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PubMed2004

Molecular biological analysis of genotyping and phylogeny of severe acute respiratory syndrome associated coronavirus.

BACKGROUND: SARS-CoV is the causative agent of severe acute respiratory syndrome (SARS) which has been associated with outbreaks of SARS in Guangdong, Hong Kong and Beijing of China, and other regions worldwide. SARS-CoV from human has shown some variations but its origin is still unknown. The genotyping and phylogeny of SARS-CoV were analyzed and reported in this paper. METHODS: Full or partial genomes of 44 SARS-CoV strains were collected from GenBank. The genotype, single nucleotide polymorphism and phylogeny of these SARS-CoV strains were analyzed by molecular biological, bioinformatic and epidemiological methods. RESULTS: There were 188 point mutations in the 33 virus full genomes with the counts of mutation mounting to 297. Further analysis was carried out among 36 of 188 loci with more than two times of mutation. All the 36 mutation loci occurred in coding sequences and 22 loci were non-synonymous. The gene mutation rates of replicase 1AB, S2 domain of spike glycoprotein and nucleocapsid protein were lower (0.079% - 0.103%). There were 4 mutation loci in S1 domain of spike glycoprotein. The gene mutation rate of ORF10 was the highest (3.333%) with 4 mutation loci in this small domain (120 bp) and 3 of 4 loci related to deletion mutation. By bioinformatics processing and analysis, the nucleotides at 7 loci of genome (T:T:A:G:T:C:T/C:G:G:A:C:T:C) can classify SARS-CoV into two types. Therefore a novel definition is put forward that according to these 7 loci of mutation, 40 strains of SARS-CoV in GenBank can be grouped into two genotypes, T:T:A:G:T:C:T and C:G:G:A:C:T:C, and named as SARS-CoV Yexin genotype and Xiaohong genotype. The two genotypes can be further divided into some sub-genotypes. These genotypes can also be approved by phylogenetic tree of three levels of 44 loci of mutation, spike glycoprotein gene and complete genome sequence. Compared to various strains among SARS-CoV Yexin genotype and Xiaohong genotype, GD01 strain of Yexin genotype is more closely related to SARS-CoV like-virus from animals. CONCLUSION: The results mentioned above suggest that SARS-CoV is responding to host immunological pressures and experiencing variation which provide clues, information and evidence of molecular biology for the clinical pathology, vaccine developing and epidemic investigation.

Evolution, Molecular↗

PubMed2003

Structural analysis of the PsbQ protein of photosystem II by Fourier transform infrared and circular dichroic spectroscopy and by bioinformatic methods.

The structure of PsbQ, one of the three main extrinsic proteins associated with the oxygen-evolving complex (OEC) of higher plants and green algae, is examined by Fourier transform infrared (FTIR) and circular dichroic (CD) spectroscopy and by computational structural prediction methods. This protein, together with two other lumenally bound extrinsic proteins, PsbO and PsbP, is essential for the stability and full activity of the OEC in plants. The FTIR spectra obtained in both H(2)O and D(2)O suggest a mainly alpha-helix structure on the basis of the relative areas of the constituents of the amide I and I' bands. The FTIR quantitative analyses indicate that PsbQ contains about 53% alpha-helix, 7% turns, 14% nonordered structure, and 24% beta-strand plus other beta-type extended structures. CD analyses indicate that PsbQ is a mainly alpha-helix protein (about 64%), presenting a small percentage assigned to beta-strand ( approximately 7%) and a larger amount assigned to turns and nonregular structures ( approximately 29%). Independent of the spectroscopic analyses, computational methods for protein structure prediction of PsbQ were utilized. First, a multiple alignment of 12 sequences of PsbQ was obtained after an extensive search in the public databases for protein and EST sequences. Based on this alignment, computational prediction of the secondary structure and the solvent accessibility suggest the presence of two different structural domains in PsbQ: a major C-terminal domain containing four alpha-helices and a minor N-terminal domain with a poorly defined secondary structure enriched in proline and glycine residues. The search for PsbQ analogues by fold recognition methods, not based on the secondary structure, also indicates that PsbQ is a four alpha-helix protein, most probably folding as an up-down bundle. The results obtained by both the spectroscopic and computational methods are in agreement, all indicating that PsbQ is mainly an alpha protein, and show the value of using both methodologies for protein structure investigation.

