Atlas of primary cell-type-specific sequence models of gene expression and variant effects

Human biology is rooted in highly specialized cell types programmed by a common genome, 98% of which is outside of genes. Genetic variation in the enormous noncoding space is linked to the majority of disease risk. To address the problem of linking these variants to expression changes in primary human cells, we introduce ExPectoSC, an atlas of modular deep-learning-based models for predicting cell-type-specific gene expression directly from sequence. We provide models for 105 primary human cell types covering 7 organ systems, demonstrate their accuracy, and then apply them to prioritize relevant cell types for complex human diseases. The resulting atlas of sequence-based gene expression and variant effects is publicly available in a user-friendly interface and readily extensible to any primary cell types. We demonstrate the accuracy of our approach through systematic evaluations and apply the models to prioritize ClinVar clinical variants of uncertain significance, verifying our top predictions experimentally.

Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data

Resolving chromatin-remodeling-linked gene expression changes at cell-type resolution is important for understanding disease states. Here we describe MAGICAL (Multiome Accessibility Gene Integration Calling and Looping), a hierarchical Bayesian approach that leverages paired single-cell RNA sequencing and single-cell transposase-accessible chromatin sequencing from different conditions to map disease-associated transcription factors, chromatin sites, and genes as regulatory circuits. By simultaneously modeling signal variation across cells and conditions in both omics data types, MAGICAL achieved high accuracy on circuit inference. We applied MAGICAL to study Staphylococcus aureus sepsis from peripheral blood mononuclear single-cell data that we generated from subjects with bloodstream infection and uninfected controls. MAGICAL identified sepsis-associated regulatory circuits predominantly in CD14 monocytes, known to be activated by bacterial sepsis. We addressed the challenging problem of distinguishing host regulatory circuit responses to methicillin-resistant and methicillin-susceptible S. aureus infections. Although differential expression analysis failed to show predictive value, MAGICAL identified epigenetic circuit biomarkers that distinguished methicillin-resistant from methicillin-susceptible S. aureus infections.

Modeling transcriptional regulation of model species with deep learning

To enable large-scale analyses of transcription regulation in model species, we developed DeepArk, a set of deep learning models of the cis-regulatory activities for four widely studied species: Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, and Mus musculus DeepArk accurately predicts the presence of thousands of different context-specific regulatory features, including chromatin states, histone marks, and transcription factors. In vivo studies show that DeepArk can predict the regulatory impact of any genomic variant (including rare or not previously observed) and enables the regulatory annotation of understudied model species.

Tissue-specific enhancer functional networks for associating distal regulatory regions to disease

Systematic study of tissue-specific function of enhancers and their disease associations is a major challenge. We present an integrative machine-learning framework, FENRIR, that integrates thousands of disparate epigenetic and functional genomics datasets to infer tissue-specific functional relationships between enhancers for 140 diverse human tissues and cell types, providing a regulatory-region-centric approach to systematically identify disease-associated enhancers. We demonstrated its power to accurately prioritize enhancers associated with 25 complex diseases. In a case study on autism, FENRIR-prioritized enhancers showed a significant proband-specific de novo mutation enrichment in a large, sibling-controlled cohort, indicating pathogenic signal. We experimentally validated transcriptional regulatory activities of eight enhancers, including enhancers not previously reported with autism, and demonstrated their differential regulatory potential between proband and sibling alleles. Thus, FENRIR is an accurate and effective framework for the study of tissue-specific enhancers and their role in disease. FENRIR can be accessed at fenrir.flatironinstitute.org/.

Modeling molecular development of breast cancer in canine mammary tumors

Understanding the changes in diverse molecular pathways underlying the development of breast tumors is critical for improving diagnosis, treatment, and drug development. Here, we used RNA-profiling of canine mammary tumors (CMTs) coupled with a robust analysis framework to model molecular changes in human breast cancer. Our study leveraged a key advantage of the canine model, the frequent presence of multiple naturally occurring tumors at diagnosis, thus providing samples spanning normal tissue and benign and malignant tumors from each patient. We showed human breast cancer signals, at both expression and mutation level, are evident in CMTs. Profiling multiple tumors per patient enabled by the CMT model allowed us to resolve statistically robust transcription patterns and biological pathways specific to malignant tumors versus those arising in benign tumors or shared with normal tissues. We showed that multiple histological samples per patient is necessary to effectively capture these progression-related signatures, and that carcinoma-specific signatures are predictive of survival for human breast cancer patients. To catalyze and support similar analyses and use of the CMT model by other biomedical researchers, we provide FREYA, a robust data processing pipeline and statistical analyses framework.

