Search

Abstract Background Journal articles and databases are two major modes of communication in the biological sciences, and thus integrating these critical resources is of urgent importance to increase the pace of discovery. Projects focused on bridging the gap between journals and databases have been on the rise over the last five years and have resulted in the development of automated tools that can recognize entities within a document and link those entities to a relevant database. Unfortunately, automated tools cannot resolve ambiguities that arise from one term being used to signify entities that are quite distinct from one another. Instead, resolving these ambiguities requires some manual oversight. Finding the right balance between the speed and portability of automation and the accuracy and flexibility of manual effort is a crucial goal to making text markup a successful venture. Results We have established a journal article mark-up pipeline that links GENETICS journal articles and the model organism database (MOD) WormBase. This pipeline uses a lexicon built with entities from the database as a first step. The entity markup pipeline results in links from over nine classes of objects including genes, proteins, alleles, phenotypes and anatomical terms. New entities and ambiguities are discovered and resolved by a database curator through a manual quality control (QC) step, along with help from authors via a web form that is provided to them by the journal. New entities discovered through this pipeline are immediately sent to an appropriate curator at the database. Ambiguous entities that do not automatically resolve to one link are resolved by hand ensuring an accurate link. This pipeline has been extended to other databases, namely Saccharomyces Genome Database (SGD) and FlyBase, and has been implemented in marking up a paper with links to multiple databases. Conclusions Our semi-automated pipeline hyperlinks articles published in GENETICS to model organism databases such as WormBase. Our pipeline results in interactive articles that are data rich with high accuracy. The use of a manual quality control step sets this pipeline apart from other hyperlinking tools and results in benefits to authors, journals, readers and databases.

The NHGRI-EBI GWAS Catalog serves as a vital resource for the genetic research community, providing access to the most comprehensive database of human GWAS results. Currently, it contains close to 7 000 publications for >15 000 traits, from which more than 625 000 lead associations have been curated. Additionally, 85 000 full genome-wide summary statistics datasets-containing association data for all variants in the analysis-are available for downstream analyses such as meta-analysis, fine-mapping, Mendelian randomisation or development of polygenic risk scores. As a centralised repository for GWAS results, the GWAS Catalog sets and implements standards for data submission and harmonisation, and encourages the use of consistent descriptors for traits, samples and methodologies. We share processes and vocabulary with the PGS Catalog, improving interoperability for a growing user group. Here, we describe the latest changes in data content, improvements in our user interface, and the implementation of the GWAS-SSF standard format for summary statistics. We address the challenges of handling the rapid increase in large-scale molecular quantitative trait GWAS and the need for sensitivity in the use of population and cohort descriptors while maintaining data interoperability and reusability., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The NHGRI-EBI GWAS Catalog serves as a vital resource for the genetic research community, providing access to the most comprehensive database of human GWAS results. Currently, it contains close to 7,000 publications for more than 15,000 traits, from which more than 625,000 lead associations have been curated. Additionally, 85,000 full genome-wide summary statistics datasets - containing association data for all variants in the analysis - are available for downstream analyses such as meta-analysis, fine-mapping, Mendelian randomisation or development of polygenic risk scores. As a centralised repository for GWAS results, the GWAS Catalog sets and implements standards for data submission and harmonisation, and encourages the use of consistent descriptors for traits, samples and methodologies. We share processes and vocabulary with the PGS Catalog, improving interoperability for a growing user group. Here, we describe the latest changes in data content, improvements in our user interface, and the implementation of the GWAS-SSF standard format for summary statistics. We address the challenges of handling the rapid increase in large-scale molecular quantitative trait GWAS and the need for sensitivity in the use of population and cohort descriptors while maintaining data interoperability and reusability., Competing Interests: Conflict of Interest statement: M.I. is a trustee of the Public Health Genomics (PHG) Foundation, a member of the Scientific Advisory Board of Open Targets, and has research collaborations with AstraZeneca, Nightingale Health and Pfizer which are unrelated to this study. No other conflicts of interest were reported by authors.