Amides↗

PubMed2005

Searching for hypothetical proteins: theory and practice based upon original data and literature.

A large part of mammalian proteomes is represented by hypothetical proteins (HP), i.e. proteins predicted from nucleic acid sequences only and protein sequences with unknown function. Databases are far from being complete and errors are expected. The legion of HP is awaiting experiments to show their existence at the protein level and subsequent bioinformatic handling in order to assign proteins a tentative function is mandatory. Two-dimensional gel-electrophoresis with subsequent mass spectrometrical identification of protein spots is an appropriate tool to search for HP in the high-throughput mode. Spots are identified by MS or by MS/MS measurements (MALDI-TOF, MALDI-TOF-TOF) and subsequent software as e.g. Mascot or ProFound. In many cases proteins can thus be unambiguously identified and characterised; if this is not the case, de novo sequencing or Q-TOF analysis is warranted. If the protein is not identified, the sequence is being sent to databases for BLAST searches to determine identities/similarities or homologies to known proteins. If no significant identity to known structures is observed, the protein sequence is examined for the presence of functional domains (databases PROSITE, PRINTS, InterPro, ProDom, Pfam and SMART), subjected to searches for motifs (ELM) and finally protein-protein interaction databases (InterWeaver, STRING) are consulted or predictions from conformations are performed. We here provide information about hypothetical proteins in terms of protein chemical analysis, independent of antibody availability and specificity and bioinformatic handling to contribute to the extension/completion of protein databases and include original work on HP in the brain to illustrate the processes of HP identification and functional assignment.

Amino Acid Sequence↗

PubMed2005

The peptidomics concept.

Peptides are a paramount example of how nature diversifies from one single gene to release multiple, regulated functionalities at the desired sites and time. To achieve this, peptides are sequentially generated by a complex network of more than 500 proteases, acting at intracellular sites, upon secretion, in extracellular environments, and, finally, serving (regulated) degradation. This cycle of maturation, activation, and degradation points out that the peptidome is mechanistically linked to the proteome: the distribution between both is regulated by proteases and counter-regulated by protease inhibitors. Given the high diversity of peptides in living systems and their involvement in key regulatory processes, a need for improved peptide discovery, ideally combining peptide sequence identification with peptide profiling, has emerged. Standard proteomic approaches are not suitable for a systematic peptide analysis, since they do not cover the low molecular mass window. The new direction in proteomic research to analyse this "terra incognita" is peptidomics. This novel concept aims at the comprehensive visualization and analysis of small polypeptides, thus covering the mass range between proteomics and metabonomics. The pacemakers for the development of peptidomics technologies are modern mass spectrometry and bioinformatics. They are ideally suited for sensitive and comprehensive peptide analysis, especially in combination with the massive information content of today's genomic and transcriptomic databases. Given the high diversity of native peptides in living systems, clinical chemistry and modern medicine are the prime application areas. The discovery of relevant peptide biomarkers and drug targets will strongly benefit from peptidomics.

Animals↗

PubMed2005

Comparative proteome analysis of secretory proteins from pathogenic and nonpathogenic Listeria species.

Extracellular proteins of bacterial pathogens play a crucial role in the infection of the host. Here we present the first comprehensive validation of the secretory subproteome of the Gram positive pathogen Listeria monocytogenes using predictive bioinformatic and experimental proteomic approaches. The previous original signal peptide (SP) prediction (Glaser et al., Science 2001, 294, 849-852) has been greatly improved by an in-depth analysis using seven different bioinformatic tools. Subsequent careful classification of the resulting data gives a probability dependent annotation of 121 putatively secreted proteins of which 45 are novel. Complementary proteomic analysis using both two-dimensional gel electrophoresis/matrix assisted laser desorption/ionization mass spectrometry and high performance liquid chromatography/electrospray ionization-mass spectrometry has identified 105 proteins in the culture supernatant of L. monocytogenes. Among these, we were able to detect all the currently known virulence factors with an SP showing the importance of this subproteome and demonstrating the reliability of the techniques used. The comparison between the L. monocytogenes wildtype and the nonpathogenic species Listeria innocua was performed to reveal proteins probably involved in pathogenicity and/or the adaptation to their respective lifestyles. In addition to the eight known virulence factors, all of which have no orthologous genes in L. innocua, eight additional proteins have been identified that exhibit the typical key feature defining the known listerial virulence factors. Further significant differences between the two species are evident in the group of cell wall and secretory proteins that warrant further study. Our investigation clearly demonstrates that the major difference between the pathogenic and nonpathogenic species, noted in the comparative genome analysis, manifests itself strongest in the secretome.