Genome-wide landscape of RNA-binding protein target site dysregulation reveals a major impact on psychiatric disorder risk

Despite the strong genetic basis of psychiatric disorders, the underlying molecular mechanisms are largely unmapped. RNA-binding proteins (RBPs) are responsible for most post-transcriptional regulation, from splicing to translation to localization. RBPs thus act as key gatekeepers of cellular homeostasis, especially in the brain. However, quantifying the pathogenic contribution of noncoding variants impacting RBP target sites is challenging. Here, we leverage a deep learning approach that can accurately predict the RBP target site dysregulation effects of mutations and discover that RBP dysregulation is a principal contributor to psychiatric disorder risk. RBP dysregulation explains a substantial amount of heritability not captured by large-scale molecular quantitative trait loci studies and has a stronger impact than common coding region variants. We share the genome-wide profiles of RBP dysregulation, which we use to identify DDHD2 as a candidate schizophrenia risk gene. This resource provides a new analytical framework to connect the full range of RNA regulation to complex disease.

Genomic RNA Elements Drive Phase Separation of the SARS-CoV-2 Nucleocapsid

We report that the SARS-CoV-2 nucleocapsid protein (N-protein) undergoes liquid-liquid phase separation (LLPS) with viral RNA. N-protein condenses with specific RNA genomic elements under physiological buffer conditions and condensation is enhanced at human body temperatures (33°C and 37°C) and reduced at room temperature (22°C). RNA sequence and structure in specific genomic regions regulate N-protein condensation while other genomic regions promote condensate dissolution, potentially preventing aggregation of the large genome. At low concentrations, N-protein preferentially crosslinks to specific regions characterized by single-stranded RNA flanked by structured elements and these features specify the location, number, and strength of N-protein binding sites (valency). Liquid-like N-protein condensates form in mammalian cells in a concentration-dependent manner and can be altered by small molecules. Condensation of N-protein is RNA sequence and structure specific, sensitive to human body temperature, and manipulatable with small molecules, and therefore presents a screenable process for identifying antiviral compounds effective against SARS-CoV-2.

Abstract 2504: Modeling molecular development of breast cancer in canine mammary tumors

Malignancy in cancer is a consequence of the progressive accumulation of mutations in a tumor, with profound implications for drug selection and treatment. However, in human studies, inter-patient variability obscures molecular signatures of tumor progression because patients usually present with a single mammary tumor. In contrast, dogs frequently exhibit multiple naturally occurring mammary tumors in the same individual. Moreover, canine mammary tumors (CMTs) and human breast cancer have similar histopathological profiles and clinical presentation. We leverage the CMT model to elucidate genome-wide molecular changes clinically relevant in human breast cancer, focusing on signals underlying tumor development. We develop a robust, generally applicable, computational analysis framework (FREYA) for analysis of CMTs for comparative oncology. Using FREYA, we RNA profile 89 samples from 16 dogs, and demonstrate that CMTs recapitulate human breast cancer subtypes. We then extract molecular profiles of breast cancer progression at three distinct stages (normal, pre-malignant and malignant) and identify signatures of gene expression reflective of tumor progression. Focusing on the transitions to malignancy, we identify transcriptional patterns and biological pathways specific to malignant tumors and distinct from those characterizing pre-malignant tumors or normal tissue. We find that human breast cancer patients whose tumors exhibit strong CMT malignancy signatures have significantly decreased survival, indicative of the importance of the tumor progression processes identified in CMTs to human breast cancer prognosis. Altogether, our comprehensive genomic characterization demonstrates that CMTs are a powerful translational model of breast cancer, providing insights that inform our understanding of tumor development in humans. To catalyze and support similar analyses and use of the CMT model by other biomedical researchers, we publicly share all of our data and provide FREYA, a robust data processing pipeline and statistical analyses framework, at freya.flatironinstitute.org.

