Manual Gene Ontology annotation workflow at the Mouse Genome Informatics Database

Integration of genome-scale data identifies candidate sleep regulators

STUDY OBJECTIVES

Genetics impacts sleep, yet, the molecular mechanisms underlying sleep regulation remain elusive. In this study, we built machine learning models to predict sleep genes based on their similarity to genes that are known to regulate sleep.

METHODS

We trained a prediction model on thousands of published datasets, representing circadian, immune, sleep deprivation, and many other processes, using a manually curated list of 109 sleep genes.

RESULTS

Our predictions fit with prior knowledge of sleep regulation and identified key genes and pathways to pursue in follow-up studies. As an example, we focused on the NF-κB pathway and showed that chronic activation of NF-κB in a genetic mouse model impacted the sleep-wake patterns.

CONCLUSION

Our study highlights the power of machine learning in integrating prior knowledge and genome-wide data to study genetic regulation of complex behaviors such as sleep.

DIA proteomics identified the potential targets associated with angiogenesis in the mammary glands of dairy cows with hemorrhagic mastitis

Hemorrhagic mastitis (HM) in dairy cows caused great economic losses in the dairy industry due to decreased milk production and increased costs associated with cattle management and treatment. However, the pathological and molecular mechanisms of HM are not well-understood. The present study aimed to investigate differentially expressed proteins (DEPs) associated with HM according to data-independent acquisition (DIA) proteomics. Compared to the mammary glands of healthylactating Holstein cows (Control, C group), the pathology of the HM group displayed massive alveolar infiltration of hemocytes and neutrophils, and the blood vessels, including arteriole, venules and capillaries were incomplete and damaged, with a loss of endothelial cells. DIA proteomics results showed that a total of 3,739 DEPs and 819 biological process terms were screened in the HM group. We focused on the blood, permeability of blood vessel, vascular and angiogenesis of mammary glands, and a total of 99 candidate DEPs, including 60 up- and 39 down-regulated DEPs, were obtained from the Gene Ontology (GO) and Pathway enrichment analyses. Phenotype prediction and function analysis of the DEPs revealed that three DEPs, particularly Caveolin-1(CAV1), were participated in the regulation of angiogenesis. Immunohistochemical and immunofluorescence staining showed that the CAV1 protein was present mainly in the mammary epithelial cells, vascular endothelial cells and vascular smooth muscle cells. The expression level of CAV1 mRNA and protein in the HM group was significantly down-regulated. The results will be helpful to the further understanding of the pathological and molecular mechanisms of HM in dairy cows.

Crowdsourcing biocuration: The Community Assessment of Community Annotation with Ontologies (CACAO)

Experimental data about gene functions curated from the primary literature have enormous value for research scientists in understanding biology. Using the Gene Ontology (GO), manual curation by experts has provided an important resource for studying gene function, especially within model organisms. Unprecedented expansion of the scientific literature and validation of the predicted proteins have increased both data value and the challenges of keeping pace. Capturing literature-based functional annotations is limited by the ability of biocurators to handle the massive and rapidly growing scientific literature. Within the community-oriented wiki framework for GO annotation called the Gene Ontology Normal Usage Tracking System (GONUTS), we describe an approach to expand biocuration through crowdsourcing with undergraduates. This multiplies the number of high-quality annotations in international databases, enriches our coverage of the literature on normal gene function, and pushes the field in new directions. From an intercollegiate competition judged by experienced biocurators, Community Assessment of Community Annotation with Ontologies (CACAO), we have contributed nearly 5,000 literature-based annotations. Many of those annotations are to organisms not currently well-represented within GO. Over a 10-year history, our community contributors have spurred changes to the ontology not traditionally covered by professional biocurators. The CACAO principle of relying on community members to participate in and shape the future of biocuration in GO is a powerful and scalable model used to promote the scientific enterprise. It also provides undergraduate students with a unique and enriching introduction to critical reading of primary literature and acquisition of marketable skills.

Review of Preferential Suspicious Genes in Microtia Patients Through Various Approaches

Recently, an increasing trend of the birth prevalence of anotia/microtia is observed in China, contributed by changes of social environment and lifestyle. There seems to be no major breakthroughs in exact pathogenesis of microtia, though the research results related to molecular genetics unceasingly appear. In this review, the authors focus on the results of various research methods which the authors regard as the preferential suspicious gene pool to facilitate the exploration of the pathogenic genes of microtia, knowing that the mechanism of microtia is very complicated. The advantages and limitations of these various approaches will also be systematically delineated. The authors believe that this review will give a deep insight in the genetic research of microtia and help plastic surgeons manage congenital microtia more effectively.

