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At least 883 records · Page 49Linked to original sources
PubMed2003

Proteomic identification of a novel protein regulated in CA1 and CA3 hippocampal regions during intermittent hypoxia.

The CA1 and CA3 regions of the hippocampus markedly differ in their susceptibility to hypoxia in general, and more particularly to the intermittent hypoxia (IH) that characterizes sleep apnea. We used proteomic analysis to build a database of proteins expressed in normoxic CA1 and CA3. The current hippocampus protein database identifies 106 proteins. A hypothetical protein with accession number AK006737 (gimid R:12839969) was strongly upregulated in the CA1, but not CA3 hippocampal region. Bioinformatic analysis revealed that the unknown protein contained a high stringency protein kinase e binding site. Domain analysis demonstrated the presence of a conserved sequence indicative of macrophage scavenger receptors. Using proteomic analysis we have previously demonstrated that acute (6 h) IH-mediated CA1 injury results from complex interactions between pathways involving increased metabolism, induction of stress-induced proteins and apoptosis, and ultimately disruption of structural proteins and cell integrity. The current findings identify a hypothetical protein that may play a key role in the response of CA1 to IH. These findings provide initial insights into mechanisms underlying differences in susceptibility to hypoxia in neural tissue and demonstrate how proteomic analysis can be used to generate new hypotheses, which define neuronal adaptation to IH.

Animals↗
PubMed2002

Conceptual data modelling for bioinformatics.

Current research in the biosciences depends heavily on the effective exploitation of huge amounts of data. These are in disparate formats, remotely dispersed, and based on the different vocabularies of various disciplines. Furthermore, data are often stored or distributed using formats that leave implicit many important features relating to the structure and semantics of the data. Conceptual data modelling involves the development of implementation-independent models that capture and make explicit the principal structural properties of data. Entities such as a biopolymer or a reaction, and their relations, eg catalyses, can be formalised using a conceptual data model. Conceptual models are implementation-independent and can be transformed in systematic ways for implementation using different platforms, eg traditional database management systems. This paper describes the basics of the most widely used conceptual modelling notations, the ER (entity-relationship) model and the class diagrams of the UML (unified modelling language), and illustrates their use through several examples from bioinformatics. In particular, models are presented for protein structures and motifs, and for genomic sequences.

Computational Biology↗
PubMed2004

Drosophila DNase I footprint database: a systematic genome annotation of transcription factor binding sites in the fruitfly, Drosophila melanogaster.

UNLABELLED: Despite increasing numbers of computational tools developed to predict cis-regulatory sequences, the availability of high-quality datasets of transcription factor binding sites limits advances in the bioinformatics of gene regulation. Here we present such a dataset based on a systematic literature curation and genome annotation of DNase I footprints for the fruitfly, Drosophila melanogaster. Using the experimental results of 201 primary references, we annotated 1367 binding sites from 87 transcription factors and 101 target genes in the D.melanogaster genome sequence. These data will provide a rich resource for future bioinformatics analyses of transcriptional regulation in Drosophila such as constructing motif models, training cis-regulatory module detectors, benchmarking alignment tools and continued text mining of the extensive literature on transcriptional regulation in this important model organism. AVAILABILITY: http://www.flyreg.org/ CONTACT: cbergman@gen.cam.ac.uk.

Binding Sites↗
PubMed2002

A bioinformatics-based strategy identifies c-Myc and Cdc25A as candidates for the Apmt mammary tumor latency modifiers.

