Clustering of Overlapping Patterns) software for the extraction of sequence motifs was developed and used to analyze genomic sequences of 1 Kb upstream of 91 retina specific genes, identifying a set of putative regulative motifs, frequently occurring in retina promoter sequences. Most of them are localized in the proximal portion of promoters and tend to be less variable in central region than in lateral regions and some of them are similar to known regulatory sequences. The performances of COOP were further evaluated by simulation approaches and by applying it to a standard positive control dataset, proposed by Tompa and colleagues for systematic evaluation and comparison of pattern discovery software. A webtool for the prediction of functional elements in promoter sequences, MOST (MOtif Searching web Tool), has been applied to different datasets under various testing conditions in order to study the influence of specific search parameters on results. Two groups of promoter sequences containing known regulatory signals were used as positive control datasets: the public yeast benchmark dataset of Tompa and colleagues and a custom produced dataset of 37 human promoter sequences, subgroups of which contained some instances of one of nine different signals. The testing of performances of the method on different benchmark datasets gave quite positive results. Taking the concepts behind COOP to a new level, a more rigorous methodology was developed for the identification of surprising and putatively regulatory motifs, by comparing their frequency in promoters sequences of co-expressed genes with that in a background set of sequences, representative of the whole set of human gene promoters. Promoter sequences are divided in overlapping regions, considered independently, for identifying positional bias in the arrangement of transcription factors binding sites along promoters. Due to the genome-wide characteristics of this approach, a new webtool for the automatic identification and retrieval of a high number of promoters in the human genome was also developed. This motif discovery methodology has been adopted to investigate structure of promoters of genes crucial during myeloid differentiation.

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Favara, David M. « The biology of ELTD1/ADGRL4 : a novel regulator of tumour angiogenesis ». Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:0d00af0a-bb43-44bc-ba0b-1f8acbe34bc5.

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Background: Our laboratory identified ELTD1, an orphan GPCR belonging to the adhesion GPCR family (aGPCR), as a novel regulator of angiogenesis and a potential anti-cancer therapeutic target. ELTD1 is normally expressed in both endothelial cells and vascular smooth muscle cells and expression is significantly increased in the tumour vasculature. The aim of this project was to analyse ELTD1's function in endothelial cells and its role in breast cancer. Method: 62 sequenced vertebrate genomes were interrogated for ELTD1 conservation and domain alterations. A phylogenetic timetree was assembled to establish time estimates for ELTD1's evolution. After ELTD1 silencing, mRNA array profiling was performed on primary human umbilical vein endothelial cells (HUVECs) and validated with qPCR and confocal microscopy. ELTD1's signalling was investigated by applying the aGPCR âStinger/tethered-agonist Hypothesis'. For this, truncated forms of ELTD1 and peptides analogous to the proposed tethered agonist region were designed. FRET-based 2^nd messenger (Cisbio IP-1;cAMP) and luciferase-reporter assays (NFAT; NFÎoB; SRE; SRF-RE; CREB) were performed to establish canonical GPCR activation. To further investigate ELTD1's role in endothelial cells, ELTD1 was stably overexpressed in HUVECS. Functional angiogenesis assays and mRNA array profiling were then performed. To investigate ELTD1 in breast cancer, a panel of cell lines representative of all molecular subtypes were screened using qPCR. Furthermore, an exploratory pilot study was performed on matched primary and regional nodal secondary breast cancers (n=43) which were stained for ELTD1 expression. Staining intensity was then scored and compared with relapse free survival and overall survival. Results: ELTD1 arose 435 million years ago (mya) in bony fish and is present in all subsequent vertebrates. ELTD1 has 3 evolutionary variants of which 2 are most common: one variant with 3 EGFs and a variant with 2 EGFs. Additionally, ELTD1 may be ancestral to members of aGPCR family 2. HUVEC mRNA expression profiling after ELTD1 silencing showed upregulation of the mitochondrial citrate transporter SLC25A1, and ACLY which converts cytoplasmic citrate to Acetyl CoA, feeding fatty acid and cholesterol synthesis, and acetylation. A review of lipid droplet (fatty acid and cholesterol) accumulation by confocal microscopy and flow cytometry (FACS) revealed no changes with ELTD1 silencing. Silencing was also shown to affect the Notch pathway (downregulating the Notch ligand JAG1 and target gene HES2; upregulating the Notch ligand DLL4) and inducing KIT, a mediator of haematopoietic (HSC) and endothelial stem cell (ESC) maintenance. Signalling experiments revealed that unlike other aGPCRs, ELTD1 does not couple to any canonical GPCR pathways (Gαi, Gαs, Gαq, Gα12/13). ELTD1 overexpression in HUVECS revealed that ELTD1 induces an endothelial tip cell phenotype by promoting sprouting and capillary formation, inhibiting lumen anastomoses in mature vessels and lowering proliferation rate. There was no effect on wound healing or adhesion to angiogenesis associated matrix components. Gene expression changes following ELTD1 overexpression included upregulation of angiogenesis associated ANTRX1 as well as JAG1 and downregulation of migration associated CCL15 as well as KIT and DLL4. In breast cancer, none of the representative breast cancer cell lines screened expressed ELTD1. ELTD1 breast cancer immunohistochemistry revealed higher levels of vascular ELTD1 staining intensity within the tumour stroma contrasted to normal stroma and expression within tumour epithelial cells. Additionally, ELTD1 expression in tumour vessels was differentially expressed between the primary breast cancer microenvironment and that of the matched regional node. Due to the small size of the pilot study population, survival comparisons between the various subgroups did not yield significant results. Conclusion: ELTD1 is a novel regulator of endothelial metabolism through its suppression of ACLY and the related citrate transporter SLC25A1. ELTD1 also represses KIT, which is known to mediate haematopoietic and endothelial progenitors stem cell maintenance, a possible mechanism through which endothelial cells maintain terminal endothelial differentiation. ELTD1 does not signal like other adhesion GPCRS with CTF and FL forms of ELTD1 not signalling canonically. Additionally, ELTD1 regulates various functions of endothelial cell behaviour and function, inducing an endothelial tip cell phenotype and is highly evolutionarily conserved. Lastly, ELTD1 is differentially expressed in tumour vessels between primary breast cancer and regional nodal metastases and is also expressed in a small subset of breast cancer cells in vivo despite no cancer cell lines expressing ELTD1. The pilot study investigating ELTD1 in the primary breast cancer and regional involved nodes will be followed up with a larger study including the investigation of ELTD1 in distant metastases.

