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At least 1,621 records · Page 90Linked to original sources
PubMed2002

In Ssarch of new anti-bacterial target genes: a comparative/structural genomics approach.

We outline a joint academic/industrial (CNRS/AVENTIS) functional genomics project aiming at the discovery of new anti-bacterial gene targets. Starting from all publicly available bacterial genomes, a subset of the most evolutionary conserved protein-coding genes has been identified. We retained genes with clear homolog in E. coli and at least one gram-positive bacterium among B.subtilis, M. tuberculosis, L. lactis or S. pyogenes. This subset was further reduced to genes encoding non-membrane proteins of unknown or hypothetical functions. The 221 E. coli Open Reading Frames (ORFs) identified through this comprehensive bioinformatic analysis are now submitted to a systematic 3-D structure determination protocol including cloning, protein expression and purification, crystallisation and X-ray diffraction. Our strategy was designed to focus on promising wide-spectrum targets as well as original biochemical pathways. Bioinformatics is used throughout all phases of project, including the initial large-scale comparative genomics analyses, the purification/expression and crystallisation stages for the detection of helpful sequence-specific features (e.g. cofactor binding motifs, non-structured N- or C- term extremities, etc ), and finally for the interpretation of the structures in conjunction with multiple sequence alignments for the identification of key residues, interaction areas on molecular surfaces, and overall function predictions.

Anti-Infective Agents↗
PubMed2005

Computational methods for protein-protein interaction and their application.

Protein-protein interactions play a central role in numerous processes in cell and are one of the main research fields in current functional proteomics. The increase of finished genomic sequences has greatly stimulated the progress for detecting the functions of the genes and their encoded proteins. As complementary ways to the high through-put experimental methods, various methods of bioinformatics have been developed for the study of the protein-protein interaction. These methods range from the sequence homology-based to the genomic-context based. Recently, it tends to integrate the data from different methods to build the protein-protein interaction network, and to predict the protein function from the analysis of the network structure. Efforts are ongoing to improve these methods and to search for novel aspects in genomes that could be exploited for function prediction. This review highlights the recent advances of the bioinformatics methods in protein-protein interaction researches. In the end, the application of the protein-protein interaction has also been discussed.

Computational Biology↗
PubMed2006

Membrane interactive alpha-helices in GPCRs as a novel drug target.

G-Protein Coupled Receptors (GPCRs) are one of the most important targets for pharmaceutical drug design. Over the past 30 years, mounting evidence has suggested the existence of homo and hetero dimers or higher-order complexes (oligomers) that are involved in signal transduction and some diseases. The number of reports describing GPCR oligomerization has increased, and in 2003, the organization of mouse rhodopsin into two-dimensional arrays of dimers was determined by an atomic force microscopic analysis. The analysis of the mouse rhodopsin complex has enabled us to discuss the oligomerization based on structural data. Although many unsolved problems still remains, the idea that GPCRs directly interact to form oligomers has been gradually accepted. One of the recent findings in the GPCR investigations is the clarification of the mechanisms of GPCR oligomerization at a molecular level. Most of these studies have suggested the importance of transmembrane alpha-helices for GPCR oligomerization. In this review, we will first summarize the importance of GPCR oligomerization and the functions of GPCRs. Then, we will explain the involvement of transmembrane alpha-helices in the oligomerization and a drug design strategy that targets these regions for GPCR oligomerization. Considering the current drug design methods, which are based on the modification of the protein-protein interactions of soluble regions of proteins, a "peptide mimic approach" that targets the transmembrane alpha-helices constituting the interfaces would be promising in drug discovery for GPCR oligomerization. For that purpose, we must know the positions of the interfaces. However, problems specific to membrane proteins have made it difficult to identify the positions of the interfaces experimentally. Therefore, information about the interfaces predicted by bioinformatics approaches is valuable. At the end of this review, several bioinformatics approaches toward interface prediction for oligomerization are introduced. The benefits and the pitfalls of these approaches are also discussed.

