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At least 1,333 records · Page 74Linked to original sources

PubMed2005

Global gene expression analysis revealed an unsuspected deo operon under the control of molybdate sensor, ModE protein, in Escherichia coli.

ModE protein, a molybdate sensor/regulator, controls the transcription of genes coding for molybdate uptake (mod), molybdopterin synthesis (moa), molybdoenzymes nitrate reductase (nap) and dimethylsulfoxide reductase (dms), as well as fermentative dihydrogen production (fdhF and hyc) and respiratory nitrate reductase (narXL) in Escherichia coli. The catalytic product of a second protein, MoeA, is also required for molybdate-dependent positive regulation of hyc and nar operons. To explore the potential role of ModE and MoeA in the regulation of other E. coli genes, the global gene expression profile of a wild type and a modE, moeA double mutant grown in glucose-minimal medium under anaerobic conditions were compared. Expression of 67 genes was affected by the modE and moeA mutations (P value <0.01). Of these, 17 differed by at least 2-fold or higher. Fourteen genes were expressed at a higher level in the mutant (2.4- to 23.9-fold) (notably, mod-molybdate transport, deo-nucleoside catabolism and opp-oligopeptide transport operons) and dmsA and yli operon were expressed at a higher level in the wild type parent (2.6- to 5.7-fold). One of the unexpected findings was repression of the deo operon by ModE. This was confirmed by quantitative RT-PCR and by the analysis of a deoC-lacZ fusion. The deo promoter/operator region contains a putative ModE-consensus sequence centered at -35 in which the adenines are replaced by guanines (TGTGT-N7-TGTGT). The ModE protein did bind to the deo upstream DNA and shifted its electrophoretic mobility. Bioinformatics analysis of the E. coli genome for ModE-consensus motif (TATAT-N7-TAYAT) identified 21 additional genes/operons including the moa as potential targets for Mo-control. The physiological role of many of the genes identified solely by bioinformatics (19/21) is unknown. Expression levels of these genes were similar in the parent and the isogenic modE, moeA mutant when cultured anaerobically in glucose-minimal medium. This study identified additional targets, such as deo and opp, for the Mo-dependent control in E. coli.

Escherichia coli↗

PubMed2005

Analysing the outer membrane subproteome of Methylococcus capsulatus (Bath) using proteomics and novel biocomputing tools.

High-resolution two-dimensional gel electrophoresis and mass spectrometry has been used to identify the outer membrane (OM) subproteome of the Gram-negative bacterium Methylococcus capsulatus (Bath). Twenty-eight unique polypeptide sequences were identified from protein samples enriched in OMs. Only six of these polypeptides had previously been identified. The predictions from novel bioinformatic methods predicting beta-barrel outer membrane proteins (OMPs) and OM lipoproteins were compared to proteins identified experimentally. BOMP ( http://www.bioinfo.no/tools/bomp ) predicted 43 beta-barrel OMPs (1.45%) from the 2,959 annotated open reading frames. This was a lower percentage than predicted from other Gram-negative proteomes (1.8-3%). More than half of the predicted BOMPs in M. capsulatus were annotated as (conserved) hypothetical proteins with significant similarity to very few sequences in Swiss-Prot or TrEMBL. The experimental data and the computer predictions indicated that the protein composition of the M. capsulatus OM subproteome was different from that of other Gram-negative bacteria studied in a similar manner. A new program, Lipo, was developed that can analyse entire predicted proteomes and give a list of recognised lipoproteins categorised according to their lipo-box similarity to known Gram-negative lipoproteins ( http://www.bioinfo.no/tools/lipo ). This report is the first using a proteomics and bioinformatics approach to identify the OM subproteome of an obligate methanotroph.

Bacterial Outer Membrane Proteins↗

PubMed2006

Analysis of the targeting sequences of an iron-containing superoxide dismutase (SOD) of the dinoflagellate Lingulodinium polyedrum suggests function in multiple cellular compartments.

