Functional Analysis of Hif1 Histone Chaperone in Saccharomyces cerevisiae

The Hif1 protein in the yeast Saccharomyces cerevisie is an evolutionarily conserved H3/H4-specific chaperone and a subunit of the nuclear Hat1 complex that catalyzes the acetylation of newly synthesized histone H4. Hif1, as well as its human homolog NASP, has been implicated in an array of chromatin-related processes including histone H3/H4 transport, chromatin assembly and DNA repair. In this study, we elucidate the functional aspects of Hif1. Initially we establish the wide distribution of Hif1 homologs with an evolutionarily conserved pattern of four tetratricopeptide repeats (TPR) motifs throughout the major fungal lineages and beyond. Subsequently, through targeted mutational analysis, we demonstrate that the acidic region that interrupts the TPR2 is essential for Hif1 physical interactions with the Hat1/Hat2-complex, Asf1, and with histones H3/H4. Furthermore, we provide evidence for the involvement of Hif1 in regulation of histone metabolism by showing that cells lacking HIF1 are both sensitive to histone H3 over expression, as well as synthetic lethal with a deletion of histone mRNA regulator LSM1. We also show that a basic patch present at the extreme C-terminus of Hif1 is essential for its proper nuclear localization. Finally, we describe a physical interaction with a transcriptional regulatory protein Spt2, possibly linking Hif1 and the Hat1 complex to transcription-associated chromatin reassembly. Taken together, our results provide novel mechanistic insights into Hif1 functions and establish it as an important protein in chromatin-associated processes

Revised fission yeast gene and allele nomenclature guidelines for machine readability

Standardized nomenclature for genes, gene products, and isoforms is crucial to prevent ambiguity and enable clear communication of scientific data, facilitating efficient biocuration and data sharing. Standardized genotype nomenclature, which describes alleles present in a specific strain that differ from those in the wild-type reference strain, is equally essential to maximize research impact and ensure that results linking genotypes to phenotypes are Findable, Accessible, Interoperable, and Reusable (FAIR). In this publication, we extend the fission yeast clade gene nomenclature guidelines to support the curation efforts at PomBase (www.pombase.org), the Schizosaccharomyces pombe Model Organism Database. This update introduces nomenclature guidelines for noncoding RNA genes, following those set forth by the Human Genome Organisation Gene Nomenclature Committee. Additionally, we provide a significant update to the allele and genotype nomenclature guidelines originally published in 1987, to standardize the diverse range of genetic modifications enabled by the fission yeast genetic toolbox. These updated guidelines reflect a community consensus between numerous fission yeast researchers. Adoption of these rules will improve consistency in gene and genotype nomenclature, and facilitate machine-readability and automated entity recognition of fission yeast genes and alleles in publications or datasets. In conclusion, our updated guidelines provide a valuable resource for the fission yeast research community, promoting consistency, clarity, and FAIRness in genetic data sharing and interpretation

Multiscale interactome analysis coupled with off-target drug predictions reveals drug repurposing candidates for human coronavirus disease

The COVID-19 pandemic has highlighted the urgent need for the identification of new antiviral drug therapies for a variety of diseases. COVID-19 is caused by infection with the human coronavirus SARS-CoV-2, while other related human coronaviruses cause diseases ranging from severe respiratory infections to the common cold. We developed a computational approach to identify new antiviral drug targets and repurpose clinically-relevant drug compounds for the treatment of a range of human coronavirus diseases. Our approach is based on graph convolutional networks (GCN) and involves multiscale host-virus interactome analysis coupled to off-target drug predictions. Cell-based experimental assessment reveals several clinically-relevant drug repurposing candidates predicted by the in silico analyses to have antiviral activity against human coronavirus infection. In particular, we identify the MET inhibitor capmatinib as having potent and broad antiviral activity against several coronaviruses in a MET-independent manner, as well as novel roles for host cell proteins such as IRAK1/4 in supporting human coronavirus infection, which can inform further drug discovery studies.We gratefully acknowledge funding that supported this research support from the Ryerson University Faculty of Science (CNA), as well as funding support in the form of a CIFAR Catalyst Grant (JPJ and CNA), an NSERC Alliance Grant (CNA) and the Ryerson COVID-19 SRC Response Fund award (CNA). BW is partly supported by CIFAR AI Chairs Program. This work was also supported by a Mitacs award (BW), the European Union’s Horizon 2020 research and innovation program under a Marie Sklodowska-Curie grant (ER), by the CIFAR Azrieli Global Scholar program (JPJ), by the Ontario Early Researcher Awards program (JPJ and CNA), and by the Canada Research Chairs program (JPJ). We also thank Dr. James Rini (University of Toronto) for the kind gift of the 9.8E12 antibody used to detect the 229E Spike protein, and Dr. Scott Gray-Owen (University of Toronto) for the kind gift of the NL63 human coronavirus.Peer reviewe

