Model organism data evolving in support of translational medicine

Lats1 and Lats2 regulate YAP and TAZ activity to control the development of mouse Sertoli cells

Recent reports suggest that the Hippo signaling pathway regulates testis development, though its exact roles in Sertoli cell differentiation remain unknown. Here, we examined the functions of the main Hippo pathway kinases, large tumor suppressor homolog kinases 1 and 2 (Lats1 and Lats2) in developing mouse Sertoli cells. Conditional inactivation of Lats1/2 in Sertoli cells resulted in the disorganization and overgrowth of the testis cords, the induction of a testicular inflammatory response and germ cell apoptosis. Stimulated by retinoic acid 8 (STRA8) expression in germ cells additionally suggested that germ cells may have been preparing to enter meiosis prior to their loss. Gene expression analyses of the developing testes of conditional knockout animals further suggested impaired Sertoli cell differentiation, epithelial-to-mesenchymal transition, and the induction of a specific set of genes associated with Yes-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ)-mediated integrin signaling. Finally, the involvement of YAP/TAZ in Sertoli cell differentiation was confirmed by concomitantly inactivating Yap/Taz in Lats1/2 conditional knockout model, which resulted in a partial rescue of the testicular phenotypic changes. Taken together, these results identify Hippo signaling as a crucial pathway for Sertoli cell development and provide novel insight into Sertoli cell fate maintenance.

Label-free metabolic optical biomarkers track stem cell fate transition in real time

Tracking stem cell fate transition is crucial for understanding their development and optimizing biomanufacturing. Destructive single-cell methods provide a pseudotemporal landscape of stem cell differentiation but cannot monitor stem cell fate in real time. We established a metabolic optical metric using label-free fluorescence lifetime imaging microscopy (FLIM), feature extraction and machine learning-assisted analysis, for real-time cell fate tracking. From a library of 205 metabolic optical biomarker (MOB) features, we identified 56 associated with hematopoietic stem cell (HSC) differentiation. These features collectively describe HSC fate transition and detect its bifurcate lineage choice. We further derived a MOB score measuring the "metabolic stemness" of single cells and distinguishing their division patterns. This score reveals a distinct role of asymmetric division in rescuing stem cells with compromised metabolic stemness and a unique mechanism of PI3K inhibition in promoting ex vivo HSC maintenance. MOB profiling is a powerful tool for tracking stem cell fate transition and improving their biomanufacturing from a single-cell perspective.

Updates to the Alliance of Genome Resources central infrastructure

The Alliance of Genome Resources (Alliance) is an extensible coalition of knowledgebases focused on the genetics and genomics of intensively studied model organisms. The Alliance is organized as individual knowledge centers with strong connections to their research communities and a centralized software infrastructure, discussed here. Model organisms currently represented in the Alliance are budding yeast, Caenorhabditis elegans, Drosophila, zebrafish, frog, laboratory mouse, laboratory rat, and the Gene Ontology Consortium. The project is in a rapid development phase to harmonize knowledge, store it, analyze it, and present it to the community through a web portal, direct downloads, and application programming interfaces (APIs). Here, we focus on developments over the last 2 years. Specifically, we added and enhanced tools for browsing the genome (JBrowse), downloading sequences, mining complex data (AllianceMine), visualizing pathways, full-text searching of the literature (Textpresso), and sequence similarity searching (SequenceServer). We enhanced existing interactive data tables and added an interactive table of paralogs to complement our representation of orthology. To support individual model organism communities, we implemented species-specific "landing pages" and will add disease-specific portals soon; in addition, we support a common community forum implemented in Discourse software. We describe our progress toward a central persistent database to support curation, the data modeling that underpins harmonization, and progress toward a state-of-the-art literature curation system with integrated artificial intelligence and machine learning (AI/ML).

GPAD: a natural language processing-based application to extract the gene-disease association discovery information from OMIM

BACKGROUND

Thousands of genes have been associated with different Mendelian conditions. One of the valuable sources to track these gene-disease associations (GDAs) is the Online Mendelian Inheritance in Man (OMIM) database. However, most of the information in OMIM is textual, and heterogeneous (e.g. summarized by different experts), which complicates automated reading and understanding of the data. Here, we used Natural Language Processing (NLP) to make a tool (Gene-Phenotype Association Discovery (GPAD)) that could syntactically process OMIM text and extract the data of interest.