Citation Format: Kiley Graim, Dmitriy Gorenshteyn, David G. Robinson, Nicholas J. Carriero, James Cahill, Rumela Chakrabarti, Michael H. Goldschmidt, Amy C. Durham, Julien Funk, John D. Storey, Vessela N. Kristensen, Chandra L. Theesfeld, Karin U. Sorenmo, Olga G. Troyanskaya. Modeling molecular development of breast cancer in canine mammary tumors [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2504.

Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development

Comprehensive information on the timing and location of gene expression is fundamental to our understanding of embryonic development and tissue formation. While high-throughput in situ hybridization projects provide invaluable information about developmental gene expression patterns for model organisms like Drosophila, the output of these experiments is primarily qualitative, and a high proportion of protein coding genes and most non-coding genes lack any annotation. Accurate data-centric predictions of spatio-temporal gene expression will therefore complement current in situ hybridization efforts. Here, we applied a machine learning approach by training models on all public gene expression and chromatin data, even from whole-organism experiments, to provide genome-wide, quantitative spatio-temporal predictions for all genes. We developed structured in silico nano-dissection, a computational approach that predicts gene expression in >200 tissue-developmental stages. The algorithm integrates expression signals from a compendium of 6,378 genome-wide expression and chromatin profiling experiments in a cell lineage-aware fashion. We systematically evaluated our performance via cross-validation and experimentally confirmed 22 new predictions for four different embryonic tissues. The model also predicts complex, multi-tissue expression and developmental regulation with high accuracy. We further show the potential of applying these genome-wide predictions to extract tissue specificity signals from non-tissue-dissected experiments, and to prioritize tissues and stages for disease modeling. This resource, together with the exploratory tools are freely available at our webserver http://find.princeton.edu, which provides a valuable tool for a range of applications, from predicting spatio-temporal expression patterns to recognizing tissue signatures from differential gene expression profiles.

Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk

We address the challenge of detecting the contribution of noncoding mutations to disease with a deep-learning-based framework that predicts the specific regulatory effects and the deleterious impact of genetic variants. Applying this framework to 1,790 autism spectrum disorder (ASD) simplex families reveals a role in disease for noncoding mutations-ASD probands harbor both transcriptional- and post-transcriptional-regulation-disrupting de novo mutations of significantly higher functional impact than those in unaffected siblings. Further analysis suggests involvement of noncoding mutations in synaptic transmission and neuronal development and, taken together with previous studies, reveals a convergent genetic landscape of coding and noncoding mutations in ASD. We demonstrate that sequences carrying prioritized mutations identified in probands possess allele-specific regulatory activity, and we highlight a link between noncoding mutations and heterogeneity in the IQ of ASD probands. Our predictive genomics framework illuminates the role of noncoding mutations in ASD and prioritizes mutations with high impact for further study, and is broadly applicable to complex human diseases.

A Computational Framework for Genome-wide Characterization of the Human Disease Landscape

A key challenge for the diagnosis and treatment of complex human diseases is identifying their molecular basis. Here, we developed a unified computational framework, URSA^HD (Unveiling RNA Sample Annotation for Human Diseases), that leverages machine learning and the hierarchy of anatomical relationships present among diseases to integrate thousands of clinical gene expression profiles and identify molecular characteristics specific to each of the hundreds of complex diseases. URSA^HD can distinguish between closely related diseases more accurately than literature-validated genes or traditional differential-expression-based computational approaches and is applicable to any disease, including rare and understudied ones. We demonstrate the utility of URSA^HD in classifying related nervous system cancers and experimentally verifying novel neuroblastoma-associated genes identified by URSA^HD. We highlight the applications for potential targeted drug-repurposing and for quantitatively assessing the molecular response to clinical therapies. URSA^HD is freely available for public use, including the use of underlying models, at ursahd.princeton.edu.

Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk

Key challenges for human genetics, precision medicine and evolutionary biology include deciphering the regulatory code of gene expression and understanding the transcriptional effects of genome variation. However, this is extremely difficult because of the enormous scale of the noncoding mutation space. We developed a deep learning-based framework, ExPecto, that can accurately predict, ab initio from a DNA sequence, the tissue-specific transcriptional effects of mutations, including those that are rare or that have not been observed. We prioritized causal variants within disease- or trait-associated loci from all publicly available genome-wide association studies and experimentally validated predictions for four immune-related diseases. By exploiting the scalability of ExPecto, we characterized the regulatory mutation space for human RNA polymerase II-transcribed genes by in silico saturation mutagenesis and profiled > 140 million promoter-proximal mutations. This enables probing of evolutionary constraints on gene expression and ab initio prediction of mutation disease effects, making ExPecto an end-to-end computational framework for the in silico prediction of expression and disease risk.

