Heterozygous mutations cause genetic instability in a yeast model of cancer evolution

OGA mutant aberrantly hydrolyzes O-GlcNAc modification from PDLIM7 to modulate p53 and cytoskeleton in promoting cancer cell malignancy

O-GlcNAcase (OGA) is the only human enzyme that catalyzes the hydrolysis (deglycosylation) of O-linked beta-N-acetylglucosaminylation (O-GlcNAcylation) from numerous protein substrates. OGA has broad implications in many challenging diseases including cancer. However, its role in cell malignancy remains mostly unclear. Here, we report that a cancer-derived point mutation on the OGA's noncatalytic stalk domain aberrantly modulates OGA interactome and substrate deglycosylation toward a specific set of proteins. Interestingly, our quantitative proteomic studies uncovered that the OGA stalk domain mutant preferentially deglycosylated protein substrates with +2 proline in the sequence relative to the O-GlcNAcylation site. One of the most dysregulated substrates is PDZ and LIM domain protein 7 (PDLIM7), which is associated with the tumor suppressor p53. We found that the aberrantly deglycosylated PDLIM7 suppressed p53 gene expression and accelerated p53 protein degradation by promoting the complex formation with E3 ubiquitin ligase MDM2. Moreover, deglycosylated PDLIM7 significantly up-regulated the actin-rich membrane protrusions on the cell surface, augmenting the cancer cell motility and aggressiveness. These findings revealed an important but previously unappreciated role of OGA's stalk domain in protein substrate recognition and functional modulation during malignant cell progression.

Signification and Application of Mutator and Antimutator Phenotype-Induced Genetic Variations in Evolutionary Adaptation and Cancer Therapeutics

Mutations present a dichotomy in their implications for cellular processes. They primarily arise from DNA replication errors or damage repair processes induced by environmental challenges. Cumulative mutations underlie genetic variations and drive evolution, yet also contribute to degenerative diseases such as cancer and aging. The mutator phenotype elucidates the heightened mutation rates observed in malignant tumors. Evolutionary adaptation, analogous to bacterial and eukaryotic systems, manifests through mutator phenotypes during changing environmental conditions, highlighting the delicate balance between advantageous mutations and their potentially detrimental consequences. Leveraging the genetic tractability of Saccharomyces cerevisiae offers unique insights into mutator phenotypes and genome instability akin to human cancers. Innovative reporter assays in yeast model organisms enable the detection of diverse genome alterations, aiding a comprehensive analysis of mutator phenotypes. Despite significant advancements, our understanding of the intricate mechanisms governing spontaneous mutation rates and preserving genetic integrity remains incomplete. This review outlines various cellular pathways affecting mutation rates and explores the role of mutator genes and mutation-derived phenotypes, particularly prevalent in malignant tumor cells. An in-depth comprehension of mutator and antimutator activities in yeast and higher eukaryotes holds promise for effective cancer control strategies.

Cancer-derived mutation in the OGA stalk domain promotes cell malignancy through dysregulating PDLIM7 and p53

O-GlcNAcase (OGA) is the sole enzyme that hydrolyzes O-GlcNAcylation from thousands of proteins and is dysregulated in many diseases including cancer. However, the substrate recognition and pathogenic mechanisms of OGA remain largely unknown. Here we report the first discovery of a cancer-derived point mutation on the OGA's non-catalytic stalk domain that aberrantly regulated a small set of OGA-protein interactions and O-GlcNAc hydrolysis in critical cellular processes. We uncovered a novel cancer-promoting mechanism in which the OGA mutant preferentially hydrolyzed the O-GlcNAcylation from modified PDLIM7 and promoted cell malignancy by down-regulating p53 tumor suppressor in different types of cells through transcription inhibition and MDM2-mediated ubiquitination. Our study revealed the OGA deglycosylated PDLIM7 as a novel regulator of p53-MDM2 pathway, offered the first set of direct evidence on OGA substrate recognition beyond its catalytic site, and illuminated new directions to interrogate OGA's precise role without perturbing global O-GlcNAc homeostasis for biomedical applications.

