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FlyBase (flybase.org) is a model organism database and knowledge base about Drosophila melanogaster, commonly known as the fruit fly. Researchers from around the world rely on the genetic, genomic, and functional information available in FlyBase, as well as its tools to view and interrogate these data. In this article, we describe the latest developments and updates to FlyBase. These include the introduction of single-cell RNA sequencing data, improved content and display of functional information, updated orthology pipelines, new chemical reports, and enhancements to our outreach resources., Competing Interests: Conflicts of interest: The author(s) declare no conflicts of interest., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Genetics Society of America.)

Research in model organisms is central to the characterization of signaling pathways in multicellular organisms. Here, we present the comprehensive and systematic curation of 17 Drosophila signaling pathways using the Gene Ontology framework to establish a dynamic resource that has been incorporated into FlyBase, providing visualization and data integration tools to aid research projects. By restricting to experimental evidence reported in the research literature and quantifying the amount of such evidence for each gene in a pathway, we captured the landscape of empirical knowledge of signaling pathways in Drosophila., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2024. Published by The Company of Biologists Ltd.)

Regulatory non-coding RNAs (ncRNAs) are increasingly recognized as integral to the control of biological processes. This is often through the targeted regulation of mRNA expression, but this is by no means the only mechanism through which regulatory ncRNAs act. The Gene Ontology (GO) has long been used for the systematic annotation of protein-coding and ncRNA gene function, but rapid progress in the understanding of ncRNAs meant that the ontology needed to be revised to accurately reflect current knowledge. Here, a targeted effort to revise GO terms used for the annotation of regulatory ncRNAs is described, focusing on microRNAs (miRNAs), long non-coding RNAs (lncRNAs), small interfering RNAs (siRNAs) and PIWI-interacting RNAs (piRNAs). This paper provides guidance to biocurators annotating ncRNA-mediated processes using the GO and serves as background for researchers wishing to make use of the GO in their studies of ncRNAs and the biological processes they regulate.

The development and application of genetic techniques in the fruit fly Drosophila melanogaster underlies some major advances in the understanding of metazoan development and biology. To examine whether the publication record for signalling pathway genes can indicate which factors have shaped pathway research, the publication history of selected genes is used to compare differences in research output over time. This is used to discuss how research trends may be shaped by a variety of factors such as advances in technology, ease of study and importance to human health., Competing Interests: The authors declare that there are no conflicts of interest present., (Copyright: © 2023 by the authors.)

Gene set enrichment analysis (GSEA) plays an important role in large-scale data analysis, helping scientists discover the underlying biological patterns over-represented in a gene list resulting from, for example, an 'omics' study. Gene Ontology (GO) annotation is the most frequently used classification mechanism for gene set definition. Here we present a new GSEA tool, PANGEA (PAthway, Network and Gene-set Enrichment Analysis; https://www.flyrnai.org/tools/pangea/), developed to allow a more flexible and configurable approach to data analysis using a variety of classification sets. PANGEA allows GO analysis to be performed on different sets of GO annotations, for example excluding high-throughput studies. Beyond GO, gene sets for pathway annotation and protein complex data from various resources as well as expression and disease annotation from the Alliance of Genome Resources (Alliance). In addition, visualizations of results are enhanced by providing an option to view network of gene set to gene relationships. The tool also allows comparison of multiple input gene lists and accompanying visualisation tools for quick and easy comparison. This new tool will facilitate GSEA for Drosophila and other major model organisms based on high-quality annotated information available for these species., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project., Competing Interests: Conflicts of interest The authors declare no conflicts of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of The Genetics Society of America.)

FlyBase provides a centralized resource for the genetic and genomic data of Drosophila melanogaster. As FlyBase enters our fourth decade of service to the research community, we reflect on our unique aspects and look forward to our continued collaboration with the larger research and model organism communities. In this study, we emphasize the dedicated reports and tools we have constructed to meet the specialized needs of fly researchers but also to facilitate use by other research communities. We also highlight ways that we support the fly community, including an external resources page, help resources, and multiple avenues by which researchers can interact with FlyBase., (© The Author(s) 2022. Published by Oxford University Press on behalf of Genetics Society of America.)