An extended set of yeast-based functional assays accurately identifies human disease mutations

We can now routinely identify coding variants within individual human genomes. A pressing challenge is to determine which variants disrupt the function of disease-associated genes. Both experimental and computational methods exist to predict pathogenicity of human genetic variation. However, a systematic performance comparison between them has been lacking. Therefore, we developed and exploited a panel of 26 yeast-based functional complementation assays to measure the impact of 179 variants (101 disease- and 78 non-disease-associated variants) from 22 human disease genes. Using the resulting reference standard, we show that experimental functional assays in a 1-billion-year diverged model organism can identify pathogenic alleles with significantly higher precision and specificity than current computational methods.

BioGRID: A Resource for Studying Biological Interactions in Yeast

The Biological General Repository for Interaction Datasets (BioGRID) is a freely available public database that provides the biological and biomedical research communities with curated protein and genetic interaction data. Structured experimental evidence codes, an intuitive search interface, and visualization tools enable the discovery of individual gene, protein, or biological network function. BioGRID houses interaction data for the major model organism species--including yeast, nematode, fly, zebrafish, mouse, and human--with particular emphasis on the budding yeast Saccharomyces cerevisiae and the fission yeast Schizosaccharomyces pombe as pioneer eukaryotic models for network biology. BioGRID has achieved comprehensive curation coverage of the entire literature for these two major yeast models, which is actively maintained through monthly curation updates. As of September 2015, BioGRID houses approximately 335,400 biological interactions for budding yeast and approximately 67,800 interactions for fission yeast. BioGRID also supports an integrated posttranslational modification (PTM) viewer that incorporates more than 20,100 yeast phosphorylation sites curated through its sister database, the PhosphoGRID.

Insulin-induced gene protein (INSIG)-dependent sterol regulation of Hmg2 endoplasmic reticulum-associated degradation (ERAD) in yeast

Insulin-induced gene proteins (INSIGs) function in control of cellular cholesterol. Mammalian INSIGs exert control by directly interacting with proteins containing sterol-sensing domains (SSDs) when sterol levels are elevated. Mammalian 3-hydroxy-3-methylglutaryl (HMG)-CoA reductase (HMGR) undergoes sterol-dependent, endoplasmic-reticulum (ER)-associated degradation (ERAD) that is mediated by INSIG interaction with the HMGR SSD. The yeast HMGR isozyme Hmg2 also undergoes feedback-regulated ERAD in response to the early pathway-derived isoprene gernanylgeranyl pyrophosphate (GGPP). Hmg2 has an SSD, and its degradation is controlled by the INSIG homologue Nsg1. However, yeast Nsg1 promotes Hmg2 stabilization by inhibiting GGPP-stimulated ERAD. We have proposed that the seemingly disparate INSIG functions can be unified by viewing INSIGs as sterol-dependent chaperones of SSD clients. Accordingly, we tested the role of sterols in the Nsg1 regulation of Hmg2. We found that both Nsg1-mediated stabilization of Hmg2 and the Nsg1-Hmg2 interaction required the early sterol lanosterol. Lowering lanosterol in the cell allowed GGPP-stimulated Hmg2 ERAD. Thus, Hmg2-regulated degradation is controlled by a two-signal logic; GGPP promotes degradation, and lanosterol inhibits degradation. These data reveal that the sterol dependence of INSIG-client interaction has been preserved for over 1 billion years. We propose that the INSIGs are a class of sterol-dependent chaperones that bind to SSD clients, thus harnessing ER quality control in the homeostasis of sterols.

