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PubMed2006

Discovery of Time-Delayed Gene Regulatory Networks based on temporal gene expression profiling.

BACKGROUND: It is one of the ultimate goals for modern biological research to fully elucidate the intricate interplays and the regulations of the molecular determinants that propel and characterize the progression of versatile life phenomena, to name a few, cell cycling, developmental biology, aging, and the progressive and recurrent pathogenesis of complex diseases. The vast amount of large-scale and genome-wide time-resolved data is becoming increasing available, which provides the golden opportunity to unravel the challenging reverse-engineering problem of time-delayed gene regulatory networks. RESULTS: In particular, this methodological paper aims to reconstruct regulatory networks from temporal gene expression data by using delayed correlations between genes, i.e., pairwise overlaps of expression levels shifted in time relative each other. We have thus developed a novel model-free computational toolbox termed TdGRN (Time-delayed Gene Regulatory Network) to address the underlying regulations of genes that can span any unit(s) of time intervals. This bioinformatics toolbox has provided a unified approach to uncovering time trends of gene regulations through decision analysis of the newly designed time-delayed gene expression matrix. We have applied the proposed method to yeast cell cycling and human HeLa cell cycling and have discovered most of the underlying time-delayed regulations that are supported by multiple lines of experimental evidence and that are remarkably consistent with the current knowledge on phase characteristics for the cell cyclings. CONCLUSION: We established a usable and powerful model-free approach to dissecting high-order dynamic trends of gene-gene interactions. We have carefully validated the proposed algorithm by applying it to two publicly available cell cycling datasets. In addition to uncovering the time trends of gene regulations for cell cycling, this unified approach can also be used to study the complex gene regulations related to the development, aging and progressive pathogenesis of a complex disease where potential dependences between different experiment units might occurs.

Algorithms↗

PubMed2004

Characterization of a new full length TMPRSS3 isoform and identification of mutant alleles responsible for nonsyndromic recessive deafness in Newfoundland and Pakistan.

BACKGROUND: Mutant alleles of TMPRSS3 are associated with nonsyndromic recessive deafness (DFNB8/B10). TMPRSS3 encodes a predicted secreted serine protease, although the deduced amino acid sequence has no signal peptide. In this study, we searched for mutant alleles of TMPRSS3 in families from Pakistan and Newfoundland with recessive deafness co-segregating with DFNB8/B10 linked haplotypes and also more thoroughly characterized the genomic structure of TMPRSS3. METHODS: We enrolled families segregating recessive hearing loss from Pakistan and Newfoundland. Microsatellite markers flanking the TMPRSS3 locus were used for linkage analysis. DNA samples from participating individuals were sequenced for TMPRSS3. The structure of TMPRSS3 was characterized bioinformatically and experimentally by sequencing novel cDNA clones of TMPRSS3. RESULTS: We identified mutations in TMPRSS3 in four Pakistani families with recessive, nonsyndromic congenital deafness. We also identified two recessive mutations, one of which is novel, of TMPRSS3 segregating in a six-generation extended family from Newfoundland. The spectrum of TMPRSS3 mutations is reviewed in the context of a genotype-phenotype correlation. Our study also revealed a longer isoform of TMPRSS3 with a hitherto unidentified exon encoding a signal peptide, which is expressed in several tissues. CONCLUSION: Mutations of TMPRSS3 contribute to hearing loss in many communities worldwide and account for 1.8% (8 of 449) of Pakistani families segregating congenital deafness as an autosomal recessive trait. The newly identified TMPRSS3 isoform e will be helpful in the functional characterization of the full length protein.

Alleles↗

PubMed2004

Use of the Serial Analysis of Gene Expression (SAGE) method in veterinary research: A concrete application in the study of the bovine trypanotolerance genetic control.

New postgenomic biotechnologies, such as transcriptome analyses, are now able to characterize the full complement of genes involved in the expression of specific biological functions. One of these is the Serial Analysis of Gene Expression (SAGE) technique, which consists of the construction of transcripts libraries for a quantitative analysis of the entire gene(s) expressed or inactivated at a particular step of cellular activation. Bioinformatic comparisons in the bovine genomic databases allow the identification of several up- and downregulated genes, expressed sequence tags, and unknown functional genes directly involved in the genetic control of the studied biological mechanism. We present and discuss the preliminary results in comparing the expressed genes in two total mRNA transcripts libraries obtained during an experimental Trypanosoma congolense infection in one trypanotolerant N'Dama animal cow. Knowing all the functional genes involved in the trypanotolerance control will permit validation of some results obtained with the quantitative trait locus approach, to set up specific microarrays sets for further metabolic and pharmacological studies, and to design field marker-assisted selection by introgression programs.