Multicellular organisms rely on cell-cell communication to exchange information necessary for developmental processes and metabolic homeostasis. Cell-cell communication pathways can be inferred from transcriptomic datasets based on ligand-receptor expression. Recently, data generated from single-cell RNA sequencing have enabled ligand-receptor interaction predictions at an unprecedented resolution. While computational methods are available to infer cell-cell communication in vertebrates such a tool does not yet exist for Drosophila. Here, we generated a high-confidence list of ligand-receptor pairs for the major fly signaling pathways and developed FlyPhoneDB, a quantification algorithm that calculates interaction scores to predict ligand-receptor interactions between cells. At the FlyPhoneDB user interface, results are presented in a variety of tabular and graphical formats to facilitate biological interpretation. To illustrate that FlyPhoneDB can effectively identify active ligands and receptors to uncover cell-cell communication events, we applied FlyPhoneDB to Drosophila single-cell RNA sequencing data sets from adult midgut, abdomen, and blood, and demonstrate that FlyPhoneDB can readily identify previously characterized cell-cell communication pathways. Altogether, FlyPhoneDB is an easy-to-use framework that can be used to predict cell-cell communication between cell types from single-cell RNA sequencing data in Drosophila., (© The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

The role of the blood-brain barrier (BBB) in Alzheimer's and other neurodegenerative diseases is still the subject of many studies. However, those studies using high-throughput methods have been compromised by the lack of Gene Ontology (GO) annotations describing the role of proteins in the normal function of the BBB. The GO Consortium provides a gold-standard bioinformatics resource used for analysis and interpretation of large biomedical data sets. However, the GO is also used by other research communities and, therefore, must meet a variety of demands on the breadth and depth of information that is provided. To meet the needs of the Alzheimer's research community we have focused on the GO annotation of the BBB, with over 100 transport or junctional proteins prioritized for annotation. This project has led to a substantial increase in the number of human proteins associated with BBB-relevant GO terms as well as more comprehensive annotation of these proteins in many other processes. Furthermore, data describing the microRNAs that regulate the expression of these priority proteins have also been curated. Thus, this project has increased both the breadth and depth of annotation for these prioritized BBB proteins. Database URLhttps://www.ebi.ac.uk/QuickGO/., (© The Author(s) 2021. Published by Oxford University Press.)

FlyBase (flybase.org) is an essential online database for researchers using Drosophila melanogaster as a model organism, facilitating access to a diverse array of information that includes genetic, molecular, genomic and reagent resources. Here, we describe the introduction of several new features at FlyBase, including Pathway Reports, paralog information, disease models based on orthology, customizable tables within reports and overview displays ('ribbons') of expression and disease data. We also describe a variety of recent important updates, including incorporation of a developmental proteome, upgrades to the GAL4 search tab, additional Experimental Tool Reports, migration to JBrowse for genome browsing and improvements to batch queries/downloads and the Fast-Track Your Paper tool., (© The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Biological processes are accomplished by the coordinated action of gene products. Gene products often participate in multiple processes, and can therefore be annotated to multiple Gene Ontology (GO) terms. Nevertheless, processes that are functionally, temporally and/or spatially distant may have few gene products in common, and co-annotation to unrelated processes probably reflects errors in literature curation, ontology structure or automated annotation pipelines. We have developed an annotation quality control workflow that uses rules based on mutually exclusive processes to detect annotation errors, based on and validated by case studies including the three we present here: fission yeast protein-coding gene annotations over time; annotations for cohesin complex subunits in human and model species; and annotations using a selected set of GO biological process terms in human and five model species. For each case study, we reviewed available GO annotations, identified pairs of biological processes which are unlikely to be correctly co-annotated to the same gene products (e.g. amino acid metabolism and cytokinesis), and traced erroneous annotations to their sources. To date we have generated 107 quality control rules, and corrected 289 manual annotations in eukaryotes and over 52 700 automatically propagated annotations across all taxa.

