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PubMed2005

Identification of hundreds of conserved and nonconserved human microRNAs.

MicroRNAs are noncoding RNAs of approximately 22 nucleotides that suppress translation of target genes by binding to their mRNA and thus have a central role in gene regulation in health and disease. To date, 222 human microRNAs have been identified, 86 by random cloning and sequencing, 43 by computational approaches and the rest as putative microRNAs homologous to microRNAs in other species. To prove our hypothesis that the total number of microRNAs may be much larger and that several have emerged only in primates, we developed an integrative approach combining bioinformatic predictions with microarray analysis and sequence-directed cloning. Here we report the use of this approach to clone and sequence 89 new human microRNAs (nearly doubling the current number of sequenced human microRNAs), 53 of which are not conserved beyond primates. These findings suggest that the total number of human microRNAs is at least 800.

Base Sequence↗

PubMed2006

Proteomics of acute myeloid leukaemia: Cytogenetic risk groups differ specifically in their proteome, interactome and post-translational protein modifications.

Acute myeloid leukaemia (AML) is characterized by specific cytogenetic aberrations that are strong determinants of prognostic outcome and therapeutic response. Because the pathological outcome of AML patients with cytogenetic abnormalities differs considerably, we hypothesized that their proteome may also differ specifically in their expression pattern, protein interaction pathways and post-translational modifications (PTM). We performed this study using 42 AML patients diagnosed for various cytogenetic abnormalities based on two-dimensional gel electrophoresis and matrix-assisted laser desorption/ionization time of flight mass spectrometry (MS) and MSMS tandem MS. We could identify significant differences in the proteome and PTM of peptides, later confirmed by other methods, between cytogenetic groups. The interactome analysis based on computational bioinformatics reveals major regulating networks: MAPK8 and MYC for complex aberrant karyotype, TP53 for t(8;21), TP53-MYC-PRKAC for 11q23 and JUN and MYC for Inv(16). Further, we analysed 42 MS spectra representative of hnRNPH1, calreticulin and hnRNPA2/B1 in a peak explorer, which reveals a cytogenetic-specific PTM of beta-O-linked N-acetyl glucosamine (O-GlcNAc) of hnRNPH1 in AML patients with 11q23 translocation, an acetylation of calreticulin in t(8;21) translocation and methylation of hnRNPA2/B1 in patients with translocations of t(8;21) and inv(16). This report may lead to a new thinking about AML pathogenesis, as differences at PTM level could be used to distinguish different subtypes of AML.

Cell Shape↗

PubMed2003

Bioinformatics for venom and toxin sciences.

Venomous animals produce a myriad of important pharmacological components. The individual components, or venoms (toxins), are used in ion channel and receptor studies, drug discovery, and formulation of insecticides. The toxin data are scattered across public databases which provide sequence and structural descriptions, but very limited functional annotation. The exponential growth of newly identified toxin data has created a need for better data management. Venominformatics is a systematic bioinformatics approach in which classified, consolidated and cleaned venom data are stored into repositories and integrated with advanced bioinformatics tools for the analysis of structure and function of toxins. Venominformatics complements experimental studies and helps reduce the number of essential experiments.

Animals↗

PubMed2005

RACE: Remote Analysis Computation for gene Expression data.

The Remote Analysis Computation for gene Expression data (RACE) suite is a collection of bioinformatics web tools designed for the analysis of DNA microarray data. RACE performs probe-level data preprocessing, extensive quality checks, data visualization and data normalization for Affymetrix GeneChips. In addition, it offers differential expression analysis on normalized expression levels from any array platform. RACE estimates the false discovery rates of lists of potentially regulated genes and provides a Gene Ontology-term analysis tool for GeneChip data to support the biological interpretation and annotation of results. The analysis is fully automated but can be customized by flexible parameter settings. To offer a convenient starting point for subsequent analyses, and to provide maximum transparency, the R scripts used to generate the results can be downloaded along with the output files. RACE is freely available for use at http://race.unil.ch.

Computational Biology↗

PubMed2006

The predicted secretome of Lactobacillus plantarum WCFS1 sheds light on interactions with its environment.

