Components of the antigen processing and presentation pathway revealed by gene expression microarray analysis following B cell antigen receptor (BCR) stimulation

A recombinant bovine adenoviral mucosal vaccine expressing mycobacterial antigen-85B generates robust protection against tuberculosis in mice

Although the BCG vaccine offers partial protection, tuberculosis remains a leading cause of infectious disease death, killing ∼1.5 million people annually. We developed mucosal vaccines expressing the autophagy-inducing peptide C5 and mycobacterial Ag85B-p25 epitope using replication-defective human adenovirus (HAdv85C5) and bovine adenovirus (BAdv85C5) vectors. BAdv85C5-infected dendritic cells (DCs) expressed a robust transcriptome of genes regulating antigen processing compared to HAdv85C5-infected DCs. BAdv85C5-infected DCs showed enhanced galectin-3/8 and autophagy-dependent in vitro Ag85B-p25 epitope presentation to CD4 T cells. BCG-vaccinated mice were intranasally boosted using HAdv85C5 or BAdv85C5 followed by infection using aerosolized Mycobacterium tuberculosis (Mtb). BAdv85C5 protected mice against tuberculosis both as a booster after BCG vaccine (>1.4-log10 reduction in Mtb lung burden) and as a single intranasal dose (>0.5-log10 reduction). Protection was associated with robust CD4 and CD8 effector (TEM), central memory (TCM), and CD103+/CD69+ lung-resident memory (TRM) T cell expansion, revealing BAdv85C5 as a promising mucosal vaccine for tuberculosis.

Sarcoidosis: Causes, Diagnosis, Clinical Features, and Treatments

Sarcoidosis is a multisystem granulomatous disease with nonspecific clinical manifestations that commonly affects the pulmonary system and other organs including the eyes, skin, liver, spleen, and lymph nodes. Sarcoidosis usually presents with persistent dry cough, eye and skin manifestations, weight loss, fatigue, night sweats, and erythema nodosum. Sarcoidosis is not influenced by sex or age, although it is more common in adults (< 50 years) of African-American or Scandinavians decent. Diagnosis can be difficult because of nonspecific symptoms and can only be verified following histopathological examination. Various factors, including infection, genetic predisposition, and environmental factors, are involved in the pathology of sarcoidosis. Exposures to insecticides, herbicides, bioaerosols, and agricultural employment are also associated with an increased risk for sarcoidosis. Due to its unknown etiology, early diagnosis and detection are difficult; however, the advent of advanced technologies, such as endobronchial ultrasound-guided biopsy, high-resolution computed tomography, magnetic resonance imaging, and 18F-fluorodeoxyglucose positron emission tomography has improved our ability to reliably diagnose this condition and accurately forecast its prognosis. This review discusses the causes and clinical features of sarcoidosis, and the improvements made in its prognosis, therapeutic management, and the recent discovery of potential biomarkers associated with the diagnostic assay used for sarcoidosis confirmation.

Engineered immunogen binding to alum adjuvant enhances humoral immunity

Adjuvants are central to the efficacy of subunit vaccines. Aluminum hydroxide (alum) is the most commonly used vaccine adjuvant, yet its adjuvanticity is often weak and mechanisms of triggering antibody responses remain poorly understood. We demonstrate that site-specific modification of immunogens with short peptides composed of repeating phosphoserine (pSer) residues enhances binding to alum and prolongs immunogen bioavailability. The pSer-modified immunogens formulated in alum elicited greatly increased germinal center, antibody, neutralizing antibody, memory and long-lived plasma cell responses compared to conventional alum-adsorbed immunogens. Mechanistically, pSer-immunogen:alum complexes form nanoparticles that traffic to lymph nodes and trigger B cell activation through multivalent and oriented antigen display. Direct uptake of antigen-decorated alum particles by B cells upregulated antigen processing and presentation pathways, further enhancing B cell activation. These data provide insights into mechanisms of action of alum and introduce a readily translatable approach to significantly improve humoral immunity to subunit vaccines using a clinical adjuvant.