Background: Ontologies are fundamental components of informatics infrastructure in domains such as biomedical, environmental, and food sciences, representing consensus knowledge in an accurate and computable form. However, their construction and maintenance demand substantial resources and necessitate substantial collaboration between domain experts, curators, and ontology experts. We present Dynamic Retrieval Augmented Generation of Ontologies using AI (DRAGON-AI), an ontology generation method employing Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). DRAGON-AI can generate textual and logical ontology components, drawing from existing knowledge in multiple ontologies and unstructured text sources., Results: We assessed performance of DRAGON-AI on de novo term construction across ten diverse ontologies, making use of extensive manual evaluation of results. Our method has high precision for relationship generation, but has slightly lower precision than from logic-based reasoning. Our method is also able to generate definitions deemed acceptable by expert evaluators, but these scored worse than human-authored definitions. Notably, evaluators with the highest level of confidence in a domain were better able to discern flaws in AI-generated definitions. We also demonstrated the ability of DRAGON-AI to incorporate natural language instructions in the form of GitHub issues., Conclusions: These findings suggest DRAGON-AI's potential to substantially aid the manual ontology construction process. However, our results also underscore the importance of having expert curators and ontology editors drive the ontology generation process., (© 2024. The Author(s).)

Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype data have been collected in many different contexts covering a variety of organisms. The emerging field of phenomics focuses on integrating and interpreting these data to inform biological hypotheses. A major impediment in phenomics is the wide range of distinct and disconnected approaches to recording the observable characteristics of an organism. Phenotype data are collected and curated using free text, single terms or combinations of terms, using multiple vocabularies, terminologies, or ontologies. Integrating these heterogeneous and often siloed data enables the application of biological knowledge both within and across species. Existing integration efforts are typically limited to mappings between pairs of terminologies; a generic knowledge representation that captures the full range of cross-species phenomics data is much needed. We have developed the Unified Phenotype Ontology (uPheno) framework, a community effort to provide an integration layer over domain-specific phenotype ontologies, as a single, unified, logical representation. uPheno comprises (1) a system for consistent computational definition of phenotype terms using ontology design patterns, maintained as a community library; (2) a hierarchical vocabulary of species-neutral phenotype terms under which their species-specific counterparts are grouped; and (3) mapping tables between species-specific ontologies. This harmonized representation supports use cases such as cross-species integration of genotype-phenotype associations from different organisms and cross-species informed variant prioritization., Competing Interests: 7.Conflict of Interest None declared.

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch's APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch's data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch's analytic tools by developing a customized plugin for OpenAI's ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos., (© 2023. The Author(s).)

The NHGRI-EBI GWAS Catalog (www.ebi.ac.uk/gwas) is a FAIR knowledgebase providing detailed, structured, standardised and interoperable genome-wide association study (GWAS) data to >200 000 users per year from academic research, healthcare and industry. The Catalog contains variant-trait associations and supporting metadata for >45 000 published GWAS across >5000 human traits, and >40 000 full P-value summary statistics datasets. Content is curated from publications or acquired via author submission of prepublication summary statistics through a new submission portal and validation tool. GWAS data volume has vastly increased in recent years. We have updated our software to meet this scaling challenge and to enable rapid release of submitted summary statistics. The scope of the repository has expanded to include additional data types of high interest to the community, including sequencing-based GWAS, gene-based analyses and copy number variation analyses. Community outreach has increased the number of shared datasets from under-represented traits, e.g. cancer, and we continue to contribute to awareness of the lack of population diversity in GWAS. Interoperability of the Catalog has been enhanced through links to other resources including the Polygenic Score Catalog and the International Mouse Phenotyping Consortium, refinements to GWAS trait annotation, and the development of a standard format for GWAS data., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Similar to managing software packages, managing the ontology life cycle involves multiple complex workflows such as preparing releases, continuous quality control checking and dependency management. To manage these processes, a diverse set of tools is required, from command-line utilities to powerful ontology-engineering environmentsr. Particularly in the biomedical domain, which has developed a set of highly diverse yet inter-dependent ontologies, standardizing release practices and metadata and establishing shared quality standards are crucial to enable interoperability. The Ontology Development Kit (ODK) provides a set of standardized, customizable and automatically executable workflows, and packages all required tooling in a single Docker image. In this paper, we provide an overview of how the ODK works, show how it is used in practice and describe how we envision it driving standardization efforts in our community. Database URL: https://github.com/INCATools/ontology-development-kit., (© The Author(s) 2022. Published by Oxford University Press.)