RESULTS

GPAD applies a series of language-based techniques to the text obtained from OMIM API to extract GDA discovery-related information. GPAD can inform when a particular gene was associated with a specific phenotype, as well as the type of validation-whether through model organisms or cohort-based patient-matching approaches-for such an association. GPAD extracted data was validated with published reports and was compared with large language model. Utilizing GPAD's extracted data, we analysed trends in GDA discoveries, noting a significant increase in their rate after the introduction of exome sequencing, rising from an average of about 150-250 discoveries each year. Contrary to hopes of resolving most GDAs for Mendelian disorders by now, our data indicate a substantial decline in discovery rates over the past five years (2017-2022). This decline appears to be linked to the increasing necessity for larger cohorts to substantiate GDAs. The rising use of zebrafish and Drosophila as model organisms in providing evidential support for GDAs is also observed.

CONCLUSIONS

GPAD's real-time analyzing capacity offers an up-to-date view of GDA discovery and could help in planning and managing the research strategies. In future, this solution can be extended or modified to capture other information in OMIM and scientific literature.

Single-cell sequencing analysis within biologically relevant dimensions

The currently predominant approach to transcriptomic and epigenomic single-cell analysis depends on a rigid perspective constrained by reduced dimensions and algorithmically derived and annotated clusters. Here, we developed Seqtometry (sequencing-to-measurement), a single-cell analytical strategy based on biologically relevant dimensions enabled by advanced scoring with multiple gene sets (signatures) for examination of gene expression and accessibility across various organ systems. By utilizing information only in the form of specific signatures, Seqtometry bypasses unsupervised clustering and individual annotations of clusters. Instead, Seqtometry combines qualitative and quantitative cell-type identification with specific characterization of diverse biological processes under experimental or disease conditions. Comprehensive analysis by Seqtometry of various immune cells as well as other cells from different organs and disease-induced states, including multiple myeloma and Alzheimer's disease, surpasses corresponding cluster-based analytical output. We propose Seqtometry as a single-cell sequencing analysis approach applicable for both basic and clinical research.

The alliance of genome resources: transforming comparative genomics

Comparing genomic and biological characteristics across multiple species is essential to using model systems to investigate the molecular and cellular mechanisms underlying human biology and disease and to translate mechanistic insights from studies in model organisms for clinical applications. Building a scalable knowledge commons platform that supports cross-species comparison of rich, expertly curated knowledge regarding gene function, phenotype, and disease associations available for model organisms and humans is the primary mission of the Alliance of Genome Resources (the Alliance). The Alliance is a consortium of seven model organism knowledgebases (mouse, rat, yeast, nematode, zebrafish, frog, fruit fly) and the Gene Ontology resource. The Alliance uses a common set of gene ortholog assertions as the basis for comparing biological annotations across the organisms represented in the Alliance. The major types of knowledge associated with genes that are represented in the Alliance database currently include gene function, phenotypic alleles and variants, human disease associations, pathways, gene expression, and both protein-protein and genetic interactions. The Alliance has enhanced the ability of researchers to easily compare biological annotations for common data types across model organisms and human through the implementation of shared programmatic access mechanisms, data-specific web pages with a unified "look and feel", and interactive user interfaces specifically designed to support comparative biology. The modular infrastructure developed by the Alliance allows the resource to serve as an extensible "knowledge commons" capable of expanding to accommodate additional model organisms.