DNA synthesis during replication or repair is a fundamental cellular process that is catalyzed by a set of evolutionary conserved polymerases. Despite a large body of research, the DNA polymerases of Drosophila melanogaster have not yet been systematically reviewed, leading to inconsistencies in their nomenclature, shortcomings in their functional (Gene Ontology, GO) annotations and an under-appreciation of the extent of their characterization. Here, we describe the complete set of DNA polymerases in D. melanogaster , applying nomenclature already in widespread use in other species, and improving their functional annotation. A total of 19 genes encode the proteins comprising three replicative polymerases (alpha-primase, delta, epsilon), five translesion/repair polymerases (zeta, eta, iota, Rev1, theta) and the mitochondrial polymerase (gamma). We also provide an overview of the biochemical and genetic characterization of these factors in D. melanogaster . This work, together with the incorporation of the improved nomenclature and GO annotation into key biological databases, including FlyBase and UniProtKB, will greatly facilitate access to information about these important proteins.

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.

High-throughput studies constitute an essential and valued source of information for researchers. However, high-throughput experimental workflows are often complex, with multiple data sets that may contain large numbers of false positives. The representation of high-throughput data in the Gene Ontology (GO) therefore presents a challenging annotation problem, when the overarching goal of GO curation is to provide the most precise view of a gene's role in biology. To address this, representatives from annotation teams within the GO Consortium reviewed high-throughput data annotation practices. We present an annotation framework for high-throughput studies that will facilitate good standards in GO curation and, through the use of new high-throughput evidence codes, increase the visibility of these annotations to the research community.

For more than 25 years, FlyBase ( flybase.org ) has served as an online database of biological information on the genus Drosophila, concentrating on the model organism D. melanogaster. Traditionally, FlyBase data have been organized and presented at a gene-by-gene level, which remains a useful perspective when the object of interest is a specific gene or gene product. However, in the modern era of a fully sequenced genome and an increasingly characterized proteome, it is often desirable to compile and analyze lists of genes related by a common function. This may be achieved in FlyBase by searching for genes annotated with relevant Gene Ontology (GO) terms and/or protein domain data. In addition, FlyBase provides preassembled lists of functionally related D. melanogaster genes within "Gene Group" reports. These are compiled manually from the published literature or expert databases and greatly facilitate access to, and analysis of, established gene sets. This chapter describes protocols to produce lists of functionally related genes in FlyBase using GO annotations, protein domain data and the Gene Groups resource, and provides guidance and advice for their further analysis and processing.

Synthesis of polypeptides from mRNA (translation) is a fundamental cellular process that is coordinated and catalyzed by a set of canonical 'translation factors'. Surprisingly, the translation factors of Drosophila melanogaster have not yet been systematically identified, leading to inconsistencies in their nomenclature and shortcomings in functional (Gene Ontology, GO) annotations. Here, we describe the complete set of translation factors in D. melanogaster, applying nomenclature already in widespread use in other species, and revising their functional annotation. The collection comprises 43 initiation factors, 12 elongation factors, 3 release factors and 6 recycling factors, totaling 64 of which 55 are cytoplasmic and 9 are mitochondrial. We also provide an overview of notable findings and particular insights derived from Drosophila about these factors. This catalog, together with the incorporation of the improved nomenclature and GO annotation into FlyBase, will greatly facilitate access to information about the functional roles of these important proteins.

FlyBase (flybase.org) is the primary online database of genetic, genomic, and functional information about Drosophila species, with a major focus on the model organism Drosophila melanogaster. The long and rich history of Drosophila research, combined with recent surges in genomic-scale and high-throughput technologies, mean that FlyBase now houses a huge quantity of data. Researchers need to be able to rapidly and intuitively query these data, and the QuickSearch tool has been designed to meet these needs. This tool is conveniently located on the FlyBase homepage and is organized into a series of simple tabbed interfaces that cover the major data and annotation classes within the database. This unit describes the functionality of all aspects of the QuickSearch tool. With this knowledge, FlyBase users will be equipped to take full advantage of all QuickSearch features and thereby gain improved access to data relevant to their research. © 2016 by John Wiley & Sons, Inc., (Copyright © 2016 John Wiley & Sons, Inc.)