Bacterial Proteins↗

PubMed2004

[Screening hepatitis B virus X-interactive proteins by yeast two-hybrid system].

BACKGROUND & OBJECTIVE: Hepatitis B virus-encoded X protein is a promiscuous transactivator and contributes to the development of hepatocellular carcinoma. Protein-protein interaction seems to be crucial for HBx transactivation. The aim of this study was to screen and identify the proteins which interact with hepatitis B virus (HBV) X protein by yeast two-hybrid system. METHODS: HBV X gene was amplified by polymerase chain reaction (PCR). HBV X bait plasmid, named pAS2-1-X, was constructed by yeast-two hybridization system 3 and verified by sequencing. pAS2-1-X was transformed into the yeast AH109, and X-BD fusion protein expressed in the yeast cells was confirmed by Western blot analysis. Yeast cells cotransformed with pAS2-1-X and normal human liver cDNA library were cultured in selective SC/-trp-leu-his-ade medium. The second screening was performed with beta-gal activity detection. The false positive clones were eliminated by segregation analysis and mating experiment. The real positive clones were amplified, sequenced, and analyzed with bioinformatics. RESULTS: Bait plasmid pAS2-1-X was successfully constructed. The result of Western blot analysis confirmed that pAS2-1-X correctly expressed X-BD fusion protein in the transformed yeast AH109. Ninety-seven clones grew in the selective SC/-trp-leu-his-ade medium; however, only one clone past through the beta-gal activity detection, segregation analysis, and mating experiment. The inserted cDNA fragment of positive clone showed high homology with Fis gene. CONCLUSION: Fis protein is a novel protein which can interact with X protein in vivo by yeast two-hybrid system.

Gene Library↗

PubMed2006

springScape: visualisation of microarray and contextual bioinformatic data using spring embedding and an 'information landscape'.

The interpretation of microarray and other high-throughput data is highly dependent on the biological context of experiments. However, standard analysis packages are poor at simultaneously presenting both the array and related bioinformatic data. We have addressed this challenge by developing a system springScape based on 'spring embedding' and an 'information landscape' allowing several related data sources to be dynamically combined while highlighting one particular feature. Each data source is represented as a network of nodes connected by weighted edges. The networks are combined and embedded in the 2-D plane by spring embedding such that nodes with a high similarity are drawn close together. Complex relationships can be discovered by varying the weight of each data source and observing the dynamic response of the spring network. By modifying Procrustes analysis, we find that the visualizations have an acceptable degree of reproducibility. The 'information landscape' highlights one particular data source, displaying it as a smooth surface whose height is proportional to both the information being viewed and the density of nodes. The algorithm is demonstrated using several microarray data sets in combination with protein-protein interaction data and GO annotations. Among the features revealed are the spatio-temporal profile of gene expression and the identification of GO terms correlated with gene expression and protein interactions. The power of this combined display lies in its interactive feedback and exploitation of human visual pattern recognition. Overall, springScape shows promise as a tool for the interpretation of microarray data in the context of relevant bioinformatic information.

Algorithms↗

PubMed2006

The family of DOF transcription factors: from green unicellular algae to vascular plants.

This article deals with the origin and evolution of the DOF transcription factor family through a phylogenetic analysis of those DOF sequences identified from a variety of representative organisms from different taxonomic groups: the green unicellular alga Chlamydomonas reinhardtii, the moss Physcomitrella patens, the fern Selaginella moellendorffii, the gymnosperm Pinus taeda, the dicotyledoneous Arabidopsis thaliana and the monocotyledoneous angiosperms Oryza sativa and Hordeum vulgare. In barley, we have identified 26 different DOF genes by sequence analyses of clones isolated from the screening of genomic libraries and of ESTs, whereas a single DOF gene was identified by bioinformatics searches in the Chlamydomonas genome. The phylogenetic analysis groups all these genes into six major clusters of orthologs originated from a primary basal grade. Our results suggest that duplications of an ancestral DOF, probably formed in the photosynthetic eukaryotic ancestor, followed by subsequent neo-, sub-functionalization and pseudogenization processes would have triggered the expansion of the DOF family. Loss, acquisition and shuffling of conserved motifs among the new DOFs likely underlie the mechanism of formation of the distinct subfamilies.