Updates to the Alliance of Genome Resources Central Infrastructure Alliance of Genome Resources Consortium

The Alliance of Genome Resources (Alliance) is an extensible coalition of knowledgebases focused on the genetics and genomics of intensively-studied model organisms. The Alliance is organized as individual knowledge centers with strong connections to their research communities and a centralized software infrastructure, discussed here. Model organisms currently represented in the Alliance are budding yeast, C. elegans, Drosophila, zebrafish, frog, laboratory mouse, laboratory rat, and the Gene Ontology Consortium. The project is in a rapid development phase to harmonize knowledge, store it, analyze it, and present it to the community through a web portal, direct downloads, and APIs. Here we focus on developments over the last two years. Specifically, we added and enhanced tools for browsing the genome (JBrowse), downloading sequences, mining complex data (AllianceMine), visualizing pathways, full-text searching of the literature (Textpresso), and sequence similarity searching (SequenceServer). We enhanced existing interactive data tables and added an interactive table of paralogs to complement our representation of orthology. To support individual model organism communities, we implemented species-specific "landing pages" and will add disease-specific portals soon; in addition, we support a common community forum implemented in Discourse. We describe our progress towards a central persistent database to support curation, the data modeling that underpins harmonization, and progress towards a state-of-the art literature curation system with integrated Artificial Intelligence and Machine Learning (AI/ML).

Intrinsic p53 Activation Restricts Gammaherpesvirus-Driven Germinal Center B Cell Expansion during Latency Establishment

Gammaherpesviruses (GHV) are DNA tumor viruses that establish lifelong latent infections in lymphocytes. For viruses such as Epstein-Barr virus (EBV) and murine gammaherpesvirus 68 (MHV68), this is accomplished through a viral gene-expression program that promotes cellular proliferation and differentiation, especially of germinal center (GC) B cells. Intrinsic host mechanisms that control virus-driven cellular expansion are incompletely defined. Using a small-animal model of GHV pathogenesis, we demonstrate in vivo that tumor suppressor p53 is activated specifically in B cells that are latently infected by MHV68. In the absence of p53, the early expansion of MHV68 latency was greatly increased, especially in GC B cells, a cell-type whose proliferation was conversely restricted by p53. We identify the B cell-specific latency gene M2, a viral promoter of GC B cell differentiation, as a viral protein sufficient to elicit a p53-dependent anti-proliferative response caused by Src-family kinase activation. We further demonstrate that EBV-encoded latent membrane protein 1 (LMP1) similarly triggers a p53 response in primary B cells. Our data highlight a model in which GHV latency gene-expression programs that promote B cell proliferation and differentiation to facilitate viral colonization of the host trigger aberrant cellular proliferation that is controlled by p53.

IMPORTANCE

Gammaherpesviruses cause lifelong infections of their hosts, commonly referred to as latency, that can lead to cancer. Latency establishment benefits from the functions of viral proteins that augment and amplify B cell activation, proliferation, and differentiation signals. In uninfected cells, off-schedule cellular differentiation would typically trigger anti-proliferative responses by effector proteins known as tumor suppressors. However, tumor suppressor responses to gammaherpesvirus manipulation of cellular processes remain understudied, especially those that occur during latency establishment in a living organism. Here we identify p53, a tumor suppressor commonly mutated in cancer, as a host factor that limits virus-driven B cell proliferation and differentiation, and thus, viral colonization of a host. We demonstrate that p53 activation occurs in response to viral latency proteins that induce B cell activation. This work informs a gap in our understanding of intrinsic cellular defense mechanisms that restrict lifelong GHV infection.

New data and collaborations at the Saccharomyces Genome Database: updated reference genome, alleles, and the Alliance of Genome Resources

Saccharomyces cerevisiae is used to provide fundamental understanding of eukaryotic genetics, gene product function, and cellular biological processes. Saccharomyces Genome Database (SGD) has been supporting the yeast research community since 1993, serving as its de facto hub. Over the years, SGD has maintained the genetic nomenclature, chromosome maps, and functional annotation, and developed various tools and methods for analysis and curation of a variety of emerging data types. More recently, SGD and six other model organism focused knowledgebases have come together to create the Alliance of Genome Resources to develop sustainable genome information resources that promote and support the use of various model organisms to understand the genetic and genomic bases of human biology and disease. Here we describe recent activities at SGD, including the latest reference genome annotation update, the development of a curation system for mutant alleles, and new pages addressing homology across model organisms as well as the use of yeast to study human disease.

Using FlyBase: A Database of Drosophila Genes and Genetics

Since 1992, FlyBase has provided a freely available online database of information about the model organism Drosophila melanogaster. Data in FlyBase is curated manually from research papers as well as computationally from a variety of relevant sources, to serve as an information hub that enables and accelerates research discovery. This chapter aims to give users new to the database an overview of the layout and types of data available, as well as introducing some tools with which to access the data. More experienced users will find useful information about recent improvements and descriptions to enable more efficient navigation of the database.

