Using ZFIN: Data Types, Organization, and Retrieval

Exploring the dynamic behavior of leukocytes with zebrafish

Cell migration is a complex and intricate network of physical, chemical, and molecular events that ultimately leads to cell motility. This phenomenon is involved in both physiological and pathological processes such as proper immune and inflammatory responses. Dysregulation of cell migration machinery in immune cells can have a tremendous impact on the trajectory of inflammation, infection, and resolution. The small vertebrate, the zebrafish, has a remarkable capacity for genetic and pharmacological manipulation aligned to transparency that enables modulation and visualization of cell migration in vivo noninvasively. Such characteristics revolutionized the field of leukocyte biology, particularly neutrophils. In this review, we will focus on leukocyte migration and highlight findings made in the zebrafish that demonstrate how this small vertebrate system is a unique model to perform in vivo imaging and study mechanisms that regulate the dynamic behavior of immune cells in their native environment under homeostasis or upon challenge.

OrthoDB v11: annotation of orthologs in the widest sampling of organismal diversity

OrthoDB provides evolutionary and functional annotations of genes in a diverse sampling of eukaryotes, prokaryotes, and viruses. Genomics continues to accelerate our exploration of gene diversity and orthology is the most precise way of bridging gene functional knowledge with the rapidly expanding universe of genomic sequences. OrthoDB samples the most diverse organisms with the best quality genomics data to provide the leading coverage of species diversity. This update of the underlying data to over 18 000 prokaryotes and almost 2000 eukaryotes with over 100 million genes propels the coverage to another level. This achievement also demonstrates the scalability of the underlying OrthoLoger software for delineation of orthologs, freely available from https://orthologer.ezlab.org. In addition to the ab-initio computations of gene orthology used for the OrthoDB release, the OrthoLoger software allows mapping of novel gene sets to precomputed orthologs and thereby links to their annotations. The LEMMI-style benchmarking of OrthoLoger ensures its state-of-the-art performance and is available from https://lemortho.ezlab.org. The OrthoDB web interface has been further developed to include a pairwise orthology view from any gene to any other sampled species. OrthoDB-computed evolutionary annotations as well as extensively collated functional annotations can be accessed via REST API or SPARQL/RDF, downloaded or browsed online from https://www.orthodb.org.

ZebraShare: a new venue for rapid dissemination of zebrafish mutant data

Background

In the past decade, the zebrafish community has widely embraced targeted mutagenesis technologies, resulting in an abundance of mutant lines. While many lines have proven to be useful for investigating gene function, many have also shown no apparent phenotype, or phenotypes not of interest to the originating lab. In order for labs to document and share information about these lines, we have created ZebraShare as a new resource offered within ZFIN.

Methods

ZebraShare involves a form-based submission process generated by ZFIN. The ZebraShare interface (https://zfin.org/action/zebrashare) can be accessed on ZFIN under "Submit Data". Users download the Submission Workbook and complete the required fields, then submit the completed workbook with associated images and captions, generating a new ZFIN publication record. ZFIN curators add the submitted phenotype and mutant information to the ZFIN database, provide mapping information about mutations, and cross reference this information across the appropriate ZFIN databases. We present here examples of ZebraShare submissions, including phf21aa, kdm1a, ctnnd1, snu13a, and snu13b mutant lines.

Results

Users can find ZebraShare submissions by searching ZFIN for specific alleles or line designations, just as for alleles submitted through the normal process. We present several potential examples of submission types to ZebraShare including a phenotypic mutants, mildly phenotypic, and early lethal mutants. Mutants for kdm1a show no apparent skeletal phenotype, and phf21aa mutants show only a mild skeletal phenotype, yet these genes have specific human disease relevance and therefore may be useful for further studies. The p120-catenin encoding gene, ctnnd1, was knocked out to investigate a potential role in brain development or function. The homozygous ctnnd1 mutant disintegrates during early somitogenesis and the heterozygote has localized defects, revealing vital roles in early development. Two snu13 genes were knocked out to investigate a role in muscle formation. The snu13a;snu13b double mutant has an early embryonic lethal phenotype, potentially related to a proposed role in the core splicing complex. In each example, the mutants submitted to ZebraShare display phenotypes that are not ideally suited to their originating lab's project directions but may be of great relevance to other researchers.