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Livres sur le sujet "Bioinformatic, Computational Biology, GPCR"

1950-, Tsigelny Igor F., dir. Protein structure prediction : Bioinformatic approach. La Jolla, Calif : International University Line, 2002.

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Strasser, Andrea. Modelling of GPCRs : A Practical Handbook. Dordrecht : Springer Netherlands, 2013.

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RNA sequence, structure, and function : Computational and bioinformatic methods. New York : Humana Press, 2014.

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Strasser, Andrea, et Hans-Joachim Wittmann. Modelling of GPCRs : A Practical Handbook. Springer, 2012.

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Strasser, Andrea, et Hans-Joachim Wittmann. Modelling of GPCRs : A Practical Handbook. Springer, 2014.

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Gorodkin, Jan, et Walter L. Ruzzo. RNA Sequence, Structure, and Function : Computational and Bioinformatic Methods. Humana Press, 2016.

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Immunoinformatics : Bioinformatic Strategies for Better Understanding of Immune Function (Novartis Foundation Symposia). Wiley, 2003.

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Chapitres de livres sur le sujet "Bioinformatic, Computational Biology, GPCR"

Junqueira, Dennis Maletich, Rúbia Marília de Medeiros, Sabrina Esteves de Matos Almeida, Vanessa Rodrigues Paixão-Cortez, Paulo Michel Roehe et Fernando Rosado Spilki. « Mapping HIV-1 Subtype C gp120Epitopes Using a Bioinformatic Approach ». Dans Advances in Bioinformatics and Computational Biology, 156–59. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03223-3_16.

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Suwa, Makiko, et Yukiteru Ono. « Computational Overview of GPCR Gene Universe to Support Reverse Chemical Genomics Study ». Dans Methods in Molecular Biology, 41–54. Totowa, NJ : Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60761-232-2_4.

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Kiriakidi, Sofia, Antonios Kolocouris, George Liapakis, Saima Ikram, Serdar Durdagi et Thomas Mavromoustakos. « Effects of Cholesterol on GPCR Function : Insights from Computational and Experimental Studies ». Dans Advances in Experimental Medicine and Biology, 89–103. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14265-0_5.

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Sgourakis, Nikolaos G., Pantelis G. Bagos et Stavros J. Hamodrakas. « Computational Methods for the Prediction of GPCRs Coupling Selectivity ». Dans Handbook of Research on Systems Biology Applications in Medicine, 167–81. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-076-9.ch009.