The predicted extracellular proteins of the bacterium Lactobacillus plantarum were analysed to gain insight into the mechanisms underlying interactions of this bacterium with its environment. Extracellular proteins play important roles in processes ranging from probiotic effects in the gastrointestinal tract to degradation of complex extracellular carbon sources such as those found in plant materials, and they have a primary role in the adaptation of a bacterium to changing environmental conditions. The functional annotation of extracellular proteins was improved using a wide variety of bioinformatics methods, including domain analysis and phylogenetic profiling. At least 12 proteins are predicted to be directly involved in adherence to host components such as collagen and mucin, and about 30 extracellular enzymes, mainly hydrolases and transglycosylases, might play a role in the degradation of substrates by L. plantarum to sustain its growth in different environmental niches. A comprehensive overview of all predicted extracellular proteins, their domains composition and their predicted function is provided through a database at http://www.cmbi.ru.nl/secretome which could serve as a basis for targeted experimental studies into the function of extracellular proteins.

Bacterial Adhesion↗

PubMed2026

Multiomic Integration Reveals Novel miRNA-mRNA-Protein Expression Profile in the Aged Female Retina.

PURPOSE: Aging is a leading risk factor for retinal degeneration. MicroRNAs (miRNAs) regulate posttranscriptional gene suppressors and influence inflammation and oxidative stress, two processes disrupted during retinal aging. This study aimed to identify age-related miRNA-mRNA-protein associations between young and older retinas and uncover dysregulated pathways that may contribute to retinal degeneration. METHODS: Retinal function was assessed using electroretinography (ERG), and microgliosis was quantified by microglial immunohistochemistry (IHC). A multiomics approach was used to examine molecular changes in older (30-month-old) female C57BL/6J mouse retinas and compared with young female (3-month-old) controls. Illumina sequencing profiled short miRNAs (20 bp) and bulk mRNAs (150 bp), while total proteomics via mass spectrometry assessed protein expression. Bioinformatic analyses included targetome analysis (miRNet), pathway enrichment (Gene Ontology), and clustering to identify age-associated molecular targets and pathways. RESULTS: Retinas from older mice displayed neuronal dysfunction and increased microgliosis. Sequencing revealed significant dysregulation of miRNAs linked to immune and inflammatory pathways, supported by enrichment of their predicted mRNA targets. In the older mice, mRNA expression showed broad inflammatory activation, though only 14% of dysregulated mRNAs overlapped with predicted miRNA targets. Proteomic profiling revealed a disconnect between RNA and protein expression, yet all omics layers showed enrichment in inflammatory pathways. Integrated analysis identified associations involving several gene regulatory networks in the older retina. CONCLUSIONS: This study demonstrates that at an advanced age, miRNA expression and their predicted downstream regulatory networks are dysregulated, highlighting potential molecular mechanisms underlying age-related retinal degeneration.

Animals↗

PubMed2006

DWARF--a data warehouse system for analyzing protein families.

BACKGROUND: The emerging field of integrative bioinformatics provides the tools to organize and systematically analyze vast amounts of highly diverse biological data and thus allows to gain a novel understanding of complex biological systems. The data warehouse DWARF applies integrative bioinformatics approaches to the analysis of large protein families. DESCRIPTION: The data warehouse system DWARF integrates data on sequence, structure, and functional annotation for protein fold families. The underlying relational data model consists of three major sections representing entities related to the protein (biochemical function, source organism, classification to homologous families and superfamilies), the protein sequence (position-specific annotation, mutant information), and the protein structure (secondary structure information, superimposed tertiary structure). Tools for extracting, transforming and loading data from public available resources (ExPDB, GenBank, DSSP) are provided to populate the database. The data can be accessed by an interface for searching and browsing, and by analysis tools that operate on annotation, sequence, or structure. We applied DWARF to the family of alpha/beta-hydrolases to host the Lipase Engineering database. Release 2.3 contains 6138 sequences and 167 experimentally determined protein structures, which are assigned to 37 superfamilies 103 homologous families. CONCLUSION: DWARF has been designed for constructing databases of large structurally related protein families and for evaluating their sequence-structure-function relationships by a systematic analysis of sequence, structure and functional annotation. It has been applied to predict biochemical properties from sequence, and serves as a valuable tool for protein engineering.

Amino Acid Sequence↗

PubMed2006

An analysis of mobile genetic elements in three Plasmodium species and their potential impact on the nucleotide composition of the P. falciparum genome.