The Rat Genome Database (RGD) facilitates genomic and phenotypic data integration across multiple species for biomedical research

Model organism research is essential for discovering the mechanisms of human diseases by defining biologically meaningful gene to disease relationships. The Rat Genome Database (RGD, ( https://rgd.mcw.edu )) is a cross-species knowledgebase and the premier online resource for rat genetic and physiologic data. This rich resource is enhanced by the inclusion and integration of comparative data for human and mouse, as well as other human disease models including chinchilla, dog, bonobo, pig, 13-lined ground squirrel, green monkey, and naked mole-rat. Functional information has been added to records via the assignment of annotations based on sequence similarity to human, rat, and mouse genes. RGD has also imported well-supported cross-species data from external resources. To enable use of these data, RGD has developed a robust infrastructure of standardized ontologies, data formats, and disease- and species-centric portals, complemented with a suite of innovative tools for discovery and analysis. Using examples of single-gene and polygenic human diseases, we illustrate how data from multiple species can help to identify or confirm a gene as involved in a disease and to identify model organisms that can be studied to understand the pathophysiology of a gene or pathway. The ultimate aim of this report is to demonstrate the utility of RGD not only as the core resource for the rat research community but also as a source of bioinformatic tools to support a wider audience, empowering the search for appropriate models for human afflictions.

Harmful effects of metal(loid) oxide nanoparticles

Abstract

The incorporation of nanomaterials (NMs), including metal(loid) oxide (MOx) nanoparticles (NPs), in the most diversified consumer products, has grown enormously in recent decades. Consequently, the contact between humans and these materials increased, as well as their presence in the environment. This fact has raised concerns and uncertainties about the possible risks of NMs to human health and the adverse effects on the environment. These concerns underline the need and importance of assessing its nanosecurity. The present review focuses on the main mechanisms underlying the MOx NPs toxicity, illustrated with different biological models: release of toxic ions, cellular uptake of NPs, oxidative stress, shading effect on photosynthetic microorganisms, physical restrain and damage of cell wall. Additionally, the biological models used to evaluate the potential hazardous of nanomaterials are briefly presented, with particular emphasis on the yeast Saccharomyces cerevisiae, as an alternative model in nanotoxicology. An overview containing recent scientific advances on cellular responses (toxic symptoms exhibited by yeasts) resulting from the interaction with MOx NPs (inhibition of cell proliferation, cell wall damage, alteration of function and morphology of organelles, presence of oxidative stress bio-indicators, gene expression changes, genotoxicity and cell dead) is critically presented. The elucidation of the toxic modes of action of MOx NPs in yeast cells can be very useful in providing additional clues about the impact of NPs on the physiology and metabolism of the eukaryotic cell. Current and future trends of MOx NPs toxicity, regarding their possible impacts on the environment and human health, are discussed.

Key points

• The potential hazardous effects of MOx NPs are critically reviewed.

• An overview of the main mechanisms associated with MOx NPs toxicity is presented.

• Scientific advances about yeast cell responses to MOx NPs are updated and discussed.

Bayesian parameter estimation for automatic annotation of gene functions using observational data and phylogenetic trees

Gene function annotation is important for a variety of downstream analyses of genetic data. But experimental characterization of function remains costly and slow, making computational prediction an important endeavor. Phylogenetic approaches to prediction have been developed, but implementation of a practical Bayesian framework for parameter estimation remains an outstanding challenge. We have developed a computationally efficient model of evolution of gene annotations using phylogenies based on a Bayesian framework using Markov Chain Monte Carlo for parameter estimation. Unlike previous approaches, our method is able to estimate parameters over many different phylogenetic trees and functions. The resulting parameters agree with biological intuition, such as the increased probability of function change following gene duplication. The method performs well on leave-one-out cross-validation, and we further validated some of the predictions in the experimental scientific literature.