Conclusion

ZebraShare provides an opportunity for researchers to directly share information about mutant lines within ZFIN, which is widely used by the community as a central database of information about zebrafish lines. Submissions of alleles with a phenotypic or unexpected phenotypes is encouraged to promote collaborations, disseminate lines, reduce redundancy of effort and to promote efficient use of time and resources. We anticipate that as submissions to ZebraShare increase, they will help build an ultimately more complete picture of zebrafish genetics and development.

The Zebrafish Information Network: major gene page and home page updates

The Zebrafish Information Network (ZFIN) (https://zfin.org/) is the database for the model organism, zebrafish (Danio rerio). ZFIN expertly curates, organizes, and provides a wide array of zebrafish genetic and genomic data, including genes, alleles, transgenic lines, gene expression, gene function, mutant phenotypes, orthology, human disease models, gene and mutant nomenclature, and reagents. New features at ZFIN include major updates to the home page and the gene page, the two most used pages at ZFIN. Data including disease models, phenotypes, expression, mutants and gene function continue to be contributed to The Alliance of Genome Resources for integration with similar data from other model organisms.

The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species

In biology and biomedicine, relating phenotypic outcomes with genetic variation and environmental factors remains a challenge: patient phenotypes may not match known diseases, candidate variants may be in genes that haven’t been characterized, research organisms may not recapitulate human or veterinary diseases, environmental factors affecting disease outcomes are unknown or undocumented, and many resources must be queried to find potentially significant phenotypic associations. The Monarch Initiative (https://monarchinitiative.org) integrates information on genes, variants, genotypes, phenotypes and diseases in a variety of species, and allows powerful ontology-based search. We develop many widely adopted ontologies that together enable sophisticated computational analysis, mechanistic discovery and diagnostics of Mendelian diseases. Our algorithms and tools are widely used to identify animal models of human disease through phenotypic similarity, for differential diagnostics and to facilitate translational research. Launched in 2015, Monarch has grown with regards to data (new organisms, more sources, better modeling); new API and standards; ontologies (new Mondo unified disease ontology, improvements to ontologies such as HPO and uPheno); user interface (a redesigned website); and community development. Monarch data, algorithms and tools are being used and extended by resources such as GA4GH and NCATS Translator, among others, to aid mechanistic discovery and diagnostics.

Human Disease Ontology 2018 update: classification, content and workflow expansion

The Human Disease Ontology (DO) (http://www.disease-ontology.org), database has undergone significant expansion in the past three years. The DO disease classification includes specific formal semantic rules to express meaningful disease models and has expanded from a single asserted classification to include multiple-inferred mechanistic disease classifications, thus providing novel perspectives on related diseases. Expansion of disease terms, alternative anatomy, cell type and genetic disease classifications and workflow automation highlight the updates for the DO since 2015. The enhanced breadth and depth of the DO’s knowledgebase has expanded the DO’s utility for exploring the multi-etiology of human disease, thus improving the capture and communication of health-related data across biomedical databases, bioinformatics tools, genomic and cancer resources and demonstrated by a 6.6× growth in DO’s user community since 2015. The DO’s continual integration of human disease knowledge, evidenced by the more than 200 SVN/GitHub releases/revisions, since previously reported in our DO 2015 NAR paper, includes the addition of 2650 new disease terms, a 30% increase of textual definitions, and an expanding suite of disease classification hierarchies constructed through defined logical axioms.