The impact of rare germline variants on human somatic mutation processes

Somatic mutations are an inevitable component of ageing and the most important cause of cancer. The rates and types of somatic mutation vary across individuals, but relatively few inherited influences on mutation processes are known. We perform a gene-based rare variant association study with diverse mutational processes, using human cancer genomes from over 11,000 individuals of European ancestry. By combining burden and variance tests, we identify 207 associations involving 15 somatic mutational phenotypes and 42 genes that replicated in an independent data set at a false discovery rate of 1%. We associate rare inherited deleterious variants in genes such as MSH3, EXO1, SETD2, and MTOR with two phenotypically different forms of DNA mismatch repair deficiency, and variants in genes such as EXO1, PAXIP1, RIF1, and WRN with deficiency in homologous recombination repair. In addition, we identify associations with other mutational processes, such as APEX1 with APOBEC-signature mutagenesis. Many of the genes interact with each other and with known mutator genes within cellular sub-networks. Considered collectively, damaging variants in the identified genes are prevalent in the population. We suggest that rare germline variation in diverse genes commonly impacts mutational processes in somatic cells.

A high-throughput microfluidic diploid yeast long-term culturing (DYLC) chip capable of bud reorientation and concerted daughter dissection for replicative lifespan determination

Background

Budding yeast, Saccharomyces cerevisiae, has been extensively favored as a model organism in aging and age-related studies, thanks to versatile microfluidic chips for cell dynamics assay and replicative lifespan (RLS) determination at single-cell resolution. However, previous microfluidic structures aiming to immobilize haploid yeast may impose excessive spatial constraint and mechanical stress on cells, especially for larger diploid cells that sprout in a bipolar pattern.

Results

We developed a high-throughput microfluidic chip for diploid yeast long-term culturing (DYLC), optical inspection and cell-aging analysis. The DYLC chip features 1100 “leaky bowl”-shaped traps formatted in an array to dock single cells under laminar-perfused medium and effectively remove daughter cells by hydraulic shear forces. The delicate microstructures of cell traps enable hydrodynamic rotation of newborn buds, so as to ensure bud reorientation towards downstream and concerted daughter dissection thereafter. The traps provide sufficient space for cell-volume enlargement during aging, and thus properly alleviate structural compression and external stress on budding yeast. Trapping efficiency and long-term maintenance of single cells were optimized according to computational fluid dynamics simulations and experimental characterization in terms of critical parameters of the trap and array geometries. Owing to the self-filling of daughter cells dissected from traps upstream, an initial trapping efficiency of about 70% can rapidly reach a high value of over 92% after 4-hour cell culturing. During yeast proliferation and aging, cellular processes of growth, budding and daughter dissection were continuously tracked for over 60 h by time-lapse imaging. Yeast RLS and budding time interval (BTI) were directly calculated by the sequential two-digit codes indicating the budding status in images. With the employed diploid yeast strain, we obtained an RLS of 24.29 ± 3.65 generations, and verified the extension of BTI in the first couple of generations after birth and the last several generations approaching death, as well as cell de-synchronization along diploid yeast aging.

Conclusions

The DYLC chip offers a promising platform for reliable capture and culturing of diploid yeast cells and for life-long tracking of cell dynamics and replicative aging processes so that grasping comprehensive insights of aging mechanism in complex eukaryotic cells.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s12951-022-01379-9.

Fumarase affects the deoxyribonucleic acid damage response by protecting the mitochondrial desulfurase Nfs1p from modification and inactivation

The Krebs cycle enzyme fumarase, which has been identified as a tumor suppressor, is involved in the deoxyribonucleic acid (DNA) damage response (DDR) in human, yeast, and bacterial cells. We have found that the overexpression of the cysteine desulfurase Nfs1p restores DNA repair in fumarase-deficient yeast cells. Nfs1p accumulates inactivating post-translational modifications in yeast cells lacking fumarase under conditions of DNA damage. Our model is that in addition to metabolic signaling of the DDR in the nucleus, fumarase affects the DDR by protecting the desulfurase Nfs1p in mitochondria from modification and inactivation. Fumarase performs this protection by directly binding to Nfs1p in mitochondria and enabling, the maintenance, via metabolism, of a non-oxidizing environment in mitochondria. Nfs1p is required for the formation of Fe-S clusters, which are essential cofactors for DNA repair enzymes. Thus, we propose that the overexpression of Nfs1p overcomes the lack of fumarase by enhancing the activity of DNA repair enzymes.