Sequencing-based network analysis provides a core set of gene resource for understanding kidney immune response against Edwardsiella tarda infection in Japanese flounder

Marine organisms are under a frequent threat from various pathogens. Edwardsiella tarda is one of the major fish pathogens infecting both cultured and wild fish species. It can also infect a variety of other vertebrates, including amphibians, reptiles, and mammals, and bacteremia caused by E. tarda can be fatal in humans. The kidney is the largest lymphoid organ in fish, and generating kidney transcriptomic information under different stresses is crucial for understanding molecular mechanisms underlying the immune responses in the kidneys. In this study, we performed transcriptome-wide gene expression profiling of the Japanese flounder (Paralichthys olivaceus) challenged by 8 and 48 h of E. tarda infection. An average of 40 million clean reads per library was obtained, and approximately 81.6% of these reads were successfully mapped to the reference genome. In addition, 1319 and 4439 differentially expressed genes (DEGs) were found at 8 and 48 h post-injection, respectively. Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to search immune-related DEGs. A protein-protein interaction network was constructed to ascertain the relationship between interacting immune genes during pathogen-induced stress. Based on the KEGG and protein association networks analysis, 24 hub genes were discovered and validated by qRT-PCR. To our knowledge, this study is the first to represent the kidney transcriptome analysis based on protein-protein interaction networks in fish. Our results provide valuable gene resources for further research on kidney immune response in fish, which can significantly improve our understanding of the molecular mechanisms underlying the immune response to E. tarda in humans and other vertebrates.

A decision analysis model for KEGG pathway analysis

Background

The knowledge base-driven pathway analysis is becoming the first choice for many investigators, in that it not only can reduce the complexity of functional analysis by grouping thousands of genes into just several hundred pathways, but also can increase the explanatory power for the experiment by identifying active pathways in different conditions. However, current approaches are designed to analyze a biological system assuming that each pathway is independent of the other pathways.

Results

A decision analysis model is developed in this article that accounts for dependence among pathways in time-course experiments and multiple treatments experiments. This model introduces a decision coefficient—a designed index, to identify the most relevant pathways in a given experiment by taking into account not only the direct determination factor of each Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway itself, but also the indirect determination factors from its related pathways. Meanwhile, the direct and indirect determination factors of each pathway are employed to demonstrate the regulation mechanisms among KEGG pathways, and the sign of decision coefficient can be used to preliminarily estimate the impact direction of each KEGG pathway. The simulation study of decision analysis demonstrated the application of decision analysis model for KEGG pathway analysis.

Conclusions

A microarray dataset from bovine mammary tissue over entire lactation cycle was used to further illustrate our strategy. The results showed that the decision analysis model can provide the promising and more biologically meaningful results. Therefore, the decision analysis model is an initial attempt of optimizing pathway analysis methodology.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1285-1) contains supplementary material, which is available to authorized users.

Influenza Research Database: An integrated bioinformatics resource for influenza virus research

The Influenza Research Database (IRD) is a U.S. National Institute of Allergy and Infectious Diseases (NIAID)-sponsored Bioinformatics Resource Center dedicated to providing bioinformatics support for influenza virus research. IRD facilitates the research and development of vaccines, diagnostics and therapeutics against influenza virus by providing a comprehensive collection of influenza-related data integrated from various sources, a growing suite of analysis and visualization tools for data mining and hypothesis generation, personal workbench spaces for data storage and sharing, and active user community support. Here, we describe the recent improvements in IRD including the use of cloud and high performance computing resources, analysis and visualization of user-provided sequence data with associated metadata, predictions of novel variant proteins, annotations of phenotype-associated sequence markers and their predicted phenotypic effects, hemagglutinin (HA) clade classifications, an automated tool for HA subtype numbering conversion, linkouts to disease event data and the addition of host factor and antiviral drug components. All data and tools are freely available without restriction from the IRD website at https://www.fludb.org.