Animals↗

PubMed2006

Beta-glucuronidase as reporter gene: advantages and limitations.

The beta-glucuronidase (GUS) gene is used extensively in plant biology studies; this analysis summarizes its advantages and limitations. With the advances in genomic sequencing and computational analyses (including bioinformatics), its application in the study of plant gene expression is now an integral component of modern day plant science. This chapter focuses on the detailed challenges of carrying out GUS studies for both qualitative and quantitative analyses, including the increasing employment of GUS from Bacillus strains, rather than E. coli; the Bacillus GUS genes encode proteins with enhanced properties, such as both increased thermostability and stability in the presence of crosslinking fixatives.

Arabidopsis↗

PubMed2026

Aqueous humour extracellular vesicle membrane protein profiling reveals pathological features of refractory macular edema.

BACKGROUND: Both diabetic macular edema (DME) and retinal vein occlusion-related macular edema (RVO-ME) can become refractory to anti-vascular endothelial growth factor (anti-VEGF) therapy, but the underlying mechanisms are unclear. Molecular discrimination of refractory disease could guide personalized treatment. This study examined whether aqueous humor-derived extracellular vesicle (EV) membrane proteins can characterize refractoriness and reveal etiology-specific pathways. METHODS: This prospective cohort study included 28 patients with DME or RVO-ME (14 each), further divided into treatment-naïve and refractory subgroups. Aqueous humour samples were collected before intravitreal anti-VEGF injection. EV membrane proteins were profiled using an EV Array chip targeting 435 antibodies. Differentially expressed proteins were analyzed by bioinformatics, including Gene Ontology, Kyoto encyclopaedia of genes and genomes (KEGG) pathway enrichment, Gene set enrichment analysis (GSEA), and cell-of-origin mapping using public single-cell RNA-seq data. RESULTS: VEGF/VEGFR2 were elevated in treatment-naïve DME and RVO-ME. Refractory DME showed upregulation of C5 and CD34 (complement/immune activation). Refractory RVO-ME exhibited increased CD68 and Annexin A1 with decreased PDGFR (chronic inflammation, vascular dysregulation). RANTES was commonly upregulated in refractory disease. Several EV proteins discriminated refractory cases with high accuracy (AUC 0.898-0.980). Cellular origin suggested immune cell-derived EVs in DME, retinal cell-derived EVs in RVO-ME. External validation confirmed key differences. CONCLUSION: Refractory ME involves distinct pathways: immune-inflammatory activation in DME versus chronic inflammation with vascular dysregulation in RVO-ME. EV membrane proteins from aqueous humor provide insights into therapeutic resistance and hold promise as biomarkers for personalized treatment decisions.

artificial intelligence↗

PubMed2003

Bioinformatics support for high-throughput proteomics.

In the "post-genome" era, mass spectrometry (MS) has become an important method for the analysis of proteome data. The rapid advancement of this technique in combination with other methods used in proteomics results in an increasing number of high-throughput projects. This leads to an increasing amount of data that needs to be archived and analyzed. To cope with the need for automated data conversion, storage, and analysis in the field of proteomics, the open source system ProDB was developed. The system handles data conversion from different mass spectrometer software, automates data analysis, and allows the annotation of MS spectra (e.g. assign gene names, store data on protein modifications). The system is based on an extensible relational database to store the mass spectra together with the experimental setup. It also provides a graphical user interface (GUI) for managing the experimental steps which led to the MS data. Furthermore, it allows the integration of genome and proteome data. Data from an ongoing experiment was used to compare manual and automated analysis. First tests showed that the automation resulted in a significant saving of time. Furthermore, the quality and interpretability of the results was improved in all cases.

Algorithms↗

PubMed2001

Radical SAM, a novel protein superfamily linking unresolved steps in familiar biosynthetic pathways with radical mechanisms: functional characterization using new analysis and information visualization methods.