Biopolymers↗
PubMed2001

Linking genomics to immunotherapy by reverse immunology--'immunomics' in the new millennium.

The disclosure of the human genome sequence and rapid advances in genomic expression profiling have revolutionized our knowledge about molecular changes in malignant diseases. Rapidly growing gene expression databases and improvements in bioinformatics tools set the stage for new approaches using large-scale molecular information to develop specific therapeutics in cancer. On one hand, the ability to detect clusters of genes differentially expressed in normal and malignant tissue may lead to widely applicable targeting of defined molecular structures. On the other hand, analyzing the 'molecular fingerprint' of an individual tumor raises the possibility of developing customized therapeutics. One approach to use the emerging new datasets for the development of novel therapeutics is to identify genes that are specifically expressed in tumors as targets for immune intervention. This review will focus on the process from in silico analysis of expression databases and screening of potential candidate genes by bioinformatics to the in vitro and in vivo analysis to determine the immunogenicity of candidate tumor antigens. Basic biological principles of 'reverse immunology' as well as technical advantages and difficulties will be addressed.

Algorithms↗
PubMed2001

Use of T cell epitopes for vaccine development.

T lymphocytes play a major role in the recognition and subsequent elimination of tumors and intracellular pathogens. Induction of epitope-specific T cell responses can help in the clearance of diseases for which no conventional vaccines exist. However, the lack of simple methods to identify relevant T cell epitopes, the high mutation rate of many pathogens, and HLA polymorphism have made the development of efficient T cell epitope-based, or "epitope-driven" vaccines difficult to achieve. Our research over the past several years has applied bioinformatics tools in conjunction with T cell assays to identify naturally processed putative T cell epitopes from several pathogens. This strategy will accelerate the development of new generation T cell epitope-based vaccines against various pathogens including viruses such as HIV and WNV, bacteria such as M.tb., and parasites such as plasmodium. This chapter will review the use of a bioinformatics-based approach to identify putative T cell epitopes. It will summarize the current state of knowledge regarding T cell-epitope-based vaccines and discuss several ways to improve their efficacy.

Algorithms↗
PubMed2006

Gene expression profile exploration of a large dataset on chronic fatigue syndrome.

OBJECTIVE: To gain understanding of the molecular basis of chronic fatigue syndrome (CFS) through gene expression analysis using a large microarray data set in conjunction with clinically administrated questionnaires. METHOD: Data from the Wichita (KS, USA) CFS Surveillance Study was used, comprising 167 participants with two self-report questionnaires (multidimensional fatigue inventory [MFI] and Zung depression scale [Zung]), microarray data, empiric classification, and others. Microarray data was analyzed using bioinformatics tools from ArrayTrack. RESULTS: Correspondence analysis was applied to the MFI questionnaire to select the 23 samples having either the most or the least fatigue, and to the Zung questionnaire to select the 26 samples having either the most or least depression; ten samples were common, resulting in a total of 39 samples. The MFI and Zung-based CFS/non-CFS (NF) classifications on the 39 samples were consistent with the empiric classification. Two differentially-expressed gene lists were determined, 188 fatigue-related genes and 164 depression-related genes, which shared 24 common genes and involved 11 common pathways. Principal component analysis based on 24 genes clearly separates 39 samples with respect to their likelihood to be CFS. Most of the 24 genes are not previously reported for CFS, yet their functions are consistent with the prevailing model of CFS, such as immune response, apoptosis, ion channel activity, signal transduction, cell-cell signaling, regulation of cell growth and neuronal activity. Hierarchical cluster analysis was performed based on 24 genes to classify 128 (=167-39) unassigned samples. Several of the 11 identified common pathways are supported by earlier findings for CFS, such as cytokine-cytokine receptor interaction and neuroactive ligand-receptor interaction. Importantly, most of the 11 common pathways are interrelated, suggesting complex biological mechanisms associated with CFS. CONCLUSION: Bioinformatics is critical in this study to select definitive sample groups, analyze gene expression data and gain insight into biological mechanisms. The 24 identified common genes and 11 common pathways could be important in future studies of CFS at the molecular level.

