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PubMed2006

pSTIING: a 'systems' approach towards integrating signalling pathways, interaction and transcriptional regulatory networks in inflammation and cancer.

pSTIING (http://pstiing.licr.org) is a new publicly accessible web-based application and knowledgebase featuring 65 228 distinct molecular associations (comprising protein-protein, protein-lipid, protein-small molecule interactions and transcriptional regulatory associations), ligand-receptor-cell type information and signal transduction modules. It has a particular major focus on regulatory networks relevant to chronic inflammation, cell migration and cancer. The web application and interface provide graphical representations of networks allowing users to combine and extend transcriptional regulatory and signalling modules, infer molecular interactions across species and explore networks via protein domains/motifs, gene ontology annotations and human diseases. pSTIING also supports the direct cross-correlation of experimental results with interaction information in the knowledgebase via the CLADIST tool associated with pSTIING, which currently analyses and clusters gene expression, proteomic and phenotypic datasets. This allows the contextual projection of co-expression patterns onto prior network information, facilitating the identification of functional modules in physiologically relevant systems.

Amino Acid Motifs↗

PubMed2004

Adaptation of enzymes to temperature: searching for basic "strategies".

The pervasive influence of temperature on biological systems necessitates a suite of temperature--compensatory adaptations that span all levels of biological organization--from behavior to fine-scale molecular structure. Beginning about 50 years ago, physiological studies conducted with whole organisms or isolated tissues, by such pioneers of comparative thermal physiology as V.Ya. Alexandrov, T.H. Bullock, F.E.J. Fry, H. Precht, C.L. Prosser, and P.F. Scholander, began to document in detail the abilities of ectothermic animals to sustain relatively similar rates of metabolic activity at widely different temperatures of adaptation or acclimation. These studies naturally led to investigation of the roles played by enzymatic proteins in metabolic temperature compensation. Peter Hochachka's laboratory became an epicenter of this new focus in comparative physiology. The studies of the enzyme lactate dehydrogenase (LDH) that he initiated as a PhD student at Duke University in the mid-1960s and continued for several years at the University of British Columbia laid much of the foundation for subsequent studies of protein adaptation to temperature. Studies of orthologs of LDH have revealed the importance of conserving kinetic properties (catalytic rate constants (kcat) and Michaelis-Menten constants (Km) and structural stability during adaptation to temperature, and recently have identified the types of amino acid substitutions causing this adaptive variation. The roles of pH and low-molecular-mass organic solutes (osmolytes) in conserving the functional and structural properties of enzymes also have been elucidated using LDH. These studies, begun in Peter Hochachka's laboratory almost 40 years ago, have been instrumental in the development of a conceptual framework for the study of biochemical adaptation, a field whose origin can be traced largely to his creative influences. This framework emphasizes the complementary roles of three "strategies" of adaptation: (1) changes in amino acid sequence that cause adaptive variation in the kinetic properties and stabilities of proteins, (2) shifts in concentrations of proteins, which are mediated through changes in gene expression and protein turnover; and (3) changes in the milieu in which proteins function, which conserve the intrinsic properties of proteins established by their primary structure and modulate protein activity in response to physiological needs. This theoretical framework has helped guide research in adaptational biochemistry for many years and now stands poised to play a critical role in the post-genomic era, as physiologists grapple with the challenge of integrating the wealth of new data on gene sequences (genome), gene expression (transcriptome and proteome), and metabolic profiles (metabolome) into a realistic physiological context that takes into account the evolutionary histories and environmental relationships of species.

Adaptation, Physiological↗

PubMed2026

Isolation of region-specific factors driving antibody class-switch recombination from the immunoglobulin heavy chain locus.

Activation-Induced Cytidine Deaminase (AID) induces DNA double-strand breaks (DSBs) at the switch (S) regions of the Immunoglobulin heavy chain (IgH) locus, which are essential for class switch recombination (CSR) and somatic hypermutation (SHM), key processes for effective antibody production. While AID activity is critical, its off-target effects, such as DSBs at the Myc locus, can cause chromosomal translocations like IgH-Myc fusions, contributing to B-cell lymphomas. The factors assembled on the IgH locus that help restrict AID-induced DSBs and subsequently CSR, remain unknown. To address this, we developed a method to isolate CSR-specific factors by inserting a 5×-GAL4-UAS sequence at the switch-mu (Sμ) region in CH12 cells. This engineered site enables recruitment of a 3-FLAG-GAL4 DNA-binding protein (3F-GAL4-DBD), allowing specific pulldown of proteins enriched at the Sμ region. Successful recovery of the known CSR regulator BRD2 from the Sμ region, along with enrichment of the DNA repair factors 53BP1 and gH2AX, validated this approach. Identification and characterization of IgH-enriched factors establish a validated methodological framework to facilitate future proteomic discovery of CSR regulators and highlight mechanisms that balance antibody diversification with genomic integrity in B cells.