The sterol-sensing domain (SSD) directly mediates signal-regulated endoplasmic reticulum-associated degradation (ERAD) of 3-hydroxy-3-methylglutaryl (HMG)-CoA reductase isozyme Hmg2

The sterol-sensing domain (SSD) is a conserved motif in membrane proteins responsible for sterol regulation. Mammalian proteins SREBP cleavage-activating protein (SCAP) and HMG-CoA reductase (HMGR) both possess SSDs required for feedback regulation of sterol-related genes and sterol synthetic rate. Although these two SSD proteins clearly sense sterols, the range of signals detected by this eukaryotic motif is not clear. The yeast HMG-CoA reductase isozyme Hmg2, like its mammalian counterpart, undergoes endoplasmic reticulum (ER)-associated degradation that is subject to feedback control by the sterol pathway. The primary degradation signal for yeast Hmg2 degradation is the 20-carbon isoprene geranylgeranyl pyrophosphate, rather than a sterol. Nevertheless, the Hmg2 protein possesses an SSD, leading us to test its role in feedback control of Hmg2 stability. We mutated highly conserved SSD residues of Hmg2 and evaluated regulated degradation. Our results indicated that the SSD was required for sterol pathway signals to stimulate Hmg2 ER-associated degradation and was employed for detection of both geranylgeranyl pyrophosphate and a secondary oxysterol signal. Our data further indicate that the SSD allows a signal-dependent structural change in Hmg2 that promotes entry into the ER degradation pathway. Thus, the eukaryotic SSD is capable of significant plasticity in signal recognition or response. We propose that the harnessing of cellular quality control pathways to bring about feedback regulation of normal proteins is a unifying theme for the action of all SSDs.

Expanded protein information at SGD: new pages and proteome browser

The recent explosion in protein data generated from both directed small-scale studies and large-scale proteomics efforts has greatly expanded the quantity of available protein information and has prompted the Saccharomyces Genome Database (SGD; ) to enhance the depth and accessibility of protein annotations. In particular, we have expanded ongoing efforts to improve the integration of experimental information and sequence-based predictions and have redesigned the protein information web pages. A key feature of this redesign is the development of a GBrowse-derived interactive Proteome Browser customized to improve the visualization of sequence-based protein information. This Proteome Browser has enabled SGD to unify the display of hidden Markov model (HMM) domains, protein family HMMs, motifs, transmembrane regions, signal peptides, hydropathy plots and profile hits using several popular prediction algorithms. In addition, a physico-chemical properties page has been introduced to provide easy access to basic protein information. Improvements to the layout of the Protein Information page and integration of the Proteome Browser will facilitate the ongoing expansion of sequence-specific experimental information captured in SGD, including post-translational modifications and other user-defined annotations. Finally, SGD continues to improve upon the availability of genetic and physical interaction data in an ongoing collaboration with BioGRID by providing direct access to more than 82 000 manually-curated interactions.

Genome Snapshot: a new resource at the Saccharomyces Genome Database (SGD) presenting an overview of the Saccharomyces cerevisiae genome

Sequencing and annotation of the entire Saccharomyces cerevisiae genome has made it possible to gain a genome-wide perspective on yeast genes and gene products. To make this information available on an ongoing basis, the Saccharomyces Genome Database (SGD) () has created the Genome Snapshot (). The Genome Snapshot summarizes the current state of knowledge about the genes and chromosomal features of S.cerevisiae. The information is organized into two categories: (i) number of each type of chromosomal feature annotated in the genome and (ii) number and distribution of genes annotated to Gene Ontology terms. Detailed lists are accessible through SGD's Advanced Search tool (), and all the data presented on this page are available from the SGD ftp site ().

Discovery of biological networks from diverse functional genomic data

BioPIXIE is a probabilistic system for query-based discovery of pathway-specific networks through integration of diverse genome-wide data.

We have developed a general probabilistic system for query-based discovery of pathway-specific networks through integration of diverse genome-wide data. This framework was validated by accurately recovering known networks for 31 biological processes in Saccharomyces cerevisiae and experimentally verifying predictions for the process of chromosomal segregation. Our system, bioPIXIE, a public, comprehensive system for integration, analysis, and visualization of biological network predictions for S. cerevisiae, is freely accessible over the worldwide web.

Fungal BLAST and Model Organism BLASTP Best Hits: new comparison resources at the Saccharomyces Genome Database (SGD)

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/) is a scientific database of gene, protein and genomic information for the yeast Saccharomyces cerevisiae. SGD has recently developed two new resources that facilitate nucleotide and protein sequence comparisons between S.cerevisiae and other organisms. The Fungal BLAST tool provides directed searches against all fungal nucleotide and protein sequences available from GenBank, divided into categories according to organism, status of completeness and annotation, and source. The Model Organism BLASTP Best Hits resource displays, for each S.cerevisiae protein, the single most similar protein from several model organisms and presents links to the database pages of those proteins, facilitating access to curated information about potential orthologs of yeast proteins.