Many publications describe sets of genes or gene products that share a common biology. For example, genome-wide studies and phylogenetic analyses identify genes related in sequence; high-throughput genetic and molecular screens reveal functionally related gene products; and advanced proteomic methods can determine the subunit composition of multi-protein complexes. It is useful for such gene collections to be presented as discrete lists within the appropriate Model Organism Database (MOD) so that researchers can readily access these data alongside other relevant information. To this end, FlyBase (flybase.org), the MOD for Drosophila melanogaster, has established a 'Gene Group' resource: high-quality sets of genes derived from the published literature and organized into individual report pages. To facilitate further analyses, Gene Group Reports also include convenient download and analysis options, together with links to equivalent gene groups at other databases. This new resource will enable researchers with diverse backgrounds and interests to easily view and analyse acknowledged D. melanogaster gene sets and compare them with those of other species., (© The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.)

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

Understanding the role of specific bilayer components in controlling the function of G-protein coupled receptors (GPCRs) will be a key factor in the development of novel pharmaceuticals. Cholesterol-dependence in particular has become an area of keen interest with respect to GPCR function; not least since the 2.6Å crystal structure of the β2 adrenergic receptor revealed a putative cholesterol binding motif conserved throughout class-A GPCRs. Furthermore, experimental evidence for cholesterol-dependent GPCR function has been demonstrated in a limited number of cases. This modulation of receptor function has been attributed to both direct interactions between cholesterol and receptor, and indirect effects caused by the influence of cholesterol on bilayer order and lateral pressure. Despite the widespread occurrence of cholesterol binding motifs, available experimental data on the functional involvement of cholesterol on GPCRs are currently limited to a small number of receptors. Here we investigate the role of cholesterol in the function of the neurotensin receptor 1 (NTS1) a class-A GPCR. Specifically we show how cholesterol, and the analogue cholesteryl hemisuccinate, influence activity, stability, and oligomerisation of both purified and reconstituted NTS1. The results caution against using such motifs as indicators of cholesterol-dependent GPCR activity., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Glucosamine-6-phosphate N-acetyltransferase 1 (GNA1) produces GlcNAc-6-phosphate from GlcN-6-phosphate and acetyl coenzyme A. Early mercury-labelling experiments implicated a conserved cysteine in the reaction mechanism, whereas recent structural data appear to support a mechanism in which this cysteine plays no role. Here, two crystal structures of Caenorhabditis elegans GNA1 are reported, revealing an unusual covalent complex between this cysteine and the coenzyme A product. Mass-spectrometric and reduction studies showed that this inactive covalent complex can be reactivated through reduction, yet mutagenesis of the cysteine supports a previously reported bi-bi mechanism. The data unify the apparently contradictory earlier reports on the role of a cysteine in the GNA1 active site.

Single-particle electron cryomicroscopy permits structural characterization of noncrystalline protein samples, but throughput is limited by problems associated with sample preparation and image processing. Three-dimensional density maps are reconstructed from high resolution but noisy images of individual molecules. We show that self-assembled DNA nanoaffinity templates can create dense, nonoverlapping arrays of protein molecules, greatly facilitating data collection. We demonstrate this technique using a G-protein-coupled membrane receptor, a soluble G-protein, and a signaling complex of both molecules.