Adult↗
PubMed2026

PCSK9 as a Key Gene of Metastasis in Lung Adenocarcinoma: A Multi-omics and Experimental Validation Study.

BACKGROUND: Lung adenocarcinoma (LUAD) is the most common form of lung cancer. Proprotein convertase subtilisin/kexin type 9 (PCSK9) is abnormally expressed in various tumor tissues and is associated with malignant phenotypes. However, the clinical significance, function, and mechanism of LUAD invasion and metastasis remain unclear. METHODS: We retrospectively enrolled 100 patients with LUAD in this study. Initially, qRT-PCR was performed to detect PCSK9 levels in clinical tissues. Subsequently, bioinformatics analysis of scRNA-seq and The Cancer Genome Atlas Program (TCGA) datasets was performed to predict the role of PCSK9 in tumor cell malignancy and its potential downstream pathways. These predictions were validated experimentally using the CCK-8 assay, TUNEL staining, wound healing, transwell invasion assay, and an in vivo lung metastasis model. Finally, Western blotting and an AKT inhibitor (MK2206) were used to verify the underlying mechanism. RESULTS: PCSK9 was significantly upregulated in LUAD tissues compared to paracancerous tissues and was associated with poorer OS and DFS. Bioinformatics analysis of scRNA-seq data and TCGA analysis predicted that PCSK9 is highly enriched in tumor cells and is involved in EMT, and that the PI3K/AKT pathway plays a significant role in LUAD development. Experiments confirmed that PCSK9 markedly promoted LUAD cell proliferation, migration, and invasion in vitro and lung metastasis in vivo. PCSK9 overexpression significantly upregulated p-AKT, p-PI3K, and p-mTOR levels. Furthermore, the AKT inhibitor, MK2206, reversed the promoting effects of PCSK9. CONCLUSIONS: PCSK9 expression is associated with the prognosis and diagnosis of LUAD. This molecule activates the PI3K/AKT signaling pathway, thereby driving invasion, metastasis, and proliferation in LUAD.

Humans↗
PubMed1997

Strategies for identifying and predicting islet autoantigen T-cell epitopes in insulin-dependent diabetes mellitus.

T cells recognize peptide epitopes bound to major histocompatibility complex molecules. Human T-cell epitopes have diagnostic and therapeutic applications in autoimmune diseases. However, their accurate definition within an autoantigen by T-cell bioassay, usually proliferation, involves many costly peptides and a large amount of blood. We have therefore developed a strategy to predict T-cell epitopes and applied it to tyrosine phosphatase IA-2, an autoantigen in IDDM, and HLA-DR4(*0401). First, the binding of synthetic overlapping peptides encompassing IA-2 was measured directly to purified DR4. Secondly, a large amount of HLA-DR4 binding data were analysed by alignment using a genetic algorithm and were used to train an artificial neural network to predict the affinity of binding. This bioinformatic prediction method was then validated experimentally and used to predict DR4 binding peptides in IA-2. The binding set encompassed 85% of experimentally determined T-cell epitopes. Both the experimental and bioinformatic methods had high negative predictive values, 92% and 95%, indicating that this strategy of combining experimental results with computer modelling should lead to a significant reduction in the amount of blood and the number of peptides required to define T-cell epitopes in humans.

Autoantigens↗
PubMed2005

Using proteomics and network analysis to elucidate the consequences of synaptic protein oxidation in a PS1 + AbetaPP mouse model of Alzheimer's disease.