Aborting meiosis allows recombination in sterile diploid yeast hybrids

Hybrids between diverged lineages contain novel genetic combinations but an impaired meiosis often makes them evolutionary dead ends. Here, we explore to what extent an aborted meiosis followed by a return-to-growth (RTG) promotes recombination across a panel of 20 Saccharomyces cerevisiae and S. paradoxus diploid hybrids with different genomic structures and levels of sterility. Genome analyses of 275 clones reveal that RTG promotes recombination and generates extensive regions of loss-of-heterozygosity in sterile hybrids with either a defective meiosis or a heavily rearranged karyotype, whereas RTG recombination is reduced by high sequence divergence between parental subgenomes. The RTG recombination preferentially arises in regions with low local heterozygosity and near meiotic recombination hotspots. The loss-of-heterozygosity has a profound impact on sexual and asexual fitness, and enables genetic mapping of phenotypic differences in sterile lineages where linkage analysis would fail. We propose that RTG gives sterile yeast hybrids access to a natural route for genome recombination and adaptation.

Gene Mutation Classification through Text Evidence Facilitating Cancer Tumour Detection

A cancer tumour consists of thousands of genetic mutations. Even after advancement in technology, the task of distinguishing genetic mutations, which act as driver for the growth of tumour with passengers (Neutral Genetic Mutations), is still being done manually. This is a time-consuming process where pathologists interpret every genetic mutation from the clinical evidence manually. These clinical shreds of evidence belong to a total of nine classes, but the criterion of classification is still unknown. The main aim of this research is to propose a multiclass classifier to classify the genetic mutations based on clinical evidence (i.e., the text description of these genetic mutations) using Natural Language Processing (NLP) techniques. The dataset for this research is taken from Kaggle and is provided by the Memorial Sloan Kettering Cancer Center (MSKCC). The world-class researchers and oncologists contribute the dataset. Three text transformation models, namely, CountVectorizer, TfidfVectorizer, and Word2Vec, are utilized for the conversion of text to a matrix of token counts. Three machine learning classification models, namely, Logistic Regression (LR), Random Forest (RF), and XGBoost (XGB), along with the Recurrent Neural Network (RNN) model of deep learning, are applied to the sparse matrix (keywords count representation) of text descriptions. The accuracy score of all the proposed classifiers is evaluated by using the confusion matrix. Finally, the empirical results show that the RNN model of deep learning has performed better than other proposed classifiers with the highest accuracy of 70%.

ALKBH1-8 and FTO: Potential Therapeutic Targets and Prognostic Biomarkers in Lung Adenocarcinoma Pathogenesis

The AlkB family consists of Fe(II)- and α-ketoglutarate-dependent dioxygenases that can catalyze demethylation on a variety of substrates, such as RNA and DNA, subsequently affecting tumor progression and prognosis. However, their detailed functional roles in lung adenocarcinoma (LUAD) have not been clarified in a comprehensive manner. In this study, several bioinformatics databases, such as ONCOMINE, TIMER, and DiseaseMeth, were used to evaluate the expression profiles and prognostic significance of the AlkB family (ALKBH1-8 and FTO) in LUAD. The expression levels of ALKBH1/2/4/5/7/8 were significantly increased in LUAD tissues, while the expression levels of ALKBH3/6 and FTO were decreased. The main functions of differentially expressed AlkB homologs are related to the hematopoietic system and cell adhesion molecules. We also found that the expression profiles of the AlkB family are highly correlated with infiltrating immune cells (i.e., B cells, CD8 + T cells, CD4 + T cells, macrophages, neutrophils and dendritic cells). In addition, DNA methylation analysis indicated that the global methylation levels of ALKBH1/2/4/5/6/8 and FTO were decreased, while the global methylation levels of ALKBH3/7 were increased. In addition, the patients with upregulated ALKBH2 have significantly poor overall survival (OS) and post-progressive survival (PPS). Taken together, our work could provide insightful information about aberrant AlkB family members as potential biomarkers for the diagnostic and prognostic evaluation of LUAD. Especially, ALKBH2 could be served as a therapeutic candidate for treating LUAD.