The epistatically interacting modifier loci (Apmt1 and Apmt2) accelerate the polyoma Middle-T (PyVT)-induced mammary tumor. To identify potential candidate genes loci, a combined bioinformatics and genomics strategy was used. On the basis of the assumption that the loci were functioning in the same or intersecting pathways, a search of the literature databases was performed to identify molecular pathways containing genes from both candidate intervals. Among the genes identified by this method were the cell cycle-associated genes Cdc25A and c-Myc, both of which have been implicated in breast cancer. Genomic sequencing revealed noncoding polymorphism in both genes, in the promoter region of Cdc25A, and in the 3' UTR of c-Myc. Molecular and in vitro analysis showed that the polymorphisms were functionally significant. In vivo analysis was performed by generating compound PyVT/Myc double-transgenic animals to mimic the hypothetical model, and was found to recapitulate the age-of-onset phenotype. These data suggest that c-Myc and Cdc25A are Apmt1 and Apmt2, and suggest that, at least in certain instances, bioinformatics can be utilized to bypass congenic construction and subsequent mapping in conventional QTL studies.

3T3 Cells↗
PubMed2004

Comparative proteomics of the Mycobacterium leprae binding protein myelin P0: its implication in leprosy and other neurodegenerative diseases.

Mycobacterium leprae, the causative agent of leprosy invades Schwann cells of the peripheral nerves leading to nerve damage and disfigurement, which is the hallmark of the disease. Wet experiments have shown that M. leprae binds to a major peripheral nerve protein, the myelin P zero (P0). This protein is specific to peripheral nerve and may be important in the initial step of M. leprae binding and invasion of Schwann cells which is the feature of leprosy. Though the receptors on Schawann cells, cytokines, chemokines and antibodies to M. leprae have been identified the molecular mechanism of nerve damage and neurodegeneration is not clearly defined. Recently pathogen and host protein/nucleotide sequence similarities (molecular mimicry) have been implicated in neurodegenerative diseases. The approach of the present study is to utilise bioinformatic tools to understand leprosy nerve damage by carrying out sequence and structural similarity searches of myelin P0 with leproma and other genomic database. Since myelin P0 is unique to peripheral nerve, its sequence and structural similarities in other neuropathogens have also been noted. Comparison of myelin P0 with the M. leprae proteins revealed two characterised proteins, Ferrodoxin NADP reductase and a conserved membrane protein, which showed similarity to the query sequence. Comparison with the entire genomic database (www.ncbi.nlm.nih.gov) by basic local alignment search tool for proteins (BLASTP) and fold classification of structure-structure alignment of proteins (FSSP) searches revealed that myelin P0 had sequence/structural similarities to the poliovirus receptor, coxsackie-adenovirus receptor, anthrax protective antigen, diphtheria toxin, herpes simplex virus, HIV gag-1 peptide, and gp120 among others. These proteins are known to be associated directly or indirectly with neruodegeneration. Sequence and structural similarities to the immunoglobin regions of myelin P0 could have implications in host-pathogen interactions, as it has homophilic adhesive properties. Although these observed similarities are not highly significant in their percentage identity, they could be functionally important in molecular mimicry, receptor binding and cell signaling events involved in neurodegeneration.

Amino Acid Sequence↗
PubMed2002

Novel homodimeric and heterodimeric rat gamma-hydroxybutyrate synthases that associate with the Golgi apparatus define a distinct subclass of aldo-keto reductase 7 family proteins.