Opposing roles for actin in Cdc42p polarization

In animal and fungal cells, the monomeric GTPase Cdc42p is a key regulator of cell polarity that itself exhibits a polarized distribution in asymmetric cells. Previous work showed that in budding yeast, Cdc42p polarization is unaffected by depolymerization of the actin cytoskeleton (Ayscough et al., J. Cell Biol. 137, 399-416, 1997). Surprisingly, we now report that unlike complete actin depolymerization, partial actin depolymerization leads to the dispersal of Cdc42p from the polarization site in unbudded cells. We provide evidence that dispersal is due to endocytosis associated with cortical actin patches and that actin cables are required to counteract the dispersal and maintain Cdc42p polarity. Thus, although Cdc42p is initially polarized in an actin-independent manner, maintaining that polarity may involve a reinforcing feedback between Cdc42p and polarized actin cables to counteract the dispersing effects of actin-dependent endocytosis. In addition, we report that once a bud has formed, polarized Cdc42p becomes more resistant to dispersal, revealing an unexpected difference between unbudded and budded cells in the organization of the polarization site.

Saccharomyces genome database: underlying principles and organisation

A scientific database can be a powerful tool for biologists in an era where large-scale genomic analysis, combined with smaller-scale scientific results, provides new insights into the roles of genes and their products in the cell. However, the collection and assimilation of data is, in itself, not enough to make a database useful. The data must be incorporated into the database and presented to the user in an intuitive and biologically significant manner. Most importantly, this presentation must be driven by the user's point of view; that is, from a biological perspective. The success of a scientific database can therefore be measured by the response of its users - statistically, by usage numbers and, in a less quantifiable way, by its relationship with the community it serves and its ability to serve as a model for similar projects. Since its inception ten years ago, the Saccharomyces Genome Database (SGD) has seen a dramatic increase in its usage, has developed and maintained a positive working relationship with the yeast research community, and has served as a template for at least one other database. The success of SGD, as measured by these criteria, is due in large part to philosophies that have guided its mission and organisation since it was established in 1993. This paper aims to detail these philosophies and how they shape the organisation and presentation of the database.

The Gene Ontology (GO) database and informatics resource

The Gene Ontology (GO) project (http://www. geneontology.org/) provides structured, controlled vocabularies and classifications that cover several domains of molecular and cellular biology and are freely available for community use in the annotation of genes, gene products and sequences. Many model organism databases and genome annotation groups use the GO and contribute their annotation sets to the GO resource. The GO database integrates the vocabularies and contributed annotations and provides full access to this information in several formats. Members of the GO Consortium continually work collectively, involving outside experts as needed, to expand and update the GO vocabularies. The GO Web resource also provides access to extensive documentation about the GO project and links to applications that use GO data for functional analyses.

Saccharomyces Genome Database (SGD) provides tools to identify and analyze sequences from Saccharomyces cerevisiae and related sequences from other organisms

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/), a scientific database of the molecular biology and genetics of the yeast Saccharomyces cerevisiae, has recently developed several new resources that allow the comparison and integration of information on a genome-wide scale, enabling the user not only to find detailed information about individual genes, but also to make connections across groups of genes with common features and across different species. The Fungal Alignment Viewer displays alignments of sequences from multiple fungal genomes, while the Sequence Similarity Query tool displays PSI-BLAST alignments of each S.cerevisiae protein with similar proteins from any species whose sequences are contained in the non-redundant (nr) protein data set at NCBI. The Yeast Biochemical Pathways tool integrates groups of genes by their common roles in metabolism and displays the metabolic pathways in a graphical form. Finally, the Find Chromosomal Features search interface provides a versatile tool for querying multiple types of information in SGD.