Punctuated Aneuploidization of the Budding Yeast Genome

Remarkably complex patterns of aneuploidy have been observed in the genomes of many eukaryotic cell types, ranging from brewing yeasts to tumor cells. Such aberrant karyotypes are generally thought to take shape progressively over many generations, but evidence also suggests that genomes may undergo faster modes of evolution. Here, we used diploid Saccharomyces cerevisiae cells to investigate the dynamics with which aneuploidies arise. We found that cells selected for the loss of a single chromosome often acquired additional unselected aneuploidies concomitantly. The degrees to which these genomes were altered fell along a spectrum, ranging from simple events affecting just a single chromosome, to systemic events involving many. The striking complexity of karyotypes arising from systemic events, combined with the high frequency at which we detected them, demonstrates that cells can rapidly achieve highly altered genomic configurations during temporally restricted episodes of genomic instability.

Spontaneous Polyploids and Antimutators Compete During the Evolution of Saccharomyces cerevisiae Mutator Cells

Mutations affecting DNA polymerase exonuclease domains or mismatch repair (MMR) generate "mutator" phenotypes capable of driving tumorigenesis. Cancers with both defects exhibit an explosive increase in mutation burden that appears to reach a threshold, consistent with selection acting against further mutation accumulation. In Saccharomyces cerevisiae haploid yeast, simultaneous defects in polymerase proofreading and MMR select for "antimutator" mutants that suppress the mutator phenotype. We report here that spontaneous polyploids also escape this "error-induced extinction" and routinely outcompete antimutators in evolved haploid cultures. We performed similar experiments to explore how diploid yeast adapt to the mutator phenotype. We first evolved cells with homozygous mutations affecting polymerase δ proofreading and MMR, which we anticipated would favor tetraploid emergence. While tetraploids arose with a low frequency, in most cultures, a single antimutator clone rose to prominence carrying biallelic mutations affecting the polymerase mutator alleles. Variation in mutation rate between subclones from the same culture suggests that there exists continued selection pressure for additional antimutator alleles. We then evolved diploid yeast modeling MMR-deficient cancers with the most common heterozygous exonuclease domain mutation (POLE-P286R). Although these cells grew robustly, within 120 generations, all subclones carried truncating or nonsynonymous mutations in the POLE-P286R homologous allele (pol2-P301R) that suppressed the mutator phenotype as much as 100-fold. Independent adaptive events in the same culture were common. Our findings suggest that analogous tumor cell populations may adapt to the threat of extinction by polyclonal mutations that neutralize the POLE mutator allele and preserve intratumoral genetic diversity for future adaptation.

Screen identifies DYRK1B network as mediator of transcription repression on damaged chromatin

DNA double-strand breaks (DSBs) trigger transient pausing of nearby transcription, an emerging ATM-dependent response that suppresses chromosomal instability. We screened a chemical library designed to target the human kinome for new activities that mediate gene silencing on DSB-flanking chromatin, and have uncovered the DYRK1B kinase as an early respondent to DNA damage. We showed that DYRK1B is swiftly and transiently recruited to laser-microirradiated sites, and that genetic inactivation of DYRK1B or its kinase activity attenuated DSB-induced gene silencing and led to compromised DNA repair. Notably, global transcription shutdown alleviated DNA repair defects associated with DYRK1B loss, suggesting that DYRK1B is strictly required for DSB repair on active chromatin. We also found that DYRK1B mediates transcription silencing in part via phosphorylating and enforcing DSB accumulation of the histone methyltransferase EHMT2. Together, our findings unveil the DYRK1B signaling network as a key branch of mammalian DNA damage response circuitries, and establish the DYRK1B-EHMT2 axis as an effector that coordinates DSB repair on transcribed chromatin.