COSMIC, the Catalogue Of Somatic Mutations In Cancer (https://cancer.sanger.ac.uk) is the most detailed and comprehensive resource for exploring the effect of somatic mutations in human cancer. The latest release, COSMIC v86 (August 2018), includes almost 6 million coding mutations across 1.4 million tumour samples, curated from over 26 000 publications. In addition to coding mutations, COSMIC covers all the genetic mechanisms by which somatic mutations promote cancer, including non-coding mutations, gene fusions, copy-number variants and drug-resistance mutations. COSMIC is primarily hand-curated, ensuring quality, accuracy and descriptive data capture. Building on our manual curation processes, we are introducing new initiatives that allow us to prioritize key genes and diseases, and to react more quickly and comprehensively to new findings in the literature. Alongside improvements to the public website and data-download systems, new functionality in COSMIC-3D allows exploration of mutations within three-dimensional protein structures, their protein structural and functional impacts, and implications for druggability. In parallel with COSMIC's deep and broad variant coverage, the Cancer Gene Census (CGC) describes a curated catalogue of genes driving every form of human cancer. Currently describing 719 genes, the CGC has recently introduced functional descriptions of how each gene drives disease, summarized into the 10 cancer Hallmarks.

COSMIC, the Catalogue of Somatic Mutations in Cancer (http://cancer.sanger.ac.uk) is a high-resolution resource for exploring targets and trends in the genetics of human cancer. Currently the broadest database of mutations in cancer, the information in COSMIC is curated by expert scientists, primarily by scrutinizing large numbers of scientific publications. Over 4 million coding mutations are described in v78 (September 2016), combining genome-wide sequencing results from 28 366 tumours with complete manual curation of 23 489 individual publications focused on 186 key genes and 286 key fusion pairs across all cancers. Molecular profiling of large tumour numbers has also allowed the annotation of more than 13 million non-coding mutations, 18 029 gene fusions, 187 429 genome rearrangements, 1 271 436 abnormal copy number segments, 9 175 462 abnormal expression variants and 7 879 142 differentially methylated CpG dinucleotides. COSMIC now details the genetics of drug resistance, novel somatic gene mutations which allow a tumour to evade therapeutic cancer drugs. Focusing initially on highly characterized drugs and genes, COSMIC v78 contains wide resistance mutation profiles across 20 drugs, detailing the recurrence of 301 unique resistance alleles across 1934 drug-resistant tumours. All information from the COSMIC database is available freely on the COSMIC website., (© The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.)

COSMIC (http://cancer.sanger.ac.uk) is an expert-curated database of somatic mutations in human cancer. Broad and comprehensive in scope, recent releases in 2016 describe over 4 million coding mutations across all human cancer disease types. Mutations are annotated across the entire genome, but expert curation is focused on over 400 key cancer genes. Now encompassing the majority of molecular mutation mechanisms in oncogenetics, COSMIC additionally describes 10 million non-coding mutations, 1 million copy-number aberrations, 9 million gene-expression variants, and almost 8 million differentially methylated CpGs. This information combines a consistent interpretation of the data from the major cancer genome consortia and cancer genome literature with exhaustive hand curation of over 22,000 gene-specific literature publications. This unit describes the graphical Web site in detail; alternative protocols overview other ways the entire database can be accessed, analyzed, and downloaded. © 2016 by John Wiley & Sons, Inc., (Copyright © 2016 John Wiley & Sons, Inc.)

Fully automated text mining (TM) systems promote efficient literature searching, retrieval, and review but are not sufficient to produce ready-to-consume curated documents. These systems are not meant to replace biocurators, but instead to assist them in one or more literature curation steps. To do so, the user interface is an important aspect that needs to be considered for tool adoption. The BioCreative Interactive task (IAT) is a track designed for exploring user-system interactions, promoting development of useful TM tools, and providing a communication channel between the biocuration and the TM communities. In BioCreative V, the IAT track followed a format similar to previous interactive tracks, where the utility and usability of TM tools, as well as the generation of use cases, have been the focal points. The proposed curation tasks are user-centric and formally evaluated by biocurators. In BioCreative V IAT, seven TM systems and 43 biocurators participated. Two levels of user participation were offered to broaden curator involvement and obtain more feedback on usability aspects. The full level participation involved training on the system, curation of a set of documents with and without TM assistance, tracking of time-on-task, and completion of a user survey. The partial level participation was designed to focus on usability aspects of the interface and not the performance per se In this case, biocurators navigated the system by performing pre-designed tasks and then were asked whether they were able to achieve the task and the level of difficulty in completing the task. In this manuscript, we describe the development of the interactive task, from planning to execution and discuss major findings for the systems tested.Database URL: http://www.biocreative.org., (Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the US.)

