Muscle Research and Gene Ontology: New standards for improved data integration

How Does the Scientific Community Contribute to Gene Ontology?

Collaborations between the scientific community and members of the Gene Ontology (GO) Consortium have led to an increase in the number and specificity of GO terms, as well as increasing the number of GO annotations. A variety of approaches have been taken to encourage research scientists to contribute to the GO, but the success of these approaches has been variable. This chapter reviews both the successes and failures of engaging the scientific community in GO development and annotation, as well as, providing motivation and advice to encourage individual researchers to contribute to GO.

Development of an Ontology for Periodontitis

BACKGROUND

In the clinical dentists and periodontal researchers' community, there is an obvious demand for a systems model capable of linking the clinical presentation of periodontitis to underlying molecular knowledge. A computer-readable representation of processes on disease development will give periodontal researchers opportunities to elucidate pathways and mechanisms of periodontitis. An ontology for periodontitis can be a model for integration of large variety of factors relating to a complex disease such as chronic inflammation in different organs accompanied by bone remodeling and immune system disorders, which has recently been referred to as osteoimmunology.

METHODS

Terms characteristic of descriptions related to the onset and progression of periodontitis were manually extracted from 194 review articles and PubMed abstracts by experts in periodontology. We specified all the relations between the extracted terms and constructed them into an ontology for periodontitis. We also investigated matching between classes of our ontology and that of Gene Ontology Biological Process.

RESULTS

We developed an ontology for periodontitis called Periodontitis-Ontology (PeriO). The pathological progression of periodontitis is caused by complex, multi-factor interrelationships. PeriO consists of all the required concepts to represent the pathological progression and clinical treatment of periodontitis. The pathological processes were formalized with reference to Basic Formal Ontology and Relation Ontology, which accounts for participants in the processes realized by biological objects such as molecules and cells. We investigated the peculiarity of biological processes observed in pathological progression and medical treatments for the disease in comparison with Gene Ontology Biological Process (GO-BP) annotations. The results indicated that peculiarities of Perio existed in 1) granularity and context dependency of both the conceptualizations, and 2) causality intrinsic to the pathological processes. PeriO defines more specific concepts than GO-BP, and thus can be added as descendants of GO-BP leaf nodes. PeriO defines causal relationships between the process concepts, which are not shown in GO-BP. The difference can be explained by the goal of conceptualization: PeriO focuses on mechanisms of the pathogenic progress, while GO-BP focuses on cataloguing all of the biological processes observed in experiments. The goal of conceptualization in PeriO may reflect the domain knowledge where a consequence in the causal relationships is a primary interest. We believe the peculiarities can be shared among other diseases when comparing processes in disease against GO-BP.

CONCLUSIONS

This is the first open biomedical ontology of periodontitis capable of providing a foundation for an ontology-based model of aspects of molecular biology and pathological processes related to periodontitis, as well as its relations with systemic diseases. PeriO is available at http://bio-omix.tmd.ac.jp/periodontitis/.

Building blocks for a clinical imaging informatics environment

Over the past 20 years, imaging informatics has been driven by the widespread adoption of radiology information and picture archiving and communication and speech recognition systems. These three clinical information systems are commonplace and are intuitive to most radiologists as they replicate familiar paper and film workflow. So what is next? There is a surge of innovation in imaging informatics around advanced workflow, search, electronic medical record aggregation, dashboarding, and analytics tools for quality measures (Nance et al., AJR Am J Roentgenol 200:1064-1070, 2013). The challenge lies in not having to rebuild the technological wheel for each of these new applications but instead attempt to share common components through open standards and modern development techniques. The next generation of applications will be built with moving parts that work together to satisfy advanced use cases without replicating databases and without requiring fragile, intense synchronization from clinical systems. The purpose of this paper is to identify building blocks that can position a practice to be able to quickly innovate when addressing clinical, educational, and research-related problems. This paper is the result of identifying common components in the construction of over two dozen clinical informatics projects developed at the University of Maryland Radiology Informatics Research Laboratory. The systems outlined are intended as a mere foundation rather than an exhaustive list of possible extensions.