Molecular Profile of Mitochondrial Dysfunction in Kidney Transplant Biopsies Is Associated With Poor Allograft Outcome

BACKGROUND

In kidney transplantation (KT), progression of chronic histological damage with subclinical inflammation is associated with poor long-term allograft survival. The role of nonimmunological pathways in chronic allograft injury has not been fully assessed.

METHODS

We analyzed a public microarray dataset that used 1-year protocol kidney transplant biopsy specimens to investigate whether nonimmunological genes and pathways might influence long-term allograft outcome. The selected microarray dataset included 3 patient/sample groups based on their histological findings: normal histology (n = 25), interstitial fibrosis alone (IF alone, n = 24), and interstitial fibrosis with inflammation (IF+i, n = 16). The IF+i group had lower death-censored graft survival and renal function in patients with a mean follow-up of 4 years. We performed statistical analysis comparing gene expression patterns in the 3 group samples.

RESULTS

Gene cluster enrichment and group-specific expression patterns demonstrated a divergent pattern between mitochondrial and immune response genes, with downregulation of mitochondrial genes in the IF+i group. Gene ontological analysis of the downregulated mitochondrial genes identified generation of precursor metabolite and energy, and response to oxidative stress as the most significant biological processes. The transcription regulation pathway analysis of downregulated gene cluster demonstrated transcription factors involved in mitochondrial biogenesis.

CONCLUSIONS

The molecular signature of mitochondrial dysfunction reflects mitochondrial energetic insufficiency, and inadequate antioxidant response involved in mitochondria biogenesis pathways is associated with IF+i and worse long-term allograft survival. Thus, mitochondria function impairment appears to be an important nonimmune factor involved in chronic allograft injury.

Response of human skin to esthetic scarification

This study was undertaken to investigate changes in RNA expression in previously healthy adult human skin following thermal injury induced by contact with hot metal that was undertaken as part of esthetic scarification, a body modification practice. Subjects were recruited to have pre-injury skin and serial wound biopsies performed. 4 mm punch biopsies were taken prior to branding and 1 h, 1 week, and 1, 2 and 3 months after injury. RNA was extracted and quality assured prior to the use of a whole-genome based bead array platform to describe expression changes in the samples using the pre-injury skin as a comparator. Analysis of the array data was performed using k-means clustering and a hypergeometric probability distribution without replacement and corrections for multiple comparisons were done. Confirmatory q-PCR was performed. Using a k of 10, several clusters of genes were shown to co-cluster together based on Gene Ontology classification with probabilities unlikely to occur by chance alone. OF particular interest were clusters relating to cell cycle, proteinaceous extracellular matrix and keratinization. Given the consistent expression changes at 1 week following injury in the cell cycle cluster, there is an opportunity to intervene early following burn injury to influence scar development.

Transcriptome analysis of Litopenaeus vannamei in response to white spot syndrome virus infection

Pacific white shrimp (Litopenaeus vannamei) is the most extensively farmed crustacean species in the world. White spot syndrome virus (WSSV) is one of the major pathogens in the cultured shrimp. However, the molecular mechanisms of the host-virus interaction remain largely unknown. In this study, the impact of WSSV infection on host gene expression in the hepatopancreas of L. vannamei was investigated through the use of 454 pyrosequencing-based RNA-Seq of cDNA libraries developed from WSSV-challenged shrimp or normal controls. By comparing the two cDNA libraries, we show that 767 host genes are significantly up-regulated and 729 genes are significantly down-regulated by WSSV infection. KEGG analysis of the differentially expressed genes indicated that the distribution of gene pathways between the up- and down-regulated genes is quite different. Among the differentially expressed genes, several are found to be involved in various processes of animal defense against pathogens such as apoptosis, mitogen-activated protein kinase (MAPK) signaling, toll-like receptor (TLR) signaling, Wnt signaling and antigen processing and presentation pathways. The present study provides valuable information on differential expression of L. vannamei genes following WSSV infection and improves our current understanding of this host-virus interaction. In addition, the large number of transcripts obtained in this study provides a strong basis for future genomic research on shrimp.