The breadth and depth of biomedical literature are increasing year upon year. To keep abreast of these increases, FlyBase, a database for Drosophila genomic and genetic information, is constantly exploring new ways to mine the published literature to increase the efficiency and accuracy of manual curation and to automate some aspects, such as triaging and entity extraction. Toward this end, we present the 'tagtog' system, a web-based annotation framework that can be used to mark up biological entities (such as genes) and concepts (such as Gene Ontology terms) in full-text articles. tagtog leverages manual user annotation in combination with automatic machine-learned annotation to provide accurate identification of gene symbols and gene names. As part of the BioCreative IV Interactive Annotation Task, FlyBase has used tagtog to identify and extract mentions of Drosophila melanogaster gene symbols and names in full-text biomedical articles from the PLOS stable of journals. We show here the results of three experiments with different sized corpora and assess gene recognition performance and curation speed. We conclude that tagtog-named entity recognition improves with a larger corpus and that tagtog-assisted curation is quicker than manual curation. DATABASE URL: www.tagtog.net, www.flybase.org.

FlyBase (http://flybase.org) is the leading website and database of Drosophila genes and genomes. Whether you are using the fruit fly Drosophila melanogaster as an experimental system or wish to understand Drosophila biological knowledge in relation to human disease or to other model systems, FlyBase can help you successfully find the information you are looking for. Here, we demonstrate some of our more advanced searching systems and highlight some of our new tools for searching the wealth of data on FlyBase. The first section explores gene function in FlyBase, using our TermLink tool to search with Controlled Vocabulary terms and our new RNA-Seq Search tool to search gene expression. The second section of this article describes a few ways to search genomic data in FlyBase, using our BLAST server and the new implementation of GBrowse 2, as well as our new FeatureMapper tool. Finally, we move on to discuss our most powerful search tool, QueryBuilder, before describing pre-computed cuts of the data and how to query the database programmatically.

Based on GC content and the observed/expected CpG ratio (oCpGr), we found three major groups among the members of subfamily Parvovirinae: Group I parvoviruses with low GC content and low oCpGr values, Group II with low GC content and high oCpGr values and Group III with high GC content and high oCpGr values. Porcine parvovirus belongs to Group I and it features an ascendant CpG distribution by position in its coding regions similarly to the majority of the parvoviruses. The entire PPV genome remains hypomethylated during the viral lifecycle independently from the tissue of origin. In vitro CpG methylation of the genome has a modest inhibitory effect on PPV replication. The in vitro hypermethylation disappears from the replicating PPV genome suggesting that beside the maintenance DNMT1 the de novo DNMT3a and DNMT3b DNA methyltransferases can't methylate replicating PPV DNA effectively either, despite that the PPV infection does not seem to influence the expression, translation or localization of the DNA methylases. SNP analysis revealed high mutability of the CpG sites in the PPV genome, while introduction of 29 extra CpG sites into the genome has no significant biological effects on PPV replication in vitro. These experiments raise the possibility that beyond natural selection mutational pressure may also significantly contribute to the low level of the CpG sites in the PPV genome.

Background: Phenotype ontologies are queryable classifications of phenotypes. They provide a widely-used means for annotating phenotypes in a form that is human-readable, programatically accessible and that can be used to group annotations in biologically meaningful ways. Accurate manual annotation requires clear textual definitions for terms. Accurate grouping and fruitful programatic usage require high-quality formal definitions that can be used to automate classification. The Drosophila phenotype ontology (DPO) has been used to annotate over 159,000 phenotypes in FlyBase to date, but until recently lacked textual or formal definitions., Results: We have composed textual definitions for all DPO terms and formal definitions for 77% of them. Formal definitions reference terms from a range of widely-used ontologies including the Phenotype and Trait Ontology (PATO), the Gene Ontology (GO) and the Cell Ontology (CL). We also describe a generally applicable system, devised for the DPO, for recording and reasoning about the timing of death in populations. As a result of the new formalisations, 85% of classifications in the DPO are now inferred rather than asserted, with much of this classification leveraging the structure of the GO. This work has significantly improved the accuracy and completeness of classification and made further development of the DPO more sustainable., Conclusions: The DPO provides a set of well-defined terms for annotating Drosophila phenotypes and for grouping and querying the resulting annotation sets in biologically meaningful ways. Such queries have already resulted in successful function predictions from phenotype annotation. Moreover, such formalisations make extended queries possible, including cross-species queries via the external ontologies used in formal definitions. The DPO is openly available under an open source license in both OBO and OWL formats. There is good potential for it to be used more broadly by the Drosophila community, which may ultimately result in its extension to cover a broader range of phenotypes.

