A Novel Genetic Screen Implicates Elm1 in the Inactivation of the Yeast Transcription Factor SBF

BACKGROUND: Despite extensive large scale analyses of expression and protein-protein interactions (PPI) in the model organism Saccharomyces cerevisiae, over a thousand yeast genes remain uncharacterized. We have developed a novel strategy in yeast that directly combines genetics with proteomics in the same screen to assign function to proteins based on the observation of genetic perturbations of sentinel protein interactions (GePPI). As proof of principle of the GePPI screen, we applied it to identify proteins involved in the regulation of an important yeast cell cycle transcription factor, SBF that activates gene expression during G1 and S phase. METHODOLOGY/PRINCIPLE FINDINGS: The principle of GePPI is that if a protein is involved in a pathway of interest, deletion of the corresponding gene will result in perturbation of sentinel PPIs that report on the activity of the pathway. We created a fluorescent protein-fragment complementation assay (PCA) to detect the interaction between Cdc28 and Swi4, which leads to the inactivation of SBF. The PCA signal was quantified by microscopy and image analysis in deletion strains corresponding to 25 candidate genes that are periodically expressed during the cell cycle and are substrates of Cdc28. We showed that the serine-threonine kinase Elm1 plays a role in the inactivation of SBF and that phosphorylation of Elm1 by Cdc28 may be a mechanism to inactivate Elm1 upon completion of mitosis. CONCLUSIONS/SIGNIFICANCE: Our findings demonstrate that GePPI is an effective strategy to directly link proteins of known or unknown function to a specific biological pathway of interest. The ease in generating PCA assays for any protein interaction and the availability of the yeast deletion strain collection allows GePPI to be applied to any cellular network. In addition, the high degree of conservation between yeast and mammalian proteins and pathways suggest GePPI could be used to generate insight into human disease

Histone H4K20 methylation mediated chromatin compaction threshold ensures genome integrity by limiting DNA replication licensing

Cell cycle and replication need to be tightly regulated to ensure genome stability in mammalian cells. Here the authors provide a link between chromatin structure and DNA replication regulation by showing that chromatin compaction limits replication licensing thereby promoting genome integrity

Contextualising adverse events of special interest to characterise the baseline incidence rates in 24 million patients with COVID-19 across 26 databases: a multinational retrospective cohort study

BACKGROUND: Adverse events of special interest (AESIs) were pre-specified to be monitored for the COVID-19 vaccines. Some AESIs are not only associated with the vaccines, but with COVID-19. Our aim was to characterise the incidence rates of AESIs following SARS-CoV-2 infection in patients and compare these to historical rates in the general population. METHODS: A multi-national cohort study with data from primary care, electronic health records, and insurance claims mapped to a common data model. This study's evidence was collected between Jan 1, 2017 and the conclusion of each database (which ranged from Jul 2020 to May 2022). The 16 pre-specified prevalent AESIs were: acute myocardial infarction, anaphylaxis, appendicitis, Bell's palsy, deep vein thrombosis, disseminated intravascular coagulation, encephalomyelitis, Guillain- Barré syndrome, haemorrhagic stroke, non-haemorrhagic stroke, immune thrombocytopenia, myocarditis/pericarditis, narcolepsy, pulmonary embolism, transverse myelitis, and thrombosis with thrombocytopenia. Age-sex standardised incidence rate ratios (SIR) were estimated to compare post-COVID-19 to pre-pandemic rates in each of the databases. FINDINGS: Substantial heterogeneity by age was seen for AESI rates, with some clearly increasing with age but others following the opposite trend. Similarly, differences were also observed across databases for same health outcome and age-sex strata. All studied AESIs appeared consistently more common in the post-COVID-19 compared to the historical cohorts, with related meta-analytic SIRs ranging from 1.32 (1.05 to 1.66) for narcolepsy to 11.70 (10.10 to 13.70) for pulmonary embolism. INTERPRETATION: Our findings suggest all AESIs are more common after COVID-19 than in the general population. Thromboembolic events were particularly common, and over 10-fold more so. More research is needed to contextualise post-COVID-19 complications in the longer term. FUNDING: None