B-cell ligand processing pathways detected by large-scale comparative analysis

The initiation of B-cell ligand recognition is a critical step for the generation of an immune response against foreign bodies. We sought to identify the biochemical pathways involved in the B-cell ligand recognition cascade and sets of ligands that trigger similar immunological responses. We utilized several comparative approaches to analyze the gene coexpression networks generated from a set of microarray experiments spanning 33 different ligands. First, we compared the degree distributions of the generated networks. Second, we utilized a pairwise network alignment algorithm, BiNA, to align the networks based on the hubs in the networks. Third, we aligned the networks based on a set of KEGG pathways. We summarized our results by constructing a consensus hierarchy of pathways that are involved in B cell ligand recognition. The resulting pathways were further validated through literature for their common physiological responses. Collectively, the results based on our comparative analyses of degree distributions, alignment of hubs, and alignment based on KEGG pathways provide a basis for molecular characterization of the immune response states of B-cells and demonstrate the power of comparative approaches (e.g., gene coexpression network alignment algorithms) in elucidating biochemical pathways involved in complex signaling events in cells.

Interferon-inducible transmembrane proteins of the innate immune response act as membrane organizers by influencing clathrin and v-ATPase localization and function

The innate response interferon-inducible transmembrane (Ifitm) proteins have been characterized as influencing proliferation, signaling complexes and restricting virus infections. Treatment of cells lacking these proteins (IfitmDel) with IFN-β resulted in the loss of clathrin from membrane compartments and the inhibition of clathrin-mediated phagocytosis, suggesting a molecular interaction between clathrin and Ifitm proteins. The pH of endosomes of IfitmDel cells, with or without IFN activation, was neutralized, suggesting the function of the vacular ATPase proton pumps in such cells was compromised. Co-immunoprecipitation of Ifitm3 with Atp6v0b demonstrated a direct interaction between the Ifitm proteins and the v-ATPase. These data suggest that the Ifitm proteins help stabilize v-ATPase complexes in cellular membranes which, in turn, facilitates the appropriate subcellular localization of clathrin.

A gene selection method for GeneChip array data with small sample sizes

Background

In microarray experiments with small sample sizes, it is a challenge to estimate p-values accurately and decide cutoff p-values for gene selection appropriately. Although permutation-based methods have proved to have greater sensitivity and specificity than the regular t-test, their p-values are highly discrete due to the limited number of permutations available in very small sample sizes. Furthermore, estimated permutation-based p-values for true nulls are highly correlated and not uniformly distributed between zero and one, making it difficult to use current false discovery rate (FDR)-controlling methods.

Results

We propose a model-based information sharing method (MBIS) that, after an appropriate data transformation, utilizes information shared among genes. We use a normal distribution to model the mean differences of true nulls across two experimental conditions. The parameters of the model are then estimated using all data in hand. Based on this model, p-values, which are uniformly distributed from true nulls, are calculated. Then, since FDR-controlling methods are generally not well suited to microarray data with very small sample sizes, we select genes for a given cutoff p-value and then estimate the false discovery rate.

Conclusion

Simulation studies and analysis using real microarray data show that the proposed method, MBIS, is more powerful and reliable than current methods. It has wide application to a variety of situations.

The impact of focused Gene Ontology curation of specific mammalian systems

The Gene Ontology (GO) resource provides dynamic controlled vocabularies to provide an information-rich resource to aid in the consistent description of the functional attributes and subcellular locations of gene products from all taxonomic groups (www.geneontology.org). System-focused projects, such as the Renal and Cardiovascular GO Annotation Initiatives, aim to provide detailed GO data for proteins implicated in specific organ development and function. Such projects support the rapid evaluation of new experimental data and aid in the generation of novel biological insights to help alleviate human disease. This paper describes the improvement of GO data for renal and cardiovascular research communities and demonstrates that the cardiovascular-focused GO annotations, created over the past three years, have led to an evident improvement of microarray interpretation. The reanalysis of cardiovascular microarray datasets confirms the need to continue to improve the annotation of the human proteome.