A monitor for bud emergence in the yeast morphogenesis checkpoint

Cell cycle transitions are subject to regulation by both external signals and internal checkpoints that monitor satisfactory progression of key cell cycle events. In budding yeast, the morphogenesis checkpoint arrests the cell cycle in response to perturbations that affect the actin cytoskeleton and bud formation. Herein, we identify a step in this checkpoint pathway that seems to be directly responsive to bud emergence. Activation of the kinase Hsl1p is dependent upon its recruitment to a cortical domain organized by the septins, a family of conserved filament-forming proteins. Under conditions that delayed or blocked bud emergence, Hsl1p recruitment to the septin cortex still took place, but hyperphosphorylation of Hsl1p and recruitment of the Hsl1p-binding protein Hsl7p to the septin cortex only occurred after bud emergence. At this time, the septin cortex spread to form a collar between mother and bud, and Hsl1p and Hsl7p were restricted to the bud side of the septin collar. We discuss models for translating cellular geometry (in this case, the emergence of a bud) into biochemical signals regulating cell proliferation.

Determinants of Swe1p degradation in Saccharomyces cerevisiae

Swe1p, the sole Wee1-family kinase in Saccharomyces cerevisiae, is synthesized during late G1 and is then degraded as cells proceed through the cell cycle. However, Swe1p degradation is halted by the morphogenesis checkpoint, which responds to insults that perturb bud formation. The Swe1p stabilization promotes cell cycle arrest through Swe1p-mediated inhibitory phosphorylation of Cdc28p until the cells can recover from the perturbation and resume bud formation. Swe1p degradation involves the relocalization of Swe1p from the nucleus to the mother-bud neck, and neck targeting requires the Swe1p-interacting protein Hsl7p. In addition, Swe1p degradation is stimulated by its substrate, cyclin/Cdc28p, and Swe1p is thought to be a target of the ubiquitin ligase SCF(Met30) acting with the ubiquitin-conjugating enzyme Cdc34p. The basis for regulation of Swe1p degradation by the morphogenesis checkpoint remains unclear, and in order to elucidate that regulation we have dissected the Swe1p degradation pathway in more detail, yielding several novel findings. First, we show here that Met30p (and by implication SCF(Met30)) is not, in fact, required for Swe1p degradation. Second, cyclin/Cdc28p does not influence Swe1p neck targeting, but can directly phosphorylate Swe1p, suggesting that it acts downstream of neck targeting in the Swe1p degradation pathway. Third, a screen for functional but nondegradable mutants of SWE1 identified two small regions of Swe1p that are key to its degradation. One of these regions mediates interaction of Swe1p with Hsl7p, showing that the Swe1p-Hsl7p interaction is critical for Swe1p neck targeting and degradation. The other region did not appear to affect interactions with known Swe1p regulators, suggesting that other as-yet-unknown regulators exist.

Septin-dependent assembly of a cell cycle-regulatory module in Saccharomyces cerevisiae

Saccharomyces cerevisiae septin mutants have pleiotropic defects, which include the formation of abnormally elongated buds. This bud morphology results at least in part from a cell cycle delay imposed by the Cdc28p-inhibitory kinase Swe1p. Mutations in three other genes (GIN4, encoding a kinase related to the Schizosaccharomyces pombe mitotic inducer Nim1p; CLA4, encoding a p21-activated kinase; and NAP1, encoding a Clb2p-interacting protein) also produce perturbations of septin organization associated with an Swe1p-dependent cell cycle delay. The effects of gin4, cla4, and nap1 mutations are additive, indicating that these proteins promote normal septin organization through pathways that are at least partially independent. In contrast, mutations affecting the other two Nim1p-related kinases in S. cerevisiae, Hsl1p and Kcc4p, produce no detectable effect on septin organization. However, deletion of HSL1, but not of KCC4, did produce a cell cycle delay under some conditions; this delay appears to reflect a direct role of Hsl1p in the regulation of Swe1p. As shown previously, Swe1p plays a central role in the morphogenesis checkpoint that delays the cell cycle in response to defects in bud formation. Swe1p is localized to the nucleus and to the daughter side of the mother bud neck prior to its degradation in G(2)/M phase. Both the neck localization of Swe1p and its degradation require Hsl1p and its binding partner Hsl7p, both of which colocalize with Swe1p at the daughter side of the neck. This localization is lost in mutants with perturbed septin organization, suggesting that the release of Hsl1p and Hsl7p from the neck may reduce their ability to inactivate Swe1p and thus contribute to the G(2) delay observed in such mutants. In contrast, treatments that perturb actin organization have little effect on Hsl1p and Hsl7p localization, suggesting that such treatments must stabilize Swe1p by another mechanism. The apparent dependence of Swe1p degradation on localization of the Hsl1p-Hsl7p-Swe1p module to a site that exists only in budded cells may constitute a mechanism for deactivating the morphogenesis checkpoint when it is no longer needed (i.e., after a bud has formed).