Immunoglobulin Class Switching↗

PubMed2006

A second fatty acid amide hydrolase with variable distribution among placental mammals.

Fatty acid amides constitute a large and diverse class of lipid transmitters that includes the endogenous cannabinoid anandamide and the sleep-inducing substance oleamide. The magnitude and duration of fatty acid amide signaling are controlled by enzymatic hydrolysis in vivo. Fatty acid amide hydrolase (FAAH) activity in mammals has been primarily attributed to a single integral membrane enzyme of the amidase signature (AS) family. Here, we report the functional proteomic discovery of a second membrane-associated AS enzyme in humans that displays FAAH activity. The gene that encodes this second FAAH enzyme was found in multiple primate genomes, marsupials, and more distantly related vertebrates, but, remarkably, not in a number of lower placental mammals, including mouse and rat. The two human FAAH enzymes, which share 20% sequence identity and are referred to hereafter as FAAH-1 and FAAH-2, hydrolyzed primary fatty acid amide substrates (e.g. oleamide) at equivalent rates, whereas FAAH-1 exhibited much greater activity with N-acyl ethanolamines (e.g. anandamide) and N-acyl taurines. Both enzymes were sensitive to the principal classes of FAAH inhibitors synthesized to date, including O-aryl carbamates and alpha-keto heterocycles. These data coupled with the overlapping, but distinct tissue distributions of FAAH-1 and FAAH-2 suggest that these proteins may collaborate to control fatty acid amide catabolism in primates. The apparent loss of the FAAH-2 gene in some lower mammals should be taken into consideration when extrapolating genetic or pharmacological findings on the fatty acid amide signaling system across species.

Amidohydrolases↗

PubMed2005

BRIGEP--the BRIDGE-based genome-transcriptome-proteome browser.

The growing amount of information resulting from the increasing number of publicly available genomes and experimental results thereof necessitates the development of comprehensive systems for data processing and analysis. In this paper, we describe the current state and latest developments of our BRIGEP bioinformatics software system consisting of three web-based applications: GenDB, EMMA and ProDB. These applications facilitate the processing and analysis of bacterial genome, transcriptome and proteome data and are actively used by numerous international groups. We are currently in the process of extensively interconnecting these applications. BRIGEP was developed in the Bioinformatics Resource Facility of the Center for Biotechnology at Bielefeld University and is freely available. A demo project with sample data and access to all three tools is available at https://www.cebitec.uni-bielefeld.de/groups/brf/software/brigep/. Code bundles for these and other tools developed in our group are accessible on our FTP server at ftp.cebitec.uni-bielefeld.de/pub/software/.

Bacterial Proteins↗

PubMed2006

POGs/PlantRBP: a resource for comparative genomics in plants.

POGs/PlantRBP (http://plantrbp.uoregon.edu/) is a relational database that integrates data from rice, Arabidopsis, and maize by placing the complete Arabidopsis and rice proteomes and available maize sequences into 'putative orthologous groups' (POGs). Annotation efforts will focus on predicted RNA binding proteins (RBPs): i.e. those with known RNA binding domains or otherwise implicated in RNA function. POGs form the heart of the database, and were assigned using a mutual-best-hit-strategy after performing BLAST comparisons of the predicted Arabidopsis and rice proteomes. Each POG entry includes orthologs in Arabidopsis and rice, annotated with domain organization, gene models, phylogenetic trees, and multiple intracellular targeting predictions. A graphical display maps maize sequences on to their most similar rice gene model. The database can be queried using any combination of gene name, accession, domain, and predicted intracellular location, or using BLAST. Useful features of the database include the ability to search for proteins with both a specified domain content and intracellular location, the concurrent display of mutual best hits and phylogenetic trees which facilitates evaluation of POG assignments, the association of maize sequences with POGs, and the display of targeting predictions and domain organization for all POG members, which reveals consistency, or lack thereof, of those predictions.

Amino Acid Sequence↗

PubMed2005

Functional role of anion channels in cardiac diseases.