Animals use TGF-β superfamily signal transduction pathways during development and tissue maintenance. The superfamily has traditionally been divided into TGF-β/Activin and BMP branches based on relationships between ligands, receptors, and R-Smads. Several previous reports have shown that, in cell culture systems, "BMP-specific" Smads can be phosphorylated in response to TGF-β/Activin pathway activation. Using Drosophila cell culture as well as in vivo assays, we find that Baboon, the Drosophila TGF-β/Activin-specific Type I receptor, can phosphorylate Mad, the BMP-specific R-Smad, in addition to its normal substrate, dSmad2. The Baboon-Mad activation appears direct because it occurs in the absence of canonical BMP Type I receptors. Wing phenotypes generated by Baboon gain-of-function require Mad, and are partially suppressed by over-expression of dSmad2. In the larval wing disc, activated Baboon cell-autonomously causes C-terminal Mad phosphorylation, but only when endogenous dSmad2 protein is depleted. The Baboon-Mad relationship is thus controlled by dSmad2 levels. Elevated P-Mad is seen in several tissues of dSmad2 protein-null mutant larvae, and these levels are normalized in dSmad2; baboon double mutants, indicating that the cross-talk reaction and Smad competition occur with endogenous levels of signaling components in vivo. In addition, we find that high levels of Activin signaling cause substantial turnover in dSmad2 protein, providing a potential cross-pathway signal-switching mechanism. We propose that the dual activity of TGF-β/Activin receptors is an ancient feature, and we discuss several ways this activity can modulate TGF-β signaling output.

The complete genome sequence of an adenovirus, isolated from turkey and proposed to be turkey adenovirus type 1 (TAdV-1), was determined to extend our knowledge about the genome organisation and phylogeny of aviadenoviruses. The longest adenovirus genome, consisting of 45,412 bp, with the highest G+C content (of 67.55%) known to date, was found. The central part of the TAdV-1 genome has the conserved gene set and arrangement that are characteristic for every other adenovirus analysed to date. This genome core is flanked by the terminal early regions 1 and 4 (E1 and E4). Aviadenovirus-specific genus-common genes were found in these regions, each containing nine such open reading frames (ORFs). Additionally a type-specific novel ORF, designated as ORF50, was found in E4. Phylogenetic analysis as well as the presence of the genus-specific genes, splice sites and protease cleavage sites confirmed the classification of TAdV-1 in the genus Aviadenovirus. Intrageneric analyses of two genus-specific genes demonstrated the distinctness of TAdV-1 from other aviadenoviruses, thus supporting the proposal for the establishment of a new species, Turkey adenovirus B for TAdV-1., (Copyright © 2010 Elsevier B.V. All rights reserved.)

Genetic evidence suggests that the Drosophila ectoderm is patterned by a spatial gradient of bone morphogenetic protein (BMP). Here we compare patterns of two related cellular responses, both signal-dependent phosphorylation of the BMP-regulated R-SMAD, MAD, and signal-dependent changes in levels and sub-cellular distribution of the co-SMAD Medea. Our data demonstrate that nuclear accumulation of the co-SMAD Medea requires a BMP signal during blastoderm and gastrula stages. During this period, nuclear co-SMAD responses occur in three distinct patterns. At the end of blastoderm, a broad dorsal domain of weak SMAD response is detected. During early gastrulation, this domain narrows to a thin stripe of strong SMAD response at the dorsal midline. SMAD response levels continue to rise in the dorsal midline region during gastrulation, and flanking plateaus of weak responses are detected in dorsolateral cells. Thus, the thresholds for gene expression responses are implicit in the levels of SMAD responses during gastrulation. Both BMP ligands, DPP and Screw, are required for nuclear co-SMAD responses during these stages. The BMP antagonist Short gastrulation (SOG) is required to elevate peak responses at the dorsal midline as well as to depress responses in dorsolateral cells. The midline SMAD response gradient can form in embryos with reduced dpp gene dosage, but the peak level is reduced. These data support a model in which weak BMP activity during blastoderm defines the boundary between ventral neurogenic ectoderm and dorsal ectoderm. Subsequently, BMP activity creates a step gradient of SMAD responses that patterns the amnioserosa and dorsomedial ectoderm.

