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PubMed2006

Positional cloning: single-gene cardiovascular disorders.

Positional cloning is a comprehensive genetic strategy used to identify a disease-causing gene without any prior knowledge of the pathogenesis or protein defects involved in the disease process. The basic process involves collection of accurately diagnosed patients and their family members, genotyping DNAs with polymorphic DNA markers mapped to specific regions on chromosomes, genetic linkage analysis to determine markers that are in close proximity to the chromosome location of the disease gene and to define the critical region by haplotype analysis, identification and selection of candidate genes residing in the critical region, and, eventually, identification of the disease-causing DNA sequence variants by various methods. Many molecular techniques are utilized in positional cloning. Bioinformatics and computation analysis are significant and indispensable components of such a study, and are detailed elsewhere in this volume. This chapter presents a few basic laboratory protocols for conducting positional cloning: genomic DNA preparation, genotyping polymorphic markers, DNA sequencing, and related procedures that serve on fluorescent-labeled and capillary electrophoresis-based semi-automated genetic analysis systems.

Cardiovascular Diseases↗

PubMed2004

The translational apparatus of Chlamydomonas reinhardtii chloroplast.

Genetic and biochemical studies have revealed that chloroplast gene expression in Chlamydomonas is controlled primarily post-transcriptionally, including events that effect mRNA processing and stability, and during the translation of plastid mRNAs into proteins. Many of the proteins required for chloroplast gene expression are encoded in the nuclear genome, and most of these proteins have yet to be identified biochemically. Emergence of the draft sequence of the Chlamydomonas nuclear genome has enabled us to carry out a prediction and comparative analysis of the proteins required for chloroplast mRNA translation. Putative translation factor genes have been identified by homology search, and functional chloroplast ribosomal protein genes have been compiled based on our recent proteomic studies. This bioinformatic and proteomic analysis shows that the translational apparatus of Chlamydomonas is related to that of bacteria, but is more complex. Chlamydomonas chloroplasts contain all of the general translation factors found in bacteria, and a majority of the ribosomal proteins are conserved between plastids and bacteria. However, Chlamydomonas contains a number of additional proteins and protein domains associated with the plastid ribosome, while some ribosomal proteins are either quite divergent or lacking. In addition, Chlamydomonas chloroplasts contain a number of mRNA specific translation factors that are not found in bacteria.

Journal Article↗

PubMed2004

Bioinformatics tools for single nucleotide polymorphism discovery and analysis.

Single nucleotide polymorphisms (SNPs) are a valuable resource for investigating the genetic basis of disease. These variants can serve as markers for fine-scale genetic mapping experiments and genome-wide association studies. Certain of these nucleotide polymorphisms may predispose individuals to illnesses such as diabetes, hypertension, or cancer, or affect disease progression. Bioinformatics techniques can play an important role in SNP discovery and analysis. We use computational methods to identify SNPs and to predict whether they are likely to be neutral or deleterious. We also use informatics to annotate genes that contain SNPs. To make this information available to the research community, we provide a variety of Internet-accessible tools for data access and display. These tools allow researchers to retrieve data about SNPs based on gene of interest, genetic or physical map location, or expression pattern.

Chromosome Mapping↗

PubMed2005

Selection of choriocarcinoma-associated genes using bioinformatics.

OBJECTIVE: To analyze the data of gene expression profiles of choriocarcinoma and screen for choriocarcinoma- related genes. METHODS: Human cDNA expression microarray containing 4 096 genes was used to study the gene expression profiles in specimens of complete hydatidiform moles (n=3) and choriocarcinomas (n=3), with normal placental villi serving as the control group. The candidate genes with similar expression profiles were identified by hierarchical cluster analysis, and their expressions in normal and neoplastic tissues analyzed by electronic Northern analysis and other bioinformatics methods. Three selected genes were analyzed by beta-actin semiquantitative reverse transcriptase-PCR to confirm the data. RESULTS: A total of 52 coexpressed candidate genes were identified from the gene expression data derived from the choriocarcinoma specimens, of which 19 genes were selected as choriocarcinoma-related genes by further cluster analysis, such as dynamin (L07807), katanin p60 (AF056022), zinc finger protein ZNF184 (U6656), calmodulin (U12022), carboxypeptidase M (BC022276), calcineurin-binding protein cabin 1 (NM_012295) and transducin-like enhancer protein TLE1(M99435). CONCLUSION: The identification of choriocarcinoma-related genes by cluster analysis provides new clues for seeking the key genes associated with the progression and metastasis of choriocarcinoma.