Managing Single-Stranded DNA during Replication Stress in Fission Yeast

Replication fork stalling generates a variety of responses, most of which cause an increase in single-stranded DNA. ssDNA is a primary signal of replication distress that activates cellular checkpoints. It is also a potential source of genome instability and a substrate for mutation and recombination. Therefore, managing ssDNA levels is crucial to chromosome integrity. Limited ssDNA accumulation occurs in wild-type cells under stress. In contrast, cells lacking the replication checkpoint cannot arrest forks properly and accumulate large amounts of ssDNA. This likely occurs when the replication fork polymerase and helicase units are uncoupled. Some cells with mutations in the replication helicase (mcm-ts) mimic checkpoint-deficient cells, and accumulate extensive areas of ssDNA to trigger the G2-checkpoint. Another category of helicase mutant (mcm4-degron) causes fork stalling in early S-phase due to immediate loss of helicase function. Intriguingly, cells realize that ssDNA is present, but fail to detect that they accumulate ssDNA, and continue to divide. Thus, the cellular response to replication stalling depends on checkpoint activity and the time that replication stress occurs in S-phase. In this review we describe the signs, signals, and symptoms of replication arrest from an ssDNA perspective. We explore the possible mechanisms for these effects. We also advise the need for caution when detecting and interpreting data related to the accumulation of ssDNA

PombeX: robust cell segmentation for fission yeast transillumination images.

Schizosaccharomyces pombe shares many genes and proteins with humans and is a good model for chromosome behavior and DNA dynamics, which can be analyzed by visualizing the behavior of fluorescently tagged proteins in vivo. Performing a genome-wide screen for changes in such proteins requires developing methods that automate analysis of a large amount of images, the first step of which requires robust segmentation of the cell. We developed a segmentation system, PombeX, that can segment cells from transmitted illumination images with focus gradient and varying contrast. Corrections for focus gradient are applied to the image to aid in accurate detection of cell membrane and cytoplasm pixels, which is used to generate initial contours for cells. Gradient vector flow snake evolution is used to obtain the final cell contours. Finally, a machine learning-based validation of cell contours removes most incorrect or spurious contours. Quantitative evaluations show overall good segmentation performance on a large set of images, regardless of differences in image quality, lighting condition, focus condition and phenotypic profile. Comparisons with recent related methods for yeast cells show that PombeX outperforms current methods, both in terms of segmentation accuracy and computational speed

Example results of distance transform-based procedure.

<p>Original images (left), pixel classification before (middle) and after (right) distance transform-based procedure. Blue pixels indicate background, red is cell membrane and green is cell interior region.</p

Quantitative performance results of yeast segmentation algorithms.

<p>¹ Relative to all cells.</p><p>% pixel mismatch compared to gold standard cell contours, relative to all cells.² Defined as the percentage of cell contours with less than 10</p><p>³ Relative to all contours.</p><p><a href="mailto:[email protected]" target="_blank">[email protected]</a> GHz, 4 GB RAM).⁴ Average CPU time per image, measured using a regular desktop PC (Windows 7, Matlab R2011a, Intel(R) Core(TM) i7-2600 </p

Segmentation results on well-known phenotypes.

<p>Cell contours are colored randomly.</p

Overview of PombeX cell segmentation method.

<p>Overview of PombeX cell segmentation method.</p