A novel protein superfamily with over 600 members was discovered by iterative profile searches and analyzed with powerful bioinformatics and information visualization methods. Evidence exists that these proteins generate a radical species by reductive cleavage of S:-adenosylmethionine (SAM) through an unusual Fe-S center. The superfamily (named here Radical SAM) provides evidence that radical-based catalysis is important in a number of previously well- studied but unresolved biochemical pathways and reflects an ancient conserved mechanistic approach to difficult chemistries. Radical SAM proteins catalyze diverse reactions, including unusual methylations, isomerization, sulfur insertion, ring formation, anaerobic oxidation and protein radical formation. They function in DNA precursor, vitamin, cofactor, antibiotic and herbicide biosynthesis and in biodegradation pathways. One eukaryotic member is interferon-inducible and is considered a candidate drug target for osteoporosis; another is observed to bind the neuronal Cdk5 activator protein. Five defining members not previously recognized as homologs are lysine 2,3-aminomutase, biotin synthase, lipoic acid synthase and the activating enzymes for pyruvate formate-lyase and anaerobic ribonucleotide reductase. Two functional predictions for unknown proteins are made based on integrating other data types such as motif, domain, operon and biochemical pathway into an organized view of similarity relationships.

Amino Acid Sequence↗

PubMed1997

Computational gene discovery and human disease.

Bioinformatics is now an essential tool in many aspects of human molecular genetics research. Methods for the prediction of gene structure are essential components in genomic sequencing projects and provide the key to deriving protein sequence and locating intron/exon junctions. Sequence comparison and database searching are the pre-eminent approaches for predicting the likely biochemical function of new genes, although sequence profiles derived from families of aligned sequences have advantages in the detection of remote sequence relationships. The use of sequence database analysis for large-scale comparative analysis of genome sequence data from model organisms is emerging as the most important recent development in the application of bioinformatics methods for characterizing candidate disease genes.

Animals↗

PubMed2006

Proteomic analysis of urine in kidney transplant patients with BK virus nephropathy.

The differentiation of BK virus-associated renal allograft nephropathy (BKVAN) from acute allograft rejection (AR) in renal transplant recipients is an important clinical problem because the treatment can be diametrically opposite for the two conditions. The aim of this discovery-phase biomarker development study was to examine feasibility of developing a noninvasive method to differentiate BKVAN from AR. Surface-enhanced laser desorption/ionization (SELDI) time-of-flight mass spectrometry analysis was used to compare proteomic profiles of urine samples of 21 patients with BKVAN, 28 patients with AR (Banff Ia to IIb), and 29 patients with stable graft function. SELDI analysis showed proteomic profiles that were significantly different in the BKVAN group versus the AR and stable transplant groups. Peaks that corresponded to m/z values of 5.872, 11.311, 11.929, 12.727, and 13.349 kD were significantly higher in patients with BKVAN. Bioinformatics analyses allowed distinction of profiles of patients with BKVAN from patients with AR and stable patients. SELDI profiles also showed a high degree of reproducibility. Proteomic analysis of urine may offer a noninvasive way to differentiate BKVAN from AR in clinical practice. The identification of individual proteomic peaks can improve further the clinical utility of this screening method.

Adult↗

PubMedDate not supplied

[What is the subject of science "bioinformatics"?].

The paper is concerned with some problems of terminology, in particular the term "bioinformatics". In the last few years, the term "bioinformatics" has been intensively used among molecular biologists to indicate a subject that is only a constituent of genomics and is considered to involve a computer-assisted analysis of all data on nucleotide sequences of DNA. However, a wide circle of scientists, including biologists, physicists, mathematicians, and specialists in the field of cybernetics, informatics, and other disciplines have accepted and accept, as a rule, the "bioinformatics" as a synonym of science cybernetics and as a successor of this science. In this case, the subject of science "bioinformatics" should embrace not only genomics but practically all sections of the biological science. It should involve a study of information processes (storage, transfer, and processing of information, etc.) participating in the regulation and control at all levels of living systems, from macromolecules to the brain of higher animals and human.

Computational Biology↗

PubMed2005

Improved access to sequential motifs: a note on the architectural bias of recurrent networks.

For many biological sequence problems the available data occupies only sparse regions of the problem space. To use machine learning effectively for the analysis of sparse data we must employ architectures with an appropriate bias. By experimentation we show that the bias of recurrent neural networks--recently analyzed by Tino et al. and Hammer and Tino--offers superior access to motifs (sequential patterns) compared to the, in bioinformatics, standardly used feedforward neural networks.

Computational Biology↗

PubMed2006

A flexible representation of omic knowledge for thorough analysis of microarray data.