The aldo-keto reductase (AKR) 7 family is composed of the dimeric aflatoxin B(1) aldehyde reductase (AFAR) isoenzymes. In the rat, two AFAR subunits exist, designated rAFAR1 and rAFAR2. Herein, we report the molecular cloning of rAFAR2, showing that it shares 76% sequence identity with rAFAR1. By contrast with rAFAR1, which comprises 327 amino acids, rAFAR2 contains 367 amino acids. The 40 extra residues in rAFAR2 are located at the N-terminus of the polypeptide as an Arg-rich domain that may form an amphipathic alpha-helical structure. Protein purification and Western blotting have shown that the two AFAR subunits are found in rat liver extracts as both homodimers and as a heterodimer. Reductase activity in rat liver towards 2-carboxybenzaldehyde (CBA) was resolved by anion-exchange chromatography into three peaks containing rAFAR1-1, rAFAR1-2 and rAFAR2-2 dimers. These isoenzymes are functionally distinct; with NADPH as cofactor, rAFAR1-1 has a low K(m) and high activity with CBA, whereas rAFAR2-2 exhibits a low K(m) and high activity towards succinic semialdehyde. These data suggest that rAFAR1-1 is a detoxication enzyme, while rAFAR2-2 serves to synthesize the endogenous neuromodulator gamma-hydroxybutyrate (GHB). Subcellular fractionation of liver extracts showed that rAFAR1-1 was recovered in the cytosol whereas rAFAR2-2 was associated with the Golgi apparatus. The distinct subcellular localization of the rAFAR1 and rAFAR2 subunits was confirmed by immunocytochemistry in H4IIE cells. Association of rAFAR2-2 with the Golgi apparatus presumably facilitates secretion of GHB, and the novel N-terminal domain may either determine the targeting of the enzyme to the Golgi or regulate the secretory process. A murine AKR protein of 367 residues has been identified in expressed sequence tag databases that shares 91% sequence identity with rAFAR2 and contains the Arg-rich extended N-terminus of 40 amino acids. Further bioinformatic evidence is presented that full-length human AKR7A2 is composed of 359 amino acids and also possesses an additional N-terminal domain. On the basis of these observations, we conclude that AKR7 proteins can be divided into two subfamilies, one of which is a Golgi-associated GHB synthase with a unique, previously unrecognized, N-terminal domain that is absent from other AKR proteins.

Alcohol Oxidoreductases↗
PubMed2026

Proteomic insights into Helicobacter pylori infection in stomach cells, revealing host response and host-targeted therapeutics repurposing.

BACKGROUND: Helicobacter pylori (H. pylori) is a globally prevalent gastric pathogen strongly associated with chronic gastritis, peptic ulcers, and gastric cancer. While bacterial factors have been extensively studied, host proteomic responses and their therapeutic potential remain largely underexplored. RESEARCH DESIGN AND METHODS: Current analyses employed a systematic proteomics-based data integration and harmonization approach (retrospective qualitative cohort study) to identify important differentially regulated host proteins. Proteomic datasets were curated from in vitro studies and analyzed for functional enrichment, protein-protein interaction networks, and hub protein identification. To explore therapeutic repurposing, drug repositioning was performed using the DrugBank database. RESULTS: Data summation describing protein differential regulation in human gastric cells as a result of the infection revealed 1672 perturbed host proteins. Bioinformatics analysis revealed 11 proteins including CSK, MET, RELA, MARK2, GRB2, FTO, PLCG1, CRKL, RPS5, RPS9, and RPS27A to be ideal host targets for therapeutic repurposing. Clinically approved drugs such as Dasatinib (targeting CSK) and Crizotinib (targeting MET) emerged as promising candidates due to favorable pharmacokinetics and known bioactivity. CONCLUSIONS: Host-directed therapeutics could offer alternative strategies to conventional antibiotic therapy, addressing challenges such as resistance and infection recurrence, providing a foundation for future experimental validation and development of host-targeted interventions for infection control.

Humans↗
PubMed2026

PSMA1, PSMA5, and PSMB2 serve as prognostic biomarkers and are correlated with tumor-infiltrating leukocytes in HCC.

Hepatocellular carcinoma (HCC) ranks as the third leading cause of cancer-related death. Proteasome (PSM) is the main intracellular proteolytic system in higher eukaryotic cells. It has been reported to be involved in the tumor's onset, metabolism, and survival, and it has been recognized as a therapeutic target for many human cancers. The 20S proteasome subunits, specifically proteasome 20S subunit alpha 1 (PSMA1), proteasome 20S subunit alpha 5 (PSMA5), and proteasome 20S subunit beta 2 (PSMB2), are overexpressed in various malignancies, including HCC. Nevertheless, the exact role of these genes in HCC prognosis remains only partially understood. Moreover, a reliable predictive biomarker for HCC is essential for supporting the implementation of personalized therapies. Therefore, this study employed a comprehensive bioinformatics approach, integrating data from cBioPortal, Human Protein Atlas (HPA), Oncomine, STRING Viruses, Kaplan-Meier plotter, and other established high-throughput databases and tools. The current study thoroughly examined the expression levels, methylation, and genomic alterations of PSM and their correlation with tumor-infiltrating leukocytes. PSMA1, PSMA5, and PSMB2 are overexpressed in various cancers, including HCC, and their expression levels are associated with tumor grade and stage. The abundance of these genes is linked to diminished DNA methylation levels and genome alterations. Moreover, there was a significant association between the expression levels of these genes and poor prognosis and immune cell infiltration. In conclusion, this study proposes PSMA1, PSMA5, and PSMB2 as biomarkers for HCC.

