Set2 regulates Ccp1 and Swc2 to ensure centromeric stability by retargeting CENP-A.

Precise positioning of the histone-H3 variant, CENP-A, ensures centromere stability and faithful chromosomal segregation. Mislocalization of CENP-A to extra-centromeric loci results in aneuploidy and compromised cell viability associated with formation of ectopic kinetochores. The mechanism that retargets mislocalized CENP-A back to the centromere is unclarified. We show here that the downregulation of the histone H3 lysine 36 (H3K36) methyltransferase Set2 can preserve centromere localization of a temperature-sensitive mutant cnp1-1 Schizosaccharomyces pombe CENP-A (SpCENP-A) protein and reverse aneuploidy by redirecting mislocalized SpCENP-A back to centromere from ribosomal DNA (rDNA) loci, which serves as a sink for the delocalized SpCENP-A. Downregulation of set2 augments Swc2 (SWR1 complex DNA-binding module) expression and releases histone chaperone Ccp1 from the centromeric reservoir. Swc2 and Ccp1 are directed to the rDNA locus to excavate the SpCENP-Acnp1-1, which is relocalized to the centromere in a manner dependent on canonical SpCENP-A loaders, including Mis16, Mis17 and Mis18, thereby conferring cell survival and safeguarding chromosome segregation fidelity. Chromosome missegregation is a severe genetic instability event that compromises cell viability. This mechanism thus promotes CENP-A presence at the centromere to maintain genomic stability.

Fission Yeast Methylenetetrahydrofolate Reductase Ensures Mitotic and Meiotic Chromosome Segregation Fidelity.

Methylenetetrahydrofolate reductase (MTHFR) is a key enzyme in the folate metabolic pathway, and its loss of function through polymorphisms is often associated with human conditions, including cancer, congenital heart disease, and Down syndrome. MTHFR is also required in the maintenance of heterochromatin, a crucial determinant of genomic stability and precise chromosomal segregation. Here, we characterize the function of a fission yeast gene met11+, which encodes a protein that is highly homologous to the mammalian MTHFR. We show that, although met11+ is not essential for viability, its disruption increases chromosome missegregation and destabilizes constitutive heterochromatic regions at pericentromeric, sub-telomeric and ribosomal DNA (rDNA) loci. Transcriptional silencing at these sites were disrupted, which is accompanied by the reduction in enrichment of histone H3 lysine 9 dimethylation (H3K9me2) and binding of the heterochromatin protein 1 (HP1)-like Swi6. The met11 null mutant also dominantly disrupts meiotic fidelity, as displayed by reduced sporulation efficiency and defects in proper partitioning of the genetic material during meiosis. Interestingly, the faithful execution of these meiotic processes is synergistically ensured by cooperation among Met11, Rec8, a meiosis-specific cohesin protein, and the shugoshin protein Sgo1, which protects Rec8 from untimely cleavage. Overall, our results suggest a key role for Met11 in maintaining pericentromeric heterochromatin for precise genetic inheritance during mitosis and meiosis.

Machine learning optimized DriverDetect software for high precision prediction of deleterious mutations in human cancers.

The detection of cancer-driving mutations is important for understanding cancer pathology and therapeutics development. Prediction tools have been created to streamline the computation process. However, most tools available have heterogeneous sensitivity or specificity. We built a machine learning-derived algorithm, DriverDetect that combines the outputs of seven pre-existing tools to improve the prediction of candidate driver cancer mutations. The algorithm was trained with cancer gene-specific mutation datasets of cancer patients to identify cancer drivers. DriverDetect performed better than the individual tools or their combinations in the validation test. It has the potential to incorporate future novel prediction algorithms and can be retrained with new datasets, offering an expanded application to pan-cancer analysis for cross-cancer study. (115 words).

A Normalization Protocol Reduces Edge Effect in High-Throughput Analyses of Hydroxyurea Hypersensitivity in Fission Yeast.

Edge effect denotes better growth of microbial organisms situated at the edge of the solid agar media. Although the precise reason underlying edge effect is unresolved, it is generally attributed to greater nutrient availability with less competing neighbors at the edge. Nonetheless, edge effect constitutes an unavoidable confounding factor that results in misinterpretation of cell fitness, especially in high-throughput screening experiments widely employed for genome-wide investigation using microbial gene knockout or mutant libraries. Here, we visualize edge effect in high-throughput high-density pinning arrays and report a normalization approach based on colony growth rate to quantify drug (hydroxyurea)-hypersensitivity in fission yeast strains. This normalization procedure improved the accuracy of fitness measurement by compensating cell growth rate discrepancy at different locations on the plate and reducing false-positive and -negative frequencies. Our work thus provides a simple and coding-free solution for a struggling problem in robotics-based high-throughput screening experiments.

Molecular mechanisms in governing genomic stability and tumor suppression by the SETD2 H3K36 methyltransferase.

Epigenetic dysregulation is an important contributor to carcinogenesis. This is not surprising, as chromatin-genomic DNA organized around structural histone scaffolding-serves as the template on which occurs essential nuclear processes, such as transcription, DNA replication and DNA repair. Histone H3 lysine 36 (H3K36) methyltransferases, such as the SET-domain 2 protein (SETD2), have emerged as critical tumor suppressors. Previous work on mammalian SETD2 and its counterpart in model organisms, Set2, has highlighted the role of this protein in governing genomic stability through transcriptional elongation and splicing, as well as in DNA damage response processes and cell cycle progression. A compendium of SETD2 mutations have been documented, garnered from sequenced cancer patient genome data, and these findings underscore the cancer-driving properties of SETD2 loss-of-function. In this review, we consolidate the molecular mechanisms regulated by SETD2/Set2 and discuss evidence of its dysregulation in tumorigenesis. Insight into the genetic interactions that exist between SETD2 and various canonical intracellular signaling pathways has not only empowered pharmacological intervention by taking advantage of synthetic lethality but underscores SETD2 as a druggable target for precision cancer therapy.

Structural and functional specificity of H3K36 methylation.

The methylation of histone H3 at lysine 36 (H3K36me) is essential for maintaining genomic stability. Indeed, this methylation mark is essential for proper transcription, recombination, and DNA damage response. Loss- and gain-of-function mutations in H3K36 methyltransferases are closely linked to human developmental disorders and various cancers. Structural analyses suggest that nucleosomal components such as the linker DNA and a hydrophobic patch constituted by histone H2A and H3 are likely determinants of H3K36 methylation in addition to the histone H3 tail, which encompasses H3K36 and the catalytic SET domain. Interaction of H3K36 methyltransferases with the nucleosome collaborates with regulation of their auto-inhibitory changes fine-tunes the precision of H3K36me in mediating dimethylation by NSD2 and NSD3 as well as trimethylation by Set2/SETD2. The identification of specific structural features and various cis-acting factors that bind to different forms of H3K36me, particularly the di-(H3K36me2) and tri-(H3K36me3) methylated forms of H3K36, have highlighted the intricacy of H3K36me functional significance. Here, we consolidate these findings and offer structural insight to the regulation of H3K36me2 to H3K36me3 conversion. We also discuss the mechanisms that underlie the cooperation between H3K36me and other chromatin modifications (in particular, H3K27me3, H3 acetylation, DNA methylation and N6-methyladenosine in RNAs) in the physiological regulation of the epigenomic functions of chromatin.