One of the proteins targeted to the peridinin plastid of the dinoflagellate Lingulodinium polyedrum is the iron-containing superoxide dismutase (LpSOD). Like dinoflagellate plastid proteins of class II, LpSOD carries a bipartite presequence comprising a signal peptide followed by a transit peptide. Our bioinformatic studies suggest that its signal peptide is atypical, however, and that the entire presequence may function as a mitochondrial targeting signal. It is possible that LpSOD represents a new class of proteins in algae with complex plastids, which are co-targeted to the plastid and mitochondrion. In addition to the ambiguous N-terminal targeting signal, LpSOD contains a potential type-1 peroxisome-targeting signal (PTS1) located at its C-terminus. In accordance with a peroxisome localization of this dismutase, its mRNA has two in-frame AUG codons. Our bioinformatic analyses indicate that the first start codon resides in a much weaker oligonucleotide context than the second one. This suggests that synthesis of the plastid/mitochondrion-targeted and peroxisome-targeted isoforms could proceed through so-called leaky scanning. Moreover, our results show that expression of the two isoforms could be regulated by a 'hairpin' structure located between the first and second start codons.

Animals↗

PubMed2003

PF-IND: probability algorithm and software for separation of plant and fungal sequences.

The separation of plant and fungal sequences in EST pools by bioinformatic methods is difficult because of sequence similarities between plants and fungi, lack of enough sequence information, and the short length of the isolated fragments. An algorithm and software that utilize the differences in codon usage bias to discriminate between plant and fungal sequences are described. The software (PF-IND) includes five pairs of fungi and their host plants that can be used to analyze a large number of related species. Analysis of a sequence provides an arbitrary value that defines the likelihood that a sequence will be a fungal or a plant gene. The software can distinguish between homologous fungal and plant genes and it helps identify the correct reading frame of unknown expressed sequence tags (ESTs) for which BLAST analyses do not provide clear information. Short sequences of 100-150 bp can be analyzed with high confidence. PF-IND analysis of 100 sequences derived from fungal infected plants identified the origin of 94 sequences. Only 66 sequences were identified by a BLASTX analysis of the same 100 ESTs. Overall, PF-IND is a novel bioinformatic tool aimed at assisting the research of fungus-plant interactions.

Algorithms↗

PubMed2004

Recent developments in the monitoring, modeling and control of biological production systems.

Current trends in the development of methods for monitoring, modeling and controlling biological production systems are reviewed from a bioengineering perspective. The ability to measure intracellular conditions in bioprocesses using genomics and other bioinformatics tools is addressed. Devices provided via micromachining techniques and new real-time optical technology are other novel methods that may facilitate biosystem engineering. Mathematical modeling of data obtained from bioinformatics or real-time monitoring methods are necessary in order to handle the dense flows of data that are generated. Furthermore, control methods must be able to cope with these data flows in efficient ways that can be implemented in plant-wide computer communication systems.

Animals↗

PubMed2006

On the advantages of multi-input single-output parallel cascade classifiers.

Parallel Cascade Identification (PCI) has been successfully applied to build dynamic nonlinear systems that address diverse challenges in the field of bioinformatics. PCI may be used to identify either single-input single-output (SISO) or multi-input single-output (MISO) models. Although SISO PCI models have typically sufficed, it has been suggested that MISO PCI systems could also be used to form bioinformatics classifiers, and indeed they were successfully applied in one study. This paper reports on the first systematic comparison of MISO and SISO PCI classifiers. Motivation for using the MISO structure is given. The construction of MISO parallel cascade models is also briefly reviewed. In order to compare the accuracy of SISO and MISO PCI classifiers, genetic algorithms are applied to optimize the model architecture on a number of equivalent single-input and multi-input biological training datasets. Through evaluation of both model structures on independent test datasets, we establish that MISO PCI is capable of building classifiers of equal accuracy to those resulting from SISO PCI models. Moreover, we discuss and illustrate the benefits of the MISO approach, including significant reduction in training and testing times, and the ability to adjust automatically the weighting of individual inputs according to information content.

Algorithms↗

PubMed2024

RNF115 aggravates tumor progression through regulation of CDK10 degradation in thyroid carcinoma.