BACKGROUND: In order to understand microarray data reasonably in the context of other existing biological knowledge, it is necessary to conduct a thorough examination of the data utilizing every aspect of available omic knowledge libraries. So far, a number of bioinformatics tools have been developed. However, each of them is restricted to deal with one type of omic knowledge, e.g., pathways, interactions or gene ontology. Now that the varieties of omic knowledge are expanding, analysis tools need a way to deal with any type of omic knowledge. Hence, we have designed the Omic Space Markup Language (OSML) that can represent a wide range of omic knowledge, and also, we have developed a tool named GSCope3, which can statistically analyze microarray data in comparison with the OSML-formatted omic knowledge data. RESULTS: In order to test the applicability of OSML to represent a variety of omic knowledge specifically useful for analysis of Arabidopsis thaliana microarray data, we have constructed a Biological Knowledge Library (BiKLi) by converting eight different types of omic knowledge into OSML-formatted datasets. We applied GSCope3 and BiKLi to previously reported A. thaliana microarray data, so as to extract any additional insights from the data. As a result, we have discovered a new insight that lignin formation resists drought stress and activates transcription of many water channel genes to oppose drought stress; and most of the 20S proteasome subunit genes show similar expression profiles under drought stress. In addition to this novel discovery, similar findings previously reported were also quickly confirmed using GSCope3 and BiKLi. CONCLUSION: GSCope3 can statistically analyze microarray data in the context of any OSML-represented omic knowledge. OSML is not restricted to a specific data type structure, but it can represent a wide range of omic knowledge. It allows us to convert new types of omic knowledge into datasets that can be used for microarray data analysis with GSCope3. In addition to BiKLi, by collecting various types of omic knowledge as OSML libraries, it becomes possible for us to conduct detailed thorough analysis from various biological viewpoints. GSCope3 and BiKLi are available for academic users at our web site http://omicspace.riken.jp.

Journal Article↗

PubMed2005

Identification of novel candidate oncogenes and tumor suppressors in malignant pleural mesothelioma using large-scale transcriptional profiling.

Malignant pleural mesothelioma (MPM) is a highly lethal, poorly understood neoplasm that is typically associated with asbestos exposure. We performed transcriptional profiling using high-density oligonucleotide microarrays containing approximately 22,000 genes to elucidate potential molecular and pathobiological pathways in MPM using discarded human MPM tumor specimens (n = 40), normal lung specimens (n = 4), normal pleura specimens (n = 5), and MPM and SV40-immortalized mesothelial cell lines (n = 5). In global expression analysis using unsupervised clustering techniques, we found two potential subclasses of mesothelioma that correlated loosely with tumor histology. We also identified sets of genes with expression levels that distinguish between multiple tumor subclasses, normal and tumor tissues, and tumors with different morphologies. Microarray gene expression data were confirmed using quantitative reverse transcriptase-polymerase chain reaction and protein analysis for three novel candidate oncogenes (NME2, CRI1, and PDGFC) and one candidate tumor suppressor (GSN). Finally, we used bioinformatics tools (ie, software) to create and explore complex physiological pathways. Combined, all of these data may advance our understanding of mesothelioma tumorigenesis, pathobiology, or both.

Blotting, Western↗

PubMed2006

A serum proteomic pattern for the detection of colorectal adenocarcinoma using surface enhanced laser desorption and ionization mass spectrometry.

PURPOSE: New serum biomarkers are needed to improve the early detection of colorectal adenocarcinoma. We performed surface enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF-MS) to screen for differentially expressed proteins in serum and build a proteomic diagnostic pattern for the detection of colorectal adenocarcinoma to improve the prognosis of patients with this disease. EXPERIMENTAL DESIGN: In an attempt to improve current approaches to the serologic diagnosis of colorectal cancer, we analyzed serum samples from subjects with or without colorectal cancer using SELDI-MS. Using a case-control study design, SELDI-MS profile of serum samples from 74 colorectal adenocarcinoma patients were compared with 48 age-and sex-matched healthy subjects using a ProteinChip reader, PBSII-C. Proteomic MS spectra were generated using IMAC3 chips, and protein peaks clustering and classification analyses were performed to build a proteomic pattern that could differentiate patients with colorectal adenocarcinoma from healthy subjects utilizing Biomarker Wizard and Biomarker Patterns software packages, respectively. The constructed pattern was then used to test an independent set of masked serum samples from 60 colorectal cancer patients and 39 healthy subjects. RESULTS: Among the differentially expressed protein peaks identified by SELDI-MS profiling that had the ability to distinguish between patients and healthy subjects, we determined a minimum set of two protein peaks for system training and for developing a decision classification pattern. Masked analysis of an independent set of serum samples showed the diagnostic pattern could differentiate patients with different stages of colorectal cancer from healthy subjects with a sensitivity of 95.00 percent and specificity of 94.87 percent. CONCLUSION: SELDI-TOF-MS profiling of serum proteins combined with bioinformatics tools can be applied to accurately differentiate patients with colorectal cancer from healthy subjects. The high sensitivity and specificity achieved by the constructed clustering analysis algorithm show great potential for the early detection of colorectal cancer.