In comparison to cation (K+, Na+, and Ca2+) channels, much less is currently known about the functional role of anion (Cl-) channels in cardiovascular physiology and pathophysiology. Over the past 15 years, various types of Cl- currents have been recorded in cardiac cells from different species including humans. All cardiac Cl- channels described to date may be encoded by five different Cl- channel genes: the PKA- and PKC-activated cystic fibrosis tansmembrane conductance regulator (CFTR), the volume-regulated ClC-2 and ClC-3, and the Ca2+-activated CLCA or Bestrophin. Recent studies using multiple approaches to examine the functional role of Cl- channels in the context of health and disease have demonstrated that Cl- channels might contribute to: 1) arrhythmogenesis in myocardial injury; 2) cardiac ischemic preconditioning; and 3) the adaptive remodeling of the heart during myocardial hypertrophy and heart failure. Therefore, anion channels represent very attractive novel targets for therapeutic approaches to the treatment of heart diseases. Recent evidence suggests that Cl- channels, like cation channels, might function as a multiprotein complex or functional module. In the post-genome era, the emergence of functional proteomics has necessitated a new paradigm shift to the structural and functional assessment of integrated Cl- channel multiprotein complexes in the heart, which could provide new insight into our understanding of the underlying mechanisms responsible for heart disease and protection.

Animals↗

PubMed2006

A regression-based K nearest neighbor algorithm for gene function prediction from heterogeneous data.

BACKGROUND: As a variety of functional genomic and proteomic techniques become available, there is an increasing need for functional analysis methodologies that integrate heterogeneous data sources. METHODS: In this paper, we address this issue by proposing a general framework for gene function prediction based on the k-nearest-neighbor (KNN) algorithm. The choice of KNN is motivated by its simplicity, flexibility to incorporate different data types and adaptability to irregular feature spaces. A weakness of traditional KNN methods, especially when handling heterogeneous data, is that performance is subject to the often ad hoc choice of similarity metric. To address this weakness, we apply regression methods to infer a similarity metric as a weighted combination of a set of base similarity measures, which helps to locate the neighbors that are most likely to be in the same class as the target gene. We also suggest a novel voting scheme to generate confidence scores that estimate the accuracy of predictions. The method gracefully extends to multi-way classification problems. RESULTS: We apply this technique to gene function prediction according to three well-known Escherichia coli classification schemes suggested by biologists, using information derived from microarray and genome sequencing data. We demonstrate that our algorithm dramatically outperforms the naive KNN methods and is competitive with support vector machine (SVM) algorithms for integrating heterogenous data. We also show that by combining different data sources, prediction accuracy can improve significantly CONCLUSION: Our extension of KNN with automatic feature weighting, multi-class prediction, and probabilistic inference, enhance prediction accuracy significantly while remaining efficient, intuitive and flexible. This general framework can also be applied to similar classification problems involving heterogeneous datasets.

Algorithms↗

PubMed2003

Integrative analysis of intraerythrocytic differentially expressed transcripts yields novel insights into the biology of Plasmodium falciparum.

BACKGROUND: The intraerythrocytic development of Plasmodium falciparum, the most virulent human malaria parasite involves asexual and gametocyte stages. There has been a significant increase in disparate datasets derived from genomic and post-genomic analysis of the parasite that necessitates delivery of integrated analysis from which biological processes important to the survival of the parasite can be determined. METHODS: In order to resolve genes associated with stage differentially expressed transcripts, we have developed and implemented an integrative approach that combines evidence from P. falciparum expressed sequence tags (ESTs), genomic, microarray, proteomic and gene ontology data. RESULTS: A total of 143 gametocyte-overexpressed and 51 asexual-overexpressed transcripts were identified. A subset of 74 genes associated with these transcripts showed evidence of stage-correlated protein expression, of which 53 have not been experimentally characterised. Our study has revealed (1) possible regulatory mechanisms in malaria parasites' gametocyte maturation, (2) correlation between EST and microarray data for a P. falciparum gene family to present unique EST-derived information, (3) candidate drug and antigenic targets on which computational and experimental studies can be performed, and (4) the need for more empirical studies on gene and protein expression in malaria parasites. CONCLUSION: Applying different domains of data to the same underlying gene set has yielded novel insights into the biology of the parasite and presents an approach to appraise critically the data quality of post-genomic datasets from malaria parasites.

Journal Article↗

PubMed2004

A multidisciplinary approach to the study of T cell migration.