BACKGROUND: RING Finger Protein 115 (RNF115), a notable E3 ligase, is known to modulate tumorigenesis and metastasis. In our investigation, we endeavor to unravel the putative function and inherent mechanism through which RNF115 influences the evolution of thyroid carcinoma (THCA). METHODS: We analyzed RNF115 expression in THCA using the Cancer Genome Atlas (TCGA) database. The influence of RNF115 on the progression of THCA was evaluated using both in vitro and in vivo experimental approaches. The protein regulated by RNF115 was identified through bioinformatics analysis, and its biological significance was further explored. RESULTS: In both THCA tissues and cells, RNF115 showed elevated expression levels. Enhanced expression of RNF115 fostered cell proliferation, tumor growth, and the exacerbation of epithelial-mesenchymal transition (EMT) in THCA, while also promoting tumor lung metastasis. Bioinformatics analysis identified cyclin-dependent kinase 10 (CDK10) as a downstream target of RNF115, which was found to be ubiquitinated and degraded by RNF115 in THCA cells. Functionally, overexpression of CDK10 was found to counteract the promotion of malignant phenotype in THCA induced by RNF115. From a mechanistic perspective, RNF115 activated the Raf-1 pathway and enhanced cancer cell cycle progression by degrading CDK10 in THCA cells. CONCLUSION: RNF115 triggers cell proliferation, EMT, and tumor metastasis by ubiquitinating and degrading CDK10. The regulation of the Raf-1 pathway and cell cycle progression in THCA may be profoundly influenced by this process.

Humans↗

PubMed2006

Bacterial expression strategies for human angiogenesis proteins.

We outline an expression strategy using Escherichia coli to obtain soluble components of a selected group of human proteins implicated in angiogenesis. These targets represent a heterogeneous group of proteins which for expression purposes were separated into cytoplasmic and helical membrane protein categories. Target selection was refined using a bioinformatic approach to generate a list of 50 experimental targets. A group consisting of forty-four cytoplasmic and signal-containing protein targets were amplified and cloned into multiple expression vectors. For this target category, we obtained 48% soluble expression products. In addition, we used a domain expression approach for six high molecular weight proteins predicted to contain membrane spanning helices to obtain soluble domain products. These results validate the utility of a bioinformatically driven high throughput approach to increase the number of soluble proteins or protein domains which can be used for multiple downstream applications.

Angiogenesis Inducing Agents↗

PubMed2026

MYBL2 promotes malignant phenotypes and M2-like macrophage polarization through CCL2 in non-small cell lung cancer.

Hub genes associated with non-small cell lung cancer (NSCLC) were identified through bioinformatics screening. In vitro experiments analyzed the potential mechanisms by which these genes regulate tumor malignant phenotypes and macrophage polarization. Differentially expressed genes were identified from The Cancer Genome Atlas (TCGA)-NSCLC and GSE32175 datasets, followed by protein-protein interaction (PPI) network analysis to screen hub genes. The effects of MYB Proto-Oncogene Like 2 (MYBL2) on NSCLC progression and macrophage polarization were evaluated using in vitro models. The regulatory relationship between MYBL2 and C-C motif chemokine ligand 2 (CCL2) was investigated by Chromatin immunoprecipitation (ChIP) and dual-luciferase reporter assays, and rescue experiments were performed to validate the role of the MYBL2-CCL2 axis. Bioinformatics screening identified BUB1B, CDCA2 and MYBL2 as key hub genes with high expression in NSCLC, among which MYBL2 was significantly upregulated in NSCLC cells. Functional experiments confirmed that MYBL2 silencing markedly inhibited the malignant proliferation, migration and invasion of NSCLC cells. Tumor cell MYBL2 knockdown effectively reversed M2-like polarization and promoted M1-like polarization in the co-culture system. Mechanistically, MYBL2 directly bound to the CCL2 promoter region to enhance CCL2 transcriptional activity and upregulate CCL2 expression in NSCLC cells. Exogenous CCL2 supplementation significantly rescued the inhibitory effect of MYBL2 knockdown on macrophage M2-like polarization, verifying the mediating role of CCL2 in this regulatory axis. MYBL2 is strongly expressed in NSCLC cells and is associated with enhanced malignant phenotypes. It may affect macrophage M2-like polarization by upregulating CCL2, thus participating in NSCLC immune microenvironment remodeling.