Humans↗
PubMed2000

Exploring protein domain structure.

The protein databank contains coordinates of over 10,000 protein structures, which constitute more than 25,000 structural domains in total. The investigation of protein structural, functional and evolutionary relationships is fundamental to many important fields in bioinformatics research, and will be crucial in determining the function of the human and other genomes. This review describes the SCOP and CATH databases of protein structure classification, which define, classify and annotate each domain in the protein databank. The hierarchical structure, use and annotation of the databases are explained. Other tools for exploring protein structure relationships are also described.

Computational Biology↗
PubMed2006

Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences.

MOTIVATION: In 2001 and 2002, we published two papers (Bioinformatics, 17, 282-283, Bioinformatics, 18, 77-82) describing an ultrafast protein sequence clustering program called cd-hit. This program can efficiently cluster a huge protein database with millions of sequences. However, the applications of the underlying algorithm are not limited to only protein sequences clustering, here we present several new programs using the same algorithm including cd-hit-2d, cd-hit-est and cd-hit-est-2d. Cd-hit-2d compares two protein datasets and reports similar matches between them; cd-hit-est clusters a DNA/RNA sequence database and cd-hit-est-2d compares two nucleotide datasets. All these programs can handle huge datasets with millions of sequences and can be hundreds of times faster than methods based on the popular sequence comparison and database search tools, such as BLAST.

Algorithms↗
PubMed2026

Casein Kinase 1 Alpha 1 Is Over-expressed in Pancreatic Adenocarcinoma Tissues and Correlates With Shorter Patient Survival.

BACKGROUND/AIM: A quarter of a century has passed since the start of the 21st century, and cancer, once considered an incurable disease, has become manageable thanks to the development of various treatments. However, pancreatic adenocarcinoma (PAAD) remains one of the deadliest cancers in the world, with over 95% of patients dying within five years. This is because the anatomical location of the pancreas makes it very difficult to detect with imaging, and symptoms often do not appear until the cancer invades the nerve plexus in its terminal stages, meaning that by the time it is diagnosed, it is often too late. Therefore, the development of prognostic markers is an urgent task. Casein kinase 1 alpha 1 (CSNK1A1) is a serine/threonine protein kinase deeply involved in Wnt signaling and the tumor suppressor mechanisms of p53. While the association between increased or decreased CSNK1A1 expression and prognosis has been reported in many types of cancer tissue, its association in PAAD is not yet fully understood. This study investigated the potential of CSNK1A1 as a prognostic marker for PAAD. MATERIALS AND METHODS: We used Gene Expression Profiling Interactive Analysis (GEPIA) and the University of Alabama Birmingham Cancer Data Analysis Portal (UALCAN) bioinformatics platforms to analyze CSNK1A1 mRNA expression, protein levels, and survival rates of patients with PAAD obtained from The Cancer Genome Atlas (TCGA) database. RESULTS: CSNK1A1 mRNA and protein levels were significantly higher in PAAD tissue compared to normal pancreatic tissue, and this increase was associated with a poor prognosis in patients with PAAD. CONCLUSION: In PAAD tissue, increased expression of CSNK1A1 mRNA and protein was observed compared to normal pancreatic tissue, and this increased expression correlated with poor patient prognosis. Therefore, CSNK1A1 is considered a promising prognostic biomarker in PAAD.

CSNK1A1↗
PubMed2025

Interconnected study of molecular pathways: miR-137 as a central element at the intersection of lipid metabolism and prostate carcinogenesis.