The Zebrafish Information Network: new support for non-coding genes, richer Gene Ontology annotations and the Alliance of Genome Resources

The Zebrafish Information Network (ZFIN) (https://zfin.org/) is the database for the model organism, zebrafish (Danio rerio). ZFIN expertly curates, organizes and provides a wide array of zebrafish genetic and genomic data, including genes, alleles, transgenic lines, gene expression, gene function, mutant phenotypes, orthology, human disease models, nomenclature and reagents. New features at ZFIN include increased support for genomic regions and for non-coding genes, and support for more expressive Gene Ontology annotations. ZFIN has recently taken over maintenance of the zebrafish reference genome sequence as part of the Genome Reference Consortium. ZFIN is also a founding member of the Alliance of Genome Resources, a collaboration of six model organism databases (MODs) and the Gene Ontology Consortium (GO). The recently launched Alliance portal (https://alliancegenome.org) provides a unified, comparative view of MOD, GO, and human data, and facilitates foundational and translational biomedical research.

A unified nomenclature for vertebrate olfactory receptors

BACKGROUND

Olfactory receptors (ORs) are G protein-coupled receptors with a crucial role in odor detection. A typical mammalian genome harbors ~ 1000 OR genes and pseudogenes; however, different gene duplication/deletion events have occurred in each species, resulting in complex orthology relationships. While the human OR nomenclature is widely accepted and based on phylogenetic classification into 18 families and further into subfamilies, for other mammals different and multiple nomenclature systems are currently in use, thus concealing important evolutionary and functional insights.

RESULTS

Here, we describe the Mutual Maximum Similarity (MMS) algorithm, a systematic classifier for assigning a human-centric nomenclature to any OR gene based on inter-species hierarchical pairwise similarities. MMS was applied to the OR repertoires of seven mammals and zebrafish. Altogether, we assigned symbols to 10,249 ORs. This nomenclature is supported by both phylogenetic and synteny analyses. The availability of a unified nomenclature provides a framework for diverse studies, where textual symbol comparison allows immediate identification of potential ortholog groups as well as species-specific expansions/deletions; for example, Or52e5 and Or52e5b represent a rat-specific duplication of OR52E5. Another example is the complete absence of OR subfamily OR6Z among primate OR symbols. In other mammals, OR6Z members are located in one genomic cluster, suggesting a large deletion in the great ape lineage. An additional 14 mammalian OR subfamilies are missing from the primate genomes. While in chimpanzee 87% of the symbols were identical to human symbols, this number decreased to ~ 50% in dog and cow and to ~ 30% in rodents, reflecting the adaptive changes of the OR gene superfamily across diverse ecological niches. Application of the proposed nomenclature to zebrafish revealed similarity to mammalian ORs that could not be detected from the current zebrafish olfactory receptor gene nomenclature.

CONCLUSIONS

We have consolidated a unified standard nomenclature system for the vertebrate OR superfamily. The new nomenclature system will be applied to cow, horse, dog and chimpanzee by the Vertebrate Gene Nomenclature Committee and its implementation is currently under consideration by other relevant species-specific nomenclature committees.

Modeling of Solid-Tumor Microenvironment in Zebrafish (Danio Rerio) Larvae

The zebrafish larvae have emerged as a powerful model for studying tumorigenesis in vivo, with remarkable conservation with mammals in genetics, molecular and cell biology. Zebrafish tumor models bear the significant advantages of optical clarity in comparison to that in the mammalian models, allowing noninvasive investigation of the tumor cell and its microenvironment at single-cell resolution. Here we review recent progressions in the field of zebrafish models of solid tumor diseases in two main categories: the genetically engineered tumor models in which all cells in the tumor microenvironment are zebrafish cells, and xenograft tumor models in which the tumor microenvironment is composed of zebrafish cells and cells from other species. Notably, the zebrafish patient-derived xenograft (zPDX) models can be used for personalized drug assessment on primary tumor biopsies, including the pancreatic cancer. For the future studies, a series of high throughput drug screenings on the library of transgenic zebrafish models of solid tumor are expected to provide systematic database of oncogenic mutation, cell-of-origin, and leading compounds; and the humanization of zebrafish in genetics and cellular composition will make it more practical hosts for zPDX modeling. Together, zebrafish tumor model systems are unique and convenient in vivo platforms, with great potential to serve as valuable tools for cancer researches.