AVAILABILITY

GO ANNOTATION DATA IS FREELY AVAILABLE FROM: ftp://ftp.geneontology.org/pub/go/gene-associations/

Mass spectrometry analysis and quantitation of peptides presented on the MHC II molecules of mouse spleen dendritic cells

Major histocompatibility complex class II (MHC II) molecules are expressed on the surface of antigen-presenting cells and display short bound peptide fragments derived from self- and nonself antigens. These peptide-MHC complexes function to maintain immunological tolerance in the case of self-antigens and initiate the CD4(+) T cell response in the case of foreign proteins. Here we report the application of LC-MS/MS analysis to identify MHC II peptides derived from endogenous proteins expressed in freshly isolated murine splenic DCs. The cell number was enriched in vivo upon treatment with Flt3L-B16 melanoma cells. In a typical experiment, starting with about 5 × 10(8) splenic DCs, we were able to reliably identify a repertoire of over 100 MHC II peptides originating from about 55 proteins localized in membrane (23%), intracellular (26%), endolysosomal (12%), nuclear (14%), and extracellular (25%) compartments. Using synthetic isotopically labeled peptides corresponding to the sequences of representative bound MHC II peptides, we quantified by LC-MS relative peptide abundance. In a single experiment, peptides were detected in a wide concentration range spanning from 2.5 fmol/μL to 12 pmol/μL or from approximately 13 to 2 × 10(5) copies per DC. These peptides were found in similar amounts on B cells where we detected about 80 peptides originating from 55 proteins distributed homogenously within the same cellular compartments as in DCs. About 90 different binding motifs predicted by the epitope prediction algorithm were found within the sequences of the identified MHC II peptides. These results set a foundation for future studies to quantitatively investigate the MHC II repertoire on DCs generated under different immunization conditions.

Saliva Ontology: an ontology-based framework for a Salivaomics Knowledge Base

Background

The Salivaomics Knowledge Base (SKB) is designed to serve as a computational infrastructure that can permit global exploration and utilization of data and information relevant to salivaomics. SKB is created by aligning (1) the saliva biomarker discovery and validation resources at UCLA with (2) the ontology resources developed by the OBO (Open Biomedical Ontologies) Foundry, including a new Saliva Ontology (SALO).

Results

We define the Saliva Ontology (SALO; http://www.skb.ucla.edu/SALO/) as a consensus-based controlled vocabulary of terms and relations dedicated to the salivaomics domain and to saliva-related diagnostics following the principles of the OBO (Open Biomedical Ontologies) Foundry.

Conclusions

The Saliva Ontology is an ongoing exploratory initiative. The ontology will be used to facilitate salivaomics data retrieval and integration across multiple fields of research together with data analysis and data mining. The ontology will be tested through its ability to serve the annotation ('tagging') of a representative corpus of salivaomics research literature that is to be incorporated into the SKB.

GO-Bayes: Gene Ontology-based overrepresentation analysis using a Bayesian approach

MOTIVATION

A typical approach for the interpretation of high-throughput experiments, such as gene expression microarrays, is to produce groups of genes based on certain criteria (e.g. genes that are differentially expressed). To gain more mechanistic insights into the underlying biology, overrepresentation analysis (ORA) is often conducted to investigate whether gene sets associated with particular biological functions, for example, as represented by Gene Ontology (GO) annotations, are statistically overrepresented in the identified gene groups. However, the standard ORA, which is based on the hypergeometric test, analyzes each GO term in isolation and does not take into account the dependence structure of the GO-term hierarchy.

RESULTS

We have developed a Bayesian approach (GO-Bayes) to measure overrepresentation of GO terms that incorporates the GO dependence structure by taking into account evidence not only from individual GO terms, but also from their related terms (i.e. parents, children, siblings, etc.). The Bayesian framework borrows information across related GO terms to strengthen the detection of overrepresentation signals. As a result, this method tends to identify sets of closely related GO terms rather than individual isolated GO terms. The advantage of the GO-Bayes approach is demonstrated with a simulation study and an application example.

ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization

Background

Statistical analysis of DNA microarray data provides a valuable diagnostic tool for the investigation of genetic components of diseases. To take advantage of the multitude of available data sets and analysis methods, it is desirable to combine both different algorithms and data from different studies. Applying ensemble learning, consensus clustering and cross-study normalization methods for this purpose in an almost fully automated process and linking different analysis modules together under a single interface would simplify many microarray analysis tasks.

Results

We present ArrayMining.net, a web-application for microarray analysis that provides easy access to a wide choice of feature selection, clustering, prediction, gene set analysis and cross-study normalization methods. In contrast to other microarray-related web-tools, multiple algorithms and data sets for an analysis task can be combined using ensemble feature selection, ensemble prediction, consensus clustering and cross-platform data integration. By interlinking different analysis tools in a modular fashion, new exploratory routes become available, e.g. ensemble sample classification using features obtained from a gene set analysis and data from multiple studies. The analysis is further simplified by automatic parameter selection mechanisms and linkage to web tools and databases for functional annotation and literature mining.

Conclusion

ArrayMining.net is a free web-application for microarray analysis combining a broad choice of algorithms based on ensemble and consensus methods, using automatic parameter selection and integration with annotation databases.

A Myc-regulated transcriptional network controls B-cell fate in response to BCR triggering

Background

The B cell antigen receptor (BCR) is a signaling complex that mediates the differentiation of stage-specific cell fate decisions in B lymphocytes. While several studies have shown differences in signal transduction components as being key to contrasting phenotypic outcomes, little is known about the differential BCR-triggered gene transcription downstream of the signaling cascades.

Results

Here we define the transcriptional changes that underlie BCR-induced apoptosis and proliferation of immature and mature B cells, respectively. Comparative genome-wide expression profiling identified 24 genes that discriminated between the early responses of the two cell types to BCR stimulation. Using mice with a conditional Myc-deletion, we validated the microarray data by demonstrating that Myc is critical to promoting BCR-triggered B-cell proliferation. We further investigated the Myc-dependent molecular mechanisms and found that Myc promotes a BCR-dependent clonal expansion of mature B cells by inducing proliferation and inhibiting differentiation.

Conclusion

This work provides the first comprehensive analysis of the early transcriptional events that lead to either deletion or clonal expansion of B cells upon antigen recognition, and demonstrates that Myc functions as the hub of a transcriptional network that control B-cell fate in the periphery.

A Distribution-Free Convolution Model for background correction of oligonucleotide microarray data

Introduction

Affymetrix GeneChip^® high-density oligonucleotide arrays are widely used in biological and medical research because of production reproducibility, which facilitates the comparison of results between experiment runs. In order to obtain high-level classification and cluster analysis that can be trusted, it is important to perform various pre-processing steps on the probe-level data to control for variability in sample processing and array hybridization. Many proposed preprocessing methods are parametric, in that they assume that the background noise generated by microarray data is a random sample from a statistical distribution, typically a normal distribution. The quality of the final results depends on the validity of such assumptions.

Results

We propose a Distribution Free Convolution Model (DFCM) to circumvent observed deficiencies in meeting and validating distribution assumptions of parametric methods. Knowledge of array structure and the biological function of the probes indicate that the intensities of mismatched (MM) probes that correspond to the smallest perfect match (PM) intensities can be used to estimate the background noise. Specifically, we obtain the smallest q2 percent of the MM intensities that are associated with the lowest q1 percent PM intensities, and use these intensities to estimate background.

Conclusion

Using the Affymetrix Latin Square spike-in experiments, we show that the background noise generated by microarray experiments typically is not well modeled by a single overall normal distribution. We further show that the signal is not exponentially distributed, as is also commonly assumed. Therefore, DFCM has better sensitivity and specificity, as measured by ROC curves and area under the curve (AUC) than MAS 5.0, RMA, RMA with no background correction (RMA-noBG), GCRMA, PLIER, and dChip (MBEI) for preprocessing of Affymetrix microarray data. These results hold for two spike-in data sets and one real data set that were analyzed. Comparisons with other methods on two spike-in data sets and one real data set show that our nonparametric methods are a superior alternative for background correction of Affymetrix data.