Transcription factors of the SMAD family relay signals from cell surface receptors to the nucleus in response to TGF-beta related soluble factors. Members of the nuclear factor I/CAAT box binding family (NFI/CTF) have been implicated as regulators of diverse biological processes such as adenovirus replication and transcription of TGF-responsive genes. There are highly conserved DNA binding domains in SMAD and NFI/CTF transcription factors that allow sequence specific DNA binding for members of each family. However, no homology relationship has been established for the DNA binding domains present in these families. For a better understanding of the structure and evolution of SMAD genes, we carried out a sensitive PSI-BLAST database search. This revealed significant similarities between the DNA binding domains of SMADs and NFI/CTF transcription factors. Enhanced graphic matrix analysis and multiple sequence alignment of the amino acid sequences of the SMAD and NFI/CTF DNA binding domains also show that these two classes of domains share considerable structural similarity. These results strongly suggest that these two classes of factors share a homologous DNA binding domain presumably resulting from a common ancestry. In contrast, the C-terminal transcription modulation domains of both SMAD and NFI/CTF families do not show any sequence similarity. Based on the structural relationship of their DNA binding domains, we propose that the SMAD and NFI/CTF transcription factors belong to new superfamily of genes.

We describe the cloning, sequencing and structure of the human fast skeletal troponin T (TNNT3) gene located on chromosome 11p15.5. The single-copy gene encodes 19 exons and 18 introns. Eleven of these exons, 1-3, 9-15 and 18, are constitutively spliced, whereas exons 4-8 are alternatively spliced. The gene contains an additional subset of developmentally regulated and alternatively spliced exons, including a foetal exon located between exon 8 and 9 and exon 16 or alpha (adult) and 17 or beta (foetal and neonatal). Exon phasing suggests that the majority of the alternatively spliced exons located at the 5' end of the gene may have evolved as a result of exon shuffling, because they are of the same phase class. In contrast, the 3' exons encoding an evolutionarily conserved heptad repeat domain, shared by both TnT and troponin I (TnI), may be remnants of an ancient ancestral gene. The sequence of the 5' flanking region shows that the putative promoter contains motifs including binding sites for MyoD, MEF-2 and several transcription factors which may play a role in transcriptional regulation and tissue-specific expression of TnT. The coding region of TNNT3 exhibits strong similarity to the corresponding rat sequence. However, unlike the rat TnT gene, TNNT3 possesses two repeat regions of CCA and TC. The exclusive presence of these repetitive elements in the human gene indicates divergence in the evolutionary dynamics of mammalian TnT genes. Homologous muscle-specific splicing enhancer motifs are present in the introns upstream and downstream of the foetal exon, and may play a role in the developmental pattern of alternative splicing of the gene. The genomic correlates of TNNT3 are relevant to our understanding of the evolution and regulation of expression of the gene, as well as the structure and function of the protein isoforms. The nucleotide sequence of TNNT3 has been submitted to EMBL/GenBank under Accession No. AF026276.

Troponin T (TnT), a thin filament myofibrillar protein, is essential for the Ca2+ regulation of striated muscle contraction in vertebrates, both in vivo and in vitro. To understand the role of TnT in this process, its interaction with two other troponin components, troponin I (TnI) and troponin C (TnC) was examined by using the yeast two hybrid system, which is a genetic approach to detect protein-protein interactions. Computer assisted analysis of phylogenetically distant TnT amino acid sequences unveiled a highly conserved protein domain that is characterized by a heptad repeat (HR) motif with a potential for alpha-helical coiled coil formation. A similar, potentially coiled coil forming domain is also conserved in all known TnI sequences. These protein motifs appeared to be the regions where TnI-TnT interaction may take place. Deletions and point mutations in TnT, which disrupted its HR motif, severely reduced or abolished TnI binding, but binding to TnC was not affected, indicating that the TnT-TnI and TnT-TnC binary interactions can be uncoupled. Remarkably, the truncated fragments of TnT and TnI in which the HR motifs were retained showed binary interaction in the yeast two hybrid system. It was also observed that the formation of the TnT-TnI heterodimers is favored over the homodimers TnT-TnT and TnI-TnI. These results indicate that the evolutionarily conserved HR motifs may play a role in TnT-TnI dimerization, presumably through the formation of alpha-helical coiled coils.

The complete nucleotide sequences of two parvoviruses isolated from goose and muscovy duck were determined. The two virus genomes share 81.9% nucleotide sequence identity, indicating that they are closely related. The coding regions are bracketed by inverted terminal repeats containing palindromes. This is similar to the genome organization of human parvoviruses, adeno-associated virus 2, and B19. Amino acid sequence comparison shows that the closest relative of the goose and muscovy duck parvoviruses is adeno-associated virus 2. This is surprising, because the goose and muscovy duck parvoviruses do not require any helper virus for productive replication, suggesting that adeno-associated virus 2 has been derived from a helper-independent ancestor.