The Bgee suite: integrated curated expression atlas and comparative transcriptomics in animals

Bgee is a database to retrieve and compare gene expression patterns in multiple animal species, produced by integrating multiple data types (RNA-Seq, Affymetrix, in situ hybridization, and EST data). It is based exclusively on curated healthy wild-type expression data (e.g., no gene knock-out, no treatment, no disease), to provide a comparable reference of normal gene expression. Curation includes very large datasets such as GTEx (re-annotation of samples as ‘healthy’ or not) as well as many small ones. Data are integrated and made comparable between species thanks to consistent data annotation and processing, and to calls of presence/absence of expression, along with expression scores. As a result, Bgee is capable of detecting the conditions of expression of any single gene, accommodating any data type and species. Bgee provides several tools for analyses, allowing, e.g., automated comparisons of gene expression patterns within and between species, retrieval of the prefered conditions of expression of any gene, or enrichment analyses of conditions with expression of sets of genes. Bgee release 14.1 includes 29 animal species, and is available at https://bgee.org/ and through its Bioconductor R package BgeeDB.

MaveQuest: a web resource for planning experimental tests of human variant effects

Summary

Fully realizing the promise of personalized medicine will require rapid and accurate classification of pathogenic human variation. Multiplexed assays of variant effect (MAVEs) can experimentally test nearly all possible variants in selected gene targets. Planning a MAVE study involves identifying target genes with clinical impact, and identifying scalable functional assays for that target. Here, we describe MaveQuest, a web-based resource enabling systematic variant effect mapping studies by identifying potential functional assays, disease phenotypes and clinical relevance for nearly all human protein-coding genes.

Availability and implementation

MaveQuest service: https://mavequest.varianteffect.org/. MaveQuest source code: https://github.com/kvnkuang/mavequest-front-end/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Predicting gene essentiality in Caenorhabditis elegans by feature engineering and machine-learning

Defining genes that are essential for life has major implications for understanding critical biological processes and mechanisms. Although essential genes have been identified and characterised experimentally using functional genomic tools, it is challenging to predict with confidence such genes from molecular and phenomic data sets using computational methods. Using extensive data sets available for the model organism Caenorhabditis elegans, we constructed here a machine-learning (ML)-based workflow for the prediction of essential genes on a genome-wide scale. We identified strong predictors for such genes and showed that trained ML models consistently achieve highly-accurate classifications. Complementary analyses revealed an association between essential genes and chromosomal location. Our findings reveal that essential genes in C. elegans tend to be located in or near the centre of autosomal chromosomes; are positively correlated with low single nucleotide polymorphim (SNP) densities and epigenetic markers in promoter regions; are involved in protein and nucleotide processing; are transcribed in most cells; are enriched in reproductive tissues or are targets for small RNAs bound to the argonaut CSR-1. Based on these results, we hypothesise an interplay between epigenetic markers and small RNA pathways in the germline, with transcription-based memory; this hypothesis warrants testing. From a technical perspective, further work is needed to evaluate whether the present ML-based approach will be applicable to other metazoans (including Drosophila melanogaster) for which comprehensive data sets (i.e. genomic, transcriptomic, proteomic, variomic, epigenetic and phenomic) are available.

FlyBase 2.0: the next generation

FlyBase (flybase.org) is a knowledge base that supports the community of researchers that use the fruit fly, Drosophila melanogaster, as a model organism. The FlyBase team curates and organizes a diverse array of genetic, molecular, genomic, and developmental information about Drosophila. At the beginning of 2018, ‘FlyBase 2.0’ was released with a significantly improved user interface and new tools. Among these important changes are a new organization of search results into interactive lists or tables (hitlists), enhanced reference lists, and new protein domain graphics. An important new data class called ‘experimental tools’ consolidates information on useful fly strains and other resources related to a specific gene, which significantly enhances the ability of the Drosophila researcher to design and carry out experiments. With the release of FlyBase 2.0, there has also been a restructuring of backend architecture and a continued development of application programming interfaces (APIs) for programmatic access to FlyBase data. In this review, we describe these major new features and functionalities of the FlyBase 2.0 site and how they support the use of Drosophila as a model organism for biological discovery and translational research.

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.