Increasing evidence suggests that oxidative injury is involved in the pathogenesis of many age-related neurodegenerative disorders, including Alzheimer's disease (AD). Identifying the protein targets of oxidative stress is critical to determine which proteins may be responsible for the neuronal impairments and subsequent cell death that occurs in AD. In this study, we have applied a high-throughput shotgun proteomic approach to identify the targets of protein carbonylation in both aged and PS1 + AbetaPP transgenic mice. However, because of the inherent difficulties associated with proteomic database searching algorithms, several newly developed bioinformatic tools were implemented to ascertain a probability-based discernment between correct protein assignments and false identifications to improve the accuracy of protein identification. Assigning a probability to each identified peptide/protein allows one to objectively monitor the expression and relative abundance of particular proteins from diverse samples, including tissue from transgenic mice of mixed genetic backgrounds. This robust bioinformatic approach also permits the comparison of proteomic data generated by different laboratories since it is instrument- and database-independent. Applying these statistical models to our initial studies, we detected a total of 117 oxidatively modified (carbonylated) proteins, 59 of which were specifically associated with PS1 + AbetaPP mice. Pathways and network component analyses suggest that there are three major protein networks that could be potentially altered in PS1 + AbetaPP mice as a result of oxidative modifications. These pathways are 1) iNOS-integrin signaling pathway, 2) CRE/CBP transcription regulation and 3) rab-lyst vesicular trafficking. We believe the results of these studies will help establish an initial AD database of oxidatively modified proteins and provide a foundation for the design of future hypothesis driven research in the areas of aging and neurodegeneration.

Activating Transcription Factor 2↗
PubMed2026

Whole-Genome Sequence Dataset of Rhodococcus qingshengii IEGM 267-Terpenoid Biotransformer Toward Genetic Functional Annotation.

Background/Objectives: Microbial biotransformation of monoterpenoids is a promising approach for obtaining bioactive compounds. Rhodococcus species are attractive biocatalysts due to their metabolic versatility and ability to transform hydrophobic substrates. In this study, we investigated the catalytic potential of Rhodococcus qingshengii IEGM 267 toward carveol isomers and explored genomic features that may underlie this activity. Methods: The strain was cultivated in mineral medium supplemented with (-)-trans-carveol. Biotransformation products were analyzed by TLC and GC-MS. The draft genome was sequenced, assembled, taxonomically assigned, and annotated using standard bioinformatics tools. Results: Rhodococcus qingshengii IEGM 267 efficiently converted (-)-trans-carveol to carvone. Genome analysis confirmed the taxonomic assignment of the strain and revealed a large repertoire of oxidoreductases, including monooxygenases, hydroxylases, and dehydrogenases. Seven genes encoding cytochrome P450-dependent oxygenases were identified as candidate enzymes potentially involved in carveol oxidation. Conclusions: R. qingshengii IEGM 267 is an efficient and stereoselective biocatalyst for (-)-trans-carveol oxidation. The results of bioinformatics analysis suggest an alternative enzymatic basis for this transformation and provide a foundation for future functional characterization.

Rhodococcus↗
PubMed2005

Genes transactivated by hepatitis C virus core protein, a microarray assay.

AIM: To explore the new target genes transactivated by hepatitis C virus (HCV) core protein and to elucidate the pathogenesis of HCV infection. METHODS: Reverse transcribed cDNA was subjected to microarray assay. The coding gene transactivated by HCV core protein was cloned and analyzed with bioinformatics methods. RESULTS: The expressive vector of pcDNA3.1(-)-core was constructed and confirmed by restriction enzyme digestion and DNA sequencing and approved correct. mRNA was purified from HepG2 and HepG2 cells transfected with pcDNA3.1(-)-core, respectively. The cDNA derived was subjected to microarray assay. A new gene named HCTP4 was cloned with molecular biological method in combination with bioinformatics method. CONCLUSION: HCV core is a potential transactivator. Microarray is an efficient and convenient method for analysis of differentially expressed genes.

Carcinoma, Hepatocellular↗
PubMed2005

Study of transactivating effect of pre-S2 protein of hepatitis B virus and cloning of genes transactivated by pre-S2 protein with suppression subtractive hybridization.