CCL2↗

PubMed2006

Fuzzy polynucleotide spaces and metrics.

The study of genetic sequences is of great importance in biology and medicine. Mathematics is playing an important role in the study of genetic sequences and, generally, in bioinformatics. In this paper, we extend the work concerning the Fuzzy Polynucleotide Space (FPS) introduced in Torres, A., Nieto, J.J., 2003. The fuzzy polynucleotide Space: Basic properties. Bioinformatics 19(5); 587-592 and Nieto, J.J., Torres, A., Vazquez-Trasande, M.M. 2003. A metric space to study differences between polynucleotides. Appl. Math. Lett. 27:1289-1294: by studying distances between nucleotides and some complete genomes using several metrics. We also present new results concerning the notions of similarity, difference and equality between polynucleotides. The results are encouraging since they demonstrate how the notions of distance and similarity between polynucleotides in the FPS can be employed in the analysis of genetic material.

Algorithms↗

PubMed2002

DNA microarrays in prostate cancer.

DNA microarray technology provides a means to examine large numbers of molecular changes related to a biological process in a high throughput manner. This review discusses plausible utilities of this technology in prostate cancer research, including definition of prostate cancer predisposition, global profiling of gene expression patterns associated with cancer initiation and progression, identification of new diagnostic and prognostic markers, and discovery of novel patient classification schemes. The technology, at present, has only been explored in a limited fashion in prostate cancer research. Some hurdles to be overcome are the high cost of the technology, insufficient sample size and repeated experiments, and the inadequate use of bioinformatics. With the completion of the Human Genome Project and the advance of several highly complementary technologies, such as laser capture microdissection, unbiased RNA amplification, customized functional arrays (eg, single-nucleotide polymorphism chips), and amenable bioinformatics software, this technology will become widely used by investigators in the field. The large amount of novel, unbiased hypotheses and insights generated by this technology is expected to have a significant impact on the diagnosis, treatment, and prevention of prostate cancer. Finally, this review emphasizes existing, but currently underutilized, data-mining tools, such as multivariate statistical analyses, neural networking, and machine learning techniques, to stimulate wider usage.

Algorithms↗

PubMed2005

The role of informatics in glycobiology research with special emphasis on automatic interpretation of MS spectra.

This paper reviews the current status of bioinformatics applications and databases in glycobiology, which are based on bioinformatics approaches as well as informatics for glycobiology where an explicit encoding of glycan structures is required. The availability of the complete sequence of the human genome has accelerated the systematic identification of so far unidentified glycogenes considerably in many areas of glycobiology using well-established bioinfomatics tools. Although there has been an immense development of new glyco-related data collections as well as informatics tools and several efforts have been started to cross-link and reference the various data deposited in distributed databases, informatics for glycobiology and glycomics is still poorly developed compared to the genomics and proteomics area. The development of algorithms for the automatic interpretation of MS spectra - currently, a severe bottleneck, which hampers the rapid and reliable interpretation of MS data in high-throughput glycomics projects - is reviewed. A comprehensive list of web resources is given. Several lines of progression are discussed. There is an urgent need for the development of decentralised input facilities of experimentally determined glycan structures. Simultaneously, agreements of standards for the structural description of glycans as well as formats for the related data have to be established. The integration of glycomics with genomics/proteomics has to increase.

Computational Biology↗

PubMed2006

Analysis of structure and function of tenascin-C.