Representing kidney development using the gene ontology

Gene Ontology (GO) provides dynamic controlled vocabularies to aid in the description of the functional biological attributes and subcellular locations of gene products from all taxonomic groups (www.geneontology.org). Here we describe collaboration between the renal biomedical research community and the GO Consortium to improve the quality and quantity of GO terms describing renal development. In the associated annotation activity, the new and revised terms were associated with gene products involved in renal development and function. This project resulted in a total of 522 GO terms being added to the ontology and the creation of approximately 9,600 kidney-related GO term associations to 940 UniProt Knowledgebase (UniProtKB) entries, covering 66 taxonomic groups. We demonstrate the impact of these improvements on the interpretation of GO term analyses performed on genes differentially expressed in kidney glomeruli affected by diabetic nephropathy. In summary, we have produced a resource that can be utilized in the interpretation of data from small- and large-scale experiments investigating molecular mechanisms of kidney function and development and thereby help towards alleviating renal disease.

A methodology for extending domain coverage in SemRep

We describe a domain-independent methodology to extend SemRep coverage beyond the biomedical domain. SemRep, a natural language processing application originally designed for biomedical texts, uses the knowledge sources provided by the Unified Medical Language System (UMLS©). Ontological and terminological extensions to the system are needed in order to support other areas of knowledge. We extended SemRep's application by developing a semantic representation of a previously unsupported domain. This was achieved by adapting well-known ontology engineering phases and integrating them with the UMLS knowledge sources on which SemRep crucially depends. While the process to extend SemRep coverage has been successfully applied in earlier projects, this paper presents in detail the step-wise approach we followed and the mechanisms implemented. A case study in the field of medical informatics illustrates how the ontology engineering phases have been adapted for optimal integration with the UMLS. We provide qualitative and quantitative results, which indicate the validity and usefulness of our methodology.

Extending SemRep to the Public Health Domain

We describe the use of a domain-independent methodology to extend a natural language processing (NLP) application, SemRep (Rindflesch, Fiszman, & Libbus, 2005), based on the knowledge sources afforded by the Unified Medical Language System (UMLS®) (Humphreys, Lindberg, Schoolman, & Barnett, 1998) to support the area of health promotion within the public health domain. Public health professionals require good information about successful health promotion policies and programs that might be considered for application within their own communities. Our effort seeks to improve access to relevant information for the public health profession, to help those in the field remain an information-savvy workforce. NLP and semantic techniques hold promise to help public health professionals navigate the growing ocean of information by organizing and structuring this knowledge into a focused public health framework paired with a user-friendly visualization application as a way to summarize results of PubMed searches in this field of knowledge.

Systems analysis of biological networks in skeletal muscle function

Skeletal muscle function depends on the efficient coordination among subcellular systems. These systems are composed of proteins encoded by a subset of genes, all of which are tightly regulated. In the cases where regulation is altered because of disease or injury, dysfunction occurs. To enable objective analysis of muscle gene expression profiles, we have defined nine biological networks whose coordination is critical to muscle function. We begin by describing the expression of proteins necessary for optimal neuromuscular junction function that results in the muscle cell action potential. That action potential is transmitted to proteins involved in excitation-contraction coupling enabling Ca(2+) release. Ca(2+) then activates contractile proteins supporting actin and myosin cross-bridge cycling. Force generated by cross-bridges is transmitted via cytoskeletal proteins through the sarcolemma and out to critical proteins that support the muscle extracellular matrix. Muscle contraction is fueled through many proteins that regulate energy metabolism. Inflammation is a common response to injury that can result in alteration of many pathways within muscle. Muscle also has multiple pathways that regulate size through atrophy or hypertrophy. Finally, the isoforms associated with fast muscle fibers and their corresponding isoforms in slow muscle fibers are delineated. These nine networks represent important biological systems that affect skeletal muscle function. Combining high-throughput systems analysis with advanced networking software will allow researchers to use these networks to objectively study skeletal muscle systems.