Mouse Genome Database (MGD) 2019

The Mouse Genome Database (MGD; http://www.informatics.jax.org) is the community model organism genetic and genome resource for the laboratory mouse. MGD is the authoritative source for biological reference data sets related to mouse genes, gene functions, phenotypes, and mouse models of human disease. MGD is the primary outlet for official gene, allele and mouse strain nomenclature based on the guidelines set by the International Committee on Standardized Nomenclature for Mice. In this report we describe significant enhancements to MGD, including two new graphical user interfaces: (i) the Multi Genome Viewer for exploring the genomes of multiple mouse strains and (ii) the Phenotype-Gene Expression matrix which was developed in collaboration with the Gene Expression Database (GXD) and allows researchers to compare gene expression and phenotype annotations for mouse genes. Other recent improvements include enhanced efficiency of our literature curation processes and the incorporation of Transcriptional Start Site (TSS) annotations from RIKEN's FANTOM 5 initiative.

Casting a wide net: use of diverse model organisms to advance toxicology

Toxicology - the study of how chemicals interact with biological systems - has clear relevance to human health and disease. Persistent exposure to natural and synthetic chemicals is an unavoidable part of living on our planet; yet, we understand very little about the effects of exposure to the vast majority of chemicals. While epidemiological studies can provide strong statistical inference linking chemical exposure to disease, research in model systems is essential to elucidate the mechanisms of action and to predict outcomes. Most research in toxicology utilizes a handful of mammalian models that represent a few distinct branches of the evolutionary tree. This narrow focus constrains the understanding of chemical-induced disease processes and systems that have evolved in response to exposures. We advocate for casting a wider net in environmental toxicology research to utilize diverse model systems, including zebrafish, and perform more mechanistic studies of cellular responses to chemical exposures to shift the perception of toxicology as an applied science to that of a basic science. This more-inclusive perspective will enrich the field and should remain central to research on chemical-induced disease.

Building a pipeline to solicit expert knowledge from the community to aid gene summary curation

Brief summaries describing the function of each gene's product(s) are of great value to the research community, especially when interpreting genome-wide studies that reveal changes to hundreds of genes. However, manually writing such summaries, even for a single species, is a daunting task; for example, the Drosophila melanogaster genome contains almost 14 000 protein-coding genes. One solution is to use computational methods to generate summaries, but this often fails to capture the key functions or express them eloquently. Here, we describe how we solicited help from the research community to generate manually written summaries of D. melanogaster gene function. Based on the data within the FlyBase database, we developed a computational pipeline to identify researchers who have worked extensively on each gene. We e-mailed these researchers to ask them to draft a brief summary of the main function(s) of the gene's product, which we edited for consistency to produce a 'gene snapshot'. This approach yielded 1800 gene snapshot submissions within a 3-month period. We discuss the general utility of this strategy for other databases that capture data from the research literature. Database URL: https://flybase.org/.

Automated generation of gene summaries at the Alliance of Genome Resources

Short paragraphs that describe gene function, referred to as gene summaries, are valued by users of biological knowledgebases for the ease with which they convey key aspects of gene function. Manual curation of gene summaries, while desirable, is difficult for knowledgebases to sustain. We developed an algorithm that uses curated, structured gene data at the Alliance of Genome Resources (Alliance; www.alliancegenome.org) to automatically generate gene summaries that simulate natural language. The gene data used for this purpose include curated associations (annotations) to ontology terms from the Gene Ontology, Disease Ontology, model organism knowledgebase (MOK)-specific anatomy ontologies and Alliance orthology data. The method uses sentence templates for each data category included in the gene summary in order to build a natural language sentence from the list of terms associated with each gene. To improve readability of the summaries when numerous gene annotations are present, we developed a new algorithm that traverses ontology graphs in order to group terms by their common ancestors. The algorithm optimizes the coverage of the initial set of terms and limits the length of the final summary, using measures of information content of each ontology term as a criterion for inclusion in the summary. The automated gene summaries are generated with each Alliance release, ensuring that they reflect current data at the Alliance. Our method effectively leverages category-specific curation efforts of the Alliance member databases to create modular, structured and standardized gene summaries for seven member species of the Alliance. These automatically generated gene summaries make cross-species gene function comparisons tenable and increase discoverability of potential models of human disease. In addition to being displayed on Alliance gene pages, these summaries are also included on several MOK gene pages.