Chi-miR-3031 regulates beta-casein via the PI3K/AKT-mTOR signaling pathway in goat mammary epithelial cells (GMECs)

BACKGROUND

MicroRNAs can regulate gene expression at the posttranscriptional level through translational repression or target degradation. Our previous investigations examined the differential expression levels of chi-miR-3031 in caprine mammary gland tissues in colostrum and common milk stages.

RESULTS

The present study detected the role of chi-miR-3031 in the lactation mechanisms of GMECs. High-throughput sequencing was used to analyze transcriptomic landscapes of GMECs transfected with chi-miR-3031 mimics (MC) and a mimic negative control (NC). In the MC and NC groups, we acquired 39,793,503 and 36,531,517 uniquely mapped reads, respectively, accounting for 85.85 and 81.66% of total reads. In the MC group, 180 differentially expressed unigenes were downregulated, whereas 157 unigenes were upregulated. KEGG pathway analyses showed that the prolactin, TNF and ErbB signaling pathways, including TGFα, PIK3R3, IGF2, ELF5, IGFBP5 and LHβ genes, played important roles in mammary development and milk secretion. Results from transcriptome sequencing, real-time PCR and western blotting showed that chi-miR-3031 suppressed the expression of IGFBP5 mRNA and protein. The expression levels of β-casein significantly increased in the MC and siRNA-IGFBP5 groups. We observed that the down-regulation of IGFBP5 activated mTOR at the Ser2448 site in GMECs transfected with MC and siRNA-IGFBP5. Previous findings and our results showed that chi-miR-3031 activated the PI3K-AKT-mTOR pathway and increased β-casein expression by down-regulating IGFBP5.

CONCLUSIONS

These findings will afford valuable information for improving milk quality and contribute the development of potential methods for amending lactation performance.

Model organism data evolving in support of translational medicine

Model organism databases (MODs) have been collecting and integrating biomedical research data for 30 years and were designed to meet specific needs of each model organism research community. The contributions of model organism research to understanding biological systems would be hard to overstate. Modern molecular biology methods and cost reductions in nucleotide sequencing have opened avenues for direct application of model organism research to elucidating mechanisms of human diseases. Thus, the mandate for model organism research and databases has now grown to include facilitating use of these data in translational applications. Challenges in meeting this opportunity include the distribution of research data across many databases and websites, a lack of data format standards for some data types, and sustainability of scale and cost for genomic database resources like MODs. The issues of widely distributed data and application of data standards are some of the challenges addressed by FAIR (Findable, Accessible, Interoperable, and Re-usable) data principles. The Alliance of Genome Resources is now moving to address these challenges by bringing together expertly curated research data from fly, mouse, rat, worm, yeast, zebrafish, and the Gene Ontology consortium. Centralized multi-species data access, integration, and format standardization will lower the data utilization barrier in comparative genomics and translational applications and will provide a framework in which sustainable scale and cost can be addressed. This article presents a brief historical perspective on how the Alliance model organisms are complementary and how they have already contributed to understanding the etiology of human diseases. In addition, we discuss four challenges for using data from MODs in translational applications and how the Alliance is working to address them, in part by applying FAIR data principles. Ultimately, combined data from these animal models are more powerful than the sum of the parts.

Sensing the cilium, digital capture of ciliary data for comparative genomics investigations

Background

Cilia are specialized, hair-like structures that project from the cell bodies of eukaryotic cells. With increased understanding of the distribution and functions of various types of cilia, interest in these organelles is accelerating. To effectively use this great expansion in knowledge, this information must be made digitally accessible and available for large-scale analytical and computational investigation. Capture and integration of knowledge about cilia into existing knowledge bases, thus providing the ability to improve comparative genomic data analysis, is the objective of this work.

Methods

We focused on the capture of information about cilia as studied in the laboratory mouse, a primary model of human biology. The workflow developed establishes a standard for capture of comparative functional data relevant to human biology. We established the 310 closest mouse orthologs of the 302 human genes defined in the SYSCILIA Gold Standard set of ciliary genes. For the mouse genes, we identified biomedical literature for curation and used Gene Ontology (GO) curation paradigms to provide functional annotations from these publications.

Results

Employing a methodology for comprehensive capture of experimental data about cilia genes in structured, digital form, we established a workflow for curation of experimental literature detailing molecular function and roles of cilia proteins starting with the mouse orthologs of the human SYSCILIA gene set. We worked closely with the GO Consortium ontology development editors and the SYSCILIA Consortium to improve the representation of ciliary biology within the GO. During the time frame of the ontology improvement project, we have fully curated 134 of these 310 mouse genes, resulting in an increase in the number of ciliary and other experimental annotations.