Transcriptional abnormalities of hamstring muscle contractures in children with cerebral palsy

Cerebral palsy (CP) is an upper motor neuron disease that results in a spectrum of movement disorders. Secondary to the neurological lesion, muscles from patients with CP are often spastic and form debilitating contractures that limit range of motion and joint function. With no genetic component, the pathology of skeletal muscle in CP is a response to aberrant complex neurological input in ways that are not fully understood. This study was designed to gain further understanding of the skeletal muscle response in CP using transcriptional profiling correlated with functional measures to broadly investigate muscle adaptations leading to mechanical deficits.Biopsies were obtained from both the gracilis and semitendinosus muscles from a cohort of patients with CP (n = 10) and typically developing patients (n = 10) undergoing surgery. Biopsies were obtained to define the unique expression profile of the contractures and passive mechanical testing was conducted to determine stiffness values in previously published work. Affymetrix HG-U133A 2.0 chips (n = 40) generated expression data, which was validated for selected transcripts using quantitative real-time PCR. Chips were clustered based on their expression and those from patients with CP clustered separately. Significant genes were determined conservatively based on the overlap of three summarization algorithms (n = 1,398). Significantly altered genes were analyzed for over-representation among gene ontologies and muscle specific networks.The majority of altered transcripts were related to increased extracellular matrix expression in CP and a decrease in metabolism and ubiquitin ligase activity. The increase in extracellular matrix products was correlated with mechanical measures demonstrating the importance in disability. These data lay a framework for further studies and development of novel therapies.

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/

The representation of heart development in the gene ontology

An understanding of heart development is critical in any systems biology approach to cardiovascular disease. The interpretation of data generated from high-throughput technologies (such as microarray and proteomics) is also essential to this approach. However, characterizing the role of genes in the processes underlying heart development and cardiovascular disease involves the non-trivial task of data analysis and integration of previous knowledge. The Gene Ontology (GO) Consortium provides structured controlled biological vocabularies that are used to summarize previous functional knowledge for gene products across all species. One aspect of GO describes biological processes, such as development and signaling. In order to support high-throughput cardiovascular research, we have initiated an effort to fully describe heart development in GO; expanding the number of GO terms describing heart development from 12 to over 280. This new ontology describes heart morphogenesis, the differentiation of specific cardiac cell types, and the involvement of signaling pathways in heart development. This work also aligns GO with the current views of the heart development research community and its representation in the literature. This extension of GO allows gene product annotators to comprehensively capture the genetic program leading to the developmental progression of the heart. This will enable users to integrate heart development data across species, resulting in the comprehensive retrieval of information about this subject. The revised GO structure, combined with gene product annotations, should improve the interpretation of data from high-throughput methods in a variety of cardiovascular research areas, including heart development, congenital cardiac disease, and cardiac stem cell research. Additionally, we invite the heart development community to contribute to the expansion of this important dataset for the benefit of future research in this area.

Microgenomic analysis in skeletal muscle: expression signatures of individual fast and slow myofibers

Background

Skeletal muscle is a complex, versatile tissue composed of a variety of functionally diverse fiber types. Although the biochemical, structural and functional properties of myofibers have been the subject of intense investigation for the last decades, understanding molecular processes regulating fiber type diversity is still complicated by the heterogeneity of cell types present in the whole muscle organ.

Methodology/Principal Findings

We have produced a first catalogue of genes expressed in mouse slow-oxidative (type 1) and fast-glycolytic (type 2B) fibers through transcriptome analysis at the single fiber level (microgenomics). Individual fibers were obtained from murine soleus and EDL muscles and initially classified by myosin heavy chain isoform content. Gene expression profiling on high density DNA oligonucleotide microarrays showed that both qualitative and quantitative improvements were achieved, compared to results with standard muscle homogenate. First, myofiber profiles were virtually free from non-muscle transcriptional activity. Second, thousands of muscle-specific genes were identified, leading to a better definition of gene signatures in the two fiber types as well as the detection of metabolic and signaling pathways that are differentially activated in specific fiber types. Several regulatory proteins showed preferential expression in slow myofibers. Discriminant analysis revealed novel genes that could be useful for fiber type functional classification.