B cell receptor-mediated internalization of salmonella: a novel pathway for autonomous B cell activation and antibody production

The present paradigm is that primary B cells are nonphagocytosing cells. In this study, we demonstrate that human primary B cells are able to internalize bacteria when the bacteria are recognized by the BCR. BCR-mediated internalization of Salmonella typhimurium results in B cell differentiation and secretion of anti-Salmonella Ab by the Salmonella-specific B cells. In addition, BCR-mediated internalization leads to efficient Ag delivery to the MHC class II Ag-loading compartments, even though Salmonella remains vital intracellularly in primary B cells. Consequently, BCR-mediated bacterial uptake induces efficient CD4(+) T cell help, which boosts Salmonella-specific Ab production. BCR-mediated internalization of Salmonella by B cells is superior over extracellular Ag extraction to induce rapid and specific humoral immune responses and efficiently combat infection.

An improved ontological representation of dendritic cells as a paradigm for all cell types

BACKGROUND

Recent increases in the volume and diversity of life science data and information and an increasing emphasis on data sharing and interoperability have resulted in the creation of a large number of biological ontologies, including the Cell Ontology (CL), designed to provide a standardized representation of cell types for data annotation. Ontologies have been shown to have significant benefits for computational analyses of large data sets and for automated reasoning applications, leading to organized attempts to improve the structure and formal rigor of ontologies to better support computation. Currently, the CL employs multiple is_a relations, defining cell types in terms of histological, functional, and lineage properties, and the majority of definitions are written with sufficient generality to hold across multiple species. This approach limits the CL's utility for computation and for cross-species data integration.

RESULTS

To enhance the CL's utility for computational analyses, we developed a method for the ontological representation of cells and applied this method to develop a dendritic cell ontology (DC-CL). DC-CL subtypes are delineated on the basis of surface protein expression, systematically including both species-general and species-specific types and optimizing DC-CL for the analysis of flow cytometry data. We avoid multiple uses of is_a by linking DC-CL terms to terms in other ontologies via additional, formally defined relations such as has_function.

CONCLUSION

This approach brings benefits in the form of increased accuracy, support for reasoning, and interoperability with other ontology resources. Accordingly, we propose our method as a general strategy for the ontological representation of cells. DC-CL is available from http://www.obofoundry.org.

Access to immunology through the Gene Ontology

The Gene Ontology (GO) is widely recognized as the premier tool for the organization and functional annotation of molecular aspects of cellular systems. However, for many immunologists the use of GO is a very foreign concept. Indeed, as a controlled vocabulary, GO can almost be considered a new language, and it can be difficult to appreciate the use and value of this approach for understanding the immune system. This review reflects on the application of GO to the field of immunology and explains the process of GO annotation. Finally, this review hopes to inspire immunologists to invest time and energy in improving both the content of the GO and the quality of GO annotations associated with genes of immunological interest.

Characterization of phototransduction gene knockouts revealed important signaling networks in the light-induced retinal degeneration

Understanding the molecular pathways mediating neuronal function in retinas can be greatly facilitated by the identification of genes regulated in the retinas of different mutants under various light conditions. We attempted to conduct a gene chip analysis study on the genes regulated during rhodopsin kinase (Rhok^−/−) and arrestin (Sag^−/−) knockout and double knockouts in mice retina. Hence, mice were exposed to constant illumination of 450 lux or 6,000 lux on dilated pupils for indicated periods. The retinas were removed after the exposure and processed for microarray analysis. Double knockout was associated with immense changes in gene expression regulating a number of apoptosis inducing transcription factors. Subsequently, network analysis revealed that during early exposure the transcription factors, p53, c-MYC, c-FOS, JUN, and, in late phase, NFκB, appeared to be essential for the initiation of light-induced retinal rod loss, and some other classical pro- and antipoptotic genes appeared to be significantly important as well.