Algal Proteins↗

PubMed2006

Data standards for flow cytometry.

Flow cytometry (FCM) is an analytical tool widely used for cancer and HIV/AIDS research, and treatment, stem cell manipulation and detecting microorganisms in environmental samples. Current data standards do not capture the full scope of FCM experiments and there is a demand for software tools that can assist in the exploration and analysis of large FCM datasets. We are implementing a standardized approach to capturing, analyzing, and disseminating FCM data that will facilitate both more complex analyses and analysis of datasets that could not previously be efficiently studied. Initial work has focused on developing a community-based guideline for recording and reporting the details of FCM experiments. Open source software tools that implement this standard are being created, with an emphasis on facilitating reproducible and extensible data analyses. As well, tools for electronic collaboration will assist the integrated access and comprehension of experiments to empower users to collaborate on FCM analyses. This coordinated, joint development of bioinformatics standards and software tools for FCM data analysis has the potential to greatly facilitate both basic and clinical research--impacting a notably diverse range of medical and environmental research areas.

Cell Separation↗

PubMed2007

Sequence handling by sequence analysis toolbox v1.0.

The fact that mass spectrometry have become a high-throughput method calls for bioinformatic tools for automated sequence handling and prediction. For efficient use of bioinformatic tools, it is important that these tools are integrated or interfaced with each other. The purpose of sequence analysis toolbox v1.0 was to have a general purpose sequence analyzing tool that can import sequences obtained by high-throughput sequencing methods. The program includes algorithms for calculation or prediction of isoelectric point, hydropathicity index, transmembrane segments, and glycosylphosphatidyl inositol-anchored proteins.

Computational Biology↗

PubMed2026

IL1RAP Is Associated With an Inflammation-Immunity-Related State in Skin Cutaneous Melanoma: Integrative Evidence From Pan-Cancer Data and Melanoma Immunotherapy Cohorts.

BACKGROUND: The crosstalk between inflammation and immunity plays a central role in tumor progression, immune evasion, and therapeutic response. Interleukin-1 receptor accessory protein (IL1RAP) is a key adaptor in inflammatory signaling, yet its immunological relevance and clinical implications in skin cutaneous melanoma (SKCM) remain largely unexplored. METHODS: We performed an integrative analysis combining pan-cancer and melanoma-focused datasets. Bulk transcriptomic, single-cell, spatial transcriptomic, genomic alteration, pharmacogenomic, and clinical survival data were obtained from TCGA, GTEx, GEO, ENA, and other public resources. IL1RAP expression was evaluated across cancer types in relation to diagnostic performance, immune subtypes, survival outcomes, functional pathway activity, immune-genomic states, somatic alterations, and drug-response metrics. Melanoma-focused analyses examined immune infiltration, methylation-derived tumor-infiltrating lymphocyte (MeTIL) scores, and exploratory survival associations in five treatment cohorts; the survival groups were defined using cohort-specific optimal cutoffs rather than median splits. RESULTS: IL1RAP expression differed between tumor and normal tissues in multiple cancers, although the direction and magnitude varied by cancer type. Pan-cancer survival associations were likewise context dependent. Single-cell and spatial transcriptomic resources indicated cell-type and spatial heterogeneity of IL1RAP expression within tumor microenvironments. Pathway, immune-genomic, and pharmacogenomic analyses identified exploratory associations with functional states, genomic features, and drug-response metrics. In SKCM, IL1RAP expression was associated with several immune-infiltration estimates and higher MeTIL scores. Across five melanoma immunotherapy cohorts, the direction and magnitude of the overall survival associations varied substantially. CONCLUSIONS: This retrospective integrative analysis suggests that IL1RAP may mark an inflammation-immunity-related state in SKCM. The heterogeneous associations across cancers and melanoma treatment cohorts support further validation but do not establish IL1RAP as a causal regulator, a treatment-response predictor, or a therapeutic target.