Conclusions

We have improved the GO annotations available for mouse genes orthologous to the human genes in the SYSCILIA Consortium’s Gold Standard set. In addition, ciliary terminology in the GO itself was improved in collaboration with GO ontology developers and the SYSCILIA Consortium. These improvements to the GO terms for the functions and roles of ciliary proteins, along with the increase in annotations of the corresponding genes, enhance the representation of ciliary processes and localizations and improve access to these data during large-scale bioinformatic analyses.

Electronic supplementary material

The online version of this article (10.1186/s13630-018-0057-0) contains supplementary material, which is available to authorized users.

Primer on the Gene Ontology

The Gene Ontology (GO) project is the largest resource for cataloguing gene function. The combination of solid conceptual underpinnings and a practical set of features have made the GO a widely adopted resource in the research community and an essential resource for data analysis. In this chapter, we provide a concise primer for all users of the GO. We briefly introduce the structure of the ontology and explain how to interpret annotations associated with the GO.

Searching the Mouse Genome Informatics (MGI) Resources for Information on Mouse Biology from Genotype to Phenotype

The Mouse Genome Informatics (MGI) resource provides the research community with access to information on the genetics, genomics, and biology of the laboratory mouse. Core data in MGI include gene characterization and function, phenotype and disease model descriptions, DNA and protein sequence data, gene expression data, vertebrate homologies, SNPs, mapping data, and links to other bioinformatics databases. Semantic integration is supported through the use of standardized nomenclature, and through the use of controlled vocabularies such as the mouse Anatomical Dictionary, the Mammalian Phenotype Ontology, and the Gene Ontologies. MGI extracts and organizes data from primary literature. MGI data are shared with and widely displayed from other bioinformatics resources. The database is updated weekly with curated annotations, and regularly adds new datasets and features. This unit provides a guide to using the MGI bioinformatics resource. © 2016 by John Wiley & Sons, Inc.

Mouse Genome Database (MGD)-2017: community knowledge resource for the laboratory mouse

The Mouse Genome Database (MGD: http://www.informatics.jax.org) is the primary community data resource for the laboratory mouse. It provides a highly integrated and highly curated system offering a comprehensive view of current knowledge about mouse genes, genetic markers and genomic features as well as the associations of those features with sequence, phenotypes, functional and comparative information, and their relationships to human diseases. MGD continues to enhance access to these data, to extend the scope of data content and visualizations, and to provide infrastructure and user support that ensures effective and efficient use of MGD in the advancement of scientific knowledge. Here, we report on recent enhancements made to the resource and new features.

Expression of FBN1 during adipogenesis: Relevance to the lipodystrophy phenotype in Marfan syndrome and related conditions

Fibrillin-1 is a large glycoprotein encoded by the FBN1 gene in humans. It provides strength and elasticity to connective tissues and is involved in regulating the bioavailability of the growth factor TGFβ. Mutations in FBN1 may be associated with depleted or abnormal adipose tissue, seen in some patients with Marfan syndrome and lipodystrophies. As this lack of adipose tissue does not result in high morbidity or mortality, it is generally under-appreciated, but is a cause of psychosocial problems particularly to young patients. We examined the role of fibrillin-1 in adipogenesis. In inbred mouse strains we found significant variation in the level of expression in the Fbn1 gene that correlated with variation in several measures of body fat, suggesting that mouse fibrillin-1 is associated with the level of fat tissue. Furthermore, we found that FBN1 mRNA was up-regulated in the adipose tissue of obese women compared to non-obese, and associated with an increase in adipocyte size. We used human mesenchymal stem cells differentiated in culture to adipocytes to show that fibrillin-1 declines after the initiation of differentiation. Gene expression results from a similar experiment (available through the FANTOM5 project) revealed that the decline in fibrillin-1 protein was paralleled by a decline in FBN1 mRNA. Examination of the FBN1 gene showed that the region commonly affected in FBN1-associated lipodystrophy is highly conserved both across the three human fibrillin genes and across genes encoding fibrillin-1 in vertebrates. These results suggest that fibrillin-1 is involved as the undifferentiated mesenchymal stem cells transition to adipogenesis but then declines as the developing adipocytes take on their final phenotype. Since the C-terminal peptide of fibrillin-1 is a glucogenic hormone, individuals with low fibrillin-1 (for example with FBN1 mutations associated with lipodystrophy) may fail to differentiate adipocytes and/or to accumulate adipocyte lipids, although this still needs to be shown experimentally.