OBJECTIVE: To evaluate the roles of miR-137 and its target genes in lipid metabolism and prostate tumorigenesis. METHODS: We used a series of bioinformatic approaches to establish the relationship between miR-137 and its target genes. We mapped the metabolic pathways of interest in the Reactome database and identified the central target genes of miR-137 in this pathway using four platforms: Reactome, miRDB, miRmap, and TargetScan. To assess the expression and association with clinical parameters, we obtained information from the UALCAN, OncoDB, and GEPIA2 databases using a dataset of patients with prostate cancer from The Cancer Genome Atlas. For functional enrichment analysis and construction of the protein-protein interaction network, we used the Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and STRING. RESULTS: Our in silico study of The Cancer Genome Atlas database revealed that miR-137 is underexpressed in tumor tissues, and its reduction is associated with poor prognosis. An intriguing set of eight genes within the PPARα pathway: PPARGC1A, PPARGC1B, NCOA1, NCOA2, NCOA3, MED1, MED27, and ESRRA displayed synergy, positive correlations, and synchronized expression patterns in adipose, hepatic, and prostatic tissues, all linked to the enigmatic processes of metabolic regulation. Among the highlighted genes, ESRRA was overexpressed in the malignant environment, whereas its counterparts remained underexpressed. The plot was thickened with associations between the expression of NCOA1, NCOA3, and MED27, lymph node involvement, and the overexpression of several genes linked to advanced prostate cancer stages. An intriguing pattern emerged, with patients exhibiting reduced disease-free survival overexpressing NCOA2, NCOA3, MED27, and ESRRA. CONCLUSION: This study elucidates the possibility that miR-137 subtly modulates metabolic genes in prostate cancer, suggesting its latent therapeutic potential as a biomarker for disease progression. BACKGROUND: ■ The reduction of miR-137 in tumor tissues is associated with a worse prognosis. BACKGROUND: ■ miR-137 has eight oncogenically relevant target genes acting in the PPARα lipid pathway. BACKGROUND: ■ NCOA1, NCOA3, MED27, and ESRRA are associated with advanced prostate cancer. BACKGROUND: ■ miR-137 exhibits significant clinical potential by repressing the activation of pathways that influence prostate tumorigenesis in hyperstimulated metabolic environments. BACKGROUND: Prostate cancer progression is sustained by the simultaneous activation of pathways involving lipid uptake and de novo synthesis. In this context, miR-137 inhibits adipogenic differentiation and may reduce lipid uptake by tumor cells by modulating the PPAR/ p160/ESRRA axis, considerably attenuating metabolic effects and suppressing prostate tumorigenesis.

Male↗
PubMed2000

Healthcare, molecular tools and applied genome research.

Biotechnology 2000 offered a rare opportunity for scientists from academia and industry to present and discuss data in fields as diverse as environmental biotechnology and applied genome research. The healthcare section of the meeting encompassed a number of gene therapy delivery systems that are successfully treating genetic disorders. Beta-thalassemia is being corrected in mice by continous erythropoeitin delivery from engineered muscles cells, and from naked DNA electrotransfer into muscles, as described by Dr JM Heard (Institut Pasteur, Paris, France). Dr Reszka (Max-Delbrueck-Centrum fuer Molekulare Medizin, Berlin, Germany), meanwhile, described a treatment for liver metastasis in the form of a drug carrier emolization system, DCES (Max-Delbrueck-Centrum fuer Molekulare Medizin), composed of surface modified liposomes and a substance for chemo-occlusion, which drastically reduces the blood supply to the tumor and promotes apoptosis, necrosis and antiangiogenesis. In the molecular tools section, Willem Stemmer (Maxygen Inc, Redwood City, CA, USA) gave an insight into the importance that techniques, such as molecular breeding (DNA shuffling), have in the evolution of molecules with improved function, over a range of fields including pharmaceuticals, vaccines, agriculture and chemicals. Technologies, such as ribosome display, which can incorporate the evolution and the specific enrichment of proteins/peptides in cycles of selection, could play an enormous role in the production of novel therapeutics and diagnostics in future years, as explained by Andreas Plückthun (Institute of Biochemistry, University of Zurich, Switzerland). Applied genome research offered technologies, such as the 'in vitro expression cloning', described by Dr Zwick (Promega Corp, Madison, WI, USA), are providing a functional analysis for the overwhelming flow of data emerging from high-throughput sequencing of genomes and from high-density gene expression microarrays (DNA chips). The importance of bioinformatics was stressed throughout the conference. In particular, its applications in the storage, analysis and visualization of data, but also the linkage of databases and data mining tools. It is the storage and linkage that generates insight into disease processes, enabling the nomination of candidate drug targets.