BACKGROUND: The completed genome sequences of the malaria parasites P. falciparum, P. y. yoelii and P. vivax have revealed some unusual features. P. falciparum contains the most AT rich genome sequenced so far--over 90% in some regions. In comparison, P. y. yoelii is approximately 77% and P. vivax is approximately 55% AT rich. The evolutionary reasons for these findings are unknown. Mobile genetic elements have a considerable impact on genome evolution but a thorough investigation of these elements in Plasmodium has not been undertaken. We therefore performed a comprehensive genome analysis of these elements and their derivatives in the three Plasmodium species. RESULTS: Whole genome analysis was performed using bioinformatic methods. Forty potential protein encoding sequences with features of transposable elements were identified in P. vivax, eight in P. y. yoelii and only six in P. falciparum. Further investigation of the six open reading frames in P. falciparum revealed that only one is potentially an active mobile genetic element. Most of the open reading frames identified in all three species are hypothetical proteins. Some represent annotated host proteins such as the putative telomerase reverse transcriptase genes in P. y. yoelii and P. falciparum. One of the P. vivax open reading frames identified in this study demonstrates similarity to telomerase reverse transcriptase and we conclude it to be the orthologue of this gene. CONCLUSION: There is a divergence in the frequencies of mobile genetic elements in the three Plasmodium species investigated. Despite the limitations of whole genome analytical methods, it is tempting to speculate that mobile genetic elements might have been a driving force behind the compositional bias of the P. falciparum genome.

Amino Acid Sequence↗

PubMed2004

Computer simulations improve university instructional laboratories.

Laboratory classes are commonplace and essential in biology departments but can sometimes be cumbersome, unreliable, and a drain on time and resources. As university intakes increase, pressure on budgets and staff time can often lead to reduction in practical class provision. Frequently, the ability to use laboratory equipment, mix solutions, and manipulate test animals are essential learning outcomes, and "wet" laboratory classes are thus appropriate. In others, however, interpretation and manipulation of the data are the primary learning outcomes, and here, computer-based simulations can provide a cheaper, easier, and less time- and labor-intensive alternative. We report the evaluation of two computer-based simulations of practical exercises: the first in chromosome analysis, the second in bioinformatics. Simulations can provide significant time savings to students (by a factor of four in our first case study) without affecting learning, as measured by performance in assessment. Moreover, under certain circumstances, performance can be improved by the use of simulations (by 7% in our second case study). We concluded that the introduction of these simulations can significantly enhance student learning where consideration of the learning outcomes indicates that it might be appropriate. In addition, they can offer significant benefits to teaching staff.

Biology↗

PubMed2004

Identification of a novel apolipoprotein, ApoN, in ovarian follicular fluid.

A novel apolipoprotein, designated ApoN, has been identified in bovine ovarian follicular fluid using chromatographic purification methods, amino acid sequence analysis, molecular biology, and bioinformatics. The apolipoprotein is a hydrophobic 12-kDa protein processed from the C terminus of a 29-kDa precursor expressed in a number of tissues, including the ovary, testis, the anterior chamber of the eye, skeletal muscle, uterus, and liver. Bovine, porcine, and murine ApoN display significant homology at the amino acid level across the entire precursor sequence. Surprisingly, there appears to be no orthologous protein in the human, although an APON-like pseudogene is found on chromosome 12. The N-terminal fragment of the ApoN precursor shows significant homology with the N-terminal sequence of the precursor of the cholesterol transport regulatory protein ApoF, but the corresponding C-terminal sequences of ApoN and ApoF possess no homology. ApoN is present in the high-density lipoprotein fraction of bovine serum and both the high-density lipoprotein and low-density lipoprotein fractions of bovine follicular fluid and is found in several tissues that are associated with local immunological privilege. These data suggest that ApoN may play a role in steroidogenesis and/or immunoregulation in the gonads of nonhuman species, as well as similar roles in other tissues.

Amino Acid Sequence↗

PubMed2004

Prediction of nuclear hormone receptor response elements.

The nuclear receptor (NR) class of transcription factors controls critical regulatory events in key developmental processes, homeostasis maintenance, and medically important diseases and conditions. Identification of the members of a regulon controlled by a NR could provide an accelerated understanding of development and disease. New bioinformatics methods for the analysis of regulatory sequences are required to address the complex properties associated with known regulatory elements targeted by the receptors because the standard methods for binding site prediction fail to reflect the diverse target site configurations. We have constructed a flexible Hidden Markov Model framework capable of predicting NHR binding sites. The model allows for variable spacing and orientation of half-sites. In a genome-scale analysis enabled by the model, we show that NRs in Fugu rubripes have a significant cross-regulatory potential. The model is implemented in a web interface, freely available for academic researchers, available at http://mordor.cgb.ki.se/NHR-scan.