Peptide synthesis is widely used for the production of small proteins and peptides, but producing uniformly isotopically labelled peptides for NMR and other biophysical studies could be limited for economic reasons. Here, we propose a use of a modified pGEV-1 plasmid to express neurotensin (NT(1-13)), pGlu(1)-Leu(2)-Tyr(3)-Glu(4)-Asn(5)-Lys(6)-Pro(7)-Arg(8)-Arg(9)-Pro(10)-Tyr(11)-Ile(12)-Leu(13)-OH, as a C-terminal fusion protein with the GB1 domain of streptococcal protein G. The free carboxyl-terminus is important for the function of several peptide hormones, including neurotensin. Therefore, for the pGEV-NT(1-13) construct, the C-terminal pGEV-encoded 6xHis tag was removed and an N-terminal 8xHis tag was introduced for affinity purification. To facilitate removal of tags using CNBr cleavage, a methionine was introduced at the N-terminal of the peptide. Furthermore, this pGEV-NT(1-13) plasmid was used as a template to include a Pro-7 to Met mutation for CNBr cleavage, giving NT(8-13), the sub-fragment crucial for the biological activity of this peptide. These two constructs are being used to produce uniformly labelled NT(1-13) and NT(8-13) in high yield and in a cost effective way, using cheap (15)N and/or (13)C source. The modification proposed here using the pGEV-1 plasmid could be an alternative option for the high expression of other isotopically labelled and unlabelled short peptides, including hormones and hydrophobic membrane peptides., (Copyright 2010 Elsevier Inc. All rights reserved.)

The G-protein coupled receptor (GPCR), rat brain neurotensin receptor type I (NTS1) is one of a small number of GPCRs that have been successfully expressed in Escherichia coli as a functional, ligand-binding receptor, but yields of purified receptor are still low for comprehensive structural studies. Here, several approaches have been examined to optimize the yields of active, ligand-binding receptor. Optimisation of E. coli strain and induction protocol yielded a significant improvement in expression of active receptor. Expression of the receptor in BL21(DE3) cells, in combination with autoinduction improved expression 10-fold when compared with previously reported expression protocols using IPTG-mediated induction in DH5alpha cells. Optimization of the purification protocol revealed that supplementation of buffers with phospholipids enhanced recovery of active receptor. The methods examined are potentially applicable to other GPCRs expressed in E. coli.

Neurotensin receptor 1 (NTS1), a Family A G-protein coupled receptor (GPCR), was expressed in Escherichia coli as a fusion with the fluorescent proteins eCFP or eYFP. A fluorophore-tagged receptor was used to study the multimerization of NTS1 in detergent solution and in brain polar lipid bilayers, using fluorescence resonance energy transfer (FRET). A detergent-solubilized receptor was unable to form FRET-competent complexes at concentrations of up to 200 nM, suggesting that the receptor is monomeric in this environment. When reconstituted into a model membrane system at low receptor density, the observed FRET was independent of agonist binding, suggesting constitutive multimer formation. In competition studies, decreased FRET in the presence of untagged NTS1 excludes the possibility of fluorescent protein-induced interactions. A simulation of the experimental data indicates that NTS1 exists predominantly as a homodimer, rather than as higher-order multimers. These observations suggest that, in common with several other Family A GPCRs, NTS1 forms a constitutive dimer in lipid bilayers, stabilized through receptor-receptor interactions in the absence of other cellular signaling components. Therefore, this work demonstrates that well-characterized model membrane systems are useful tools for the study of GPCR multimerization, allowing fine control over system composition and complexity, provided that rigorous control experiments are performed.

The siglecs are a group of mammalian sialic acid binding receptors expressed predominantly in the immune system. The CD33-related siglecs show complex recognition patterns for sialylated glycans. Siglec-7 shows a preference for alpha(2,8)-disialylated ligands and provides a structural template for studying the key interactions that drive this selectivity. We have co-crystallized Siglec-7 with a synthetic oligosaccharide corresponding to the alpha(2,8)-disialylated ganglioside GT1b. The crystal structure of the complex offers a first glimpse into how this important family of lectins binds the structurally diverse gangliosides. The structure reveals that the C-C' loop, a region implicated in previous studies as driving siglec specificity, undergoes a dramatic conformational shift, allowing it to interact with the underlying neutral glycan core of the ganglioside. The structural data in combination with mutagenesis studies show that binding of the ganglioside is driven by extensive hydrophobic contacts together with key polar interactions and that the binding site structure is complementary to preferred solution conformations of GT1b.