AIM: To investigate the transactivating effect of pre-S2 protein of hepatitis B virus (HBV) and construct a subtractive cDNA library of genes transactivated by pre-S2 protein with suppression subtractive hybridization (SSH) technique, and to pave the way for elucidating the pathogenesis of HBV infection. METHODS: pcDNA3.1(-)-pre-S2 containing pre-S2 region of HBV genome was constructed by routine molecular methods. HepG2 cells were cotransfected with pcDNA3.1(-)-pre-S2/pSV-lacZ and empty pcDNA3.1(-)/pSV-lacZ. After 48 h, cells were collected and detected for the expression of beta-galactosidase (beta-gal). SSH and bioinformatics techniques were used, the mRNA of HepG2 cells transfected with pcDNA3.1(-)-pre-S2 and pcDNA3.1(-) empty vector was isolated, respectively, cDNA was synthesized. After digestion with restriction enzyme RsaI, cDNA fragments were obtained. Tester cDNA was then divided into two groups and ligated to the specific adaptor 1 and adaptor 2, respectively. After tester cDNA was hybridized with driver cDNA twice and underwent two times of nested PCR, amplified cDNA fragments were subcloned into pGEM-Teasy vectors to set up the subtractive library. Amplification of the library was carried out with E.coli strain DH5alpha. The cDNA was sequenced and analyzed in GenBank with Blast search after PCR. RESULTS: The pre-S2 mRNA could be detected in HepG2 cells transfected with pcDNA3.1(-)-pre-S2 plasmid. The activity of beta-gal in HepG2 cells transfected with pcDNA3.1(-)-pre-S2/pSV-lacZ was 7.0 times higher than that of control plasmid (P<0.01). The subtractive library of genes transactivated by HBV pre-S2 protein was constructed successfully. The amplified library contains 96 positive clones. Colony PCR showed that 86 clones contained 200-1 000 bp inserts. Sequence analysis was performed in 50 clones randomly, and the full length sequences were obtained with bioinformatics method and searched for homologous DNA sequence from GenBank, altogether 25 coding sequences were obtained, these cDNA sequences might be the target genes transactivated by pre-S2 protein. CONCLUSION: The pre-S2 protein of HBV has transactivating effect on SV40 early promoter. The obtained sequences may be target genes transactivated by pre-S2 protein among which some genes coding proteins involved in cell cycle regulation, metabolism, immunity, signal transduction and cell apoptosis. This finding brings some new clues for studying the biological functions of pre-S2 protein and further understanding of HBV hepatocarcinogesis.

Base Sequence↗
PubMed2003

Isolation and analysis of a novel gene over-expressed during liver regeneration.

AIM: To isolate and analyze a novel gene over-expressed during liver regeneration. METHODS: Total RNA of regenerating liver was extracted from liver tissue after 0-4-36-36-36 hr short interval successive partial hepatectomy (SISPH). Reverse transcription-polymerase chain reaction was used to synthesize double strand cDNA, after the tissue was digested by proteinase K and Sfi A/B. The double-strand cDNA was ligated to lambdaTriplEx2. lambdaphage packaging reaction was performed and E. coli XL1-Blue was infected for titering and amplifying. One expressed sequence tag was probed by Dig and phage in situ hybridization was carried out to isolate positive clones. Positive recombinant lambdaTriplEx2 was converted to the corresponding pTriplEx2, and bioinformatics was used to analyze full-length cDNA. RESULTS: We isolated a novel full-length cDNA during liver regeneration following SISPH. CONCLUSION: We have succeeded in cloning a novel gene, based on bioinformatics. We postulate that this gene may function in complicated network in liver regeneration. On the one hand, it may exert initiation of liver regeneration via regulating nitric oxide synthesis. On the other hand, it may protect damaged residue lobus following SISPH.

Amino Acid Sequence↗
PubMed2026

[Study of a patient with azoospermia due to variant of MOV10L1 gene].