Training and evaluation corpora for the extraction of causal relationships encoded in biological expression language (BEL)

Success in extracting biological relationships is mainly dependent on the complexity of the task as well as the availability of high-quality training data. Here, we describe the new corpora in the systems biology modeling language BEL for training and testing biological relationship extraction systems that we prepared for the BioCreative V BEL track. BEL was designed to capture relationships not only between proteins or chemicals, but also complex events such as biological processes or disease states. A BEL nanopub is the smallest unit of information and represents a biological relationship with its provenance. In BEL relationships (called BEL statements), the entities are normalized to defined namespaces mainly derived from public repositories, such as sequence databases, MeSH or publicly available ontologies. In the BEL nanopubs, the BEL statements are associated with citation information and supportive evidence such as a text excerpt. To enable the training of extraction tools, we prepared BEL resources and made them available to the community. We selected a subset of these resources focusing on a reduced set of namespaces, namely, human and mouse genes, ChEBI chemicals, MeSH diseases and GO biological processes, as well as relationship types ‘increases’ and ‘decreases’. The published training corpus contains 11 000 BEL statements from over 6000 supportive text excerpts. For method evaluation, we selected and re-annotated two smaller subcorpora containing 100 text excerpts. For this re-annotation, the inter-annotator agreement was measured by the BEL track evaluation environment and resulted in a maximal F-score of 91.18% for full statement agreement. In addition, for a set of 100 BEL statements, we do not only provide the gold standard expert annotations, but also text excerpts pre-selected by two automated systems. Those text excerpts were evaluated and manually annotated as true or false supportive in the course of the BioCreative V BEL track task.

Database URL:http://wiki.openbel.org/display/BIOC/Datasets

Application of comparative biology in GO functional annotation: the mouse model

The Gene Ontology (GO) is an important component of modern biological knowledge representation with great utility for computational analysis of genomic and genetic data. The Gene Ontology Consortium (GOC) consists of a large team of contributors including curation teams from most model organism database groups as well as curation teams focused on representation of data relevant to specific human diseases. Key to the generation of consistent and comprehensive annotations is the development and use of shared standards and measures of curation quality. The GOC engages all contributors to work to a defined standard of curation that is presented here in the context of annotation of genes in the laboratory mouse. Comprehensive understanding of the origin, epistemology, and coverage of GO annotations is essential for most effective use of GO resources. Here the application of comparative approaches to capturing functional data in the mouse system is described.

A method for increasing expressivity of Gene Ontology annotations using a compositional approach

Background

The Gene Ontology project integrates data about the function of gene products across a diverse range of organisms, allowing the transfer of knowledge from model organisms to humans, and enabling computational analyses for interpretation of high-throughput experimental and clinical data. The core data structure is the annotation, an association between a gene product and a term from one of the three ontologies comprising the GO. Historically, it has not been possible to provide additional information about the context of a GO term, such as the target gene or the location of a molecular function. This has limited the specificity of knowledge that can be expressed by GO annotations.

Results

The GO Consortium has introduced annotation extensions that enable manually curated GO annotations to capture additional contextual details. Extensions represent effector–target relationships such as localization dependencies, substrates of protein modifiers and regulation targets of signaling pathways and transcription factors as well as spatial and temporal aspects of processes such as cell or tissue type or developmental stage. We describe the content and structure of annotation extensions, provide examples, and summarize the current usage of annotation extensions.

Conclusions

The additional contextual information captured by annotation extensions improves the utility of functional annotation by representing dependencies between annotations to terms in the different ontologies of GO, external ontologies, or an organism’s gene products. These enhanced annotations can also support sophisticated queries and reasoning, and will provide curated, directional links between many gene products to support pathway and network reconstruction.

A guide to best practices for Gene Ontology (GO) manual annotation

The Gene Ontology Consortium (GOC) is a community-based bioinformatics project that classifies gene product function through the use of structured controlled vocabularies. A fundamental application of the Gene Ontology (GO) is in the creation of gene product annotations, evidence-based associations between GO definitions and experimental or sequence-based analysis. Currently, the GOC disseminates 126 million annotations covering >374 000 species including all the kingdoms of life. This number includes two classes of GO annotations: those created manually by experienced biocurators reviewing the literature or by examination of biological data (1.1 million annotations covering 2226 species) and those generated computationally via automated methods. As manual annotations are often used to propagate functional predictions between related proteins within and between genomes, it is critical to provide accurate consistent manual annotations. Toward this goal, we present here the conventions defined by the GOC for the creation of manual annotation. This guide represents the best practices for manual annotation as established by the GOC project over the past 12 years. We hope this guide will encourage research communities to annotate gene products of their interest to enhance the corpus of GO annotations available to all.

Database URL:http://www.geneontology.org