An RNAi Screen for Genes Required for Growth of Drosophila Wing Tissue

Cell division and tissue growth must be coordinated with development. Defects in these processes are the basis for a number of diseases, including developmental malformations and cancer. We have conducted an unbiased RNAi screen for genes that are required for growth in the Drosophila wing, using GAL4-inducible short hairpin RNA (shRNA) fly strains made by the Drosophila RNAi Screening Center. shRNA expression down the center of the larval wing disc using dpp-GAL4, and the central region of the adult wing was then scored for tissue growth and wing hair morphology. Out of 4,753 shRNA crosses that survived to adulthood, 18 had impaired wing growth. FlyBase and the new Alliance of Genome Resources knowledgebases were used to determine the known or predicted functions of these genes and the association of their human orthologs with disease. The function of eight of the genes identified has not been previously defined in Drosophila The genes identified included those with known or predicted functions in cell cycle, chromosome segregation, morphogenesis, metabolism, steroid processing, transcription, and translation. All but one of the genes are similar to those in humans, and many are associated with disease. Knockdown of lin-52, a subunit of the Myb-MuvB transcription factor, or βNACtes6, a gene involved in protein folding and trafficking, resulted in a switch from cell proliferation to an endoreplication growth program through which wing tissue grew by an increase in cell size (hypertrophy). It is anticipated that further analysis of the genes that we have identified will reveal new mechanisms that regulate tissue growth during development.

Xenbase: Facilitating the Use of Xenopus to Model Human Disease

At a fundamental level most genes, signaling pathways, biological functions and organ systems are highly conserved between man and all vertebrate species. Leveraging this conservation, researchers are increasingly using the experimental advantages of the amphibian Xenopus to model human disease. The online Xenopus resource, Xenbase, enables human disease modeling by curating the Xenopus literature published in PubMed and integrating these Xenopus data with orthologous human genes, anatomy, and more recently with links to the Online Mendelian Inheritance in Man resource (OMIM) and the Human Disease Ontology (DO). Here we review how Xenbase supports disease modeling and report on a meta-analysis of the published Xenopus research providing an overview of the different types of diseases being modeled in Xenopus and the variety of experimental approaches being used. Text mining of over 50,000 Xenopus research articles imported into Xenbase from PubMed identified approximately 1,000 putative disease- modeling articles. These articles were manually assessed and annotated with disease ontologies, which were then used to classify papers based on disease type. We found that Xenopus is being used to study a diverse array of disease with three main experimental approaches: cell-free egg extracts to study fundamental aspects of cellular and molecular biology, oocytes to study ion transport and channel physiology and embryo experiments focused on congenital diseases. We integrated these data into Xenbase Disease Pages to allow easy navigation to disease information on external databases. Results of this analysis will equip Xenopus researchers with a suite of experimental approaches available to model or dissect a pathological process. Ideally clinicians and basic researchers will use this information to foster collaborations necessary to interrogate the development and treatment of human diseases.

Towards comprehensive annotation of Drosophila melanogaster enzymes in FlyBase

The catalytic activities of enzymes can be described using Gene Ontology (GO) terms and Enzyme Commission (EC) numbers. These annotations are available from numerous biological databases and are routinely accessed by researchers and bioinformaticians to direct their work. However, enzyme data may not be congruent between different resources, while the origin, quality and genomic coverage of these data within any one resource are often unclear. GO/EC annotations are assigned either manually by expert curators or inferred computationally, and there is potential for errors in both types of annotation. If such errors remain unchecked, false positive annotations may be propagated across multiple resources, significantly degrading the quality and usefulness of these data. Similarly, the absence of annotations (false negatives) from any one resource can lead to incorrect inferences or conclusions. We are systematically reviewing and enhancing the functional annotation of the enzymes of Drosophila melanogaster, focusing on improvements within the FlyBase (www.flybase.org) database. We have reviewed four major enzyme groups to date: oxidoreductases, lyases, isomerases and ligases. Herein, we describe our review workflow, the improvement in the quality and coverage of enzyme annotations within FlyBase and the wider impact of our work on other related databases.