Whole-Genome Sequencing of Yeast Cells

The budding yeast, Saccharomyces cerevisiae, has been widely used for genetic studies of fundamental cellular functions. The isolation and analysis of yeast mutants is a commonly used and powerful technique to identify the genes that are involved in a process of interest. Furthermore, natural genetic variation among wild yeast strains has been studied for analysis of polygenic traits by quantitative trait loci mapping. Whole-genome sequencing, often combined with bulk segregant analysis, is a powerful technique that helps determine the identity of mutations causing a phenotype. Here, we describe protocols for the construction of libraries for S. cerevisiae whole-genome sequencing. We also present a bioinformatic pipeline to determine the genetic variants in a yeast strain using whole-genome sequencing data. This pipeline can also be used for analyzing Schizosaccharomyces pombe mutants. © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: Generation of haploid spores for bulk segregant analysis Basic Protocol 2: Extraction of genomic DNA from yeast cells Basic Protocol 3: Shearing of genomic DNA for library preparation Basic Protocol 4: Construction and amplification of DNA libraries Support Protocol 1: Annealing oligonucleotides for forming Y-adapters Support Protocol 2: Size selection and cleanup using SPRI beads Basic Protocol 5: Identification of genomic variants from sequencing data.

Insights into the Conserved Regulatory Mechanisms of Human and Yeast Aging

Aging represents a significant biological process having strong associations with cancer, diabetes, and neurodegenerative and cardiovascular disorders, which leads to progressive loss of cellular functions and viability. Astonishingly, age-related disorders share several genetic and molecular mechanisms with the normal aging process. Over the last three decades, budding yeast Saccharomyces cerevisiae has emerged as a powerful yet simple model organism for aging research. Genetic approaches using yeast RLS have led to the identification of hundreds of genes impacting lifespan in higher eukaryotes. Numerous interventions to extend yeast lifespan showed an analogous outcome in multi-cellular eukaryotes like fruit flies, nematodes, rodents, and humans. We collected and analyzed a multitude of observations from published literature and provide the contribution of yeast in the understanding of aging hallmarks most applicable to humans. Here, we discuss key pathways and molecular mechanisms that underpin the evolutionarily conserved aging process and summarize the current understanding and clinical applicability of its trajectories. Gathering critical information on aging biology would pave the way for future investigation targeted at the discovery of aging interventions.

Mutation severity spectrum of rare alleles in the human genome is predictive of disease type

The human genome harbors a variety of genetic variations. Single-nucleotide changes that alter amino acids in protein-coding regions are one of the major causes of human phenotypic variation and diseases. These single-amino acid variations (SAVs) are routinely found in whole genome and exome sequencing. Evaluating the functional impact of such genomic alterations is crucial for diagnosis of genetic disorders. We developed DeepSAV, a deep-learning convolutional neural network to differentiate disease-causing and benign SAVs based on a variety of protein sequence, structural and functional properties. Our method outperforms most stand-alone programs, and the version incorporating population and gene-level information (DeepSAV+PG) has similar predictive power as some of the best available. We transformed DeepSAV scores of rare SAVs in the human population into a quantity termed "mutation severity measure" for each human protein-coding gene. It reflects a gene's tolerance to deleterious missense mutations and serves as a useful tool to study gene-disease associations. Genes implicated in cancer, autism, and viral interaction are found by this measure as intolerant to mutations, while genes associated with a number of other diseases are scored as tolerant. Among known disease-associated genes, those that are mutation-intolerant are likely to function in development and signal transduction pathways, while those that are mutation-tolerant tend to encode metabolic and mitochondrial proteins.