Algorithms↗

PubMed2005

Identification of Peptide tumor markers in a tumor graft model in immunodeficient mice.

The medical demand for useful biomarkers is large and still increasing. This is especially true for cancer, because for this disease adequate diagnostic markers with high specificity and sensitivity are still lacking. Despite advances in imaging technologies for early detection of cancer, peptidomic multiplex techniques evolved in recent years will provide new opportunities for detection of low molecular weight (LMW) proteome biomarker (peptides) by mass spectrometry. Improvements in peptidomics research were made based on separation of peptides and/or proteins by their physico-chemical properties in combination with mass spectrometric detection, respectively identification, and sophisticated bioinformatic tools for data analysis. To evaluate the potential of serological tumor marker detection by differential peptide display (DPD) we analyzed plasma samples from a tumor graft model. After subcutaneous injection of HCT-116 cells in immunodeficient mice and their growth to a palpable tumor, plasma samples were analyzed by DPD. The comparison of obtained mass spectrometric data allows discovery of tumor specific peptides which fit well into the biological context of cancer pathogenesis and show a strong correlation to tumor growth. The identified peptides indicate events associated with hyper-proliferation and dedifferentiation of cells from an epithelial origin, which are typical characteristics of human carcinomas. We conclude that these findings are a "proof of principle" to detect differentially expressed, tumor-related peptides in plasma of tumor-bearing mice.

Animals↗

PubMed2005

In silico identification of supertypes for class II MHCs.

The development of epitope-based vaccines, which have wide population coverage, is greatly complicated by MHC polymorphism. The grouping of alleles into supertypes, on the basis of common structural and functional features, addresses this problem directly. In the present study we applied a combined bioinformatics approach, based on analysis of both protein sequence and structure, to identify similarities in the peptide binding sites of 2225 human class II MHC molecules, and thus define supertypes and supertype fingerprints. Two chemometric techniques were used: hierarchical clustering using three-dimensional Comparative Similarity Indices Analysis fields and nonhierarchical k-means clustering using sequence-based z-descriptors. An average consensus of 84% was achieved, i.e., 1872 of 2225 class II molecules were classified in the same supertype by both techniques. Twelve class II supertypes were defined: five DRs, three DQs, and four DPs. The HLA class II supertypes and their fingerprints given in parenthesis are DR1 (Trp(9beta)), DR3 (Glu(9beta), Gln(70beta), and Gln/Arg(74beta)), DR4 (Glu(9beta), Gln/Arg(70beta), and Glu/Ala(74beta)), DR5 (Glu(9beta), Asp(70beta)), and DR9 (Lys/Gln(9beta)); DQ1 (Ala/Gly(86beta)), DQ2 (Glu(86beta), Lys(71beta)), and DQ3 (Glu(86beta), Thr/Asp(71beta)); DPw1 (Asp(84beta) and Lys(69beta)), DPw2 (Gly/Val(84beta) and Glu(69beta)), DPw4 (Gly/Val(84beta) and Lys(69beta)), and DPw6 (Asp(84beta) and Glu(69beta)). Apart from the good agreement between known binding motifs and our classification, several new supertypes, and corresponding thematic binding motifs, were also defined.

Alleles↗

PubMed2002

Representation and processing of complex DNA spatial architecture and its annotated genomic content.

This paper presents a new general approach for the spatial representation and visualization of DNA molecule and its annotated information. This approach is based on a biological 3D model that predicts the complex spatial trajectory of huge naked DNA. With such modeling, a global vision of the sequence is possible, which is different and complementary to other representations as textual, linguistics or syntactic ones. The DNA is well known as a three-dimensional structure. Whereas, the spatial information plays a great part during its evolution and its interaction with the other biological elements This work will motivate investigations in order to launch new bioinformatics studies for the analysis of the spatial architecture of the genome. Besides, in order to obtain a friendly interactive visualization, a powerful graphic modeling is proposed including DNA complex trajectory management and its annotated-based content structuring. The paper describes spatial architecture modeling, with consideration of both biological and computational constraints. This work is implemented through a powerful graphic software tool, named ADN-Viewer. Several examples of visualization are shown for various organisms and biological elements.

DNA↗

PubMedDate not supplied

DNA microarrays: applicability to renal physiology and disease.