Tenascin-C is a multidomain large extracellular matrix glycoprotein composed of six monomers. The size of tenascin-C monomers (180-250 kDa) varies as a result of an alternative splicing of the fibronectin repeats at the pre-mRNA level. For the first time we applied bioinformatic and molecular modeling procedures, for detailed analysis of the organization of tenascin-C and we performed bioinformatic analysis of tenascin-C gene. We detected the presence of heat shock protein 33 in the tenascin-C N-terminal domain that may suggest its role in the protein-protein interactions and stress response. The number of fibronectin type III-like repeats and epidermal growth factor-like repeats were corrected to 15 and 14, respectively. Using polyactylamide gel electophoresis, RT/PCR analysis and microarrays data, we showed the higher level of tenascin-C in the human tumor tissues: brain, intestine and breast. These results suggested a new role of tenascin-C as the potential tumor marker and drug target.

Alternative Splicing↗

PubMed2005

Automated derivation and refinement of sequence length patterns for protein sequences using evolutionary computation.

Several stratagems are used in protein bioinformatics for the classification of proteins based on sequence, structure or function. We explore the concept of a minimal signature embedded in a sequence that defines the likely position of a protein in a classification. Specifically, we address the derivation of sparse profiles for the G-protein coupled receptor (GPCR) clan of integral membrane proteins. We present an evolutionary algorithm (EA) for the derivation of sparse profiles (signatures) without the need to supply a multiple alignment. We also apply an evolution strategy (ES) to the problem of pattern and profile refinement. Patterns were derived for the GPCR 'superfamily' and GPCR families 1-3 individually from starting populations of randomly generated signatures, using a database of integral membrane protein sequences and an objective function using a modified receiver operator characteristic (ROC) statistic. The signature derived for the family 1 GPCR sequences was shown to perform very well in a stringent cross-validation test, detecting 76% of unseen GPCR sequences at 5% error. Application of the ES refinement method to a signature developed by a previously described method [Sadowski, M.I., Parish, J.H., 2003. Automated generation and refinement of protein signatures: case study with G-protein coupled receptors. Bioinformatics 19, 727-734] resulted in a 6% increase of coverage for 5% error as measured in the validation test. We note that there might be a limit to this or any classification of proteins based on patterns or schemata.

Algorithms↗

PubMed2005

2D RNA-QSAR: assigning ACC oxidase family membership with stochastic molecular descriptors; isolation and prediction of a sequence from Psidium guajava L.

Quantitative structure-activity relationship (QSAR) techniques for small molecules could be applied to nucleic acids. Unfortunately, almost all molecular descriptors are more successful at encoding branching information than sequences and/or cannot be back-projected. A solution for scaling the QSAR problem up to RNA may be to transform sequences into secondary structures first. Our group has used Markovian negentropies as molecular descriptors for drug design with preliminary results in bioinformatics [Bioinformatics 2003, 19, 2079]. However, RNA-QSAR studies on RNA molecules have not been described to date. Novel Markovian negentropies have been introduced here as molecular descriptors for 2D-RNA structures. An RNA-QSAR study of the ACC proteins from different plants has been carried out. The QSAR recognizes 19/20 sequences (95.0%) within the ACC family and 12/17 (70.6%) of the control group sequences. The model has a high Matthews' regression coefficient (C = 0.68). Overall cross-validation average accuracies were 14 out of 15 for ACC sequences (93.3%) and 10 out of 13 for control sequences (76.9%). Finally, ACC oxidase family membership was assigned to a new sequence isolated for the first time in this work from Psidium guajava L. A backprojection map for this sequence identifies the left stem (40%) and the main stem (45%) as highly important substructures. Results of an nBLAST experiment are consistent with this finding and indicate a high conservation score (>70) for left stem and main stem; whereas major loop, right stem, cap and major loop right half were hardly conserved.

Amino Acid Oxidoreductases↗

PubMed2004

Opioid research in amphibians: an alternative pain model yielding insights on the evolution of opioid receptors.