OBJECTIVE: To explore the clinical and genotypic characteristics of a patient with Sertoli cell-only syndrome (SCOS) due to variants of MOV10L1 gene. METHODS: A 27-year-old patient with Non-obstructive azoospermia (NOA) underwent routine semen analysis. Serum levels of follicle-stimulating hormone (FSH), luteinizing hormone (LH), progesterone (P), estradiol (E2), prolactin (PRL), and testosterone (T) were determined by chemiluminescence assays. Peripheral blood samples were collected for G-banded karyotyping analysis. Multiplex PCR fluorescence detection was used to screen for AZF gene microdeletions. Whole exome sequencing (WES) and Sanger sequencing were performed simultaneously. Testicular biopsy tissues were subjected to Hematoxylin-Eosin (HE) staining to assess seminiferous tubule cell composition, and MOV10L1 protein expression was detected by immunohistochemical staining. Bioinformatics tools were employed to predict the pathogenicity of variants and their impact on protein structure and function. This study was approved by the Medical Ethics Committee of the Guangdong Institute of Reproductive Sciences [Ethics No.: 2023(01)]. RESULTS: The patient's two semen analyses had failed to detect any sperm. Hormone tests indicated elevated FSH (22.32 mIU/mL) and PRL (397.6 mIU/mL), while T (3.68 nmol/L) and E2 (38.32 pmol/L) were reduced. Chromosomal karyotyping revealed 46,XY, and no AZF gene deletion was detected. WES and Sanger sequencing detected compound heterozygous variants of the MOV10L1 gene, including a c.345C>A (p.C115X) nonsense variant and a c.3323C>T (p.T1108I) missense variant, with the former being unreported previously. HE staining showed only Sertoli cells in the seminiferous tubules, confirming the diagnosis of SCOS. Immunohistochemical staining revealed absent MOV10L1 protein expression in the testicular tissue. Based on the guidelines from American College of Medical Genetics and Genomics (ACMG), the c.345C>A (p.C115X) was classified as a pathogenic variant (PVS1+PM2_Supporting+PP4), while the c.3323C>T (p.T1108I) was deemed variant of uncertain significance (PM2_Supporting+PP3_Supporting+PP4). Bioinformatics analysis demonstrated that c.345C>A (p.C115X) may cause premature termination of protein translation, while c.3323C>T (p.T1108I) may disrupt the hydrophobicity of the RNA helicase domain, reducing the active pocket volume and decreasing its affinity for MILI protein. CONCLUSION: This study has diagnosed a case of SCOS due to compound heterozygous variants of the MOV10L1 gene, which also enriched its mutational spectrum.

Humans↗
PubMed2005

[Establishment of diagnostic model of cerebrospinal protein fingerprint pattern for glioma and its clinical application].

OBJECTIVE: To establish the diagnostic model of cerebrospinal protein profile for gliomas by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) and bioinformatics. METHODS: Seventy-five samples of cerebrospinal fluid from patients with gliomas, benign brain tumors and mild brain traumas were collected. A total of 50 samples from gliomas and non-brain-tumors were divided into training sets (33 cases including 17 gliomas and 16 non-brain-tumors) and testing sets (17 cases including 5 gliomas and 12 non-brain-tumors). The cerebrospinal proteins bound to H4 chip were detected by SELDI-TOF MS, the profiles of cerebrospinal protein were gained and then analyzed with artificial neural network algorithm (ANN); and the diagnostic model of cerebrospinal protein profiles for differentiating gliomas from non-brain-tumors was established. Forty-seven of cerebrospinal samples of gliomas and benign brain tumors were divided into training sets (31 cases including 13 gliomas and 18 benign brain tumors) and testing sets (16 cases including 9 gliomas and 7 benign brain tumors), the diagnostic model of cerebrospinal protein profiles for differentiating gliomas from benign brain tumors was established based on the same method. The support vector machine (SVM) algorithm was also used for evaluation, both results were very similar, but the result derived from ANN was more stable than that from SVM. RESULT: The diagnostic model of cerebrospinal protein profiles for differentiating gliomas from non-brain-tumors was established and was challenged with the test set randomly, the sensitivity and specificity were 100% and 91.7%, respectively. The cerebrospinal protein profiling model for differentiating gliomas from benign brain tumors was also developed and was challenged with the test set randomly, the sensitivity and specificity were 88.9%, and 100%, respectively. CONCLUSION: The technology of SELDI-TOF MS which combined with analysis tools of bioinformatics is a novel effective method for screening and identifying tumor biomarkers of gliomas and it may provide a new approach for the clinical diagnosis of glioma.