IL1RAP↗

PubMed2005

Application of phylogenetic algorithms to assess Rab functional relationships.

Researchers looking to solve biological problems have access to enormous amounts of sequence information and the desktop computational infrastructure to personally interrogate and analyze large datasets. Many powerful bioinformatics tools are available online; however, this discourages the customized analysis of data that is necessary for the experimental scientist to make maximally effective use of the information. In addition, a customized environment facilitates the critical evaluation of bioinformatic methods. This chapter presents a protocol developed to aid in classification of subfamilies and subclasses of a superfamily using the personal desktop computer. The visual representation of the qualitative and quantitative results of data analyses is also considered. The examples are focused on Rab GTPases but are more widely applicable to the classification of any given protein family.

Algorithms↗

PubMed2006

A Web-based assessment of bioinformatics end-user support services at US universities.

OBJECTIVES: This study was conducted to gauge the availability of bioinformatics end-user support services at US universities and to identify the providers of those services. The study primarily focused on the availability of short-term workshops that introduce users to molecular biology databases and analysis software. METHODS: Websites of selected US universities were reviewed to determine if bioinformatics educational workshops were offered, and, if so, what organizational units in the universities provided them. RESULTS: Of 239 reviewed universities, 72 (30%) offered bioinformatics educational workshops. These workshops were located at libraries (N = 15), bioinformatics centers (N = 38), or other facilities (N = 35). No such training was noted on the sites of 167 universities (70%). Of the 115 bioinformatics centers identified, two-thirds did not offer workshops. CONCLUSIONS: This analysis of university Websites indicates that a gap may exist in the availability of workshops and related training to assist researchers in the use of bioinformatics resources, representing a potential opportunity for libraries and other facilities to provide training and assistance for this growing user group.

Computational Biology↗

PubMedDate not supplied

Clinical applications of DNA microarray analysis.

Microarray technology provides a revolutionary macro-genetic and bioinformatic-rich platform for understanding human diseases. DNA microarrays facilitate the study of complex diseases, enabling several observations simultaneously that can become foundations for newer hypotheses-shifting us towards a non-reductionist approach to biological phenomenon. This appears of particular value for scientific and clinical dissection of tumor pathologies. Despite the tremendous potential presented by microarray technology for the investigation of disease, concrete insights and advances that translate to the clinical setting are only recently beginning to be tapped. Here, we discuss specific examples of how microarray technology is being integrated into our ever-evolving approach to clinical disease. We focus on molecular strategies for (a) disease classification, (b) disease outcome, and (c) disease mechanisms.

Gene Expression Profiling↗

PubMed2005

Comparison for functional aberration of G-6-PD deficiency variants with exon 10 mutations.

Glucose-6-phosphate dehydrogenase (G-6-PD) deficiency is a common inherited enzyme deficiency in many parts of the world and there are many different variants described. Every G-6-PD deficiency variant has a unique underlying genetic defect, therefore it manifests specific properties. The single amino acid substitution in the globin chain is the commonest form of G-6-PD deficiency variant. Usually, the G-6-PD deficiency variant with the pathogenesis of a single amino acid substitution presents with only one aberration in secondary structure. Although many G-6-PD deficiency variants present similar structural abnormal points their functions sometimes are discordant. Here, the author performed a functional analysis on some alpha haemoglobinopathies using a novel bioinformatic tool, Polyphen. The mutations of five G-6-PD deficiency variants with exon 10 mutations, Guadalajara (386 Arg-->Cys), Beverly Hills (387 Arg-->His), Serres (361 Ala-->Val), Iowa (385 Lys-->Glu), and Clinic (405 Met-->Ile) were selected for further study in this investigation. According to the in silico mutation study, the functional change in the G-6-PD deficiency variants with exon 10 mutations studied is variable. Here, it indicates that the functional aberration in the G-6-PD deficiency variant is based on complex pathogenesis. The identification of the structural aberration only in a G-6-PD deficiency variant is not sufficient and should be supplemented with a further functional analysis for a better insight in this topic.