Journal Article↗
PubMed2005

Linking experimental results, biological networks and sequence analysis methods using Ontologies and Generalised Data Structures.

The structure of a closely integrated data warehouse is described that is designed to link different types and varying numbers of biological networks, sequence analysis methods and experimental results such as those coming from microarrays. The data schema is inspired by a combination of graph based methods and generalised data structures and makes use of ontologies and meta-data. The core idea is to consider and store biological networks as graphs, and to use generalised data structures (GDS) for the storage of further relevant information. This is possible because many biological networks can be stored as graphs: protein interactions, signal transduction networks, metabolic pathways, gene regulatory networks etc. Nodes in biological graphs represent entities such as promoters, proteins, genes and transcripts whereas the edges of such graphs specify how the nodes are related. The semantics of the nodes and edges are defined using ontologies of node and relation types. Besides generic attributes that most biological entities possess (name, attribute description), further information is stored using generalised data structures. By directly linking to underlying sequences (exons, introns, promoters, amino acid sequences) in a systematic way, close interoperability to sequence analysis methods can be achieved. This approach allows us to store, query and update a wide variety of biological information in a way that is semantically compact without requiring changes at the database schema level when new kinds of biological information is added. We describe how this datawarehouse is being implemented by extending the text-mining framework ONDEX to link, support and complement different bioinformatics applications and research activities such as microarray analysis, sequence analysis and modelling/simulation of biological systems. The system is developed under the GPL license and can be downloaded from http://sourceforge.net/projects/ondex/

Algorithms↗
PubMed2004

Database development in toxicogenomics: issues and efforts.

The marriage of toxicology and genomics has created not only opportunities but also novel informatics challenges. As with the larger field of gene expression analysis, toxicogenomics faces the problems of probe annotation and data comparison across different array platforms. Toxicogenomics studies are generally built on standard toxicology studies generating biological end point data, and as such, one goal of toxicogenomics is to detect relationships between changes in gene expression and in those biological parameters. These challenges are best addressed through data collection into a well-designed toxicogenomics database. A successful publicly accessible toxicogenomics database will serve as a repository for data sharing and as a resource for analysis, data mining, and discussion. It will offer a vehicle for harmonizing nomenclature and analytical approaches and serve as a reference for regulatory organizations to evaluate toxicogenomics data submitted as part of registrations. Such a database would capture the experimental context of in vivo studies with great fidelity such that the dynamics of the dose response could be probed statistically with confidence. This review presents the collaborative efforts between the European Molecular Biology Laboratory-European Bioinformatics Institute ArrayExpress, the International Life Sciences Institute Health and Environmental Science Institute, and the National Institute of Environmental Health Sciences National Center for Toxigenomics Chemical Effects in Biological Systems knowledge base. The goal of this collaboration is to establish public infrastructure on an international scale and examine other developments aimed at establishing toxicogenomics databases. In this review we discuss several issues common to such databases: the requirement for identifying minimal descriptors to represent the experiment, the demand for standardizing data storage and exchange formats, the challenge of creating standardized nomenclature and ontologies to describe biological data, the technical problems involved in data upload, the necessity of defining parameters that assess and record data quality, and the development of standardized analytical approaches.

Animals↗