The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration

The value of any kind of data is greatly enhanced when it exists in a form that allows it to be integrated with other data. One approach to integration is through the annotation of multiple bodies of data using common controlled vocabularies or 'ontologies'. Unfortunately, the very success of this approach has led to a proliferation of ontologies, which itself creates obstacles to integration. The Open Biomedical Ontologies (OBO) consortium is pursuing a strategy to overcome this problem. Existing OBO ontologies, including the Gene Ontology, are undergoing coordinated reform, and new ontologies are being created on the basis of an evolving set of shared principles governing ontology development. The result is an expanding family of ontologies designed to be interoperable and logically well formed and to incorporate accurate representations of biological reality. We describe this OBO Foundry initiative and provide guidelines for those who might wish to become involved.

Transcriptomics-based identification of novel factors enhancing heterologous protein secretion in yeasts

Efficient production of heterologous proteins with yeasts and other eukaryotic hosts is often hampered by inefficient secretion of the product. Limitation of protein secretion has been attributed to a low folding rate, and a rational solution is the overexpression of proteins supporting folding, like protein disulfide isomerase (Pdi), or the unfolded protein response transcription factor Hac1. Assuming that other protein factors which are not directly involved in protein folding may also support secretion of heterologous proteins, we set out to analyze the differential transcriptome of a Pichia pastoris strain overexpressing human trypsinogen versus that of a nonexpressing strain. Five hundred twenty-four genes were identified to be significantly regulated. Excluding those genes with totally divergent functions (like, e.g., core metabolism), we reduced this number to 13 genes which were upregulated in the expression strain having potential function in the secretion machinery and in stress regulation. The respective Saccharomyces cerevisiae homologs of these genes, including the previously characterized secretion helpers PDI1, ERO1, SSO2, KAR2/BiP, and HAC1 as positive controls, were cloned and overexpressed in a P. pastoris strain expressing a human antibody Fab fragment. All genes except one showed a positive effect on Fab fragment secretion, as did the controls. Six out of these novel secretion helper factors, more precisely Bfr2 and Bmh2 (involved in protein transport), the chaperones Ssa4 and Sse1, the vacuolar ATPase subunit Cup5, and Kin2 (a protein kinase connected to exocytosis), proved their benefits for practical application in laboratory-scale production processes by increasing both specific production rates and the volumetric productivity of an antibody fragment up to 2.5-fold in fed-batch fermentations of P. pastoris.

Ontology development for biological systems: immunology

We recently implemented improvements to the representation of immunology content of the biological process branch of the Gene Ontology (GO). The aims of the revision were to provide a comprehensive representation of immunological processes and to improve the organization of immunology related terms in the GO to match current concepts in the field of immunology. With these improvements, the GO will better reflect current understanding in the field of immunology and thus prove to be a more valuable resource for knowledge representation in gene annotation and analysis in the areas of immunology related to genomics and bioinformatics.

AVAILABILITY

http://www.geneontology.org.

A distribution free summarization method for Affymetrix GeneChip(R) arrays

MOTIVATION

Affymetrix GeneChip arrays require summarization in order to combine the probe-level intensities into one value representing the expression level of a gene. However, probe intensity measurements are expected to be affected by different levels of non-specific- and cross-hybridization to non-specific transcripts. Here, we present a new summarization technique, the Distribution Free Weighted method (DFW), which uses information about the variability in probe behavior to estimate the extent of non-specific and cross-hybridization for each probe. The contribution of the probe is weighted accordingly during summarization, without making any distributional assumptions for the probe-level data.

RESULTS

We compare DFW with several popular summarization methods on spike-in datasets, via both our own calculations and the 'Affycomp II' competition. The results show that DFW outperforms other methods when sensitivity and specificity are considered simultaneously. With the Affycomp spike-in datasets, the area under the receiver operating characteristic curve for DFW is nearly 1.0 (a perfect value), indicating that DFW can identify all differentially expressed genes with a few false positives. The approach used is also computationally faster than most other methods in current use.

AVAILABILITY

The R code for DFW is available upon request.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.