Choriocarcinoma↗

PubMed2000

Functional analysis of eight open reading frames on chromosomes XII and XIV of Saccharomyces cerevisiae.

Deletion, together with basic functional and bioinformatic analyses has been carried out on eight novel ORFs discovered during the sequencing of the Saccharomyces cerevisiae genome. Six ORFs (YLL049w, YLL051c, YLL052c, YLL053c, YLL054c and YLL055w) located on the left arm, and one (YLR130c) on the right arm, of chromosome XII, and an eighth ORF (YNL331c) on the left arm of the chromosome XIV, have been investigated. ORFs were deleted by the SFH-PCR gene-replacement strategy. Basic functional analysis revealed no obvious phenotype for any of the eight ORFs. Bioinformatic analysis, however, revealed possible functions for seven of the ORFs on the basis of the amino acid sequence similarity of their predicted protein products to those of proteins with known functions. ORF YLL051c (FRE6) shows similarity to iron transport proteins, such as ferric reductase. YLL052c and YLL053c appear to be aquaporins. The product of YLL054c (Yll054p) is highly similar to the oleate-specific transcriptional activator protein (Pip2p), which is involved in the peroxisomal induction pathway (pip). ORF YLL055w is similar to Dal5p, allantoate permease, and may play role in allantoin transport. YLR130c (ZRT2) is a low-affinity zinc transporter protein. YNL331c is also named AAD14, which is induced by chemicals that induce oxidative stress by depleting the cell of glutathione.

Chromosomes, Fungal↗

PubMed2002

RADARS, a bioinformatics solution that automates proteome mass spectral analysis, optimises protein identification, and archives data in a relational database.

RADARS, a rapid, automated, data archiving and retrieval software system for high-throughput proteomic mass spectral data processing and storage, is described. The majority of mass spectrometer data files are compatible with RADARS, for consistent processing. The system automatically takes unprocessed data files, identifies proteins via in silico database searching, then stores the processed data and search results in a relational database suitable for customized reporting. The system is robust, used in 24/7 operation, accessible to multiple users of an intranet through a web browser, may be monitored by Virtual Private Network, and is secure. RADARS is scalable for use on one or many computers, and is suited to multiple processor systems. It can incorporate any local database in FASTA format, and can search protein and DNA databases online. A key feature is a suite of visualisation tools (many available gratis), allowing facile manipulation of spectra, by hand annotation, reanalysis, and access to all procedures. We also described the use of Sonar MS/MS, a novel, rapid search engine requiring 40 MB RAM per process for searches against a genomic or EST database translated in all six reading frames. RADARS reduces the cost of analysis by its efficient algorithms: Sonar MS/MS can identifiy proteins without accurate knowledge of the parent ion mass and without protein tags. Statistical scoring methods provide close-to-expert accuracy and brings robust data analysis to the non-expert user.

Amino Acid Sequence↗

PubMed2005

A POU factor binding site upstream of the Chx10 homeobox gene is required for Chx10 expression in subsets of retinal progenitor cells and bipolar cells.

Retinal progenitor cells (RPCs) undergo a series of changes over time that affect their competency to produce different cell types at different times in development. The transcriptional machinery that regulates these changes, as well as associated gene expression changes, have not been characterized. An analysis of the regulatory region of the retinal homeodomain transcription factor, Chx10, was carried out using in ovo electroporations in chick and transgenic mice. An RPC enhancer was defined that mediates reporter activity in subsets of RPCs and directs high-level expression in intermediate and late RPCs. Using bioinformatic and biochemical analysis, a key binding site in this enhancer was found and was shown to be bound by the POU domain factors, Brn-2 and Tst-1/SCIP, in retinal extracts. Analysis of the Brn-2 expression pattern shows that it is expressed in intermediate and late RPCs, but not early RPCs, and thus partially overlaps with expression of the reporter activated by the defined Chx10 enhancer. Biochemical analysis also revealed binding of both Chx10 and Brn-2 to an enhancer of the CNS progenitor cell marker, Nestin. Nestin expression in the retina is restricted to intermediate/late RPC subsets, and genetic evidence is presented that demonstrates that Chx10 represses Nestin expression in early RPCs. A bipolar cell enhancer for Chx10 also was defined, and a role for Brn-2 in expression of Chx10 in bipolar cells is predicted. These data identify Brn-2 as a new marker of subsets of RPCs and suggest a mechanism by which a combination of POU factors and Chx10 define RPC gene expression patterns, such as that of Nestin.