Amino Acid Substitution↗

PubMed2005

Gene expression differences in mice divergently selected for methamphetamine sensitivity.

In an effort to identify genes that may be important for drug-abuse liability, we mapped behavioral quantitative trait loci (bQTL) for sensitivity to the locomotor stimulant effect of methamphetamine (MA) using two mouse lines that were selectively bred for high MA-induced activity (HMACT) or low MA-induced activity (LMACT). We then examined gene expression differences between these lines in the nucleus accumbens, using 20 U74Av2 Affymetrix microarrays and quantitative polymerase chain reaction (qPCR). Expression differences were detected for several genes, including Casein Kinase 1 Epsilon (Csnkle), glutamate receptor, ionotropic, AMPA1 (GluR1), GABA B1 receptor (Gabbr1), and dopamine- and cAMP-regulated phosphoprotein of 32 kDa (Darpp-32). We used the www.WebQTL.org database to identify QTL that regulate the expression of the genes identified by the microarrays (expression QTL; eQTL). This approach identified an eQTL for Csnkle on Chromosome 15 (LOD = 3.8) that comapped with a bQTL for the MA stimulation phenotype (LOD = 4.5), suggesting that a single allele may cause both traits. The chromosomal region containing this QTL has previously been associated with sensitivity to the stimulant effects of cocaine. These results suggest that selection was associated with (and likely caused) altered gene expression that is partially attributable to different frequencies of gene expression polymorphisms. Combining classical genetics with analysis of whole-genome gene expression and bioinformatic resources provides a powerful method for provisionally identifying genes that influence complex traits. The identified genes provide excellent candidates for future hypothesis-driven studies, translational genetic studies, and pharmacological interventions.

Animals↗

PubMed2004

New approaches for in silico identification of cytokine-modified beta cell gene networks.

Beta cell dysfunction and death in type 1 diabetes mellitus (T1DM) is caused by direct contact with activated macrophages and T lymphocytes and by exposure to soluble mediators secreted by these cells, such as cytokines and nitric oxide. Cytokine-induced apoptosis depends on the expression of pro- and anti-apoptotic genes that remain to be characterized. Using microarray analyses, we identified several transcription factor and "effector" gene networks regulated by interleukin-1beta and/or interferon-gamma in beta cells. This suggests that beta cell fate following exposure to cytokines is a complex and highly regulated process, depending on the duration and severity of perturbation of key gene networks. In order to draw correct conclusions from these massive amounts of data, we need to utilize novel bioinformatics and statistical tools. Thus, we are presently performing in silico analysis for the localization of binding sites for the transcription factor NF-kappaB (previously shown to be pivotal for beta cell apoptosis) in 15 temporally related gene clusters, identified by time-course microarray analysis. In silico analysis is based on a broad range of computational techniques used to detect motifs in a DNA sequence corresponding to the binding site of a transcription factor. These computer-based findings must be validated by use of positive and negative controls, and by "ChIP on chip" analysis. Moreover, new statistical approaches are required to decrease false positive findings. These novel approaches will constitute a "proof of principle" for the integrated use of bioinformatics and functional genomics in the characterization of relevant cytokine-regulated beta cell gene networks leading to beta cell apoptosis in T1DM.

Animals↗

PubMed2005

Specific loss of connexin 26 expression in ductal sweat gland epithelium associated with the deletion mutation del(GJB6-D13S1830).

A whole array of cutaneous syndromes is associated with distinct dominant mutations in GJB2 encoding the gap junction protein connexin 26 (C x 26), including Vohwinkel's syndrome and keratitis-ichthyosis-deafness syndrome. In contrast, recessive GJB2 mutations occur in a large proportion of individuals with hearing loss but no obvious dermatological phenotype. Recently, a large deletion of approximately 342 kb, encompassing the coding region of GJB6 encoding C x 30, but not affecting GJB2, was shown to be associated with hearing loss. From analysis of patient skin, we provide immunohistochemical and bioinformatic data to show that the expression of C x 26 is affected by del(GJB6-D13S1830) in a cell-type-specific manner within the sweat gland. This putative regulatory element of C x 26 expression may be a key factor related to the severe or profound deafness associated with del(GJB6-D13S1830).

Chromosome Deletion↗