Accurate Tracking of the Mutational Landscape of Diploid Hybrid Genomes

Mutations, recombinations, and genome duplications may promote genetic diversity and trigger evolutionary processes. However, quantifying these events in diploid hybrid genomes is challenging. Here, we present an integrated experimental and computational workflow to accurately track the mutational landscape of yeast diploid hybrids (MuLoYDH) in terms of single-nucleotide variants, small insertions/deletions, copy-number variants, aneuploidies, and loss-of-heterozygosity. Pairs of haploid Saccharomyces parents were combined to generate ancestor hybrids with phased genomes and varying levels of heterozygosity. These diploids were evolved under different laboratory protocols, in particular mutation accumulation experiments. Variant simulations enabled the efficient integration of competitive and standard mapping of short reads, depending on local levels of heterozygosity. Experimental validations proved the high accuracy and resolution of our computational approach. Finally, applying MuLoYDH to four different diploids revealed striking genetic background effects. Homozygous Saccharomyces cerevisiae showed a ∼4-fold higher mutation rate compared with its closely related species S. paradoxus. Intraspecies hybrids unveiled that a substantial fraction of the genome (∼250 bp per generation) was shaped by loss-of-heterozygosity, a process strongly inhibited in interspecies hybrids by high levels of sequence divergence between homologous chromosomes. In contrast, interspecies hybrids exhibited higher single-nucleotide mutation rates compared with intraspecies hybrids. MuLoYDH provided an unprecedented quantitative insight into the evolutionary processes that mold diploid yeast genomes and can be generalized to other genetic systems.

The Impact of Gene Dosage and Heterozygosity on The Diploid Pathobiont Candida albicans

Candida albicans is a fungal species that can colonize multiple niches in the human host where it can grow either as a commensal or as an opportunistic pathogen. The genome of C. albicans has long been of considerable interest, given that it is highly plastic and can undergo a wide variety of alterations. These changes play a fundamental role in determining C. albicans traits and have been shown to enable adaptation both to the host and to antifungal drugs. C. albicans isolates contain a heterozygous diploid genome that displays variation from the level of single nucleotides to largescale rearrangements and aneuploidy. The heterozygous nature of the genome is now increasingly recognized as being central to C. albicans biology, as the relative fitness of isolates has been shown to correlate with higher levels of overall heterozygosity. Moreover, loss of heterozygosity (LOH) events can arise frequently, either at single polymorphisms or at a chromosomal level, and both can alter the behavior of C. albicans cells during infection or can modulate drug resistance. In this review, we examine genome plasticity in this pathobiont focusing on how gene dosage variation and loss of heterozygosity events can arise and how these modulate C. albicans behavior.

Baker's Yeast Clinical Isolates Provide a Model for How Pathogenic Yeasts Adapt to Stress

Global outbreaks of drug-resistant fungi such as Candida auris are thought to be due at least in part to excessive use of antifungal drugs. Baker's yeast Saccharomyces cerevisiae has gained importance as an emerging opportunistic fungal pathogen that can cause infections in immunocompromised patients. Analyses of over 1000 S. cerevisiae isolates are providing rich resources to better understand how fungi can grow in human environments. A large percentage of clinical S. cerevisiae isolates are heterozygous across many nucleotide sites, and a significant proportion are of mixed ancestry and/or are aneuploid or polyploid. Such features potentially facilitate adaptation to new environments. These observations provide strong impetus for expanding genomic and molecular studies on clinical and wild isolates to understand the prevalence of genetic diversity and instability-generating mechanisms, and how they are selected for and maintained. Such work can also lead to the identification of new targets for antifungal drugs.

The Mutator Phenotype: Adapting Microbial Evolution to Cancer Biology

The mutator phenotype hypothesis was postulated almost 40 years ago to reconcile the observation that while cancer cells display widespread mutational burden, acquisition of mutations in non-transformed cells is a rare event. Moreover, it also suggested that cancer evolution could be fostered by increased genome instability. Given the evolutionary conservation throughout the tree of life and the genetic tractability of model organisms, yeast and bacterial species pioneered studies to dissect the functions of genes required for genome maintenance (caretaker genes) or for cell growth control (gatekeeper genes). In this review, we first provide an overview of what we learned from model organisms about the roles of these genes and the genome instability that arises as a consequence of their dysregulation. We then discuss our current understanding of how mutator phenotypes shape the evolution of bacteria and yeast species. We end by bringing clinical evidence that lessons learned from single-cell organisms can be applied to tumor evolution.