Animals↗

PubMed2003

Characterisation of epitopes of pan-IgG/anti-G3m(u) and anti-Fc monoclonal antibodies.

The characterisation of monoclonal antibodies (MAbs) and their epitopes is important prior to their application as molecular probes. In this study, Western blotting using IgG1 Fc and pFc' fragments was employed to screen seven MAbs before pepscan analysis to determine their reactivity to potentially linear epitopes. MAbs PNF69C, PNF110A, X1A11 and MAbs WC2, G7C, JD312, 1A1 detected epitopes within the C(H)3 and C(H)2 domains, respectively. However, only four MAbs showed pepscan profiles that highlighted likely target residues. In particular, MAbs PNF69C and PNF110A that have previously been characterised with pan-IgG and anti-G3m(u) specificity, detected the peptide motif 338-KAKGQPR-344 which was located within the N-terminal region of the C(H)3 domain. Furthermore the majority of residues were present in all four IgG subclasses. Consequently the peptide identified was consistent with the pan-IgG nature of these antibodies. By using PCImdad, a molecular display programme, this sequence was visualised as surface accessible, located in the C(H)2/C(H)3 inter-domain region and proximal to the residue arginine(435). It is speculated that this residue may be important for phenotypic expression of G3m(u) and specificity of these reagents. Pepscan analysis of MAbs G7C and JD312 (both pan-IgG) highlighted the core peptide sequence 290-KPREE-294, which was present in the C(H)2 domain and was common to all four IgG subclasses. PCImdad also showed this region to be highly accessible and was consistent with previous bioinformatic and autoimmune analysis of IgG. Overall these MAbs may serve as useful anti-IgG or anti-G3m(u) reagents and probes of immunoglobulin structure.

Animals↗

PubMed2025

Identification of key genes related to bone metastasis of breast cancer using bioinformatics methods and construction of a prognostic model.

Breast cancer (BC) ranks among the most prevalent cancers in females, with bone metastasis significantly compromising patients' quality of life and survival rates. Enhancing our comprehension of BC bone metastasis mechanisms at the molecular level holds promise for improving BC treatment and prognosis. Leveraging bioinformatics tools, we integrated multiple datasets, conducted comprehensive analyses across various databases, identified biomarkers associated with BC bone metastasis, and constructed a prognostic model. Firstly, 3 BC bone metastasis-related datasets were downloaded from gene expression omnibus, the data were merged, and batch effects were removed, followed by identification of differentially expressed genes (DEGs). Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the DEGs. A protein-protein interaction network was constructed using the STRING database to screen hub genes. Then, survival analysis of hub genes was performed using the Cancer Genome Atlas (TCGA) database. A prognostic model was constructed using key genes with survival differences, and the model was evaluated. Two hundred ninety-two DEGs were identified. Gene ontology and KEGG pathway enrichment analysis yielded 769 biological processes (BPs), 78 cellular components, 43 molecular functions, and 50 KEGG pathways. Fifteen hub genes were selected from the protein-protein interaction network. Survival analysis revealed 6 genes related to BC survival. The prognostic model identified 4 genes with important predictive value for BC prognosis. Our study utilized bioinformatics analysis to identify a series of DEGs related to BC bone metastasis. Based on further selection of hub genes, we constructed a relatively ideal prognostic model for BC, and identified 4 genes (DLGAP5, TPX2, PLK1, and CENPN) with valuable predictive value for BC prognosis.

Humans↗

PubMed2006

Frequency and dynamics of recombination within different species of human enteroviruses.