This review summarizes the work from our laboratory investigating mechanisms of opioid analgesia using the Northern grass frog, Rana pipiens. Over the last dozen years, we have accumulated data on the characterization of behavioral effects after opioid administration on radioligand binding by using opioid agonist and antagonist ligands in amphibian brain and spinal cord homogenates, and by cloning and sequencing opioid-like receptor cDNA from amphibian central nervous system (CNS) tissues. The relative analgesic potency of mu, delta, and kappa opioids is highly correlated between frogs and other mammals, including humans. Radioligand binding studies using selective opioid agonists show a similar selectivity profile in amphibians and mammals. In contrast, opioid antagonists that are highly selective for mammalian mu, delta, and kappa opioid receptors were not selective in behavioral and binding studies in amphibians. Three opioid-like receptor cDNAs were cloned and sequenced from amphibian brain tissues and are orthologs to mammalian mu, delta, and kappa opioid receptors. Bioinformatics analysis of the three types of opioid receptor cDNAs from all vertebrate species with full datasets gave a pattern of the molecular evolution of opioid receptors marked by the divergence of mu, delta, and kappa opioid receptor sequences during vertebrate evolution. This divergence in receptor amino acid sequence in later-evolved vertebrates underlies the hypothesis that opioid receptors are more type-selective in mammals than in nonmammalian vertebrates. The apparent order of receptor type evolution is kappa, then delta, and, most recently, the mu opioid receptor. Finally, novel bioinformatics analyses suggest that conserved extracellular receptor domains determine the type selectivity of vertebrate opioid receptors.

Amphibians↗

PubMed2006

DNA microarray technology in toxicogenomics of aquatic models: methods and applications.

Toxicogenomics represents the merging of toxicology with genomics and bioinformatics to investigate biological functions of genome in response to environmental contaminants. Aquatic species have traditionally been used as models in toxicology to characterize the actions of environmental stresses. Recent completion of the DNA sequencing for several fish species has spurred the development of DNA microarrays allowing investigators access to toxicogenomic approaches. However, since microarray technology is thus far limited to only a few aquatic species and derivation of biological meaning from microarray data is highly dependent on statistical arguments, the full potential of microarray in aquatic species research has yet to be realized. Herein we review some of the issues related to construction, probe design, statistical and bioinformatical data analyses, and current applications of DNA microarrays. As a model a recently developed medaka (Oryzias latipes) oligonucleotide microarray was described to highlight some of the issues related to array technology and its application in aquatic species exposed to hypoxia. Although there are known non-biological variations present in microarray data, it remains unquestionable that array technology will have a great impact on aquatic toxicology. Microarray applications in aquatic toxicogenomics will range from the discovery of diagnostic biomarkers, to establishment of stress-specific signatures and molecular pathways hallmarking the adaptation to new environmental conditions.

Animals↗

PubMed2005

Novel biological networks modulated by complement.

The almost complete deciphering of the human genome has paved the way for the application of new technology platforms in understanding the contribution of complex biological pathways to human pathophysiology and disease. In the post-genomic era, the concept of systems biology has gained significant momentum and biomedical research is now being conducted on an integrated and cross-disciplinary platform that pulls together its resources from diverse fields such as computational biology, bioinformatics, functional genomics, structural biology, and proteomics. In this perspective, the identity of established biologic systems is being re-examined in the light of novel findings that suggest novel associations between otherwise unrelated pathways and individual proteins. Complement exemplifies such a system that, transcending its innate immune identity, has forged functional associations with multiple pathways and networks in modulating basic biologic processes. In the present article, we provide a global overview of these unusual system associations of complement with the aid of a powerful and high-throughput bioinformatics platform. Using a novel approach called systems literature analysis that allows the rapid extraction of text-based associations between genes and pathways from the ever expanding scientific article database, we have selected a broad range of biologic processes modulated by complement proteins and have constructed an integrated map of complement-mediated networks that incorporates well over 85 diverse biologic pathways. Expanding the complement cascade beyond its approximately 35 designated components, we discuss protein-protein interactions involving novel ligands and associations with signaling cascades and cellular networks that affect both inflammatory and non-inflammatory processes. This integrated consideration of complement within a unified 'systems biology' framework underscores the concept that innate immunity goes well beyond the protection of 'self' extending links to critical developmental, homeostatic, and metabolic processes.

Complement System Proteins↗