The utilization of DNA microarray is increasing in all areas of medical research as evidenced by the exponential increase in the number of publications on MedLine. Improvements in DNA microarray technology and the bioinformatic techniques for data analysis continue to take place and result in improved reliability, reproducibility, and volume of data. Replicate analysis remains important in order to allow statistical examination of significance. Expression of key genes should also be confirmed by alternative techniques. Single microarray experiments yield such vast amounts of data that shortage of manpower is the limiting factor to progression of full scientific discovery. Sharing of microarray data on the internet and its distribution to a broader scientific community will help overcome this manpower shortage. Currently, DNA microarray technology serves as a research tool. Ultimately, microarray technology may be used widely in the clinical setting as fingerprinting of cells, tissue, and disease states, though more targeted strategies using a limited repertoire of genes may be most valuable.

Animals↗

PubMed2002

Artificial neural networks and gene filtering distinguish between global gene expression profiles of Barrett's esophagus and esophageal cancer.

cDNAmicroarrays, combined with bioinformatics analyses, are becomingincreasingly used in current medical research. Existing analytic methods,particularly those that are unsupervised, often have difficulty recognizing subtle differences among predefined subgroups. In contrast, supervised methods, such as Artificial Neural Networks (ANNs), are able to recognize subtly different biological entities. We applied ANNs in a proof-of-principle study of cDNA microarray data in esophageal cancer (CA) and premalignancy. cDNA microarrays, each containing 8064 clones, were hybridized to RNAs from 22 esophageal lesions, including 14 Barrett's esophagus (BA) metaplasias and 8 esophageal carcinomas (3 squamous cell carcinomas and 5 adenocarcinomas). Scanned cDNA microarray data were analyzed using the bioinformatics software Cluster/TreeView, Significance Analysis of Microarrays (SAM), and ANNs. Cluster analysis based on all 8064 clones on the microarrays was unable to correctly distinguish BA specimens from CA specimens. SAM then selected 160 differentially expressed genes between Barrett's and cancer. Cluster analysis based on this reduced set still misclassified 2 Barrett's as cancers. The ANN was trained on 12 samples and tested against the remaining 10 samples. Using the 160 selected genes, the ANN correctly diagnosed all 10 samples in the test set. Finally, the 160 genes selected by SAM may merit further study as biomarkers of neoplastic progression in the esophagus, as well as in elucidating pathological mechanisms underlying BA and CA.

Adenocarcinoma↗

PubMed2003

Screening of augmenter of liver regeneration-binding proteins by yeast-two hybrid technique.

OBJECTIVE: To investigate the biological function of augmenter of liver regeneration (ALR), we used yeast-two hybrid technique to detect proteins in hepatocytes interacting with ALR. METHODS: ALR bait plasmid was constructed by using yeast-two hybrid system 3, then transformed into yeast AH109. The transformed yeast was mated with yeast Y187 containing liver cDNA library plasmid in a 2XYPDA medium. Diploid yeast was plated on a synthetic dropout nutrient medium (SD/-Trp-Leu-His-Ade) containing x-alpha-gal for selection and screening. After extracting and sequencing of the plasmid from blue colonies. Analysis was performed by bioinformatics. RESULTS: Of 36 colonies sequenced, 14 are metallothionein, 12 albumin, and 3 selenoprotein P. One colony is a new gene with unknown function. CONCLUSION: The successful cloning of gene of ALR interacting protein has paved the way for studying the physiological function of ALR and associated proteins.

Animals↗

PubMed2004

Penalized binary regression for gene expression profiling.

OBJECTIVES: A typical bioinformatics task in microarray analysis is the classification of biological samples into two alternative categories. A procedure is needed which, based on the expression levels measured, allows us to compute the probability that a new sample belongs to a certain class. METHODS: For the purpose of classification the statistical approach of binary regression is considered. Highdimensionality and at the same time small sample sizes make it a challenging task. Standard logit or probit regression fails because of condition problems and poor predictive performance. The concepts of frequentist and of Bayesian penalization for binary regression are introduced. A Bayesian interpretation of the penalized log-likelihood is given. Finally the role of cross-validation for regularization and feature selection is discussed. RESULTS: Penalization makes classical binary regression a suitable tool for microarray analysis. We illustrate penalized logit and Bayesian probit regression on a well-known data set and compare the obtained results, also with respect to published results from decision trees. CONCLUSIONS: The frequentist and the Bayesian penalization concept work equally well on the example data, however some method-specific differences can be made out. Moreover the Bayesian approach yields a quantification (posterior probabilities) of the bias due to the constraining assumptions.

Bayes Theorem↗