Enteroviruses are members of the family Picornaviridae that cause widespread infections in human and other mammalian populations. Enteroviruses are genetically and antigenically highly variable, and recombination within and between serotypes contributes to their genetic diversity. To investigate the dynamics of the recombination process, sequence phylogenies between three regions of the genome (VP4, VP1, and 3Dpol) were compared among species A and B enterovirus variants detected in a human population-based survey in Scotland between 2000 and 2001, along with contemporary virus isolates collected in the same geographical region. This analysis used novel bioinformatic methods to quantify phylogenetic compatibility and correlations with serotype assignments of evolutionary trees constructed for different regions of the enterovirus genome. Species B enteroviruses showed much more frequent, time-correlated recombination events than those found for species A, despite the equivalence in population sampling, concordant with a linkage analysis of previously characterized enterovirus sequences obtained over longer collection periods. An analysis of recombination among complete genome sequences by computation of a phylogenetic compatibility matrix (PCM) demonstrated sharply defined boundaries between the VP2/VP3/VP1 block and sequences to either side in phylogenetic compatibility. The PCM also revealed equivalent or frequently greater degrees of incompatibility between different parts within the nonstructural region (2A-3D), indicating the occurrence of extensive recombination events in the past evolution of this part of the genome. Together, these findings provide new insights into the dynamics of species A and B enterovirus recombination and evolution and into the contribution of structured sampling to documenting reservoirs, emergence, and spread of novel recombinant forms in human populations.

Capsid Proteins↗

PubMed2006

Biochemical characterization of Trypanosoma brucei RNA polymerase II.

In Trypanosoma brucei, transcription by RNA polymerase II accounts for the expression of the spliced leader (SL) RNA and most protein coding mRNAs. To understand the regulation of RNA polymerase II transcription in these parasites, we have purified a transcriptionally active enzyme through affinity chromatography of its essential subunit, RPB4. The enzyme preparation is active in both promoter-independent and promoter-dependent in vitro transcription assays. Importantly, the enzyme is sensitive to alpha-amanitin inhibition, a hallmark of eukaryotic RNA polymerase II enzymes. Using mass spectrometric analysis we have identified the previously unobserved RPB12 subunit of T. brucei RNA polymerase II. TbRPB12 contains a conserved CX(2)CX(10-15)CX(2)C zinc binding motif that is characteristic of other eukaryotic RPB12 polypeptides. We also identified seven proteins that associate with T. brucei RNA polymerase II. While both bioinformatics and biochemical analysis have focused on the subunit structure of trypanosome RNA polymerases, this is the first study that reveals a functional RNA polymerase II enzyme.

Amanitins↗

PubMed2004

Molecular signature of late-stage human ALS revealed by expression profiling of postmortem spinal cord gray matter.

Little is known about global gene expression patterns in the human neurodegenerative disease amyotrophic lateral sclerosis (ALS). To address this, we used high-density oligonucleotide microarray technology to compare expression levels of approximately 6,800 genes in postmortem spinal cord gray matter obtained from individuals with ALS as well as normal individuals. Using Fisher discriminant analysis (FDA) and leave-one-out cross-validation (LOOCV), we discerned an ALS-specific signature. Moreover, it was possible to distinguish familial ALS (FALS) from sporadic ALS (SALS) gene expression profiles. Characterization of the specific genes significantly altered in ALS uncovered a pro-inflammatory terminal state. Moreover, we found alterations in genes involved in mitochondrial function, oxidative stress, excitotoxicity, apoptosis, cytoskeletal architecture, RNA transcription and translation, proteasomal function, and growth and signaling. It is apparent from this study that DNA microarray analysis and appropriate bioinformatics can reveal distinct phenotypic changes that underlie the terminal stages of neurodegeneration in ALS.

Amyotrophic Lateral Sclerosis↗

PubMed2006

Cloning microRNAs from mammalian tissues.