Adult↗
PubMed2000

[Molecular cloning of a novel gene located on chromosome 3p25.3 and an analysis of its expression in nasopharyngeal carcinoma].

OBJECTIVE: To obtain the novel genes associated with human nasopharyngeal carcinoma(NPC) on chromosome 3p24-26. METHODS: Twenty epithelial-derived expressed sequence tags(EST) were selected from chromosome 3p24-26 where loss of heterozygosity(LOH) frequently occurs in NPC tissues. Primers were designed based on the sequences of these ESTs. RT-PCR was used to amply their corresponding cDNA fragments from NPC cell line HNE1 and primary cultures of normal nasopharyngeal epithelial cells. The differential expression of two ESTs, T93093 and R41598, was confirmed by Northern blot. Then, expression of EST T93093 was further detected in 7 normal nasopharyngeal and 19 NPC biopsies. cDNA library screening was used to get its full cDNA sequence and the sequence of this novel gene was analyzed by bioinformatics. RESULTS: Thirteen ESTs(T62511, N39155, N68660, R61275, T95314, R06143, H52697, H66521, AA128685, AA284537, N52379, AA054180, and H98090) showed the similar expression level and 5 ESTs(R00732, R07573, R98052, H91759, H17566) showed no expression in both types of cells. EST T93093 was down-expressed, whereas EST R41598 up-expressed in NPC HNE1 cells. The EST T93093 was also found to be down-expressed in 26.3%(5/19) of NPC biopsies. The full length cDNA of this gene was obtained and named NAG-7, which is located at chromosome 3p25.3. Its 1677 bp full length cDNA has a potential open reading frame(ORF) predicting a 94 amino acid protein with a molecular weight of 11023.87 Dalton. Bioinformatics analysis of the NAG-7 gene shows that it is a transmembrane protein containing a protein kinase C(PKC) phosphorylation site and a myristyl site. It has no significant homology to any reported genes in database of GenBank(AF086709). CONCLUSION: NAG-7 is a novel gene down-expressed in NPC, which may be involved in the development of NPC.

Chromosome Mapping↗
PubMed2000

Mining genomes and mapping proteomes: identification and characterization of protein subunit vaccines.

Currently, there is an extensive and unprecedented effort to obtain the complete nucleotide sequence of the complex genomes of many micro-organisms. In this post-genomic era, based on the availability of the entire genome sequence of an organism, three new disciplines of molecular biology have emerged: genomics, transcriptional profiling and proteomics. All these technologies have the potential to accelerate the process of identifying protective protein antigens as subunit vaccine targets as well as validating and extending the range of available candidate antigens. The progress of these technologies has led to the origination of the science of bioinformatics for management and critical evaluation of the large amount of information generated. Although genomics, transcriptional profiling and proteomics are each based on different principles, there is considerable synergy between them. Appropriate application of any one, or a combination of two or more of these approaches, coupled with bioinformatics, would allow identification of a short-list of vaccine candidates from the entire list of several hundreds to thousands of proteins encoded by the genome. These candidates would then require usual channelling through the subsequent process involving recombinant expression, purification and testing for immunogenicity and protective efficacy.

Animals↗
PubMed2001

A web-based tool to retrieve human genome polymorphisms from public databases.

Single Nucleotide Polymorphisms (SNPs) are the most important source of variation in our genome, and an invaluable tool in the hands of researchers who investigate genetic diseases. Databases of SNPs are growing at a very fast rate, and the ability to perform large-scale, high-resolution association studies is quickly becoming a reality. In this paper we describe SNPper, a web-based tool to search for SNPs in public databases. The system allows searching for all SNPs in a given set of genes (for candidate gene studies) or in a specified region of a chromosome. The information displayed for each gene or each SNP is fully annotated and linked to the leading bioinformatics web sites. The first release of SNPper is available on the web, and has received positive feedback from the genetic and bioinformatics community.

Databases, Genetic↗