Conclusions/Significance

As gene expression analyses at the single fiber level significantly increased the resolution power, this innovative approach would allow a better understanding of the adaptive transcriptomic transitions occurring in myofibers under physiological and pathological conditions.

High density gene expression microarrays and gene ontology analysis for identifying processes in implanted tissue engineering constructs

The in vivo performance of tissue-engineered constructs is often based on generally accepted read-out parameters, like (immuno)histology. In this study, high-density gene expression microarrays and gene ontology (GO) analysis were used as a read-out tool to identify the biological processes occurring after implantation of an acellular collagen-based skin construct using a rat full-thickness wound model. A freely-available program (DAVID) was used to identify up/downregulated biological processes (GO-terms) and results were compared to wound healing/regeneration without a construct. The entire process from RNA isolation to biological interpretation is explained step-by-step. Conventional (immuno)histology was used to validate the biological processes identified and indicate that microarray analysis may provide a valuable, fast and unbiased tool to evaluate the in vivo performance of tissue-engineered constructs. However, challenges remain e.g. with regards to the development of specific GO-terms and annotation of the (rat) genome.

Representing ontogeny through ontology: a developmental biologist's guide to the gene ontology

Developmental biology, like many other areas of biology, has undergone a dramatic shift in the perspective from which developmental processes are viewed. Instead of focusing on the actions of a handful of genes or functional RNAs, we now consider the interactions of large functional gene networks and study how these complex systems orchestrate the unfolding of an organism, from gametes to adult. Developmental biologists are beginning to realize that understanding ontogeny on this scale requires the utilization of computational methods to capture, store and represent the knowledge we have about the underlying processes. Here we review the use of the Gene Ontology (GO) to study developmental biology. We describe the organization and structure of the GO and illustrate some of the ways we use it to capture the current understanding of many common developmental processes. We also discuss ways in which gene product annotations using the GO have been used to ask and answer developmental questions in a variety of model developmental systems. We provide suggestions as to how the GO might be used in more powerful ways to address questions about development. Our goal is to provide developmental biologists with enough background about the GO that they can begin to think about how they might use the ontology efficiently and in the most powerful ways possible.

Community annotation in biology

Attempts to engage the scientific community to annotate biological data (such as protein/gene function) stored in databases have not been overly successful. There are several hypotheses on why this has not been successful but it is not clear which of these hypotheses are correct. In this study we have surveyed 50 biologists (who have recently published a paper characterizing a gene or protein) to better understand what would make them interested in providing input/contributions to biological databases. Based on our survey two things become clear: a) database managers need to proactively contact biologists to solicit contributions; and b) potential contributors need to be provided with an easy-to-use interface and clear instructions on what to annotate. Other factors such as 'reward' and 'employer/funding agency recognition' previously perceived as motivators was found to be less important. Based on this study we propose community annotation projects should devote resources to direct solicitation for input and streamlining of the processes or interfaces used to collect this input.

Reviewers

This article was reviewed by I. King Jordan, Daniel Haft and Yuriy Gusev

Acid stress response of a mycobacterial proteome: insight from a gene ontology analysis

Acidity in vesicles of macrophages is a general signal that bacteria respond to during infection. Mycobacteria are particularly capable of resisting the acidification in macrophages that engulf the bacteria. In this work, we used label-free quantitative proteomics to study the Mycobacterium smegmatis proteome under acid stress so as to gain an insight into the acidic adaptation in mycobacteria. We quantified 1032 proteins. With a 3-fold change threshold, 20 and 52 proteins were found regulated at false discovery rates of 5% and 14% respectively. We performed a systems analysis based on gene ontology for the global proteome expression profile. We found that the most significant changes induced by the acid stress include a downregulation of transmembrane transporter activity and an upregulation of enzymes involved in fatty acid metabolism. The results suggest that reduced transmembrane transport and increased fatty acid metabolism probably contribute to or associate with acid tolerance in mycobacteria.