A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data

Background and objective As a response to the ongoing COVID-19 pandemic, several prediction models in the existing literature were rapidly developed, with the aim of providing evidence-based guidance. However, none of these COVID-19 prediction models have been found to be reliable. Models are commonly assessed to have a risk of bias, often due to insufficient reporting, use of non-representative data, and lack of large-scale external validation. In this paper, we present the Observational Health Data Sciences and Informatics (OHDSI) analytics pipeline for patient-level prediction modeling as a standardized approach for rapid yet reliable development and validation of prediction models. We demonstrate how our analytics pipeline and open-source software tools can be used to answer important prediction questions while limiting potential causes of bias (e.g., by validating phenotypes, specifying the target population, performing large-scale external validation, and publicly providing all analytical source code). Methods We show step-by-step how to implement the analytics pipeline for the question: ‘In patients hospitalized with COVID-19, what is the risk of death 0 to 30 days after hospitalization?’. We develop models using six different machine learning methods in a USA claims database containing over 20,000 COVID-19 hospitalizations and externally validate the models using data containing over 45,000 COVID-19 hospitalizations from South Korea, Spain, and the USA. Results Our open-source software tools enabled us to efficiently go end-to-end from problem design to reliable Model Development and evaluation. When predicting death in patients hospitalized with COVID-19, AdaBoost, random forest, gradient boosting machine, and decision tree yielded similar or lower internal and external validation discrimination performance compared to L1-regularized logistic regression, whereas the MLP neural network consistently resulted in lower discrimination. L1-regularized logistic regression models were well calibrated. Conclusion Our results show that following the OHDSI analytics pipeline for patient-level prediction modelling can enable the rapid development towards reliable prediction models. The OHDSI software tools and pipeline are open source and available to researchers from all around the world

A two-state activation mechanism controls the histone methyltransferase Suv39h1

Specialized chromatin domains contribute to nuclear organization and regulation of gene expression. Gene-poor regions are di- and trimethylated at lysine 9 of histone H3 (H3K9me2 and H3K9me3) by the histone methyltransferase Suv39h1. This enzyme harnesses a positive feedback loop to spread H3K9me2 and H3K9me3 over extended heterochromatic regions. However, little is known about how feedback loops operate on complex biopolymers such as chromatin, in part because of the difficulty in obtaining suitable substrates. Here we describe the synthesis of multidomain 'designer chromatin' templates and their application to dissecting the regulation of human Suv39h1. We uncovered a two-step activation switch where H3K9me3 recognition and subsequent anchoring of the enzyme to chromatin allosterically promotes methylation activity and confirmed that this mechanism contributes to chromatin recognition in cells. We propose that this mechanism serves as a paradigm in chromatin biochemistry, as it enables highly dynamic sampling of chromatin state combined with targeted modification of desired genomic regions

Degradation of Hof1 by SCF(Grr1) is important for actomyosin contraction during cytokinesis in yeast

SCF-type (SCF: Skp1–Cullin–F-box protein complex) E3 ligases regulate ubiquitin-dependent degradation of many cell cycle regulators, mainly at the G1/S transition. Here, we show that SCF(Grr1) functions during cytokinesis by degrading the PCH protein Hof1. While Hof1 is required early in mitosis to assemble a functional actomyosin ring, it is specifically degraded late in mitosis and remains unstable during the entire G1 phase of the cell cycle. Degradation of Hof1 depends on its PEST motif and a functional 26S proteasome. Interestingly, degradation of Hof1 is independent of APC(Cdh1), but instead requires the SCF(Grr1) E3 ligase. Grr1 is recruited to the mother–bud neck region after activation of the mitotic-exit network, and interacts with Hof1 in a PEST motif-dependent manner. Our results also show that downregulation of Hof1 at the end of mitosis is necessary to allow efficient contraction of the actomyosin ring and cell separation during cytokinesis. SCF(Grr1)-mediated degradation of Hof1 may thus represent a novel mechanism to couple exit from mitosis with initiation of cytokinesis