Active T cell locomotion depends on efficient repeated cycles of integrin receptor/ligand interactions mediating cell adhesion and detachment, intracellular signaling cascades orchestrating posttranslation modifications of interacting proteins, dynamic reassembly of participating cytoskeletal elements, and structural support of associated scaffolding molecules. Using an integrated approach based on novel cutting edge technologies of live cell imaging, cell transfection, proteomics, and nanotechnology, we provide here a detailed characterisation of crucial mechanisms involved in LFA-1 integrin-mediated T cell migration. Polarization and phenotypic changes associated with LFA-1-triggered T cell locomotion is largely dependent on the intact functioning of the microtubule cytoskeleton. Experiments utilizing 4-D (3-D over time) confocal live imaging of T cells, microinjected with fully functional constructs encoding protein kinase C beta (PKC-beta) isoenzyme tagged with enhanced green fluorescent protein (GFP), elucidate that LFA-1-induced activation is associated with translocation of PKC-beta to sites associated with centrosomes and tubulin cytoskeleton in locomotory T lymphocytes. We also provide here a characterization of a novel microfluidics-based multichannel platform enabling detailed analysis of leukocyte adhesion and migration under regulated shear stress conditions. Using precision machined surfaces, we demonstrate that the substrate topography can influence the motile response of the two different T cell types in different ways, and this can be quantified in terms of specified motility parameters. Finally, using an original in situ immunoprecipitation method, in which LFA-1 antibodies are utilized to induce intracellular association of proteins in the cytoskeletal/signaling complex, we demonstrate that this complex includes a number of structural and signaling proteins, which have been identified by 2-D electrophoresis and MALDI-TOF protein sequencing.

Cell Adhesion↗

PubMed2006

Two-dimensional difference gel electrophoresis.

The two-dimensional (2D) polyacrylamide gel-based approach to protein profiling has been successful because it is an accessible, inexpensive, and powerful tool for the analysis of global patterns of protein expression. All protein spots that are resolved and detected within the 104 to 105 dynamic range of gel capacity can be studied qualitatively and quantitatively in relation to each other, and viewed as a single image. Two-dimensional difference gel electrophoresis (DIGE) has strengthened the 2D platform by allowing the detection and quantitation of differences between samples resolved on the same gel, or across multiple gels, when linked by an internal standard. The technology is based on modification of protein extracts with fluorescent cyanine dyes, which have distinct excitation and emission spectra and are migration (charge and size) matched so that the same protein labeled with any of the dyes (Cy2, Cy3, Cy5) will migrate to the same position within a 2D gel, greatly enhancing reproducibility. It is becoming clear that each technology that is currently available for quantifying differential protein expression has its own weaknesses and strengths, and that multiplatform, integrated approaches will be necessary to provide the most complete analysis of any given proteome. We believe that DIGE is, and will remain in the future, a key front-end proteomic tool that will strongly complement other protein-profiling technologies.

Animals↗

PubMed2006

Isolation and mass spectrometry of specific DNA binding proteins.

A subset of the proteome that binds to specific DNA sequences is at the center of genome function, integrity, and dynamics. We present a detailed protocol that allows the isolation of any specific DNA binding protein and its subsequent identification by mass spectrometry. The procedure involves prefractionation of crude nuclear extract by phosphocellulose (P11) chromatography, followed by a series of positive/negative selections on wild-type and site-mutated ligand DNA in a magnetic microparticulate format. DNA-affinity capture requires concatamerized and biotinylated ligand, selective salt conditions, and improved competitor DNAs. The amount of protein(s) captured on DNA-magnetic beads is generally sufficient for successful MALDI-TOF and TOF/TOF MS-based protein identification. As an example, we describe the procedures used to isolate and identify four specific transcription factors from 2 x 10(9) promyelocytic NB4 cells.

Blotting, Western↗

PubMed2001

The total laboratory solution: a new laboratory E-business model based on a vertical laboratory meta-network.

Major forces are now reshaping all businesses on a global basis, including the healthcare and clinical laboratory industries. One of the major forces at work is information technology (IT), which now provides the opportunity to create a new economic and business model for the clinical laboratory industry based on the creation of an integrated vertical meta-network, referred to here as the "total laboratory solution" (TLS). Participants at the most basic level of such a network would include a hospital-based laboratory, a reference laboratory, a laboratory information system/application service provider/laboratory portal vendor, an in vitro diagnostic manufacturer, and a pharmaceutical/biotechnology manufacturer. It is suggested that each of these participants would add value to the network primarily in its area of core competency. Subvariants of such a network have evolved over recent years, but a TLS comprising all or most of these participants does not exist at this time. Although the TLS, enabled by IT and closely akin to the various e-businesses that are now taking shape, offers many advantages from a theoretical perspective over the current laboratory business model, its success will depend largely on (a) market forces, (b) how the collaborative networks are organized and managed, and (c) whether the network can offer healthcare organizations higher quality testing services at lower cost. If the concept is successful, new demands will be placed on hospital-based laboratory professionals to shift the range of professional services that they offer toward clinical consulting, integration of laboratory information from multiple sources, and laboratory information management. These information management and integration tasks can only increase in complexity in the future as new genomic and proteomics testing modalities are developed and come on-line in clinical laboratories.