MicroRNAs (miRNAs) are ubiquitous regulators of gene expression in plants and animals. Their distinctive structure, as very short RNAs with a 5'-phosphate and 3'-hydroxyl group, has enabled the development of protocols to clone miRNAs. After enrichment of these small molecules by size, serial ligation of adapter oligonucleotides to each terminus allows amplification using reverse transcription (RT)-polymerase chain reaction (PCR). Plasmid cloning of multiple miRNA sequences and subsequent DNA sequence analysis enable both bioinformatic characterization of the various miRNAs and experimental validation of their accumulation in cells.

Animals↗

PubMed2006

Biochemical, molecular characterization, and glycoproteomic analyses of alpha(1)-proteinase inhibitor products used for replacement therapy.

BACKGROUND: Isoelectric focusing (IEF) of alpha(1)-proteinase inhibitor (A1PI) shows that commercial products and plasma have different glycoisoform band patterns. Those in Aralast (Grifols Biologicals) reflect an anodal shift of glycoisoforms, which has caused concern. The protein, including glycoproteomic analyses, and structural features of A1PI products were investigated by state-of-the-art techniques. STUDY DESIGN AND METHODS: Batches from Aralast, Prolastin (Bayer), and Zemaira (Aventis Behring LLC) were analyzed by high-resolution IEF and high-performance size-exclusion chromatography (HP-SEC). Preparative separated isoforms from IEF were further purified by chromatography and subjected to mass spectrometry for sequence analyses, peptide mapping, and glycosylation analysis. Deamidation was quantified by enzymatic isoaspartate detection. Multiple sequence alignments and structural bioinformatics analyses were performed. RESULTS: In HP-SEC, Prolastin had the highest aggregate content at approximately 30 percent. Isoforms from all products purified by high-resolution IEF were sequenced with an amino acid coverage of more than 98 percent. Deamidation of Asn116 and Asn314 in A1PI was to found to some extent in all products and confirmed quantitatively by enzymatic analysis. There were no signs of methionine oxidation. Cys232 was found to be cysteinylated in A1PI in Prolastin and Aralast as in plasma, but not in Zemaira. All products showed truncation of the C-terminal lysine. Intact A1PI concentrates contained mainly diantennary, disialylated and smaller amounts of triantennary, trisialylated N-glycans. The percentage of fucosylation was similar in all products. Site-specific glycan analysis revealed bands M6 contained only diantennary glycans, whereas the more acidic bands M4 and M2 also carried triantennary structures. The most acidic isoforms, M2 in Prolastin and Zemaira and M0 in Aralast, additionally exhibited tetraantennary N-glycans. CONCLUSION: Protein chemical characterization of A1PI showed that all A1PI products to some extent differ from A1PI circulating in human plasma. Bioinformatic analysis indicated that removal of C-terminal Lys394 and cysteinylation of Cys232 are unlikely to affect structure and/or function of A1PI but cysteinylation may influence interaction between A1PI and its physiologic ligands. Aralast, Prolastin, and Zemaira contain the same set of N-glycans in the same ratios as those in normal human plasma A1PI. Tri- and tetraantennary structures are responsible for the partitioning into IEF isoforms, with the migration shift of Aralast not being due to any difference in the N-glycosylation, but to the partial loss of the C-terminal lysine.

Chromatography, High Pressure Liquid↗

PubMed2006

Differentiation of normal skin and melanoma using high resolution hyperspectral imaging.

We investigated the use of high resolution hyperspectral imaging microscopy to detect abnormalities in skin tissue using hematoxylin eosin stained preparations of normal and abnormal skin, benign nevi and melanomas. A goal of this study was to provide objective data that could be utilized by any researcher; and form the beginnings of a reference spectral data base. All spectral characterizations were acquired in percent transmission, and absorption, with contiguous wavelength acquisition between 400 and 800 nm; and a spectral resolution of approximately 1 nm. Biopsy sections were characterized with varying sample thickness, staining and magnification in order to determine their impact on spectral characterizations. Spectra were classified using spectral waveform cross correlation analysis, an algorithm that is linearity invariant. Classified spectra were incorporated into spectral libraries; and all spectra acquired from the field of view were correlated with library spectra to a quantified, user determined, confidence threshold (minimum correlation coefficient). The results revealed that all skin conditions in our initial data sets could be objectively differentiated providing that staining and section thickness was controlled. We also demonstrated that it is likely that a reference spectral library database could be created to include bioinformatics and cluster analysis. This would assist multiple laboratories to participate in the input and retrieval of target spectral information.