Siglecs (sialic acid binding Ig-like lectins) are transmembrane receptors for sialylated glycoconjugates that modulate cellular interactions and signalling events in the haematopoietic, immune and nervous systems. Siglec-7 is a structural prototype for the recently described family of immune inhibitory CD33-related siglecs and is predominantly expressed on natural killer cells and monocytes, as well as subsets of CD8 T-cells. Siglec-specific inhibitors are desired for the detection of masked and unmasked forms of siglecs, to aid in dissection of signalling pathways and as tools to investigate siglecs as potential therapeutic targets. As a first step towards this end, we present the crystal structure of siglec-7 in complex with a sialylated ligand, the ganglioside analogue DSLc4 [alpha(2,3)/alpha(2,6) disialyl lactotetraosyl 2-(trimethylsilyl)ethyl], which allows for a detailed description of the binding site, required for structure-guided inhibitor design. Mutagenesis and binding assays were used to demonstrate a key structural role for Lys131, a residue that changes conformation upon sialic acid binding. Differences between the binding sites of siglec family members were then exploited using alpha-methyl Neu5Ac (N-acetylneuraminic acid) as a basic scaffold. A co-crystal of siglec-7 in complex with the sialoside inhibitor, oxamido-Neu5Ac [methyl alpha-9-(amino-oxalyl-amino)-9-deoxy-Neu5Ac] and inhibition data for the sialosides gives clear leads for future inhibitor design.

Siglec-5 (CD170) is a member of the recently described human CD33-related siglec subgroup of sialic acid binding Ig-like lectins and is expressed on myeloid cells of the hemopoietic system. Similar to other CD33-related siglecs, Siglec-5 contains two tyrosine-based motifs in its cytoplasmic tail implicated in signaling functions. To investigate the role of these motifs in Siglec-5-dependent signaling, we used transfected rat basophil leukemia cells as a model system. Tyrosine phosphorylation of Siglec-5 led to recruitment of the tyrosine phosphatases SHP-1 and SHP-2, as seen in both pull-down assays and microscopy. Siglec-5 could efficiently inhibit FcepsilonRI-mediated calcium fluxing and serotonin release after co-cross-linking. Surprisingly, a double tyrosine to alanine mutant of Siglec-5 could still mediate strong inhibition of serotonin release in the absence of detectable tyrosine phosphorylation, whereas a double tyrosine to phenylalanine mutant lost all inhibitory activity. In comparison, suppression of Siglec-5-dependent adhesion to red blood cells was reversed by either tyrosine to alanine or tyrosine to phenylalanine mutations of the membrane proximal tyrosine-based motif. Using an in vitro phosphatase assay with synthetic and recombinant forms of the cytoplasmic tail, it was shown that a double alanine mutant of Siglec-5 had weak, but significant SHP-1 activating properties similar to those of wild type, non-phosphorylated cytoplasmic tail, whereas a double phenylalanine mutant was inactive. These findings establish that Siglec-5 can be classified as an inhibitory receptor with the potential to mediate SHP-1 and/or SHP-2-dependent signaling in the absence of tyrosine phosphorylation.

Sialic acid-binding immunoglobulin-like lectins (Siglecs) recognize sialylated glycoconjugates and play a role in cell-cell recognition. Siglec-7 is expressed on natural killer cells and displays unique ligand binding properties different from other members of the Siglec family. Here we describe the high resolution structures of the N-terminal V-set Ig-like domain of Siglec-7 in two crystal forms, at 1.75 and 1.9 A. The latter crystal form reveals the full structure of this domain and allows us to speculate on the differential ligand binding properties displayed by members of the Siglec family. A fully ordered N-linked glycan is observed, tethered by tight interactions with symmetry-related protein molecules in the crystal. Comparison of the structure with that of sialoadhesin and a model of Siglec-9 shows that the unique preference of Siglec-7 for alpha(2,8)-linked disialic acid is likely to reside in the C-C' loop, which is variable in the Siglec family. In the Siglec-7 structure, the ligand-binding pocket is occupied by a loop of a symmetry-related molecule, mimicking the interactions with sialic acid.