Chemistry, Clinical↗

PubMed2001

Applying functional genomics research to the study of pig reproduction.

Functional genomics is an experimental approach that incorporates genome-wide or system-wide experimentation, expanding the scope of biological investigation from studying single genes to studying potentially all genes at once in a systematic manner. This technology is highly appealing because of its high throughput and relatively low cost. Furthermore, analysis of gene expression using microarrays is likely to be more biologically relevant than the conventional paradigm of reductionism, because it has the potential to uncover new biological connections between genes and biochemical pathways. However, functional genomics is still in its infancy, especially with regard to the study of pig reproduction. Currently, efforts are centred on developing the necessary resources to enable high throughput evaluation and comparison of gene expression. However, it is clear that in the near future functional genomics will be applied on a large scale to study the biology and physiology of reproduction in pigs, and to understand better the complex nature of genetic control over polygenic characteristics, such as ovulation rate and litter size. We can look forward to generating a significant amount of new data on differences in gene expression between genotypes, treatments, or at various temporal and spatial coordinates within a variety of reproductively relevant systems. Along with this capability will be the challenge of collating, analysing and interpreting datasets that are orders of magnitude more extensive and complex than those currently used. Furthermore, integration of functional genomics with traditional genetic approaches and with detailed analysis of the proteome and relevant whole animal phenotypes will be required to make full use of this powerful new experimental paradigm as a beneficial research tool.

Animals↗

PubMed2007

The PROTICdb database for 2-DE proteomics.

PROTICdb is a web-based database mainly designed to store and analyze plant proteome data obtained by 2D polyacrylamide gel electrophoresis (2D PAGE) and mass spectrometry (MS). The goals of PROTICdb are (1) to store, track, and query information related to proteomic experiments, i.e., from tissue sampling to protein identification and quantitative measurements; and (2) to integrate information from the user's own expertise and other sources into a knowledge base, used to support data interpretation (e.g., for the determination of allelic variants or products of posttranslational modifications). Data insertion into the relational database of PROTICdb is achieved either by uploading outputs from Mélanie, PDQuest, IM2d, ImageMaster(tm) 2D Platinum v5.0, Progenesis, Sequest, MS-Fit, and Mascot software, or by filling in web forms (experimental design and methods). 2D PAGE-annotated maps can be displayed, queried, and compared through the GelBrowser. Quantitative data can be easily exported in a tabulated format for statistical analyses with any third-party software. PROTICdb is based on the Oracle or the PostgreSQLDataBase Management System (DBMS) and is freely available upon request at http://cms.moulon.inra.fr/content/view/14/44/.

Databases, Protein↗

PubMed2003

Hit and lead generation: beyond high-throughput screening.

The identification of small-molecule modulators of protein function, and the process of transforming these into high-content lead series, are key activities in modern drug discovery. The decisions taken during this process have far-reaching consequences for success later in lead optimization and even more crucially in clinical development. Recently, there has been an increased focus on these activities due to escalating downstream costs resulting from high clinical failure rates. In addition, the vast emerging opportunities from efforts in functional genomics and proteomics demands a departure from the linear process of identification, evaluation and refinement activities towards a more integrated parallel process. This calls for flexible, fast and cost-effective strategies to meet the demands of producing high-content lead series with improved prospects for clinical success.

Amino Acid Motifs↗

PubMed2003

SPINE 2: a system for collaborative structural proteomics within a federated database framework.

We present version 2 of the SPINE system for structural proteomics. SPINE is available over the web at http://nesg.org. It serves as the central hub for the Northeast Structural Genomics Consortium, allowing collaborative structural proteomics to be carried out in a distributed fashion. The core of SPINE is a laboratory information management system (LIMS) for key bits of information related to the progress of the consortium in cloning, expressing and purifying proteins and then solving their structures by NMR or X-ray crystallography. Originally, SPINE focused on tracking constructs, but, in its current form, it is able to track target sample tubes and store detailed sample histories. The core database comprises a set of standard relational tables and a data dictionary that form an initial ontology for proteomic properties and provide a framework for large-scale data mining. Moreover, SPINE sits at the center of a federation of interoperable information resources. These can be divided into (i) local resources closely coupled with SPINE that enable it to handle less standardized information (e.g. integrated mailing and publication lists), (ii) other information resources in the NESG consortium that are inter-linked with SPINE (e.g. crystallization LIMS local to particular laboratories) and (iii) international archival resources that SPINE links to and passes on information to (e.g. TargetDB at the PDB).

Cooperative Behavior↗

PubMed2024

Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast.

Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein coexpression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship.

Saccharomyces cerevisiae↗