The morphogenesis checkpoint in Saccharomyces cerevisiae: cell cycle control of Swe1p degradation by Hsl1p and Hsl7p

In Saccharomyces cerevisiae, the Wee1 family kinase Swe1p is normally stable during G(1) and S phases but is unstable during G(2) and M phases due to ubiquitination and subsequent degradation. However, perturbations of the actin cytoskeleton lead to a stabilization and accumulation of Swe1p. This response constitutes part of a morphogenesis checkpoint that couples cell cycle progression to proper bud formation, but the basis for the regulation of Swe1p degradation by the morphogenesis checkpoint remains unknown. Previous studies have identified a protein kinase, Hsl1p, and a phylogenetically conserved protein of unknown function, Hsl7p, as putative negative regulators of Swe1p. We report here that Hsl1p and Hsl7p act in concert to target Swe1p for degradation. Both proteins are required for Swe1p degradation during the unperturbed cell cycle, and excess Hsl1p accelerates Swe1p degradation in the G(2)-M phase. Hsl1p accumulates periodically during the cell cycle and promotes the periodic phosphorylation of Hsl7p. Hsl7p can be detected in a complex with Swe1p in cell lysates, and the overexpression of Hsl7p or Hsl1p produces an effective override of the G(2) arrest imposed by the morphogenesis checkpoint. These findings suggest that Hsl1p and Hsl7p interact directly with Swe1p to promote its recognition by the ubiquitination complex, leading ultimately to its destruction.

The role of actin in spindle orientation changes during the Saccharomyces cerevisiae cell cycle

In the budding yeast Saccharomyces cerevisiae, the mitotic spindle must align along the mother-bud axis to accurately partition the sister chromatids into daughter cells. Previous studies showed that spindle orientation required both astral microtubules and the actin cytoskeleton. We now report that maintenance of correct spindle orientation does not depend on F-actin during G2/M phase of the cell cycle. Depolymerization of F-actin using Latrunculin-A did not perturb spindle orientation after this stage. Even an early step in spindle orientation, the migration of the spindle pole body (SPB), became actin-independent if it was delayed until late in the cell cycle. Early in the cell cycle, both SPB migration and spindle orientation were very sensitive to perturbation of F-actin. Selective disruption of actin cables using a conditional tropomyosin double-mutant also led to defects in spindle orientation, even though cortical actin patches were still polarized. This suggests that actin cables are important for either guiding astral microtubules into the bud or anchoring them in the bud. In addition, F-actin was required early in the cell cycle for the development of the actin-independent spindle orientation capability later in the cell cycle. Finally, neither SPB migration nor the switch from actin-dependent to actin-independent spindle behavior required B-type cyclins.

Involvement of integrins alpha v beta 3 and alpha v beta 5 in ocular neovascular diseases

Angiogenesis underlies the majority of eye diseases that result in catastrophic loss of vision. Recent evidence has implicated the integrins alpha v beta 3 and alpha v beta 5 in the angiogenic process. We examined the expression of alpha v beta 3 and alpha v beta 5 in neovascular ocular tissue from patients with subretinal neovascularization from age-related macular degeneration or the presumed ocular histoplasmosis syndrome or retinal neovascularization from proliferative diabetic retinopathy (PDR). Only alpha v beta 3 was observed on blood vessels in ocular tissues with active neovascularization from patients with age-related macular degeneration or presumed ocular histoplasmosis, whereas both alpha v beta 3 and alpha v beta 5 were present on vascular cells in tissues from patients with PDR. Since we observed both integrins on vascular cells from tissues of patients with retinal neovascularization from PDR, we examined the effects of a systemically administered cyclic peptide antagonist of alpha v beta 3 and alpha v beta 5 on retinal angiogenesis in a murine model. This antagonist specifically blocked new blood vessel formation with no effect on established vessels. These results not only reinforce the concept that retinal and subretinal neovascular diseases are distinct pathological processes, but that antagonists of alpha v beta 3 and/or alpha v beta 5 may be effective in treating individuals with blinding eye disease associated with angiogenesis.