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GPCRs comprise a wide and diverse class of eukaryotic transmembrane proteins with well-established pharmacological significance. As a consequence of recent genome projects, there is a wealth of information at the sequence level that lacks any functional annotation. These receptors, often quoted as orphan GPCRs, could potentially lead to novel drug targets. However, typical experiments that aim at elucidating their function are hampered by the lack of knowledge on their selective coupling partners at the interior of the cell, the G-proteins. Up-to-date, computational efforts to predict properties of GPCRs have been focused mainly on the ligand-binding specificity, while the aspect of coupling has been less studied. Here, we present the main motivations, drawbacks, and results from the application of bioinformatics techniques to predict the coupling specificity of GPCRs to G-proteins, and discuss the application of the most successful methods in both experimental works that focus on a single receptor and large-scale genome annotation studies.

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Wang, Jinan, et Yinglong Miao. « Recent advances in computational studies of GPCR-G protein interactions ». Dans Advances in Protein Chemistry and Structural Biology, 397–419. Elsevier, 2019. http://dx.doi.org/10.1016/bs.apcsb.2018.11.011.

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Dragan, Paulina, Alessandro Atzei, Swapnil Ganesh Sanmukh et Dorota Latek. « Computational and experimental approaches to probe GPCR activation and signaling ». Dans Progress in Molecular Biology and Translational Science. Elsevier, 2022. http://dx.doi.org/10.1016/bs.pmbts.2022.06.001.

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Sailapathi, Ananthasri, Seshan Gunalan, Kanagasabai Somarathinam, Gugan Kothandan et Diwakar Kumar. « Importance of Homology Modeling for Predicting the Structures of GPCRs ». Dans Homology Molecular Modeling - Perspectives and Applications [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.94402.

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Homology modeling is one of the key discoveries that led to a rapid paradigm shift in the field of computational biology. Homology modeling obtains the three dimensional structure of a target protein based on the similarity between template and target sequences and this technique proves to be efficient when it comes to studying membrane proteins that are hard to crystallize like GPCR as it provides a higher degree of understanding of receptor-ligand interaction. We get profound insights on structurally unsolved, yet clinically important drug targeting proteins through single or multiple template modeling. The advantages of homology modeling studies are often used to overcome various problems in crystallizing GPCR proteins that are involved in major disease-related pathways, thus paving way to more structural insights via in silico models when there is a lack of experimentally solved structures. Owing to their pharmaceutical significance, structural analysis of various GPCR proteins using techniques like homology modeling is of utmost importance.

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Oylumlu, Ece, Goksu Uzel, Lubeyne Durmus, Meric Tas, Damla Gunes et Ceren Ciraci. « Pattern Recognition Receptor-Mediated Regulatory T Cell Functions in Diseases ». Dans Regulatory T Cells [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.105693.

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The advent of new technologies in gene expression, immunology, molecular biology, and computational modeling studies has expedited the discovery process and provided us with a holistic view of host immune responses that are highly regulated. The regulatory mechanisms of the immune system lie not only in weakening the attacker directly but also in fortifying the defender for the development of an efficient adaptive immune response. This chapter reviews a comprehensive set of experimental and bioinformatic studies designed to deepen the current knowledge on the regulatory T cells (Tregs) in the context of Pattern Recognition Receptors (PRRs). Initially, we examined both membrane-bound Toll-like Receptors (TLRs) and C Type Lectin Receptors (CLRs); and cytosolic NOD-like Receptors (NLRs) and RIG-I like Receptors (RLRs) in Tregs. Then, we revisited the disease conditions associated with regulatory T cells by emphasizing the essential roles of PRRs. Expanding our knowledge and strategies on the regulatory mechanisms are likely to provide our best chances for long-term disease control and maintenance of homeostasis.

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Actes de conférences sur le sujet "Bioinformatic, Computational Biology, GPCR"

Chengzhang, Li, et Xu Jiucheng. « Identification of Potentially Therapeutic Target Genes in Ovarian Cancer via Bioinformatic Approach ». Dans 2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB). IEEE, 2021. http://dx.doi.org/10.1109/icbcb52223.2021.9459203.

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Yang, Heng-Yi, et Tian-Ni Mao. « ITGAX : A Potential Biomarker of Acute Myeloid Leukemia (AML) through Bioinformatic Analysis ». Dans 2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB). IEEE, 2021. http://dx.doi.org/10.1109/icbcb52223.2021.9459204.

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Xu, Nuo, Changjiang Zhang, Leng Leng Lim et Aloysius Wong. « Bioinformatic Analysis of Nucleotide Cyclase Functional Centers and Development of ACPred Webserver ». Dans BCB '18 : 9th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA : ACM, 2018. http://dx.doi.org/10.1145/3233547.3233549.

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