Adenocarcinoma↗

PubMed2004

Bioinformatics: harvesting information for plant and crop science.

Bioinformatics is an integral aspect of plant and crop science research. Developments in data management and analytical software are reviewed with an emphasis on applications in functional genomics. This includes information resources for Arabidopsis and crop species, and tools available for analysis and visualisation of comparative genomic data. Approaches used to explore relationships between plant genes and expressed sequences are compared, including use of ontologies. The impact of bioinformatics in forward and reverse genetics is described, together with the potential from data mining. The role of bioinformatics is explored in the wider context of plant and crop science.

Algorithms↗

PubMed2004

FunnyBase: a systems level functional annotation of Fundulus ESTs for the analysis of gene expression.

BACKGROUND: While studies of non-model organisms are critical for many research areas, such as evolution, development, and environmental biology, they present particular challenges for both experimental and computational genomic level research. Resources such as mass-produced microarrays and the computational tools linking these data to functional annotation at the system and pathway level are rarely available for non-model species. This type of "systems-level" analysis is critical to the understanding of patterns of gene expression that underlie biological processes. RESULTS: We describe a bioinformatics pipeline known as FunnyBase that has been used to store, annotate, and analyze 40,363 expressed sequence tags (ESTs) from the heart and liver of the fish, Fundulus heteroclitus. Primary annotations based on sequence similarity are linked to networks of systematic annotation in Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) and can be queried and computationally utilized in downstream analyses. Steps are taken to ensure that the annotation is self-consistent and that the structure of GO is used to identify higher level functions that may not be annotated directly. An integrated framework for cDNA library production, sequencing, quality control, expression data generation, and systems-level analysis is presented and utilized. In a case study, a set of genes, that had statistically significant regression between gene expression levels and environmental temperature along the Atlantic Coast, shows a statistically significant (P < 0.001) enrichment in genes associated with amine metabolism. CONCLUSION: The methods described have application for functional genomics studies, particularly among non-model organisms. The web interface for FunnyBase can be accessed at http://genomics.rsmas.miami.edu/funnybase/super_craw4/. Data and source code are available by request at jpaschall@bioinfobase.umkc.edu.

Animals↗

PubMed2005

[Proteomic analysis of prostate cancer using surface enhanced laser desorption/ionization mass spectrometry].

OBJECTIVE: To identify the serum biomarkers of prostate cancer by using protein chip and bioinformatics. METHODS: Eighty three prostate cancer (PCA) patients and ninety five healthy people from mass screen in Changchun were detected by surface-enhanced laser desorption/ionization mass spectrometry (SELDI-MS). The data of spectra were analyzed by bioinformatics tools-Biomarker Wizard and Biomarker Pattern. RESULTS: Compared with the spectra of healthy people, there were 18 potential markers detected in the spectra of the PCA patients, the protein expression was high in 4 of which and low in the 10 of which. The softwares Biomarkerwizard and Biomarker Pattern automatically, under given conditions, selected 8 biomarker proteins to be used to establish a five layer decision tree differentiate to diagnose PCA and differentiate PCA from healthy people with a specificity of 92.632% and a sensitivity of 96.386%. CONCLUSION: New serum biomarkers of PCA have been identified, and this SELDI mass spectrometry coupled with decision tree classification algorithm will provide a highly accurate and innovative approach for the early diagnosis of PCA.

Aged↗

PubMed2001

Microarray applications in neuroscience.

Advances in all facets of technology from molecular biology to imaging and computational biology offer unprecedented opportunities for improving our understanding of the brain in health and disease. Oligonucleotide and cDNA microarray analysis, using a variety of "DNA chips," is a recently developed high-throughput technique that allows for tour-de-force analysis of gene expression. We review this powerful technique, developed in genetics laboratories, with reference to applications in neurologic diseases in humans and the use of animal models. The typical microarray experiment is multistaged and includes preparation or purchase of arrays, preparation of target DNA and probe, target DNA hybridization, microarray scanning, and image analysis. The power and pitfalls of this technology are discussed in the context of neuroscience paradigms. Since unprecedented amounts of data are produced from microarray experiments, bioinformatics and modeling expertise are increasingly becoming critical components of this approach.

Animals↗