Biopsy↗

PubMed2001

A new network-based biologic database system.

Bioinformatics is playing an increasingly important role in the processing and analysis of biomedical data. The collection, storage and analysis of biologic information are key components of bioinformatics. In the genome era, with the explosion of sequence and structural information available to researchers, the structure of biologic databases is becoming more complicated, the contents is becoming larger, and the management and development is also becoming more difficult. A network-based biologic database has been developed. This database system integrates administration, development and analysis of bioinformatics with common and friendly interface. For the life scientists without much computer programming expertise, the system is easy to master.

Computational Biology↗

PubMed2005

Molecular processes during fat cell development revealed by gene expression profiling and functional annotation.

BACKGROUND: Large-scale transcription profiling of cell models and model organisms can identify novel molecular components involved in fat cell development. Detailed characterization of the sequences of identified gene products has not been done and global mechanisms have not been investigated. We evaluated the extent to which molecular processes can be revealed by expression profiling and functional annotation of genes that are differentially expressed during fat cell development. RESULTS: Mouse microarrays with more than 27,000 elements were developed, and transcriptional profiles of 3T3-L1 cells (pre-adipocyte cells) were monitored during differentiation. In total, 780 differentially expressed expressed sequence tags (ESTs) were subjected to in-depth bioinformatics analyses. The analysis of 3'-untranslated region sequences from 395 ESTs showed that 71% of the differentially expressed genes could be regulated by microRNAs. A molecular atlas of fat cell development was then constructed by de novo functional annotation on a sequence segment/domain-wise basis of 659 protein sequences, and subsequent mapping onto known pathways, possible cellular roles, and subcellular localizations. Key enzymes in 27 out of 36 investigated metabolic pathways were regulated at the transcriptional level, typically at the rate-limiting steps in these pathways. Also, coexpressed genes rarely shared consensus transcription-factor binding sites, and were typically not clustered in adjacent chromosomal regions, but were instead widely dispersed throughout the genome. CONCLUSIONS: Large-scale transcription profiling in conjunction with sophisticated bioinformatics analyses can provide not only a list of novel players in a particular setting but also a global view on biological processes and molecular networks.

3T3-L1 Cells↗

PubMed2005

Bioinformatics approaches for cross-species liver cancer analysis based on microarray gene expression profiling.

BACKGROUND: The completion of the sequencing of human, mouse and rat genomes and knowledge of cross-species gene homologies enables studies of differential gene expression in animal models. These types of studies have the potential to greatly enhance our understanding of diseases such as liver cancer in humans. Genes co-expressed across multiple species are most likely to have conserved functions. We have used various bioinformatics approaches to examine microarray expression profiles from liver neoplasms that arise in albumin-SV40 transgenic rats to elucidate genes, chromosome aberrations and pathways that might be associated with human liver cancer. RESULTS: In this study, we first identified 2223 differentially expressed genes by comparing gene expression profiles for two control, two adenoma and two carcinoma samples using an F-test. These genes were subsequently mapped to the rat chromosomes using a novel visualization tool, the Chromosome Plot. Using the same plot, we further mapped the significant genes to orthologous chromosomal locations in human and mouse. Many genes expressed in rat 1q that are amplified in rat liver cancer map to the human chromosomes 10, 11 and 19 and to the mouse chromosomes 7, 17 and 19, which have been implicated in studies of human and mouse liver cancer. Using Comparative Genomics Microarray Analysis (CGMA), we identified regions of potential aberrations in human. Lastly, a pathway analysis was conducted to predict altered human pathways based on statistical analysis and extrapolation from the rat data. All of the identified pathways have been known to be important in the etiology of human liver cancer, including cell cycle control, cell growth and differentiation, apoptosis, transcriptional regulation, and protein metabolism. CONCLUSION: The study demonstrates that the hepatic gene expression profiles from the albumin-SV40 transgenic rat model revealed genes, pathways and chromosome alterations consistent with experimental and clinical research in human liver cancer. The bioinformatics tools presented in this paper are essential for cross species extrapolation and mapping of microarray data, its analysis and interpretation.

Animals↗